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Zahra A, van Smeden M, Abbink EJ, van den Berg JM, Blom MT, van den Dries CJ, Gussekloo J, Wouters F, Joling KJ, Melis R, Mooijaart SP, Peters JB, Polinder-Bos HA, van Raaij BFM, Appelman B, la Roi-Teeuw HM, Moons KGM, Luijken K. External validation of six COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting. J Clin Epidemiol 2024; 168:111270. [PMID: 38311188 DOI: 10.1016/j.jclinepi.2024.111270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/26/2024] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
OBJECTIVES To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes. STUDY DESIGN AND SETTING This retrospective external validation study included 14,092 older individuals of ≥70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation. MAIN OUTCOME MEASURE In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting. RESULTS All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large -1.45 to 7.46, calibration slopes 0.24-0.81, and C-statistic 0.55-0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of -2.35 to -0.15 indicating overestimation, calibration slopes of 0.24-0.81 indicating signs of overfitting, and C-statistic of 0.55-0.71. CONCLUSION Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic.
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Affiliation(s)
- Anum Zahra
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Evertine J Abbink
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jesse M van den Berg
- Department of General Practice, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands; PHARMO Institute for Drug Outcomes Research, Utrecht, The Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands
| | - Carline J van den Dries
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jacobijn Gussekloo
- Section Gerontology and Geriatrics, LUMC Center for Medicine for Older People & Department of Public Health and Primary Care & Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Fenne Wouters
- Department of Medicine for Older People, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Aging & Later Life, Amsterdam, The Netherlands
| | - Karlijn J Joling
- Department of Medicine for Older People, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Aging & Later Life, Amsterdam, The Netherlands
| | - René Melis
- Department of Geriatric Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Simon P Mooijaart
- LUMC Center for Medicine for Older People, LUMC, Leiden, The Netherlands
| | - Jeannette B Peters
- Department of Pulmonary Diseases, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Harmke A Polinder-Bos
- Section of Geriatric Medicine, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Bas F M van Raaij
- LUMC Center for Medicine for Older People, LUMC, Leiden, The Netherlands
| | - Brent Appelman
- Amsterdam UMC Location University of Amsterdam, Center for Experimental and Molecular Medicine, Amsterdam, The Netherlands
| | - Hannah M la Roi-Teeuw
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kim Luijken
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Balagopalan A, Baldini I, Celi LA, Gichoya J, McCoy LG, Naumann T, Shalit U, van der Schaar M, Wagstaff KL. Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact. PLOS DIGITAL HEALTH 2024; 3:e0000474. [PMID: 38620047 PMCID: PMC11018283 DOI: 10.1371/journal.pdig.0000474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/18/2024] [Indexed: 04/17/2024]
Abstract
Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for healthcare (MLHC) community which broadly reward technical novelty over tangible, equitable impact. We structure our work as a healthcare-focused echo of the 2012 paper "Machine Learning that Matters", which highlighted such structural issues in the ML community at large, and offered a series of clearly defined "Impact Challenges" to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare-the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access.
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Affiliation(s)
- Aparna Balagopalan
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology; Cambridge, Massachusetts, United States of America
| | - Ioana Baldini
- IBM Research; Yorktown Heights, New York, United States of America
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology; Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center; Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health; Boston, Massachusetts, United States of America
| | - Judy Gichoya
- Department of Radiology and Imaging Sciences, School of Medicine, Emory University; Atlanta, Georgia, United States of America
| | - Liam G. McCoy
- Division of Neurology, Department of Medicine, University of Alberta; Edmonton, Alberta, Canada
| | - Tristan Naumann
- Microsoft Research; Redmond, Washington, United States of America
| | - Uri Shalit
- The Faculty of Data and Decision Sciences, Technion; Haifa, Israel
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge; Cambridge, United Kingdom
- The Alan Turing Institute; London, United Kingdom
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Lee JE, Kang DH, Kim SY, Kim DK, Lee SI. Clinical Manifestations and Outcomes of Older Patients with COVID-19: A Comprehensive Review. Tuberc Respir Dis (Seoul) 2024; 87:145-154. [PMID: 38368903 PMCID: PMC10990616 DOI: 10.4046/trd.2023.0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/17/2023] [Accepted: 01/13/2024] [Indexed: 02/20/2024] Open
Abstract
The consequences of coronavirus disease 2019 (COVID-19) are particularly severe in older adults with a disproportionate number of severe and fatal outcomes. Therefore, this integrative review aimed to provide a comprehensive overview of the clinical characteristics, management approaches, and prognosis of older patients diagnosed with COVID-19. Common clinical presentations in older patients include fever, cough, and dyspnea. Additionally, preexisting comorbidities, especially diabetes and pulmonary and cardiovascular diseases, were frequently observed and associated with adverse outcomes. Management strategies varied, however, early diagnosis, vigilant monitoring, and multidisciplinary care were identified as key factors for enhancing patient outcomes. Nonetheless, the prognosis remains guarded for older patients, with increased rates of hospitalization, mechanical ventilation, and mortality. However, timely therapeutic interventions, especially antiviral and supportive treatments, have demonstrated some efficacy in mitigating the severe consequences in this age group. In conclusion, while older adults remain highly susceptible to severe outcomes from COVID-19, early intervention, rigorous monitoring, and comprehensive care can play a pivotal role in improving their clinical outcomes.
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Affiliation(s)
- Jeong Eun Lee
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Da Hyun Kang
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - So-Yun Kim
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Duk Ki Kim
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Song I Lee
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea
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Hinson JS, Zhao X, Klein E, Badaki‐Makun O, Rothman R, Copenhaver M, Smith A, Fenstermacher K, Toerper M, Pekosz A, Levin S. Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza. J Am Coll Emerg Physicians Open 2024; 5:e13117. [PMID: 38500599 PMCID: PMC10945311 DOI: 10.1002/emp2.13117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 03/20/2024] Open
Abstract
Objective Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged. Methods We developed random forest machine learning (ML) models to estimate needs for critical care within 24 h and inpatient care within 72 h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score. Results Among 8032 patients with laboratory-confirmed influenza, incidence of critical care needs was 6.3% and incidence of inpatient care needs was 19.6%. The most common reasons for ED visit were symptoms of respiratory tract infection, fever, and shortness of breath. Model AUCs were 0.89 (95% CI 0.86-0.93) for prediction of critical care and 0.90 (95% CI 0.88-0.93) for inpatient care needs; Brier scores were 0.026 and 0.042, respectively. Importantpredictors included shortness of breath, increasing respiratory rate, and a high number of comorbid diseases. Conclusions ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision-making.
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Affiliation(s)
- Jeremiah S. Hinson
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Malone Center for Engineering in HealthcareJohns Hopkins University Whiting School of EngineeringBaltimoreMarylandUSA
| | - Xihan Zhao
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Eili Klein
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- One Health TrustWashingtonDistrict of ColumbiaUSA
| | - Oluwakemi Badaki‐Makun
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of PediatricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Richard Rothman
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Martin Copenhaver
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Aria Smith
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Malone Center for Engineering in HealthcareJohns Hopkins University Whiting School of EngineeringBaltimoreMarylandUSA
| | - Katherine Fenstermacher
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Matthew Toerper
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Andrew Pekosz
- Department of Microbiology and ImmunologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Scott Levin
- Department of Emergency MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Malone Center for Engineering in HealthcareJohns Hopkins University Whiting School of EngineeringBaltimoreMarylandUSA
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Indrayan A, Malhotra RK, Pawar M. Use of ROC curve analysis for prediction gives fallacious results: Use predictivity-based indices. J Postgrad Med 2024; 70:91-96. [PMID: 38668827 PMCID: PMC11160993 DOI: 10.4103/jpgm.jpgm_753_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/18/2024] [Accepted: 02/26/2024] [Indexed: 05/22/2024] Open
Abstract
ABSTRACT The area under the ROC curve is frequently used for assessing the predictive efficacy of a model, and the Youden index is commonly used to provide the optimal cut-off. Both are misleading tools for predictions. A ROC curve is drawn for the sensitivity of a quantitative test against its (1 - specificity) at different values of the test. Both sensitivity and specificity are retrospective in nature as these are indicators of correct classification of already known conditions. They are not indicators of future events and are not valid for predictions. Predictivity intimately depends on the prevalence which may be ignored by sensitivity and specificity. We explain this fallacy in detail and illustrate with several examples that the actual predictivity could differ greatly from the ROC curve-based predictivity reported by many authors. The predictive efficacy of a test or a model is best assessed by the percentage correctly predicted in a prospective framework. We propose predictivity-based ROC curves as tools for providing predictivities at varying prevalence in different populations. For optimal cut-off for prediction, in place of the Youden index, we propose a P-index where the sum of positive and negative predictivities is maximum after subtracting 1. To conclude, for correctly assessing adequacy of a prediction models, predictivity-based ROC curves should be used instead of the usual sensitivity-specificity-based ROC curves and the P-index should replace the Youden index.
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Affiliation(s)
- A Indrayan
- Department of Clinical Research, Max Healthcare, New Delhi, India
| | - RK Malhotra
- Dr BRA Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - M Pawar
- Department of Clinical Research, Max Healthcare, New Delhi, India
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Challa SR, Oskrochi G, Singh GP, Chirumamilla LG, Shayegh N, Nair VK, Littleton M, Byer DT, Morrison NA, Grossi BL, Ashanna B, Dusmatova S, Thompson T, Dawodu DO, Brim H, Ashktorab H. Predictors of mortality in hospitalized African American COVID-19 patients with cancer. Transl Cancer Res 2024; 13:1314-1322. [PMID: 38617523 PMCID: PMC11009794 DOI: 10.21037/tcr-23-166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 10/29/2023] [Indexed: 04/16/2024]
Abstract
Background Coronavirus disease 2019 (COVID-19) manifest differently depending on patients' background and pre-existing conditions. It remains unclear how African Americans with cancer have been affected in comparison to those without. In this study, we aim to identify demographic, clinical, and laboratory markers associated with mortality in COVID-19 patients with cancer. Methods We reviewed all COVID-19 hospitalized patients' records from Dec. 2019 to Oct. 2021 at Howard University Hospital. Patients having a history of, or active, cancer were reviewed. Clinical, treatment, lab test values, and pathological data were extracted. Univariable and multivariable analyses were conducted on the entire cohort as well as on cases and controls separately, using SPSS software. Results Out of 512 COVID-19 infected patients, 49 had cancer, either active or history of cancer (cases) and 463 COVID-19 were cancer-free (controls), allowing for comparison. African American race was predominant in both cases and controls, 83.7% and 66.7% respectively. Cancer patients were older than non-cancer patients (mean age: 70.6 vs. 56.3 years) and had an increased length of hospital stay (mean 13.9 vs. 9.4 days). Mortality is significantly higher among cancer patients (n=10, 20.4%, P=0.03) compared to non-cancer COVID-19 patients (n=41, 8.9%). Among cancer patients, breast cancer was more prevalent in females and prostate cancer in males (54% and 52%, respectively). A comparison of patients with active vs. previous cancer showed no significant difference in the clinical outcome, death vs. discharge (P=0.34). A higher reduction in albumin level in cancer cases, from the time of admission to day 5, was significantly associated with death during the hospital stay compared to those discharged (n=24, 49.0%, P<0.001). In controls, lymphopenia (n=436, 94.2%, P=0.05), aspartate aminotransferase (AST) (n=59, 12.7%, P=0.008) and albumin (n=40, 8.6%, P=0.02) have shown an association with increased mortality. Conclusions Albumin level has an inverse relationship with clinical outcomes among all COVID-19 infected cancer patients. Reduction in albumin level during the hospital stay, particularly in COVID-19 cancer patients should be considered as a predictor of mortality. Further research with a large cohort size is needed to verify and identify other predictors of outcomes in COVID-19 patients with cancer.
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Affiliation(s)
| | - Gholamreza Oskrochi
- College of Engineering and Technology, American University of the Middle East, Egalia, Kuwait
| | - Gagan Paul Singh
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Lakshmi G. Chirumamilla
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Nader Shayegh
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Vaisakh K. Nair
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Megan Littleton
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Danae T. Byer
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Nicole A. Morrison
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Brittany L. Grossi
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Barclay Ashanna
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Shahnoza Dusmatova
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Trae Thompson
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Dideolu O. Dawodu
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Hassan Brim
- Department of Pathology and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
| | - Hassan Ashktorab
- Department of Medicine and Cancer Center, College of Medicine, Howard University, Washington, DC, USA
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Victorino-Aguilar M, Lerma A, Badillo-Alonso H, Ramos-Lojero VM, Ledesma-Amaya LI, Ruiz-Velasco Acosta S, Lerma C. Individualized Prediction of SARS-CoV-2 Infection in Mexico City Municipality during the First Six Waves of the Pandemic. Healthcare (Basel) 2024; 12:764. [PMID: 38610186 PMCID: PMC11011518 DOI: 10.3390/healthcare12070764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/27/2024] [Accepted: 03/30/2024] [Indexed: 04/14/2024] Open
Abstract
After COVID-19 emerged, alternative methods to laboratory tests for the individualized prediction of SARS-CoV-2 were developed in several world regions. The objective of this investigation was to develop models for the individualized prediction of SARS-CoV-2 infection in a large municipality of Mexico. The study included data from 36,949 patients with suspected SARS-CoV-2 infection who received a diagnostic tested at health centers of the Alvaro Obregon Jurisdiction in Mexico City registered in the Epidemiological Surveillance System for Viral Respiratory Diseases (SISVER-SINAVE). The variables that were different between a positive test and a negative test were used to generate multivariate binary logistic regression models. There was a large variation in the prediction variables for the models of different pandemic waves. The models obtained an overall accuracy of 73% (63-82%), sensitivity of 52% (18-71%), and specificity of 84% (71-92%). In conclusion, the individualized prediction models of a positive COVID-19 test based on SISVER-SINAVE data had good performance. The large variation in the prediction variables for the models of different pandemic waves highlights the continuous change in the factors that influence the spread of COVID-19. These prediction models could be applied in early case identification strategies, especially in vulnerable populations.
