1
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Nau C, Butler RK, Huang CW, Khang VK, Chen A, Creekmur B, Broder B, Subject C, Sharp AL, Moreta-Sainz LM, Park JS, Manek AJ, Cooper RM, Mendoza SM, Luo G, Gould MK. Development and validation of the COVID-19 Hospitalized Patient Deterioration Index. THE AMERICAN JOURNAL OF MANAGED CARE 2023; 29:e365-e371. [PMID: 38170527 PMCID: PMC10843847 DOI: 10.37765/ajmc.2023.89470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
OBJECTIVES To develop a COVID-19-specific deterioration index for hospitalized patients: the COVID Hospitalized Patient Deterioration Index (COVID-HDI). This index builds on the proprietary Epic Deterioration Index, which was not developed for predicting respiratory deterioration events among patients with COVID-19. STUDY DESIGN A retrospective observational cohort was used to develop and validate the COVID-HDI model to predict respiratory deterioration or death among hospitalized patients with COVID-19. Deterioration events were defined as death or requiring high-flow oxygen, bilevel positive airway pressure, mechanical ventilation, or intensive-level care within 72 hours of run time. The sample included hospitalized patients with COVID-19 diagnoses or positive tests at Kaiser Permanente Southern California between May 3, 2020, and October 17, 2020. METHODS Machine learning models and 118 candidate predictors were used to generate benchmark performance. Logit regression with least absolute shrinkage and selection operator and physician input were used to finalize the model. Split-sample cross-validation was used to train and test the model. RESULTS The area under the receiver operating curve was 0.83. COVID-HDI identifies patients at low risk (negative predictive value [NPV] > 98.5%) and borderline low risk (NPV > 95%) of an event. Of all patients, 74% were identified as being at low or borderline low risk at some point during their hospitalization and could be considered for discharge with or without home monitoring. A high-risk group with a positive predictive value of 51% included 12% of patients. Model performance remained high in a recent cohort of patients. CONCLUSIONS COVID-HDI is a parsimonious, well-calibrated, and accurate model that may support clinical decision-making around discharge and escalation of care.
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Affiliation(s)
- Claudia Nau
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, 98 S Los Robles Ave, Pasadena, CA 91101.
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2
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Ge J, Digitale JC, Fenton C, McCulloch CE, Lai JC, Pletcher MJ, Gennatas ED. Predicting post-liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning. Am J Transplant 2023; 23:1908-1921. [PMID: 37652176 PMCID: PMC11018271 DOI: 10.1016/j.ajt.2023.08.022] [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: 03/03/2023] [Revised: 08/04/2023] [Accepted: 08/25/2023] [Indexed: 09/01/2023]
Abstract
Liver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF), but high post-LT mortality has been reported. Existing post-LT models in ACLF have been limited. We developed an Expert-Augmented Machine Learning (EAML) model to predict post-LT outcomes. We identified ACLF patients who underwent LT in the University of California Health Data Warehouse. We applied the RuleFit machine learning (ML) algorithm to extract rules from decision trees and create intermediate models. We asked human experts to rate the rules generated by RuleFit and incorporated these ratings to generate final EAML models. We identified 1384 ACLF patients. For death at 1 year, areas under the receiver-operating characteristic curve were 0.707 (confidence interval [CI] 0.625-0.793) for EAML and 0.719 (CI 0.640-0.800) for RuleFit. For death at 90 days, areas under the receiver-operating characteristic curve were 0.678 (CI 0.581-0.776) for EAML and 0.707 (CI 0.615-0.800) for RuleFit. In pairwise comparisons, both EAML and RuleFit models outperformed cross-sectional models. Significant discrepancies between experts and ML occurred in rankings of biomarkers used in clinical practice. EAML may serve as a method for ML-guided hypothesis generation in further ACLF research.
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Affiliation(s)
- Jin Ge
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California-San Francisco, San Francisco, California, USA.
| | - Jean C Digitale
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
| | - Cynthia Fenton
- Division of Hospital Medicine, Department of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Charles E McCulloch
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
| | - Jennifer C Lai
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Mark J Pletcher
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
| | - Efstathios D Gennatas
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
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3
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Ghosheh GO, St John TL, Wang P, Ling VN, Orquiola LR, Hayat N, Shamout FE, Almallah YZ. Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits. PLOS DIGITAL HEALTH 2023; 2:e0000306. [PMID: 37910466 PMCID: PMC10619807 DOI: 10.1371/journal.pdig.0000306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/22/2023] [Indexed: 11/03/2023]
Abstract
Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease of use, urine dipstick test may lack sensitivity and specificity. In this paper, we use a real-world dataset consisting of 17,572 outpatient encounters who underwent urine cultures, collected between 2015 and 2021 at a large multi-specialty hospital in Abu Dhabi, United Arab Emirates. We develop and evaluate a simple parsimonious prediction model for positive urine cultures based on a minimal input set of ten features selected from the patient's presenting vital signs, history, and dipstick results. In a test set of 5,339 encounters, the parsimonious model achieves an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI: 0.810-0.844) for predicting a bacterial count ≥ 105 CFU/ml, outperforming a model that uses dipstick features only that achieves an AUROC of 0.786 (95% CI: 0.769-0.806). Our proposed model can be easily deployed at point-of-care, highlighting its value in improving the efficiency of clinical workflows, especially in low-resource settings.