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Affiliation(s)
- Mariel Victorino-Aguilar
- Master’s Program in Biomedical Sciences, Institute of Health Sciences, Autonomous University of the State of Hidalgo, San Agustín Tlaxiaca 42160, Mexico;
| | - Abel Lerma
- Area of Psychology, Institute of Health Sciences, Autonomous University of the State of Hidalgo, San Agustín Tlaxiaca 42160, Mexico;
| | | | | | - Luis Israel Ledesma-Amaya
- Area of Psychology, Institute of Health Sciences, Autonomous University of the State of Hidalgo, San Agustín Tlaxiaca 42160, Mexico;
| | - Silvia Ruiz-Velasco Acosta
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
| | - Claudia Lerma
- Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México Campus Norte, Huixquilucan Edo. de Mexico 52786, Mexico
- Department of Molecular Biology, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City 04480, Mexico
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Ince FM, Alkan Bilik O, Ince H. Evaluating Mortality Predictors in COVID-19 Intensive Care Unit Patients: Insights into Age, Procalcitonin, Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, and Ferritin Lactate Index. Diagnostics (Basel) 2024; 14:684. [PMID: 38611597 PMCID: PMC11011413 DOI: 10.3390/diagnostics14070684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
INTRODUCTION Numerous studies suggest that alterations in blood parameters, such as changes in platelet, lymphocyte, hemoglobin, eosinophil, and basophil counts; increased neutrophil counts; and elevated neutrophil/lymphocyte and platelet/lymphocyte ratios, signal COVID-19 infection and predict worse outcomes. Leveraging these insights, our study seeks to create a predictive mortality model by assessing age and crucial laboratory markers. MATERIALS AND METHODS Patients were categorized into two groups based on their hospital outcomes: 130 survivors who recovered from their Intensive Care Unit (ICU) stay (Group 1) and 74 who died (Group 2). We then developed a predictive mortality model using patients' age, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), procalcitonin levels, and ferritin lactate (FL) index results. RESULTS A total of 204 patients were included. Patients in Group 2 had a notably higher mean age compared to those in Group 1 (76 ± 11 vs. 66 ± 15 years) (p < 0.001). Using specific cut-off values, our analysis revealed varying effectiveness in predicting COVID-19 mortality: Those aged over 73 years showed 74% sensitivity and 60% specificity, with an area under the curve (AUC) of 0.701. Procalcitonin levels above 0.35 ng/mL balanced true-positive and -negative identifications well, achieving an AUC of 0.752. The FL index, with a threshold of 1228 mg/dL, had 68% sensitivity and 65% specificity with an AUC of 0.707. A PLR higher than 212 resulted in 48% sensitivity and 69% specificity, with an AUC of 0.582. An NLR higher than 5.8 resulted in 55% sensitivity and 63% specificity, with an AUC of 0.640, showcasing diverse predictive accuracies across parameters. The statistical analysis evaluated the effects of age (>73), procalcitonin levels (>0.35), FL > 1228, PLR > 212, and NLR > 5.8 on mortality variables using logistic regression. Ages over 73 significantly increased event odds by 2.1 times (p = 0.05), procalcitonin levels above 0.35 nearly quintupled the odds (OR = 5.6, p < 0.001), high FL index levels more than tripled the odds (OR = 3.5, p = 0.003), a PLR > 212 significantly increased event odds by 3.5 (p = 0.030), and an NLR > 5.8 significantly increased event odds by 1.6 (p = 0.043). CONCLUSIONS Our study highlights significant predictors of mortality in COVID-19 ICU patients, including advanced age, elevated procalcitonin, FL index levels, the PLR, and the NLR.
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Affiliation(s)
- Fatma Meral Ince
- Infectious Diseases and Clinical Microbiology, Selahaddin Eyyubi State Hospital, 21100 Diyarbakir, Turkey
| | - Ozge Alkan Bilik
- Medical Microbiology, Selahaddin Eyyubi State Hospital, 21100 Diyarbakir, Turkey
| | - Hasan Ince
- Internal Medicine, Selahaddin Eyyubi State Hospital, 21100 Diyarbakir, Turkey;
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Zinna G, Pipitò L, Colomba C, Scichilone N, Licata A, Barbagallo M, Russo A, Almasio PL, Coppola N, Cascio A. COVID-19: The Development and Validation of a New Mortality Risk Score. J Clin Med 2024; 13:1832. [PMID: 38610597 PMCID: PMC11012743 DOI: 10.3390/jcm13071832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/14/2024] Open
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has found the whole world unprepared for its correct management. Italy was the first European country to experience the spread of the SARS-CoV-2 virus at the end of February 2020. As a result of hospital overcrowding, the quality of care delivered was not always optimal. A substantial number of patients admitted to non-ICU units could have been treated at home. It would have been extremely useful to have a score that, based on personal and clinical characteristics and simple blood tests, could have predicted with sufficient reliability the probability that a patient had or did not have a disease that could have led to their death. This study aims to develop a scoring system to identify which patients with COVID-19 are at high mortality risk upon hospital admission, to expedite and enhance clinical decision making. Methods: A retrospective analysis was performed to develop a multivariable prognostic prediction model. Results: Derivation and external validation cohorts were obtained from two Italian University Hospital databases, including 388 (10.31% deceased) and 1357 (7.68% deceased) patients with confirmed COVID-19, respectively. A multivariable logistic model was used to select seven variables associated with in-hospital death (age, baseline oxygen saturation, hemoglobin value, white blood cell count, percentage of neutrophils, platelet count, and creatinine value). Calibration and discrimination were satisfactory with a cumulative AUC for prediction mortality of 0.924 (95% CI: 0.893-0.944) in derivation cohorts and 0.808 (95% CI: 0.886-0.828) in external validation cohorts. The risk score obtained was compared with the ISARIC 4C Mortality Score, and with all the other most important scores considered so far, to evaluate the risk of death of patients with COVID-19. It performed better than all the above scores to evaluate the predictability of dying. Its sensitivity, specificity, and AUC were higher than the other COVID-19 scoring systems when the latter were calculated for the 388 patients in our derivation cohort. Conclusions: In conclusion, the CZ-COVID-19 Score may help all physicians by identifying those COVID-19 patients who require more attention to provide better therapeutic regimens or, on the contrary, by identifying those patients for whom hospitalization is not necessary and who could therefore be sent home without overcrowding healthcare facilities. We developed and validated a new risk score based on seven variables for upon-hospital admission of COVID-19 patients. It is very simple to calculate and performs better than all the other similar scores to evaluate the predictability of dying.
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Affiliation(s)
- Giuseppe Zinna
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; (G.Z.); (L.P.); (C.C.); (N.S.); (A.L.); (M.B.); (P.L.A.)
- Department of Surgery, Dentistry, Paediatrics, and Gynaecology, Division of Cardiac Surgery, University of Verona Medical School, 37129 Verona, Italy
| | - Luca Pipitò
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; (G.Z.); (L.P.); (C.C.); (N.S.); (A.L.); (M.B.); (P.L.A.)
| | - Claudia Colomba
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; (G.Z.); (L.P.); (C.C.); (N.S.); (A.L.); (M.B.); (P.L.A.)
- Pediatric Infectious Disease Unit, ARNAS Civico-Di Cristina-Benfratelli Hospital, 90127 Palermo, Italy
| | - Nicola Scichilone
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; (G.Z.); (L.P.); (C.C.); (N.S.); (A.L.); (M.B.); (P.L.A.)
| | - Anna Licata
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; (G.Z.); (L.P.); (C.C.); (N.S.); (A.L.); (M.B.); (P.L.A.)
| | - Mario Barbagallo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; (G.Z.); (L.P.); (C.C.); (N.S.); (A.L.); (M.B.); (P.L.A.)
| | - Antonio Russo
- Section of Infectious Diseases, Department of Mental Health and Public Medicine, University of Campania “Luigi Vanvitelli”, Via Luciano Armanni 5, 80131 Naples, Italy; (A.R.); (N.C.)
| | - Piero Luigi Almasio
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; (G.Z.); (L.P.); (C.C.); (N.S.); (A.L.); (M.B.); (P.L.A.)
| | - Nicola Coppola
- Section of Infectious Diseases, Department of Mental Health and Public Medicine, University of Campania “Luigi Vanvitelli”, Via Luciano Armanni 5, 80131 Naples, Italy; (A.R.); (N.C.)
| | - Antonio Cascio
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy; (G.Z.); (L.P.); (C.C.); (N.S.); (A.L.); (M.B.); (P.L.A.)
- Infectious and Tropical Disease Unit, AOU Policlinico “P. Giaccone”, Via del Vespro 129, 90127 Palermo, Italy
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Douillet D, Riou J, Morin F, Mahieu R, Chauvin A, Gennai S, Ferrant L, Lopez R, Sebbane M, Plantefeve G, Brice C, Cayeux C, Savary D, Moumneh T, Penaloza A, Roy PM. Derivation and validation of a risk-stratification model for patients with probable or proven COVID-19 in EDs: the revised HOME-CoV score. Emerg Med J 2024; 41:218-225. [PMID: 38365436 DOI: 10.1136/emermed-2022-212631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 02/05/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND The HOME-CoV (Hospitalisation or Outpatient ManagEment of patients with SARS-CoV-2 infection) score is a validated list of uniquely clinical criteria indicating which patients with probable or proven COVID-19 can be treated at home. The aim of this study was to optimise the score to improve its ability to discriminate between patients who do and do not need admission. METHODS A revised HOME-CoV score was derived using data from a previous prospective multicentre study which evaluated the original Home-CoV score. Patients with proven or probable COVID-19 attending 34 EDs in France, Monaco and Belgium between April and May 2020 were included. The population was split into a derivation and validation sample corresponding to the observational and interventional phases of the original study. The main outcome was non-invasive or invasive ventilation or all-cause death within 7 days following inclusion. Two threshold values were defined using a sensitivity of >0.9 and a specificity of >0.9 to identify low-risk and high-risk patients, respectively. The revised HOME-CoV score was then validated by retrospectively applying it to patients in the same EDs with proven or probable COVID-19 during the interventional phase. The revised HOME-CoV score was also tested against original HOME-CoV, qCSI, qSOFA, CRB65 and SMART-COP in this validation cohort. RESULTS There were 1696 patients in the derivation cohort, of whom 65 (3.8%) required non-invasive ventilation or mechanical ventilation or died within 7 days and 1304 patients in the validation cohort, of whom 22 (1.7%) had a progression of illness. The revised score included seven clinical criteria. The area under the curve (AUC) was 87.6 (95% CI 84.7 to 90.6). The cut-offs to define low-risk and high-risk patients were <2 and >3, respectively. In the validation cohort, the AUC was 85.8 (95% CI 80.6 to 91.0). A score of <2 qualified 73% of patients as low risk with a sensitivity of 0.77 (0.55-0.92) and a negative predictive value of 0.99 (0.99-1.00). CONCLUSION The revised HOME-CoV score, which does not require laboratory testing, may allow accurate risk stratification and safely qualify a significant proportion of patients with probable or proven COVID-19 for home treatment.
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Affiliation(s)
- Delphine Douillet
- Emergency Department, CHU Angers, University of Angers, CHU Angers, Angers, France
- UMR MitoVasc CNRS 6015 - INSERM 1083, Health Faculty, University of Angers; FCRIN, INNOVTE, Universite Angers Faculte des sciences, Angers, France
| | - Jérémie Riou
- Micro et Nano médecines Translationnelles, MINT, UNIV Angers, UMR INSERM 1066, UMR CNRS 6021, CHU Angers, Angers, France
- Methodology and Biostatistics Department, Delegation to Clinical Research and Innovation, Angers University Hospital, Université Angers Faculté des Sciences, Angers, France
| | - François Morin
- Emergency Department, CHU Angers, University of Angers, CHU Angers, Angers, France
| | - Rafaël Mahieu
- Department of Infectious Disease, Angers University Hospital; University of Angers, CHU Angers, Angers, France
- CRCINA, Inserm U1232, University of Nantes-Angers, Universite Angers Faculte Des Sciences, Angers, France
| | - Anthony Chauvin
- Emergency Department, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Assistance Publique - Hopitaux de Paris, Paris, France
| | - Stéphane Gennai
- Emergency Department, Reims University Hospital, University Hospital Centre Reims, Reims, France
- UFR Médecine, Université de Reims Champagne-Ardenne, Reims, France
| | - Lionel Ferrant
- Emergency Department, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Raphaëlle Lopez
- Emergency Department, Sart Tilman University Hospital, Centre hospitalier universitaire de Liège, Liege, Belgium
| | - Mustapha Sebbane
- Emergency Department, Montpellier University Hospital, Montpellier, France
| | | | - Christian Brice
- Emergency Department, Centre Hospitalier de Saint Brieuc, Saint Brieuc, France
| | - Coralie Cayeux
- Emergency Department, Centre Hospitalier de Remiremont, Remiremont, France
| | - Dominique Savary
- Department of Emergency Medicine, University of Angers, ANGERS, France
- Inserm IRSET UMR_S1085, I, EHESP, Angers, France
| | | | - Andrea Penaloza
- Emergency, Cliniques universitaires Saint-Luc, Bruxelles, Belgium
| | - Pierre Marie Roy
- Emergency Department, CHU Angers, University of Angers, CHU Angers, Angers, France
- UMR MitoVasc CNRS 6015 - INSERM 1083, Health Faculty, University of Angers; FCRIN, INNOVTE, Universite Angers Faculte des sciences, Angers, France
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Lapi F, Marconi E, Domnich A, Cricelli I, Rossi A, Grattagliano I, Icardi G, Cricelli C. A Vulnerability Index to Assess the Risk of SARS-CoV-2-Related Hospitalization/Death: Urgent Need for an Update after Diffusion of Anti-COVID Vaccines. Infect Dis Rep 2024; 16:260-268. [PMID: 38525768 PMCID: PMC10961815 DOI: 10.3390/idr16020021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 03/26/2024] Open
Abstract
Background: There are algorithms to predict the risk of SARS-CoV-2-related complications. Given the spread of anti-COVID vaccination, which sensibly modified the burden of risk of the infection, these tools need to be re-calibrated. Therefore, we updated our vulnerability index, namely, the Health Search (HS)-CoVulnerabiltyIndex (VI)d (HS-CoVId), to predict the risk of SARS-CoV-2-related hospitalization/death in the primary care setting. Methods: We formed a cohort of individuals aged ≥15 years and diagnosed with COVID-19 between 1 January and 31 December 2021 in the HSD. The date of COVID-19 diagnosis was the study index date. These patients were eligible if they had received an anti-COVID vaccine at least 15 days before the index date. Patients were followed up from the index date until one of the following events, whichever came first: COVID-19-related hospitalization/death (event date), end of registration with their GPs, and end of the study period (31 December 2022). To calculate the incidence rate of COVID-19-related hospitalization/death, a patient-specific score was derived through linear combination of the coefficients stemming from a multivariate Cox regression model. Its prediction performance was evaluated by obtaining explained variation, discrimination, and calibration measures. Results: We identified 2192 patients who had received an anti-COVID vaccine from 1 January to 31 December 2021. With this cohort, we re-calibrated the HS-CoVId by calculating optimism-corrected pseudo-R2, AUC, and calibration slope. The final model reported a good predictive performance by explaining 58% (95% CI: 48-71%) of variation in the occurrence of hospitalizations/deaths, the AUC was 83 (95% CI: 77-93%), and the calibration slope did not reject the equivalence hypothesis (p-value = 0.904). Conclusions: Two versions of HS-CoVId need to be differentially adopted to assess the risk of COVID-19-related complications among vaccinated and unvaccinated subjects. Therefore, this functionality should be operationalized in related patient- and population-based informatic tools intended for general practitioners.
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Affiliation(s)
- Francesco Lapi
- Health Search, Italian College of General Practitioners and Primary Care, 50142 Florence, Italy
| | - Ettore Marconi
- Health Search, Italian College of General Practitioners and Primary Care, 50142 Florence, Italy
| | - Alexander Domnich
- Hygiene Unit, San Martino Policlinico Hospital-IRCCS for Oncology and Neurosciences, 16132 Genoa, Italy; (A.D.); (G.I.)
| | | | - Alessandro Rossi
- Italian College of General Practitioners and Primary Care, 50142 Florence, Italy; (A.R.); (I.G.); (C.C.)
| | - Ignazio Grattagliano
- Italian College of General Practitioners and Primary Care, 50142 Florence, Italy; (A.R.); (I.G.); (C.C.)
| | - Giancarlo Icardi
- Hygiene Unit, San Martino Policlinico Hospital-IRCCS for Oncology and Neurosciences, 16132 Genoa, Italy; (A.D.); (G.I.)
- Department of Health Sciences, University of Genoa, 16132 Genoa, Italy
| | - Claudio Cricelli
- Italian College of General Practitioners and Primary Care, 50142 Florence, Italy; (A.R.); (I.G.); (C.C.)
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Seyedsalehi A, Fazel S. Suicide risk assessment tools and prediction models: new evidence, methodological innovations, outdated criticisms. BMJ MENTAL HEALTH 2024; 27:e300990. [PMID: 38485246 PMCID: PMC11021746 DOI: 10.1136/bmjment-2024-300990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/04/2024] [Indexed: 03/19/2024]
Abstract
The number of prediction models for suicide-related outcomes has grown substantially in recent years. These models aim to assist in stratifying risk, improve clinical decision-making, and facilitate a personalised medicine approach to the prevention of suicidal behaviour. However, there are contrasting views as to whether prediction models have potential to inform and improve assessment of suicide risk. In this perspective, we discuss common misconceptions that characterise criticisms of suicide risk prediction research. First, we discuss the limitations of a classification approach to risk assessment (eg, categorising individuals as low-risk vs high-risk), and highlight the benefits of probability estimation. Second, we argue that the preoccupation with classification measures (such as positive predictive value) when assessing a model's predictive performance is inappropriate, and discuss the importance of clinical context in determining the most appropriate risk threshold for a given model. Third, we highlight that adequate discriminative ability for a prediction model depends on the clinical area, and emphasise the importance of calibration, which is almost entirely overlooked in the suicide risk prediction literature. Finally, we point out that conclusions about the clinical utility and health-economic value of suicide prediction models should be based on appropriate measures (such as net benefit and decision-analytic modelling), and highlight the role of impact assessment studies. We conclude that the discussion around using suicide prediction models and risk assessment tools requires more nuance and statistical expertise, and that guidelines and suicide prevention strategies should be informed by the new and higher quality evidence in the field.
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Affiliation(s)
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
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Tariq MU, Ismail SB. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PLoS One 2024; 19:e0294289. [PMID: 38483948 PMCID: PMC10939212 DOI: 10.1371/journal.pone.0294289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 10/28/2023] [Indexed: 03/17/2024] Open
Abstract
The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.
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Affiliation(s)
- Muhammad Usman Tariq
- Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia
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Sá MVBDO, de Morais CNL, Gonçalves RSG, Sarteschi C, Vasconcelos LRS. Predicting the outcome of death by CALL Score in COVID-19 patients. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2024; 70:e20230688. [PMID: 38451572 PMCID: PMC10913782 DOI: 10.1590/1806-9282.20230688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 10/08/2023] [Indexed: 03/08/2024]
Abstract
OBJECTIVE The aim of this study was to assess the performance of the CALL Score tool in predicting the death outcome in COVID-19 patients. METHODS A total of 897 patients were analyzed. Univariate and multivariate logistic regression analyses were conducted to determine the association between characteristics of the CALL Score and the occurrence of death. The relationship between CALL Score risk classification and the occurrence of death was also examined. Receiver operating characteristic curve analysis was performed to identify optimal cutoff points for the CALL Score and the outcome. RESULTS The study revealed that age>60 years, DHL>500, and lymphocyte count ≤1000 emerged as independent predictors of death. Higher risk classifications of the CALL Score were associated with an increased likelihood of death. The optimal CALL Score cutoff point for predicting the death outcome was 9.5 (≥9.5), with a sensitivity of 70.4%, specificity of 80.3%, and accuracy of 80%. CONCLUSION The CALL Score showed promising discriminatory ability for death outcomes in COVID-19 patients. Age, DHL level, and lymphocyte count were identified as independent predictors. Further validation and external evaluation are necessary to establish the robustness and generalizability of the CALL Score in diverse clinical settings.
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Affiliation(s)
- Marcus Villander Barros de Oliveira Sá
- Royal Portuguese Charitable Hospital, Royal Medical Clinic – Recife (PE), Brazil
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Department of Parasitology and Immunology – Recife (PE), Brazil
| | | | | | - Camila Sarteschi
- Royal Portuguese Charitable Hospital, Royal Medical Clinic – Recife (PE), Brazil
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Cappelletti L, Rekerle L, Fontana T, Hansen P, Casiraghi E, Ravanmehr V, Mungall CJ, Yang JJ, Spranger L, Karlebach G, Caufield JH, Carmody L, Coleman B, Oprea TI, Reese J, Valentini G, Robinson PN. Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning. BIOINFORMATICS ADVANCES 2024; 4:vbae036. [PMID: 38577542 PMCID: PMC10994718 DOI: 10.1093/bioadv/vbae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 01/31/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Motivation Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.
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Affiliation(s)
- Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
| | - Peter Hansen
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Vida Ravanmehr
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Jeremy J Yang
- Department of Internal Medicine and UNM Comprehensive Cancer Center, UNM School of Medicine, Albuquerque, NM 87102, United States
| | - Leonard Spranger
- Institute of Bioinformatics, Freie Universität Berlin, Berlin, 14195, Germany
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - J Harry Caufield
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Leigh Carmody
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, United States
| | - Tudor I Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, UNM School of Medicine, Albuquerque, NM 87102, United States
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
- ELLIS—European Laboratory for Learning and Intelligent Systems
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, United States
- ELLIS—European Laboratory for Learning and Intelligent Systems
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, 10117, Germany
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Talimtzi P, Ntolkeras A, Kostopoulos G, Bougioukas KI, Pagkalidou E, Ouranidis A, Pataka A, Haidich AB. The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews. J Clin Epidemiol 2024; 167:111264. [PMID: 38266742 DOI: 10.1016/j.jclinepi.2024.111264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/08/2024] [Accepted: 01/13/2024] [Indexed: 01/26/2024]
Abstract
OBJECTIVES To conduct a methodological overview of reviews to evaluate the reporting completeness and transparency of systematic reviews (SRs) of prognostic prediction models (PPMs) for COVID-19. STUDY DESIGN AND SETTING MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Epistemonikos (epistemonikos.org) were searched for SRs of PPMs for COVID-19 until December 31, 2022. The risk of bias in systematic reviews tool was used to assess the risk of bias. The protocol for this overview was uploaded in the Open Science Framework (https://osf.io/7y94c). RESULTS Ten SRs were retrieved; none of them synthesized the results in a meta-analysis. For most of the studies, there was absence of a predefined protocol and missing information on study selection, data collection process, and reporting of primary studies and models included, while only one SR had its data publicly available. In addition, for the majority of the SRs, the overall risk of bias was judged as being high. The overall corrected covered area was 6.3% showing a small amount of overlapping among the SRs. CONCLUSION The reporting completeness and transparency of SRs of PPMs for COVID-19 was poor. Guidance is urgently required, with increased awareness and education of minimum reporting standards and quality criteria. Specific focus is needed in predefined protocol, information on study selection and data collection process, and in the reporting of findings to improve the quality of SRs of PPMs for COVID-19.
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Affiliation(s)
- Persefoni Talimtzi
- Department of Hygiene, Social-Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece
| | - Antonios Ntolkeras
- School of Biology, Aristotle University of Thessaloniki, University Campus, 54636, Thessaloniki, Greece
| | | | - Konstantinos I Bougioukas
- Department of Hygiene, Social-Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece
| | - Eirini Pagkalidou
- Department of Hygiene, Social-Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece
| | - Andreas Ouranidis
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Athanasia Pataka
- Department of Respiratory Deficiency, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece
| | - Anna-Bettina Haidich
- Department of Hygiene, Social-Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece.
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da Cunha CBC, Lima TA, Ferraz DLDM, Silva ITC, Santiago MKD, Sena GR, Monteiro VS, Andrade LB. Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population. Braz J Cardiovasc Surg 2024; 39:e20230212. [PMID: 38426717 PMCID: PMC10903744 DOI: 10.21470/1678-9741-2023-0212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/17/2023] [Indexed: 03/02/2024] Open
Abstract
INTRODUCTION Blood transfusion is a common practice in cardiac surgery, despite its well-known negative effects. To mitigate blood transfusion-associated risks, identifying patients who are at higher risk of needing this procedure is crucial. Widely used risk scores to predict the need for blood transfusions have yielded unsatisfactory results when validated for the Brazilian population. METHODS In this retrospective study, machine learning (ML) algorithms were compared to predict the need for blood transfusions in a cohort of 495 cardiac surgery patients treated at a Brazilian reference service between 2019 and 2021. The performance of the models was evaluated using various metrics, including the area under the curve (AUC), and compared to the commonly used Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST) scoring systems. RESULTS The study found that the model had the highest performance, achieving an AUC of 0.7350 (confidence interval [CI]: 0.7203 to 0.7497). Importantly, all ML algorithms performed significantly better than the commonly used TRACK and TRUST scoring systems. TRACK had an AUC of 0.6757 (CI: 0.6609 to 0.6906), while TRUST had an AUC of 0.6622 (CI: 0.6473 to 0.6906). CONCLUSION The findings of this study suggest that ML algorithms may offer a more accurate prediction of the need for blood transfusions than the traditional scoring systems and could enhance the accuracy of predicting blood transfusion requirements in cardiac surgery patients. Further research could focus on optimizing and refining ML algorithms to improve their accuracy and make them more suitable for clinical use.