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Affiliation(s)
| | | | - Pengyu Wang
- NYU Abu Dhabi, Abu Dhabi, The United Arab Emirates
| | - Vee Nis Ling
- NYU Abu Dhabi, Abu Dhabi, The United Arab Emirates
| | | | - Nasir Hayat
- NYU Abu Dhabi, Abu Dhabi, The United Arab Emirates
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4
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Hardy-Werbin M, Maiques JM, Busto M, Cirera I, Aguirre A, Garcia-Gisbert N, Zuccarino F, Carbullanca S, Del Carpio LA, Ramal D, Gayete Á, Martínez-Roldan J, Marquez-Colome A, Bellosillo B, Gibert J. MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis. Sci Rep 2023; 13:18761. [PMID: 37907750 PMCID: PMC10618492 DOI: 10.1038/s41598-023-46126-8] [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: 02/10/2023] [Accepted: 10/27/2023] [Indexed: 11/02/2023] Open
Abstract
The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnosis on different prevalence scenarios. With the objective of improving and accelerating the diagnosis of COVID-19, a multi modal prediction algorithm (MultiCOVID) based on CXR and blood test was developed, to discriminate between COVID-19, Heart Failure and Non-COVID Pneumonia and healthy (Control) patients. This retrospective single-center study includes CXR and blood test obtained between January 2017 and May 2020. Multi modal prediction models were generated using opensource DL algorithms. Performance of the MultiCOVID algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar-Bowker test. A total of 8578 samples from 6123 patients (mean age 66 ± 18 years of standard deviation, 3523 men) were evaluated across datasets. For the entire test set, the overall accuracy of MultiCOVID was 84%, with a mean AUC of 0.92 (0.89-0.94). For 300 random test images, overall accuracy of MultiCOVID was significantly higher (69.6%) compared with individual radiologists (range, 43.7-58.7%) and the consensus of all five radiologists (59.3%, P < .001). Overall, we have developed a multimodal deep learning algorithm, MultiCOVID, that discriminates among COVID-19, heart failure, non-COVID pneumonia and healthy patients using both CXR and blood test with a significantly better performance than experienced thoracic radiologists.
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Affiliation(s)
- Max Hardy-Werbin
- Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Emergency Department, Hospital del Mar, Barcelona, Spain
| | | | - Marcos Busto
- Radiology Department, Hospital del Mar, Barcelona, Spain
| | - Isabel Cirera
- Emergency Department, Hospital del Mar, Barcelona, Spain
| | - Alfons Aguirre
- Emergency Department, Hospital del Mar, Barcelona, Spain
| | - Nieves Garcia-Gisbert
- Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | | | | | | | - Didac Ramal
- Radiology Department, Hospital del Mar, Barcelona, Spain
| | - Ángel Gayete
- Radiology Department, Hospital del Mar, Barcelona, Spain
| | - Jordi Martínez-Roldan
- Innovation and Digital Transformation Department, Hospital del Mar, Barcelona, Spain
| | | | - Beatriz Bellosillo
- Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Pathology Department, Hospital del Mar, Barcelona, Spain
| | - Joan Gibert
- Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.
- Pathology Department, Hospital del Mar, Barcelona, Spain.
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5
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Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics (Basel) 2023; 13:1749. [PMID: 37238232 PMCID: PMC10217633 DOI: 10.3390/diagnostics13101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/29/2023] [Indexed: 05/28/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
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Affiliation(s)
| | - Murat Koyuncu
- Department of Information Systems Engineering, Atilim University, 06830 Ankara, Turkey;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
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6
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Banoei MM, Rafiepoor H, Zendehdel K, Seyyedsalehi MS, Nahvijou A, Allameh F, Amanpour S. Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study. Front Med (Lausanne) 2023; 10:1170331. [PMID: 37215714 PMCID: PMC10192907 DOI: 10.3389/fmed.2023.1170331] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/11/2023] [Indexed: 05/24/2023] Open
Abstract
Background At the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic increased the hospital burden of COVID-19 caused by the SARS-Cov-2 and became the most significant health challenge for nations worldwide. The severity and high mortality of COVID-19 have been correlated with various demographic characteristics and clinical manifestations. Prediction of mortality rate, identification of risk factors, and classification of patients played a crucial role in managing COVID-19 patients. Our purpose was to develop machine learning (ML)-based models for the prediction of mortality and severity among patients with COVID-19. Identifying the most important predictors and unraveling their relationships by classification of patients to the low-, moderate- and high-risk groups might guide prioritizing treatment decisions and a better understanding of interactions between factors. A detailed evaluation of patient data is believed to be important since COVID-19 resurgence is underway in many countries. Results The findings of this study revealed that the ML-based statistically inspired modification of the partial least square (SIMPLS) method could predict the in-hospital mortality among COVID-19 patients. The prediction model was developed using 19 predictors including clinical variables, comorbidities, and blood markers with moderate predictability (Q2 = 0.24) to separate survivors and non-survivors. Oxygen saturation level, loss of consciousness, and chronic kidney disease (CKD) were the top mortality predictors. Correlation analysis showed different correlation patterns among predictors for each non-survivor and survivor cohort separately. The main prediction model was verified using other ML-based analyses with a high area under the curve (AUC) (0.81-0.93) and specificity (0.94-0.99). The obtained data revealed that the mortality prediction model can be different for males and females with diverse predictors. Patients were classified into four clusters of mortality risk and identified the patients at the highest risk of mortality, which accentuated the most significant predictors correlating with mortality. Conclusion An ML model for predicting mortality among hospitalized COVID-19 patients was developed considering the interactions between factors that may reduce the complexity of clinical decision-making processes. The most predictive factors related to patient mortality were identified by assessing and classifying patients into different groups based on their sex and mortality risk (low-, moderate-, and high-risk groups).