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Affiliation(s)
- Cristiano Berardo Carneiro da Cunha
- Department of Cardiovascular Research, Harvard Medical School,
Boston, Massachusetts, United States of America
- Department of Cardiovascular Research, Brigham and Women’s
Hospital, Boston, Massachusetts, United States of America
- Department of Cardiovascular Surgery, Instituto de Medicina
Integral Professor Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
| | - Tiago Andrade Lima
- Department of Systems Analysis and Development, Instituto Federal
de Pernambuco, Recife, Pernambuco, Brazil
| | - Diogo Luiz de Magalhães Ferraz
- Department of Cardiovascular Surgery, Instituto de Medicina
Integral Professor Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
| | - Igor Tiago Correia Silva
- Department of Cardiovascular Surgery, Instituto de Medicina
Integral Professor Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
| | | | | | - Verônica Soares Monteiro
- Department of Cardiology, Instituto de Medicina Integral Professor
Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
| | - Lívia Barbosa Andrade
- Department of Post-Graduation, Instituto de Medicina Integral
Professor Fernando Figueira (IMIP), Recife, Pernambuco, Brazil
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Bate S, McGovern D, Costigliolo F, Tan PG, Kratky V, Scott J, Chapman GB, Brown N, Floyd L, Brilland B, Martín-Nares E, Aydın MF, Ilyas D, Butt A, Nic an Riogh E, Kollar M, Lees JS, Yildiz A, Hinojosa-Azaola A, Dhaygude A, Roberts SA, Rosenberg A, Wiech T, Pusey CD, Jones RB, Jayne DR, Bajema I, Jennette JC, Stevens KI, Augusto JF, Mejía-Vilet JM, Dhaun N, McAdoo SP, Tesar V, Little MA, Geetha D, Brix SR. The Improved Kidney Risk Score in ANCA-Associated Vasculitis for Clinical Practice and Trials. J Am Soc Nephrol 2024; 35:335-346. [PMID: 38082490 PMCID: PMC10914211 DOI: 10.1681/asn.0000000000000274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/03/2023] [Indexed: 01/27/2024] Open
Abstract
SIGNIFICANCE STATEMENT Reliable prediction tools are needed to personalize treatment in ANCA-associated GN. More than 1500 patients were collated in an international longitudinal study to revise the ANCA kidney risk score. The score showed satisfactory performance, mimicking the original study (Harrell's C=0.779). In the development cohort of 959 patients, no additional parameters aiding the tool were detected, but replacing the GFR with creatinine identified an additional cutoff. The parameter interstitial fibrosis and tubular atrophy was modified to allow wider access, risk points were reweighted, and a fourth risk group was created, improving predictive ability (C=0.831). In the validation, the new model performed similarly well with excellent calibration and discrimination ( n =480, C=0.821). The revised score optimizes prognostication for clinical practice and trials. BACKGROUND Reliable prediction tools are needed to personalize treatment in ANCA-associated GN. A retrospective international longitudinal cohort was collated to revise the ANCA renal risk score. METHODS The primary end point was ESKD with patients censored at last follow-up. Cox proportional hazards were used to reweight risk factors. Kaplan-Meier curves, Harrell's C statistic, receiver operating characteristics, and calibration plots were used to assess model performance. RESULTS Of 1591 patients, 1439 were included in the final analyses, 2:1 randomly allocated per center to development and validation cohorts (52% male, median age 64 years). In the development cohort ( n =959), the ANCA renal risk score was validated and calibrated, and parameters were reinvestigated modifying interstitial fibrosis and tubular atrophy allowing semiquantitative reporting. An additional cutoff for kidney function (K) was identified, and serum creatinine replaced GFR (K0: <250 µ mol/L=0, K1: 250-450 µ mol/L=4, K2: >450 µ mol/L=11 points). The risk points for the percentage of normal glomeruli (N) and interstitial fibrosis and tubular atrophy (T) were reweighted (N0: >25%=0, N1: 10%-25%=4, N2: <10%=7, T0: none/mild or <25%=0, T1: ≥ mild-moderate or ≥25%=3 points), and four risk groups created: low (0-4 points), moderate (5-11), high (12-18), and very high (21). Discrimination was C=0.831, and the 3-year kidney survival was 96%, 79%, 54%, and 19%, respectively. The revised score performed similarly well in the validation cohort with excellent calibration and discrimination ( n =480, C=0.821). CONCLUSIONS The updated score optimizes clinicopathologic prognostication for clinical practice and trials.
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Affiliation(s)
- Sebastian Bate
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Population Health, Health Services Research, and Primary Care, Centre for Biostatistics, University of Manchester, Manchester, United Kingdom
| | - Dominic McGovern
- Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Renal Medicine, Vasculitis Clinic, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Francesca Costigliolo
- Division of Nephrology, Dialysis and Transplantation, University of Genova, Genova, Italy
- Department of Internal Medicine and IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Pek Ghe Tan
- Imperial College Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Renal Unit, Northern Health, Victoria, Australia
| | - Vojtech Kratky
- 1st Faculty of Medicine, Charles University, Prague, Czechia
- Department of Nephrology, General University Hospital, Prague, Czechia
| | - Jennifer Scott
- Trinity Kidney Centre, Trinity College Dublin, Dublin, Ireland
| | - Gavin B. Chapman
- University/BHF Centre for Cardiovascular Science, University of Edinburgh and Department of Renal Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Nina Brown
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
- Renal Department, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom
| | - Lauren Floyd
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
- Renal Department, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom
| | - Benoit Brilland
- Service de Néphrologie-Dialyse-Transplantation, CHU d’Angers, Angers, France
| | - Eduardo Martín-Nares
- Departments of Immunology and Rheumatology, Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | | | - Duha Ilyas
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
- Renal, Transplantation and Urology Unit, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Arslan Butt
- Renal Department, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom
| | | | - Marek Kollar
- Department of Pathology, Institute for Clinical and Experimental Medicine, Prague, Czechia
| | - Jennifer S. Lees
- Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | - Abdülmecit Yildiz
- Division of Nephrology, Bursa Uludağ University School of Medicine, Bursa, Turkey
| | - Andrea Hinojosa-Azaola
- Departments of Immunology and Rheumatology, Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Ajay Dhaygude
- Renal Department, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom
| | - Stephen A. Roberts
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Population Health, Health Services Research, and Primary Care, Centre for Biostatistics, University of Manchester, Manchester, United Kingdom
| | - Avi Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Thorsten Wiech
- University Medical Center Hamburg-Eppendorf, Institute of Pathology, Hamburg, Germany
| | - Charles D. Pusey
- Imperial College Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
| | - Rachel B. Jones
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Renal Medicine, Vasculitis Clinic, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - David R.W. Jayne
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Renal Medicine, Vasculitis Clinic, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Ingeborg Bajema
- Department of Pathology, Groningen University Medical Center, Groningen, The Netherlands
| | - J. Charles Jennette
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Kate I. Stevens
- Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | | | - Juan Manuel Mejía-Vilet
- Departments of Immunology and Rheumatology, Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Neeraj Dhaun
- University/BHF Centre for Cardiovascular Science, University of Edinburgh and Department of Renal Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Stephen P. McAdoo
- Imperial College Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
| | - Vladimir Tesar
- 1st Faculty of Medicine, Charles University, Prague, Czechia
- Department of Nephrology, General University Hospital, Prague, Czechia
| | - Mark A. Little
- Trinity Kidney Centre, Trinity College Dublin, Dublin, Ireland
| | - Duruvu Geetha
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Silke R. Brix
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Renal, Transplantation and Urology Unit, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Cell Matrix Biology and Regenerative Medicine, University of Manchester, Manchester, United Kingdom
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Ryan DK, Maclean RH, Balston A, Scourfield A, Shah AD, Ross J. Artificial intelligence and machine learning for clinical pharmacology. Br J Clin Pharmacol 2024; 90:629-639. [PMID: 37845024 DOI: 10.1111/bcp.15930] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023] Open
Abstract
Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.
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Affiliation(s)
- David K Ryan
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Rory H Maclean
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Alfred Balston
- Department of Clinical Pharmacology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Andrew Scourfield
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anoop D Shah
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Jack Ross
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
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70
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How to support the transition to AI-powered healthcare. Nat Med 2024; 30:609-610. [PMID: 38504014 DOI: 10.1038/s41591-024-02897-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
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71
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Hill DLG. AI in imaging: the regulatory landscape. Br J Radiol 2024; 97:483-491. [PMID: 38366148 PMCID: PMC11027239 DOI: 10.1093/bjr/tqae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/03/2023] [Accepted: 12/26/2023] [Indexed: 02/18/2024] Open
Abstract
Artificial intelligence (AI) methods have been applied to medical imaging for several decades, but in the last few years, the number of publications and the number of AI-enabled medical devices coming on the market have significantly increased. While some AI-enabled approaches are proving very valuable, systematic reviews of the AI imaging field identify significant weaknesses in a significant proportion of the literature. Medical device regulators have recently become more proactive in publishing guidance documents and recognizing standards that will require that the development and validation of AI-enabled medical devices need to be more rigorous than required for tradition "rule-based" software. In particular, developers are required to better identify and mitigate risks (such as bias) that arise in AI-enabled devices, and to ensure that the devices are validated in a realistic clinical setting to ensure their output is clinically meaningful. While this evolving regulatory landscape will mean that device developers will take longer to bring novel AI-based medical imaging devices to market, such additional rigour is necessary to address existing weaknesses in the field and ensure that patients and healthcare professionals can trust AI-enabled devices. There would also be benefits in the academic community taking into account this regulatory framework, to improve the quality of the literature and make it easier for academically developed AI tools to make the transition to medical devices that impact healthcare.
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Takashima Y, Inaba T, Matsuyama T, Yoshii K, Tanaka M, Matsumoto K, Sudo K, Tokuda Y, Omi N, Nakano M, Nakaya T, Fujita N, Sotozono C, Sawa T, Tashiro K, Ohta B. Potential marker subset of blood-circulating cytokines on hematopoietic progenitor-to-Th1 pathway in COVID-19. Front Med (Lausanne) 2024; 11:1319980. [PMID: 38476443 PMCID: PMC10927758 DOI: 10.3389/fmed.2024.1319980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/31/2024] [Indexed: 03/14/2024] Open
Abstract
In this study, we analyzed a relatively large subset of proteins, including 109 kinds of blood-circulating cytokines, and precisely described a cytokine storm in the expression level and the range of fluctuations during hospitalization for COVID-19. Of the proteins analyzed in COVID-19, approximately 70% were detected with Bonferroni-corrected significant differences in comparison with disease severity, clinical outcome, long-term hospitalization, and disease progression and recovery. Specifically, IP-10, sTNF-R1, sTNF-R2, sCD30, sCD163, HGF, SCYB16, IL-16, MIG, SDF-1, and fractalkine were found to be major components of the COVID-19 cytokine storm. Moreover, the 11 cytokines (i.e., SDF-1, SCYB16, sCD30, IL-11, IL-18, IL-8, IFN-γ, TNF-α, sTNF-R2, M-CSF, and I-309) were associated with the infection, mortality, disease progression and recovery, and long-term hospitalization. Increased expression of these cytokines could be explained in sequential pathways from hematopoietic progenitor cell differentiation to Th1-derived hyperinflammation in COVID-19, which might also develop a novel strategy for COVID-19 therapy with recombinant interleukins and anti-chemokine drugs.
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Affiliation(s)
- Yasuo Takashima
- Department of Genomic Medical Sciences, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Tohru Inaba
- Department of Infection Control and Laboratory Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Tasuku Matsuyama
- Department of Emergency Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kengo Yoshii
- Department of Mathematics and Statistics in Medical Sciences, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Masami Tanaka
- Department of Genomic Medical Sciences, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kazumichi Matsumoto
- Faculty of Clinical Laboratory, University Hospital Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kazuki Sudo
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yuichi Tokuda
- Department of Genomic Medical Sciences, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Natsue Omi
- Department of Genomic Medical Sciences, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Masakazu Nakano
- Department of Genomic Medical Sciences, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Takaaki Nakaya
- Department of Infectious Diseases, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Naohisa Fujita
- Department of Infection Control and Laboratory Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
- Kyoto Prefectural Institute of Public Health and Environment, Kyoto, Japan
| | - Chie Sotozono
- Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Teiji Sawa
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
- University Hospital Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kei Tashiro
- Department of Genomic Medical Sciences, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Bon Ohta
- Department of Emergency Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Rauscher FG, Bernardes R. Retinal OCT biomarkers and their association with cognitive function-clinical and AI approaches. DIE OPHTHALMOLOGIE 2024:10.1007/s00347-024-01988-9. [PMID: 38381373 DOI: 10.1007/s00347-024-01988-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 02/22/2024]
Abstract
Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in monitoring disease progression and evaluating the effectiveness of interventions targeting cognitive decline. The association between retinal OCT biomarkers and cognitive performance has been demonstrated in several studies, and their importance in cognitive assessment is increasingly being recognized. Machine learning (ML) is a branch of artificial intelligence (AI) with an exponential number of applications in the medical field, particularly its deep learning (DL) subset, which is widely used for the analysis of medical images. These techniques efficiently deal with novel biomarkers when their outcome for the applications of interest is unclear, e.g., for diagnosis, prognosis prediction, disease staging, or any other relevance to clinical practice. However, using AI-based tools for medical purposes must be approached with caution, despite the many efforts to address the black-box nature of such approaches, especially due to the general underperformance in datasets other than those used for their development. Retinal OCT biomarkers are promising as potential indicators for decline in cognitive function. The underlying mechanisms are currently being explored to gain deeper insights into this relationship linking retinal health and cognitive function. Insights from neurovascular coupling and retinal microvascular changes play an important role. Further research is needed to establish the validity and utility of retinal OCT biomarkers as early indicators of cognitive decline and neurodegenerative diseases in routine clinical practice. Retinal OCT biomarkers could then provide a new avenue for early detection, monitoring and intervention in cognitive impairment with the potential to improve patient care and outcomes.
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Affiliation(s)
- Franziska G Rauscher
- Leipzig Research Centre for Civilisation Diseases (LIFE), Leipzig University, Leipzig, Germany.
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Haertelstraße 16-18, 04107, Leipzig, Germany.
- Centre for Medical Informatics - Department of Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.
| | - Rui Bernardes
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Azinhaga de Santa Comba, 3000-548, Coimbra, Portugal
- Clinical Academic Center of Coimbra (CACC), Faculty of Medicine (FMUC), University of Coimbra, Azinhaga de Santa Comba, 3000-548, Coimbra, Portugal
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Fehr J, Citro B, Malpani R, Lippert C, Madai VI. A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare. Front Digit Health 2024; 6:1267290. [PMID: 38455991 PMCID: PMC10919164 DOI: 10.3389/fdgth.2024.1267290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/05/2024] [Indexed: 03/09/2024] Open
Abstract
Trustworthy medical AI requires transparency about the development and testing of underlying algorithms to identify biases and communicate potential risks of harm. Abundant guidance exists on how to achieve transparency for medical AI products, but it is unclear whether publicly available information adequately informs about their risks. To assess this, we retrieved public documentation on the 14 available CE-certified AI-based radiology products of the II b risk category in the EU from vendor websites, scientific publications, and the European EUDAMED database. Using a self-designed survey, we reported on their development, validation, ethical considerations, and deployment caveats, according to trustworthy AI guidelines. We scored each question with either 0, 0.5, or 1, to rate if the required information was "unavailable", "partially available," or "fully available." The transparency of each product was calculated relative to all 55 questions. Transparency scores ranged from 6.4% to 60.9%, with a median of 29.1%. Major transparency gaps included missing documentation on training data, ethical considerations, and limitations for deployment. Ethical aspects like consent, safety monitoring, and GDPR-compliance were rarely documented. Furthermore, deployment caveats for different demographics and medical settings were scarce. In conclusion, public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks. We call on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health.
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Affiliation(s)
- Jana Fehr
- Digital Health & Machine Learning, Hasso Plattner Institute, Potsdam, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Brian Citro
- Independent Researcher, Chicago, IL, United States
| | | | - Christoph Lippert
- Digital Health & Machine Learning, Hasso Plattner Institute, Potsdam, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Vince I. Madai
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom
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Barreñada L, Ledger A, Dhiman P, Collins G, Wynants L, Verbakel JY, Timmerman D, Valentin L, Van Calster B. ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies. BMJ MEDICINE 2024; 3:e000817. [PMID: 38375077 PMCID: PMC10875560 DOI: 10.1136/bmjmed-2023-000817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/25/2024] [Indexed: 02/21/2024]
Abstract
Objectives To conduct a systematic review of studies externally validating the ADNEX (Assessment of Different Neoplasias in the adnexa) model for diagnosis of ovarian cancer and to present a meta-analysis of its performance. Design Systematic review and meta-analysis of external validation studies. Data sources Medline, Embase, Web of Science, Scopus, and Europe PMC, from 15 October 2014 to 15 May 2023. Eligibility criteria for selecting studies All external validation studies of the performance of ADNEX, with any study design and any study population of patients with an adnexal mass. Two independent reviewers extracted the data. Disagreements were resolved by discussion. Reporting quality of the studies was scored with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) reporting guideline, and methodological conduct and risk of bias with PROBAST (Prediction model Risk Of Bias Assessment Tool). Random effects meta-analysis of the area under the receiver operating characteristic curve (AUC), sensitivity and specificity at the 10% risk of malignancy threshold, and net benefit and relative utility at the 10% risk of malignancy threshold were performed. Results 47 studies (17 007 tumours) were included, with a median study sample size of 261 (range 24-4905). On average, 61% of TRIPOD items were reported. Handling of missing data, justification of sample size, and model calibration were rarely described. 91% of validations were at high risk of bias, mainly because of the unexplained exclusion of incomplete cases, small sample size, or no assessment of calibration. The summary AUC to distinguish benign from malignant tumours in patients who underwent surgery was 0.93 (95% confidence interval 0.92 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX with the serum biomarker, cancer antigen 125 (CA125), as a predictor (9202 tumours, 43 centres, 18 countries, and 21 studies) and 0.93 (95% confidence interval 0.91 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX without CA125 (6309 tumours, 31 centres, 13 countries, and 12 studies). The estimated probability that the model has use clinically in a new centre was 95% (with CA125) and 91% (without CA125). When restricting analysis to studies with a low risk of bias, summary AUC values were 0.93 (with CA125) and 0.91 (without CA125), and estimated probabilities that the model has use clinically were 89% (with CA125) and 87% (without CA125). Conclusions The results of the meta-analysis indicated that ADNEX performed well in distinguishing between benign and malignant tumours in populations from different countries and settings, regardless of whether the serum biomarker, CA125, was used as a predictor. A key limitation was that calibration was rarely assessed. Systematic review registration PROSPERO CRD42022373182.
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Affiliation(s)
- Lasai Barreñada
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ashleigh Ledger
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Paula Dhiman
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford, UK
| | - Gary Collins
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford, UK
| | - Laure Wynants
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Epidemiology, Universiteit Maastricht Care and Public Health Research Institute, Maastricht, Netherlands
| | - Jan Y Verbakel
- Department of Public Health and Primary care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Leuven Unit for Health Technology Assessment Research (LUHTAR), KU Leuven, Leuven, Belgium
| | - Dirk Timmerman
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Obstetrics and Gynaecology, UZ Leuven campus Gasthuisberg Dienst gynaecologie en verloskunde, Leuven, Belgium
| | - Lil Valentin
- Department of Obstetrics and Gynaecology, Skåne University Hospital, Malmo, Sweden
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Leuven Unit for Health Technology Assessment Research (LUHTAR), KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
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76
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Rachman A, Rahmaniyah R, Khomeini A, Iriani A. The association between vitamin D deficiency and the clinical outcomes of hospitalized COVID-19 patients. F1000Res 2024; 12:394. [PMID: 38434628 PMCID: PMC10905025 DOI: 10.12688/f1000research.132214.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/01/2024] [Indexed: 03/05/2024] Open
Abstract
Background Vitamin D deficiency is an emerging public health problem that affects more than one billion people worldwide. Vitamin D has been shown to be effective in preventing and reducing the severity of viral respiratory diseases, including influenza. However, the role of vitamin D in COVID-19 infection remains controversial. This study aimed to analyze the association of vitamin D deficiency on the clinical outcome of hospitalized COVID-19 patients. Methods A prospective cohort study was conducted among hospitalized COVID-19 patients at two COVID-19 referral hospitals in Indonesia from October 2021 until February 2022. Results The median serum 25(OH)D level in 191 hospitalized COVID-19 patients was 13.6 [IQR=10.98] ng/mL. The serum 25(OH)D levels were significantly lower among COVID-19 patients with vitamin D deficiency who had cardiovascular disease (p-value=0.04), the use of a ventilator (p-value=0.004), more severe COVID-19 cases (p-value=0.047), and mortality (p-value=0.002). Furthermore, serum 25(OH)D levels were significantly different between patients with mild and severe COVID-19 cases (p-value=0.019). Serum 25(OH)D levels in moderate and severe COVID-19 cases were significantly different (p-value=0.031). Lower serum 25(OH)D levels were significantly associated with an increased number of comorbidities (p-value=0.03), the severity of COVID-19 (p-value=0.002), and the use of mechanical ventilation (p-value=0.032). Mortality was found in 7.3% of patients with deficient vitamin D levels. However, patients with either sufficient or insufficient vitamin D levels did not develop mortality. Conclusions COVID-19 patients with vitamin D deficiency were significantly associated with having cardiovascular disease, mortality, more severe COVID-19 cases, and the used of mechanical ventilation. Lower serum 25(OH)D levels were associated with an increased number of comorbidities, COVID-19 severity, and the use of mechanical-ventilation. Thus, we suggest hospitalized COVID-19 patients to reach a sufficient vitamin D status to improve the clinical outcome of the disease.
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Affiliation(s)
- Andhika Rachman
- Division of Hematology and Oncology, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine, Universitas Indonesia, Centra Jakarta, DKI Jakarta, 10430, Indonesia
| | - Rizky Rahmaniyah
- Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Central Jakarta, DKI Jakarta, 10430, Indonesia
| | - Andi Khomeini
- Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Central Jakarta, DKI Jakarta, 10430, Indonesia
- Department of Internal Medicine, Wisma Atlet COVID-19 Emergency Hospital, North Jakarta, DKI Jakarta, 14360, Indonesia
| | - Anggraini Iriani
- Department of Clinical Pathology, Yarsi University, Central Jakarta, DKI Jakarta, 10510, Indonesia
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77
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Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Mousavi Baigi SF, Rezaei Sarsari M, Dahmardeh Kemmak F, Mazaheri Habibi MR. The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review. Health Sci Rep 2024; 7:e1893. [PMID: 38357491 PMCID: PMC10865276 DOI: 10.1002/hsr2.1893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Background and Aims This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.
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Affiliation(s)
- Kosar Ghaddaripouri
- Department of Health Information Management, School of Health Management and Information SciencesShiraz University of Medical SciencesShirazIran
| | - Maryam Ghaddaripouri
- Department of Laboratory Sciences, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
| | | | - Seyyedeh Fatemeh Mousavi Baigi
- Mashhad University of Medical SciencesMashhadIran
- Student Research CommitteeMashhad University of Medical SciencesMashhadIran
| | | | - Fatemeh Dahmardeh Kemmak
- Mashhad University of Medical SciencesMashhadIran
- Student Research CommitteeMashhad University of Medical SciencesMashhadIran
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78
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Ozden MF, Sogut O, Az A, Dogan Y. Analysis of Age-Specific Predictors of Mortality in Patients with Coronavirus Disease 2019. Niger J Clin Pract 2024; 27:244-251. [PMID: 38409154 DOI: 10.4103/njcp.njcp_507_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/16/2023] [Indexed: 02/28/2024]
Abstract
AIM We investigated predictors of mortality, including demographic, clinical, and laboratory parameters, in hospitalized patients with coronavirus disease 2019 (COVID-19) pneumonia. PATIENTS AND METHODS This retrospective, single-center study included 651 consecutive Turkish adults who had been admitted to the emergency department with a diagnosis of COVID-19. We recorded the demographic, clinical, and laboratory parameters of the patients. The patients were divided into two groups: patients aged ≥65 years and patients aged <65 years. The predictors of mortality for hospitalized COVID-19 patients were evaluated. RESULTS The study included 651 patients (354 [54.4%] men and 297 [45.6%] women; mean age, 56.40 ± 15.70 years). The most common comorbidities were hypertension (37.6%), diabetes mellitus (28.9%), and coronary artery disease (CAD) (16.1%). The overall mortality rate was 10.6% (n = 69); the mortality rate was higher in men than in women. Advanced age; chronic renal failure (CRF); prolonged activated partial thromboplastin time; high serum neutrophil and platelet counts; high C-reactive protein to albumin (CRP/albumin) ratio; and high levels of albumin, lactate dehydrogenase (LDH), and high-sensitivity troponin I (TnI-hs) were independent predictors of mortality in all age groups. CONCLUSION Multivariate logistic regression analysis showed that chronic obstructive pulmonary disease (COPD), high serum platelet count, high CRP/albumin ratio, and high levels of albumin, TnI-hs, and D-dimer were independent predictors of mortality in patients aged <65 years. Conversely, advanced age, CAD, CRF, and high levels of serum CRP and LDH were independent predictors of mortality in patients aged ≥65 years.
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Affiliation(s)
- M F Ozden
- Department of Emergency Medicine, University of Health Sciences, Haseki Training and Research Hospital, Istanbul, Turkey
| | - O Sogut
- Department of Emergency Medicine, University of Health Sciences, Haseki Training and Research Hospital, Istanbul, Turkey
| | - A Az
- Department of Emergency Medicine, Istanbul Beylikdüzü State Hospital, Istanbul, Turkey
| | - Y Dogan
- Department of Emergency Medicine, Mus State Hospital, Mus, Turkey
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79
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Rauscher FG, Bernardes R. [Retinal optical coherence tomography biomarkers and their association with cognitive functions : Clinical and artificial intelligence approaches. German version]. DIE OPHTHALMOLOGIE 2024; 121:105-115. [PMID: 38285070 DOI: 10.1007/s00347-024-01985-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/04/2024] [Indexed: 01/30/2024]
Abstract
Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in monitoring disease progression and evaluating the effectiveness of interventions targeting cognitive decline. The association between retinal OCT biomarkers and cognitive performance has been demonstrated in several studies, and their importance in cognitive assessment is increasingly being recognized. Machine learning (ML) is a branch of artificial intelligence (AI) with an exponential number of applications in the medical field, particularly its deep learning (DL) subset, which is widely used for the analysis of medical images. These techniques efficiently deal with novel biomarkers when their outcome for the applications of interest are unclear, e.g., for the diagnosis, prognosis prediction and disease staging. However, using AI-based tools for medical purposes must be approached with caution, despite the many efforts to address the black-box nature of such approaches, especially due to the general underperformance in datasets other than those used for their development. Retinal OCT biomarkers are promising as potential indicators for decline in cognitive function. The underlying mechanisms are currently being explored to gain deeper insights into this relationship linking retinal health and cognitive function. Insights from neurovascular coupling and retinal microvascular changes play an important role. Further research is needed to establish the validity and utility of retinal OCT biomarkers as early indicators of cognitive decline and neurodegenerative diseases in routine clinical practice. Retinal OCT biomarkers could then provide a new avenue for early detection, monitoring and intervention in cognitive impairment with the potential to improve patient care and outcomes.
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Affiliation(s)
- Franziska G Rauscher
- Leipzig Research Centre for Civilisation Diseases (LIFE), Leipzig University, Leipzig, Deutschland.
- Institute for Medical Informatics, Statistics, and Epidemiology , Leipzig University, Härtelstr 16-18, 04107, Leipzig, Deutschland.
- Centre for Medical Informatics - Department of Medical Data Science, Leipzig University Medical Center, Leipzig, Deutschland.
| | - Rui Bernardes
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Azinhaga de Santa Comba, 3000-548, Coimbra, Portugal
- Clinical Academic Center of Coimbra (CACC), Faculty of Medicine (FMUC), University of Coimbra, Azinhaga de Santa Comba, 3000-548, Coimbra, Portugal
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Hopkins R, Young KG, Thomas NJ, Godwin J, Raja D, Mateen BA, Challen RJ, Vollmer SJ, Shields BM, McGovern AP, Dennis JM. Risk factor associations for severe COVID-19, influenza and pneumonia in people with diabetes to inform future pandemic preparations: UK population-based cohort study. BMJ Open 2024; 14:e078135. [PMID: 38296292 PMCID: PMC10831438 DOI: 10.1136/bmjopen-2023-078135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/17/2024] [Indexed: 02/03/2024] Open
Abstract
OBJECTIVE This study aimed to compare clinical and sociodemographic risk factors for severe COVID-19, influenza and pneumonia, in people with diabetes. DESIGN Population-based cohort study. SETTING UK primary care records (Clinical Practice Research Datalink) linked to mortality and hospital records. PARTICIPANTS Individuals with type 1 and type 2 diabetes (COVID-19 cohort: n=43 033 type 1 diabetes and n=584 854 type 2 diabetes, influenza and pneumonia cohort: n=42 488 type 1 diabetes and n=585 289 type 2 diabetes). PRIMARY AND SECONDARY OUTCOME MEASURES COVID-19 hospitalisation from 1 February 2020 to 31 October 2020 (pre-COVID-19 vaccination roll-out), and influenza and pneumonia hospitalisation from 1 September 2016 to 31 May 2019 (pre-COVID-19 pandemic). Secondary outcomes were COVID-19 and pneumonia mortality. Associations between clinical and sociodemographic risk factors and each outcome were assessed using multivariable Cox proportional hazards models. In people with type 2 diabetes, we explored modifying effects of glycated haemoglobin (HbA1c) and body mass index (BMI) by age, sex and ethnicity. RESULTS In type 2 diabetes, poor glycaemic control and severe obesity were consistently associated with increased risk of hospitalisation for COVID-19, influenza and pneumonia. The highest HbA1c and BMI-associated relative risks were observed in people aged under 70 years. Sociodemographic-associated risk differed markedly by respiratory infection, particularly for ethnicity. Compared with people of white ethnicity, black and south Asian groups had a greater risk of COVID-19 hospitalisation, but a lesser risk of pneumonia hospitalisation. Risk factor associations for type 1 diabetes and for type 2 diabetes mortality were broadly consistent with the primary analysis. CONCLUSIONS Clinical risk factors of high HbA1c and severe obesity are consistently associated with severe outcomes from COVID-19, influenza and pneumonia, especially in younger people. In contrast, associations with sociodemographic risk factors differed by type of respiratory infection. This emphasises that risk stratification should be specific to individual respiratory infections.
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Affiliation(s)
- Rhian Hopkins
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Katherine G Young
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Nicholas J Thomas
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - James Godwin
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Daniyal Raja
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Bilal A Mateen
- The Alan Turing Institute, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Robert J Challen
- Engineering Mathematics, University of Bristol, Bristol, UK
- NIHR Applied Research Collaboration South West Peninsula, Exeter, UK
| | | | - Beverley M Shields
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Andrew P McGovern
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - John M Dennis
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
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81
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Plášek J, Dodulík J, Gai P, Hrstková B, Škrha J, Zlatohlávek L, Vlasáková R, Danko P, Ondráček P, Čubová E, Čapek B, Kollárová M, Fürst T, Václavík J. A Simple Risk Formula for the Prediction of COVID-19 Hospital Mortality. Infect Dis Rep 2024; 16:105-115. [PMID: 38391586 PMCID: PMC10887710 DOI: 10.3390/idr16010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
SARS-CoV-2 respiratory infection is associated with significant morbidity and mortality in hospitalized patients. We aimed to assess the risk factors for hospital mortality in non-vaccinated patients during the 2021 spring wave in the Czech Republic. A total of 991 patients hospitalized between January 2021 and March 2021 with a PCR-confirmed SARS-CoV-2 acute respiratory infection in two university hospitals and five rural hospitals were included in this analysis. After excluding patients with unknown outcomes, 790 patients entered the final analyses. Out of 790 patients included in the analysis, 282/790 (35.7%) patients died in the hospital; 162/790 (20.5) were male and 120/790 (15.2%) were female. There were 141/790 (18%) patients with mild, 461/790 (58.3%) with moderate, and 187/790 (23.7%) with severe courses of the disease based mainly on the oxygenation status. The best-performing multivariate regression model contains only two predictors-age and the patient's state; both predictors were rendered significant (p < 0.0001). Both age and disease state are very significant predictors of hospital mortality. An increase in age by 10 years raises the risk of hospital mortality by a factor of 2.5, and a unit increase in the oxygenation status raises the risk of hospital mortality by a factor of 20.
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Affiliation(s)
- Jiří Plášek
- Department of Internal Medicine and Cardiology, University Hospital Ostrava, 708 52 Ostrava, Czech Republic
- Centre for Research on Internal Medicine and Cardiovascular Diseases, University of Ostrava, 703 00 Ostrava, Czech Republic
| | - Jozef Dodulík
- Department of Internal Medicine and Cardiology, University Hospital Ostrava, 708 52 Ostrava, Czech Republic
| | - Petr Gai
- Department of Pulmonary Medicine and Tuberculosis, University Hospital Ostrava, 708 52 Ostrava, Czech Republic
| | - Barbora Hrstková
- Department of Infectious Diseases, University Hospital Ostrava, 708 52 Ostrava, Czech Republic
| | - Jan Škrha
- Department of Internal Medicine, General University Hospital, 128 08 Prague, Czech Republic
| | - Lukáš Zlatohlávek
- Department of Internal Medicine, General University Hospital, 128 08 Prague, Czech Republic
| | - Renata Vlasáková
- Department of Internal Medicine, General University Hospital, 128 08 Prague, Czech Republic
| | - Peter Danko
- Department of Internal Medicine, Havířov Regional Hospital, 736 01 Havířov, Czech Republic
| | - Petr Ondráček
- Department of Internal Medicine, Bílovec Regional Hospital, 743 01 Bílovec, Czech Republic
| | - Eva Čubová
- Department of Internal Medicine, Fifejdy City Hospital, 728 80 Ostrava, Czech Republic
| | - Bronislav Čapek
- Department of Internal Medicine, Associated Medical Facilities, 794 01 Krnov, Czech Republic
| | - Marie Kollárová
- Department of Internal Medicine, Třinec Regional Hospital, 739 61 Třinec, Czech Republic
| | - Tomáš Fürst
- Department of Mathematical Analysis and Application of Mathematics, Palacky University, 771 46 Olomouc, Czech Republic
| | - Jan Václavík
- Department of Internal Medicine and Cardiology, University Hospital Ostrava, 708 52 Ostrava, Czech Republic
- Centre for Research on Internal Medicine and Cardiovascular Diseases, University of Ostrava, 703 00 Ostrava, Czech Republic
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82
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Niazkar HR, Moshari J, Khajavi A, Ghorbani M, Niazkar M, Negari A. Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables. Sci Rep 2024; 14:2043. [PMID: 38263446 PMCID: PMC10806074 DOI: 10.1038/s41598-024-52529-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 01/19/2024] [Indexed: 01/25/2024] Open
Abstract
Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020-2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60-92%, the duration of hospital stay with an accuracy of 53-65%, and admission to the ICU with an accuracy of 76-91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended.
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Affiliation(s)
- Hamid Reza Niazkar
- Gonabad University of Medical Sciences, Gonabad, Iran.
- Breast Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Jalil Moshari
- Pediatric Department, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Abdoljavad Khajavi
- Community Medicine Department, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Mohammad Ghorbani
- Laboratory hematology and Transfusion medicine, Department of Medical Laboratory Sciences, Faculty of Allied Medicine, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Majid Niazkar
- Faculty of Engineering, Free University of Bozen-Bolzano, Piazza Università 5, 39100 Bolzano, Italy
| | - Aida Negari
- Gonabad University of Medical Sciences, Gonabad, Iran
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83
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Okada N, Umemura Y, Shi S, Inoue S, Honda S, Matsuzawa Y, Hirano Y, Kikuyama A, Yamakawa M, Gyobu T, Hosomi N, Minami K, Morita N, Watanabe A, Yamasaki H, Fukaguchi K, Maeyama H, Ito K, Okamoto K, Harano K, Meguro N, Unita R, Koshiba S, Endo T, Yamamoto T, Yamashita T, Shinba T, Fujimi S. "KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals. Sci Rep 2024; 14:1672. [PMID: 38243054 PMCID: PMC10799049 DOI: 10.1038/s41598-024-52135-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 01/14/2024] [Indexed: 01/21/2024] Open
Abstract
Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the "KAIZEN checklist", which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models' AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.
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Affiliation(s)
| | | | - Shoi Shi
- University of Tsukuba, Tsukuba, Japan
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ken Okamoto
- Juntendo University Urayasu Hospital, Urayasu, Japan
| | | | | | - Ryo Unita
- National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | | | - Takuro Endo
- International University of Health and Welfare, School of Medicine, Narita Hospital, Narita, Japan
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84
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Yamaguchi F, Suzuki A, Hashiguchi M, Kondo E, Maeda A, Yokoe T, Sasaki J, Shikama Y, Hayashi M, Kobayashi S, Suzuki H. Combination of rRT-PCR and Clinical Features to Predict Coronavirus Disease 2019 for Nosocomial Infection Control. Infect Drug Resist 2024; 17:161-170. [PMID: 38260181 PMCID: PMC10802122 DOI: 10.2147/idr.s432198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/29/2023] [Indexed: 01/24/2024] Open
Abstract
Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), immediately became a pandemic. Therefore, nosocomial infection control is necessary to screen for patients with possible COVID-19. Objective This study aimed to investigate commonly measured clinical variables to predict COVID-19. Methods This cross-sectional study enrolled 1087 patients in the isolation ward of a university hospital. Conferences were organized to differentiate COVID-19 from non-COVID-19 cases, and multiple nucleic acid tests were mandatory when COVID-19 could not be excluded. Multivariate logistic regression models were employed to determine the clinical factors associated with COVID-19 at the time of hospitalization. Results Overall, 352 (32.4%) patients were diagnosed with COVID-19. The majority of the non-COVID-19 cases were predominantly caused by bacterial infections. Multivariate analysis indicated that COVID-19 was significantly associated with age, sex, body mass index, lactate dehydrogenase, C-reactive protein, and malignancy. Conclusion Some clinical factors are useful to predict patients with COVID-19 among those with symptoms similar to COVID-19. This study suggests that at least two real-time reverse-transcription polymerase chain reactions of SARS-CoV-2 are recommended to exclude COVID-19.
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Affiliation(s)
- Fumihiro Yamaguchi
- Department of Respiratory Medicine, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Ayako Suzuki
- Department of Pharmacy, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Miyuki Hashiguchi
- Department of Infection Control, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Emiko Kondo
- Department of Infection Control, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Atsuo Maeda
- Department of Emergency and Critical Care Medicine, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Takuya Yokoe
- Department of Respiratory Medicine, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Jun Sasaki
- Department of Emergency and Critical Care Medicine, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Yusuke Shikama
- Department of Respiratory Medicine, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Munetaka Hayashi
- Department of Emergency and Critical Care Medicine, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Sei Kobayashi
- Department of Otolaryngology, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Hiroshi Suzuki
- Department of Cardiology, Showa University Fujigaoka Hospital, Yokohama, Japan
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Tabja Bortesi JP, Ranisau J, Di S, McGillion M, Rosella L, Johnson A, Devereaux PJ, Petch J. Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review. J Med Internet Res 2024; 26:e52880. [PMID: 38236623 PMCID: PMC10835585 DOI: 10.2196/52880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/09/2023] [Accepted: 12/12/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)-based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment. OBJECTIVE The objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images. METHODS We conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not address SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using tables. Employment of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines was evaluated and PROBAST (Prediction Model Risk of Bias Assessment Tool) was used to assess risk of bias (RoB). RESULTS In total, 10 of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly color images, and the volume of images used ranged from under 50 to thousands. Further, 10 TRIPOD items were reported in at least 4 studies, though 15 items were reported in fewer than 4 studies. PROBAST assessment led to 9 studies being identified as having an overall high RoB, with 1 study having overall unclear RoB. CONCLUSIONS Research on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future.
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Affiliation(s)
| | - Jonathan Ranisau
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
| | - Shuang Di
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | | | - Laura Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | | | - P J Devereaux
- Population Health Research Institute, Hamilton, ON, Canada
| | - Jeremy Petch
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton, ON, Canada
- Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Division of Cardiology, McMaster University, Hamilton, ON, Canada
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86
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Shah NH, Halamka JD, Saria S, Pencina M, Tazbaz T, Tripathi M, Callahan A, Hildahl H, Anderson B. A Nationwide Network of Health AI Assurance Laboratories. JAMA 2024; 331:245-249. [PMID: 38117493 DOI: 10.1001/jama.2023.26930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Importance Given the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed. Observations While there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings. Conclusion and Relevance The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.
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Affiliation(s)
- Nigam H Shah
- Stanford Medicine, Palo Alto, California
- Coalition for Health AI, Dover, Delaware
| | - John D Halamka
- Coalition for Health AI, Dover, Delaware
- Mayo Clinic Platform, Mayo Clinic, Rochester, Minnesota
| | - Suchi Saria
- Coalition for Health AI, Dover, Delaware
- Bayesian Health, New York, New York
- Johns Hopkins University, Baltimore, Maryland
- Johns Hopkins Medicine, Baltimore, Maryland
| | - Michael Pencina
- Coalition for Health AI, Dover, Delaware
- Duke AI Health, Duke University School of Medicine, Durham, North Carolina
| | - Troy Tazbaz
- US Food and Drug Administration, Silver Spring, Maryland
| | - Micky Tripathi
- US Office of the National Coordinator for Health IT, Washington, DC
| | | | | | - Brian Anderson
- Coalition for Health AI, Dover, Delaware
- MITRE Corporation, Bedford, Massachusetts
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87
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Baek S, Jeong YJ, Kim YH, Kim JY, Kim JH, Kim EY, Lim JK, Kim J, Kim Z, Kim K, Chung MJ. Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study. J Med Internet Res 2024; 26:e52134. [PMID: 38206673 PMCID: PMC10811577 DOI: 10.2196/52134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/03/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. OBJECTIVE The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. METHODS We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. RESULTS Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). CONCLUSIONS RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.
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Affiliation(s)
- Sangwon Baek
- Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Center for Data Science, New York University, New York, NY, United States
| | - Yeon Joo Jeong
- Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Yun-Hyeon Kim
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Jin Young Kim
- Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea
| | - Jin Hwan Kim
- Department of Radiology, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Eun Young Kim
- Department of Radiology, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Jae-Kwang Lim
- Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Jungok Kim
- Department of Infectious Diseases, Chungnam National University Sejong Hospital, Sejong, Republic of Korea
| | - Zero Kim
- Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Myung Jin Chung
- Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Radiology, Samsung Medical Center, Seoul, Republic of Korea
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Oliveira FMS, Caetano MMM, de Godoy ARV, de Oliveira LL, de Melo Mambrini JV, Rezende MS, Fantini MPR, Oliveira Mendes TAD, Medeiros NI, Guimarães HC, Fiuza JA, Gaze ST. Retrospective cohort study to evaluate the continuous use of anticholesterolemics and diuretics in patients with COVID-19. Front Med (Lausanne) 2024; 10:1252556. [PMID: 38274462 PMCID: PMC10808793 DOI: 10.3389/fmed.2023.1252556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 12/12/2023] [Indexed: 01/27/2024] Open
Abstract
Purpose The purpose of this study is to evaluate the interference of the continuous use of drug classes in the expression of biomarkers during the first week of hospitalization and in the prognosis of patients with COVID-19. Methods The patients diagnosed with COVID-19 and confirmed with SARS-CoV-2 by RT-qPCR assay underwent the collection of fasting whole blood samples for further analysis. Other data also extracted for this study included age, sex, clinical symptoms, related comorbidities, smoking status, and classes of continuous use. Routine serum biochemical parameters, including alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, C-reactive protein, N-terminal fragment of B-type natriuretic peptide, and cardiac troponin, were measured. Results In this cross-sectional study, a total of 176 patients with COVID-19 hospitalizations were included. Among them, 155 patients were discharged (88.5%), and 21 patients died (12%). Among the drug classes evaluated, we verified that the continuous use of diuretic 4.800 (1.853-11.67) (p = 0.0007) and antihypercholesterolemic 3.188 (1.215-7.997) (p = 0.0171) drug classes presented a significant relative risk of death as an outcome when compared to the group of patients who were discharged. We evaluated biomarkers in patients who used continuous antihypercholesterolemic and diuretic drug classes in the first week of hospitalization. We observed significant positive correlations between the levels of CRP with cardiac troponin (r = 0.714), IL-6 (r = 0.600), and IL-10 (r = 0.900) in patients who used continuous anticholesterolemic and diuretic drug classes and were deceased. In these patients, we also evaluated the possible correlations between the biomarkers AST, NT-ProBNP, cardiac troponin, IL-6, IL-8, and IL-10. We observed a significantly negative correlations in AST levels with NT-ProBNP (r = -0.500), cardiac troponin (r = -1.00), IL-6 (r = -1.00), and IL-10 (r = -1.00) and a positive correlation with IL-8 (r = 0.500). We also observed significant negative correlation in the levels of NT-ProBNP with IL-10 (r = -0.800) and a positive correlation with cardiac troponin (r = 0.800). IL-6 levels exhibited positive correlations with cardiac troponin (r = 0.800) and IL-10 (r = 0.700). Conclusion In this study, we observed that hospitalized COVID-19 patients who continued using anticholesterolemic and diuretic medications showed a higher number of correlations between biomarkers, indicating a poorer clinical prognosis. These correlations suggest an imbalanced immune response to injuries caused by SARS-CoV-2.
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Affiliation(s)
- Fabrício Marcus Silva Oliveira
- Cellular and Molecular Immunology Group, Rene Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, Brazil
| | - Mônica Maria Magalhães Caetano
- Cellular and Molecular Immunology Group, Rene Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, Brazil
| | - Ana Raquel Viana de Godoy
- Cellular and Molecular Immunology Group, Rene Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, Brazil
| | - Larissa Lilian de Oliveira
- Cellular and Molecular Immunology Group, Rene Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, Brazil
| | - Juliana Vaz de Melo Mambrini
- Cellular and Molecular Immunology Group, Rene Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, Brazil
| | | | | | | | - Nayara Ingrid Medeiros
- Cellular and Molecular Immunology Group, Rene Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, Brazil
- Department of Morphology, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Jacqueline Araújo Fiuza
- Cellular and Molecular Immunology Group, Rene Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, Brazil
| | - Soraya Torres Gaze
- Cellular and Molecular Immunology Group, Rene Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, Brazil
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Hai CN, Duc TB, Minh TN, Quang LN, Tung SLC, Duc LT, Duong-Quy S. Predicting mortality risk in hospitalized COVID-19 patients: an early model utilizing clinical symptoms. BMC Pulm Med 2024; 24:24. [PMID: 38200490 PMCID: PMC10777603 DOI: 10.1186/s12890-023-02838-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Despite global efforts to control the COVID-19 pandemic, the emergence of new viral strains continues to pose a significant threat. Accurate patient stratification, optimized resource allocation, and appropriate treatment are crucial in managing COVID-19 cases. To address this, a simple and accurate prognostic tool capable of rapidly identifying individuals at high risk of mortality is urgently needed. Early prognosis facilitates predicting treatment outcomes and enables effective patient management. The aim of this study was to develop an early predictive model for assessing mortality risk in hospitalized COVID-19 patients, utilizing baseline clinical factors. METHODS We conducted a descriptive cross-sectional study involving a cohort of 375 COVID-19 patients admitted and treated at the COVID-19 Patient Treatment Center in Military Hospital 175 from October 2021 to December 2022. RESULTS Among the 375 patients, 246 and 129 patients were categorized into the survival and mortality groups, respectively. Our findings revealed six clinical factors that demonstrated independent predictive value for mortality in COVID-19 patients. These factors included age greater than 50 years, presence of multiple underlying diseases, dyspnea, acute confusion, saturation of peripheral oxygen below 94%, and oxygen demand exceeding 5 L per minute. We integrated these factors to develop the Military Hospital 175 scale (MH175), a prognostic scale demonstrating significant discriminatory ability with an area under the curve (AUC) of 0.87. The optimal cutoff value for predicting mortality risk using the MH175 score was determined to be ≥ 3 points, resulting in a sensitivity of 96.1%, specificity of 63.4%, positive predictive value of 58%, and negative predictive value of 96.9%. CONCLUSIONS The MH175 scale demonstrated a robust predictive capacity for assessing mortality risk in patients with COVID-19. Implementation of the MH175 scale in clinical settings can aid in patient stratification and facilitate the application of appropriate treatment strategies, ultimately reducing the risk of death. Therefore, the utilization of the MH175 scale holds significant potential to improve clinical outcomes in COVID-19 patients. TRIAL REGISTRATION An independent ethics committee approved the study (Research Ethics Committee of Military Hospital 175 (No. 3598GCN-HDDD; date: October 8, 2021), which was performed in accordance with the Declaration of Helsinki, Guidelines for Good Clinical Practice.
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Affiliation(s)
- Cong Nguyen Hai
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam.
| | | | - The Nguyen Minh
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Lich Ngo Quang
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Son Luong Cao Tung
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Loi Trinh Duc
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Sy Duong-Quy
- Clinical Research Unit, Lam Dong Medical College and Bio-Medical Research Centre, Dalat City, Vietnam
- Immuno-Allergology Division, Hershey Medical Center, Penn State College of Medicine, Hershey, Pennsylvania, USA
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90
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Collins GS, Dhiman P, Ma J, Schlussel MM, Archer L, Van Calster B, Harrell FE, Martin GP, Moons KGM, van Smeden M, Sperrin M, Bullock GS, Riley RD. Evaluation of clinical prediction models (part 1): from development to external validation. BMJ 2024; 384:e074819. [PMID: 38191193 PMCID: PMC10772854 DOI: 10.1136/bmj-2023-074819] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 01/10/2024]
Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Michael M Schlussel
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Lucinda Archer
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
| | - Ben Van Calster
- KU Leuven, Department of Development and Regeneration, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
- EPI-Centre, KU Leuven, Belgium
| | - Frank E Harrell
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Matthew Sperrin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Garrett S Bullock
- Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
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Hien NTK, Tsai FJ, Chang YH, Burton W, Phuc PT, Nguyen PA, Harnod D, Lam CSK, Lu TC, Chen CI, Hsu MH, Lu CY, Huang CW, Yang HC, Hsu JC. Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases. Front Med (Lausanne) 2024; 10:1289968. [PMID: 38249981 PMCID: PMC10797111 DOI: 10.3389/fmed.2023.1289968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024] Open
Abstract
Background Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods. Objective This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19. Methods Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. Results The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902. Conclusion This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.
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Affiliation(s)
- Nguyen Thi Kim Hien
- Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Feng-Jen Tsai
- Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Yu-Hui Chang
- PharmD Program, Division of Clinical Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Whitney Burton
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Phan Thanh Phuc
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Dorji Harnod
- Department of Emergency, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Carlos Shu-Kei Lam
- Department of Emergency, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Emergency, Department of Emergency and Critical Care Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Tsung-Chien Lu
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chang-I Chen
- Department of Healthcare Administration, School of Management, Taipei Medical University, Taipei, Taiwan
| | - Min-Huei Hsu
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Christine Y. Lu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, United States
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Kolling Institute, Faculty of Medicine and Health, The University of Sydney and the Northern Sydney Local Health District, Sydney, NSW, Australia
| | - Chih-Wei Huang
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Hsuan-Chia Yang
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-analysis, Wanfang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Jason C. Hsu
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
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Schut MC, Dongelmans DA, de Lange DW, Brinkman S, de Keizer NF, Abu-Hanna A. Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges. BMC Med Inform Decis Mak 2024; 24:7. [PMID: 38166918 PMCID: PMC10762959 DOI: 10.1186/s12911-023-02401-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data. METHODS Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status. RESULTS A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables. CONCLUSIONS We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.
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Affiliation(s)
- M C Schut
- Department of Medical Informatics, Amsterdam Public Health research institute, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands.
- Department of Laboratory Medicine, Amsterdam UMC location Vrije Universiteit, De Boelelaan 1117, 1081, HV, Amsterdam, The Netherlands.
| | - D A Dongelmans
- Department of Intensive Care Medicine, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands
| | - D W de Lange
- Department of Intensive Care Medicine and Dutch Poisons Information Center (DPIC), University Medical Center Utrecht, University Utrecht, Heidelberglaan 100, 3584, CX, Utrecht, The Netherlands
| | - S Brinkman
- Department of Medical Informatics, Amsterdam Public Health research institute, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands
| | - N F de Keizer
- Department of Medical Informatics, Amsterdam Public Health research institute, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands
| | - A Abu-Hanna
- Department of Medical Informatics, Amsterdam Public Health research institute, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands
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93
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Chao PJ, Chang CH, Wu JJ, Liu YH, Shiau J, Shih HH, Lin GZ, Lee SH, Lee TF. Improving Prediction of Complications Post-Proton Therapy in Lung Cancer Using Large Language Models and Meta-Analysis. Cancer Control 2024; 31:10732748241286749. [PMID: 39307562 PMCID: PMC11418344 DOI: 10.1177/10732748241286749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 08/26/2024] [Accepted: 09/06/2024] [Indexed: 09/25/2024] Open
Abstract
PURPOSE This study enhances the efficiency of predicting complications in lung cancer patients receiving proton therapy by utilizing large language models (LLMs) and meta-analytical techniques for literature quality assessment. MATERIALS AND METHODS We integrated systematic reviews with LLM evaluations, sourcing studies from Web of Science, PubMed, and Scopus, managed via EndNote X20. Inclusion and exclusion criteria ensured literature relevance. Techniques included meta-analysis, heterogeneity assessment using Cochran's Q test and I2 statistics, and subgroup analyses for different complications. Quality and bias risk were assessed using the PROBAST tool and further analyzed with models such as ChatGPT-4, Llama2-13b, and Llama3-8b. Evaluation metrics included AUC, accuracy, precision, recall, F1 score, and time efficiency (WPM). RESULTS The meta-analysis revealed an overall effect size of 0.78 for model predictions, with high heterogeneity observed (I2 = 72.88%, P < 0.001). Subgroup analysis for radiation-induced esophagitis and pneumonitis revealed predictive effect sizes of 0.79 and 0.77, respectively, with a heterogeneity index (I2) of 0%, indicating that there were no significant differences among the models in predicting these specific complications. A literature assessment using LLMs demonstrated that ChatGPT-4 achieved the highest accuracy at 90%, significantly outperforming the Llama3 and Llama2 models, which had accuracies ranging from 44% to 62%. Additionally, LLM evaluations were conducted 3229 times faster than manual assessments were, markedly enhancing both efficiency and accuracy. The risk assessment results identified nine studies as high risk, three as low risk, and one as unknown, confirming the robustness of the ChatGPT-4 across various evaluation metrics. CONCLUSION This study demonstrated that the integration of large language models with meta-analysis techniques can significantly increase the efficiency of literature evaluations and reduce the time required for assessments, confirming that there are no significant differences among models in predicting post proton therapy complications in lung cancer patients.
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Affiliation(s)
- Pei-Ju Chao
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chu-Ho Chang
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Jyun-Jie Wu
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Yen-Hsien Liu
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Junping Shiau
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Hsin-Hung Shih
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Guang-Zhi Lin
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Shen-Hao Lee
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
- Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Linkou, Taiwan
| | - Tsair-Fwu Lee
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
- Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
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94
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Quintana‐Lopez JM, Rodríguez L, Portuondo J, García J, Legarreta MJ, Gascón M, Larrea N, Barrio I. Relevance of comorbidities for main outcomes during different periods of the COVID-19 pandemic. Influenza Other Respir Viruses 2024; 18:e13240. [PMID: 38229871 PMCID: PMC10790186 DOI: 10.1111/irv.13240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/02/2023] [Accepted: 12/03/2023] [Indexed: 01/18/2024] Open
Abstract
Background Throughout the evolution of the COVID-19 pandemic, the severity of the disease has varied. The aim of this study was to determine how patients' comorbidities affected and were related to, different outcomes during this time. Methods Retrospective cohort study of all patients testing positive for SARS-CoV-2 infection between March 1, 2020, and January 9, 2022. We extracted sociodemographic, basal comorbidities, prescribed treatments, COVID-19 vaccination data, and outcomes such as death and admission to hospital and intensive care unit (ICU) during the different periods of the pandemic. We used logistic regression to quantify the effect of each covariate in each outcome variable and a random forest algorithm to select the most relevant comorbidities. Results Predictors of death included having dementia, heart failure, kidney disease, or cancer, while arterial hypertension, diabetes, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia were also relevant. Heart failure, dementia, kidney disease, diabetes, and cancer were predictors of adverse evolution (death or ICU admission) with arterial hypertension, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia also relevant. Arterial hypertension, heart failure, diabetes, kidney, ischemic heart diseases, and cancer were predictors of hospitalization, while dyslipidemia and respiratory, cerebrovascular, and peripheral vascular diseases were also relevant. Conclusions Preexisting comorbidities such as dementia, cardiovascular and renal diseases, and cancers were those most related to adverse outcomes. Of particular note were the discrepancies between predictors of adverse outcomes and predictors of hospitalization and the fact that patients with dementia had a lower probability of being admitted in the first wave.
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Affiliation(s)
- José M. Quintana‐Lopez
- Research Unit, Osakidetza Basque Health ServiceGaldakao‐Usansolo University HospitalGaldakaoSpain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS)BarakaldoSpain
- Health Service Research Network on Chronic Diseases (REDISSEC)BilbaoSpain
- Kronikgune Institute for Health Services ResearchBarakaldoSpain
| | - Lander Rodríguez
- Basque Center for Applied Mathematics, BCAM, Organization and EvaluationBilbaoSpain
| | - Janire Portuondo
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS)BarakaldoSpain
- Osakidetza Basque Health ServiceSub‐Directorate for Primary Care CoordinationVitoria‐GasteizSpain
- Biocruces Bizkaia Health Research InstituteBarakaldoSpain
| | - Julia García
- Basque Government Department of HealthOffice of Healthcare PlanningVitoria‐GasteizSpain
| | - Maria Jose Legarreta
- Research Unit, Osakidetza Basque Health ServiceGaldakao‐Usansolo University HospitalGaldakaoSpain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS)BarakaldoSpain
- Health Service Research Network on Chronic Diseases (REDISSEC)BilbaoSpain
- Kronikgune Institute for Health Services ResearchBarakaldoSpain
| | - María Gascón
- Research Unit, Osakidetza Basque Health ServiceGaldakao‐Usansolo University HospitalGaldakaoSpain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS)BarakaldoSpain
- Health Service Research Network on Chronic Diseases (REDISSEC)BilbaoSpain
- Kronikgune Institute for Health Services ResearchBarakaldoSpain
| | - Nere Larrea
- Research Unit, Osakidetza Basque Health ServiceGaldakao‐Usansolo University HospitalGaldakaoSpain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS)BarakaldoSpain
- Health Service Research Network on Chronic Diseases (REDISSEC)BilbaoSpain
- Kronikgune Institute for Health Services ResearchBarakaldoSpain
| | - Irantzu Barrio
- Basque Center for Applied Mathematics, BCAM, Organization and EvaluationBilbaoSpain
- Department of MathematicsUniversity of the Basque Country UPV/EHULeioaSpain
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95
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Lohmann A, Groenwold RHH, van Smeden M. Comparison of likelihood penalization and variance decomposition approaches for clinical prediction models: A simulation study. Biom J 2024; 66:e2200108. [PMID: 37199142 DOI: 10.1002/bimj.202200108] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/30/2022] [Accepted: 11/10/2022] [Indexed: 05/19/2023]
Abstract
Logistic regression is one of the most commonly used approaches to develop clinical risk prediction models. Developers of such models often rely on approaches that aim to minimize the risk of overfitting and improve predictive performance of the logistic model, such as through likelihood penalization and variance decomposition techniques. We present an extensive simulation study that compares the out-of-sample predictive performance of risk prediction models derived using the elastic net, with Lasso and ridge as special cases, and variance decomposition techniques, namely, incomplete principal component regression and incomplete partial least squares regression. We varied the expected events per variable, event fraction, number of candidate predictors, presence of noise predictors, and the presence of sparse predictors in a full-factorial design. Predictive performance was compared on measures of discrimination, calibration, and prediction error. Simulation metamodels were derived to explain the performance differences within model derivation approaches. Our results indicate that, on average, prediction models developed using penalization and variance decomposition approaches outperform models developed using ordinary maximum likelihood estimation, with penalization approaches being consistently superior over the variance decomposition approaches. Differences in performance were most pronounced on the calibration of the model. Performance differences regarding prediction error and concordance statistic outcomes were often small between approaches. The use of likelihood penalization and variance decomposition techniques methods was illustrated in the context of peripheral arterial disease.
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Affiliation(s)
- Anna Lohmann
- Department of Welfare, EAH Jena University of Applied Sciences, Jena, Germany
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rolf H H Groenwold
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Maarten van Smeden
- Julius Center for Health Science and Primary Care, University Medical Center Utrecht, Utrecht, The Netherland
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96
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Imène K, Mohamed K, Amal G, Mohamed A, Asma C, Asma A, Wael K, Kalboussi H, Olfa EM, Walid N, Maher M, Nejib M. Olfactory Dysfunction in Healthcare Workers with COVID-19: Prevalence and Associated Factors. RECENT ADVANCES IN INFLAMMATION & ALLERGY DRUG DISCOVERY 2024; 18:67-77. [PMID: 37867280 DOI: 10.2174/0127722708249126231006061438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/01/2023] [Accepted: 08/31/2023] [Indexed: 10/24/2023]
Abstract
BACKGROUND The COVID-19 pandemic is a real global health crisis. Its clinical presentation has evolved over time with an increasing number of symptoms. Olfactory dysfunction (OD) has recently been recognized as a frequent symptom relevant to screening for COVID-19, especially in pauci-asymptomatic forms. However, the underlying mechanisms of OD are not yet fully understood. AIM To determine the prevalence of OD in healthcare workers with SARS-CoV-2 and to identify its associated factors. METHODS This is a cross-sectional, analytical study, carried out during a period of six months and including all healthcare workers at Farhat Hached Academic Hospital (Tunisia) who were diagnosed with SARS-CoV-2 by PCR, RAT, or chest CT scan. RESULTS A total of 474 healthcare workers were included, representing a participation rate of 85.4%. The mean age was 41.02±10.67 years with a sex ratio of 0.2. The distribution of this population by department noted that it was mainly maternity (13.9%). The most presented workstation was nursing (31.4%). OD represented 39.2% of the reasons for consultation. Hospitalization was indicated in 16 patients (3.4%). The average duration of hospitalization was 8.87 ± 7.8 days. The average time off work was 17.04 ± 11.6 days. OD persisted for more than 90 days in 35 patients (7.4%). After multiple binary logistic regression, OD was statistically associated with female gender (p =0.001; OR 95% CI: 2.46 [1.4-4.2]) and blue-collar occupational category (p =0.002; OR IC95%:3.1 [1.5-6.5]). A significant association was also noted between OD and professional seniority and absence from work duration (p =0.019; OR 95% CI: 0.97 [0.95-0.99] and p =0.03; OR 95% CI: 0.97 [0.95-0.99]) respectively. CONCLUSION OD is common in COVID-19 patients. The identification of its associated factors may contribute to enhancing the understanding of its mechanism and drive therapeutic options.
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Affiliation(s)
- Kacem Imène
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
| | - Kahloul Mohamed
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Anesthesia and Intensive Care, University Hospital Sahloul, Sousse, Tunisia
| | - Ghenim Amal
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
| | - Ajmi Mohamed
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Anesthesia and Intensive Care, University Hospital Sahloul, Sousse, Tunisia
| | - Chouchane Asma
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
| | - Aloui Asma
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
| | - Khalefa Wael
- Family and Community Medicine Department, Faculty of Medicine of Sousse, Sousse, Tunisia
| | - H Kalboussi
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
| | - El Maalel Olfa
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
| | - Naija Walid
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Anesthesia and Intensive Care, University Hospital Sahloul, Sousse, Tunisia
| | - Maoua Maher
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
| | - Mrizak Nejib
- Faculty of Medicine of Sousse, University of Sousse, 4000 Sousse, Tunisia
- Department of Occupational Medicine, University Hospital Farhat Hached, Sousse, Tunisia
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97
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Judy J, Yehoshua A, Gouveia-Pisano J, Brook RA, Kleinman NL, Drnach AA, Rosenberg EM, Ghanjanasak T, Winter DA, Dai F, Escobar JM, Sell H. Impact of COVID-19 on work loss in the United States- A retrospective database analysis. J Med Econ 2024; 27:941-951. [PMID: 38984895 DOI: 10.1080/13696998.2024.2379056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/07/2024] [Indexed: 07/11/2024]
Abstract
OBJECTIVES This study investigates the utilization of work absence benefits among United States (US) employees diagnosed with COVID-19, examining frequency, duration, cost, and types of work loss benefits used. METHODS This retrospective analysis of the Workpartners Research Reference Database (RRDb) included employees eligible for short- and long-term disability (STD and LTD employer-sponsored benefits, respectively), and other paid work absence benefits from 2018 to 2022. Workpartners RRDb includes over 3.5 million employees from over 500 self-insured employers across the US. Employees were identified by codes from adjudicated medical and disability claims for COVID-19 (2020-2022) and influenza, as well as prescription claims for COVID-19 treatments. Associated payments were quantified for each absence reason. RESULTS Approximately 1 million employees were eligible for employer-sponsored paid leave benefits between January 2018 and December 2022. The mean age was 37 years (22% >50 years), and 49.4% were females. COVID-19 was the 2nd most common reason for an STD claim (6.9% of all STD claims) and 13th for an LTD claim (1.7% of all LTD claims) from 2020-2022. The mean duration for COVID-19 STD claims was 24 days (N = 3,731, mean claim=$3,477) versus 10 days for influenza (N = 283, mean claim=$1,721). The mean duration for an LTD claim for COVID-19 was 153 days (N = 11, mean claim=$19,254). Only 21.5% of employees with STD claims in the COVID-19 cohort had prior COVID-19-associated medical or pharmacy claims; over half (range 53%-61%) had documented high risk factors for severe COVID-19. CONCLUSION COVID-19 and influenza have the potential to cause work loss in otherwise healthy employees. In this analysis, COVID-19 was the second most frequent reason for an STD claim at the start of the pandemic and remained high (ranked 5th) in 2022. These results highlight the impact of COVID-19 on work loss beyond the acute phase. Comprehensively evaluating work loss implications may help employers prioritize strategies, such as vaccinations and timely treatments, to mitigate the impact of COVID-19 on employees and their companies.
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98
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Pawel S, Kook L, Reeve K. Pitfalls and potentials in simulation studies: Questionable research practices in comparative simulation studies allow for spurious claims of superiority of any method. Biom J 2024; 66:e2200091. [PMID: 36890629 DOI: 10.1002/bimj.202200091] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 03/10/2023]
Abstract
Comparative simulation studies are workhorse tools for benchmarking statistical methods. As with other empirical studies, the success of simulation studies hinges on the quality of their design, execution, and reporting. If not conducted carefully and transparently, their conclusions may be misleading. In this paper, we discuss various questionable research practices, which may impact the validity of simulation studies, some of which cannot be detected or prevented by the current publication process in statistics journals. To illustrate our point, we invent a novel prediction method with no expected performance gain and benchmark it in a preregistered comparative simulation study. We show how easy it is to make the method appear superior over well-established competitor methods if questionable research practices are employed. Finally, we provide concrete suggestions for researchers, reviewers, and other academic stakeholders for improving the methodological quality of comparative simulation studies, such as preregistering simulation protocols, incentivizing neutral simulation studies, and code and data sharing.
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Affiliation(s)
- Samuel Pawel
- Epidemiology, Biostatistics and Prevention Institute, Center for Reproducible Science, University of Zurich, Zurich, Switzerland
| | - Lucas Kook
- Epidemiology, Biostatistics and Prevention Institute, Center for Reproducible Science, University of Zurich, Zurich, Switzerland
| | - Kelly Reeve
- Epidemiology, Biostatistics and Prevention Institute, Center for Reproducible Science, University of Zurich, Zurich, Switzerland
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99
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Bell LC, Shimron E. Sharing Data Is Essential for the Future of AI in Medical Imaging. Radiol Artif Intell 2024; 6:e230337. [PMID: 38231036 PMCID: PMC10831510 DOI: 10.1148/ryai.230337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/16/2023] [Accepted: 11/20/2023] [Indexed: 01/18/2024]
Abstract
If we want artificial intelligence to succeed in radiology, we must share data and learn how to share data.
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Affiliation(s)
- Laura C. Bell
- From the Clinical Imaging Group, Genentech, 1 DNA Way, South San
Francisco, CA 94080 (L.C.B.); and Department of Electrical and Computer
Engineering and Department of Biomedical Engineering, Technion-Israel Institute
of Technology, Haifa, Israel (E.S.)
| | - Efrat Shimron
- From the Clinical Imaging Group, Genentech, 1 DNA Way, South San
Francisco, CA 94080 (L.C.B.); and Department of Electrical and Computer
Engineering and Department of Biomedical Engineering, Technion-Israel Institute
of Technology, Haifa, Israel (E.S.)
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100
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Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, Matsui Y, Nozaki T, Nakaura T, Fujima N, Tatsugami F, Yanagawa M, Hirata K, Yamada A, Tsuboyama T, Kawamura M, Fujioka T, Naganawa S. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 2024; 42:3-15. [PMID: 37540463 PMCID: PMC10764412 DOI: 10.1007/s11604-023-01474-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/17/2023] [Indexed: 08/05/2023]
Abstract
In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.
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Affiliation(s)
- Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan.
| | | | - Shohei Fujita
- Department of Radiology, University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-ku, Okayama, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-ku, Kumamoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-ku, Hiroshima, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita-ku, Sapporo, Hokkaido, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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