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Affiliation(s)
| | - Haniyeh Rafiepoor
- Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kazem Zendehdel
- Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
- Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Monireh Sadat Seyyedsalehi
- Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Azin Nahvijou
- Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Farshad Allameh
- Gastroenterology Ward, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences, Tehran, Iran
| | - Saeid Amanpour
- Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
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7
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Mahran GSK, Gadallah MA, Ahmed AE, Abouzied WR, Obiedallah AA, Sayed MMM, Abbas MS, Mohamed SAA. Development of a Discharge Criteria Checklist for COVID-19 Patients From the Intensive Care Unit. Crit Care Nurs Q 2023; 46:227-238. [PMID: 36823749 DOI: 10.1097/cnq.0000000000000455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
This study aims to develop and validate a checklist of discharge readiness criteria for COVID-19 patients from the intensive care unit (ICU). We conducted a Delphi design study. The degree of agreement among 7 experts had been evaluated using the content validity index (CVI) through a 4-point Likert scale. The instrument was validated with 17 items. All the experts rated all items as very relevant which scored the item-CVI 1, which validates all checklist items. Using the mean of all items, the scale-CVI was calculated, and it was 1. This meant validation of the checklist as a whole. With regard to the overall checklist evaluation, the mean expert proportion of the instrument was 1, and the S-CVI/UA was 1. This discharge criteria checklist improves transition of care for COVID-19 patients and can help nurses, doctors, and academics to discharge COVID-19 patients from the ICU safely.
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Affiliation(s)
- Ghada S K Mahran
- Departments of Critical Care and Emergency Nursing (Dr Mahran) and Pediatric Nursing (Drs Gadallah and Ahmed), Faculty of Nursing, Assiut University, Assiut, Egypt; Department of Critical and Emergency Care Nursing, Faculty of Nursing, South Valley University, Qena, Egypt (Dr Abouzied); and Departments of Internal Medicine, Cardiology and Critical Care Medicine Unit (Dr Obiedallah), Anesthesia and Intensive Care (Drs Sayed and Abbas), and Chest Diseases and Tuberculosis (Dr Mohamed), Faculty of Medicine, Assiut University, Assiut, Egypt
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8
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The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19. Clin Imaging 2023. [PMID: 37301052 PMCID: PMC10014481 DOI: 10.1016/j.clinimag.2023.03.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
Objectives We aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19). Material and methods This retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1–33%), moderate (34–66%), or severe (67–100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis. Results Patients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia. Conclusions COVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia.
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Yan C, Yan Y, Wan Z, Zhang Z, Omberg L, Guinney J, Mooney SD, Malin BA. A Multifaceted benchmarking of synthetic electronic health record generation models. Nat Commun 2022; 13:7609. [PMID: 36494374 PMCID: PMC9734113 DOI: 10.1038/s41467-022-35295-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
Synthetic health data have the potential to mitigate privacy concerns in supporting biomedical research and healthcare applications. Modern approaches for data generation continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a systematic benchmarking framework to appraise key characteristics with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic health data and further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.
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Affiliation(s)
- Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yao Yan
- Sage Bionetworks, Seattle, WA, USA
| | - Zhiyu Wan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ziqi Zhang
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | - Justin Guinney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
- Tempus Labs, Chicago, IL, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
| | - Bradley A Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
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10
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Levy TJ, Coppa K, Cang J, Barnaby DP, Paradis MD, Cohen SL, Makhnevich A, van Klaveren D, Kent DM, Davidson KW, Hirsch JS, Zanos TP. Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients. Nat Commun 2022; 13:6812. [PMID: 36357420 PMCID: PMC9648888 DOI: 10.1038/s41467-022-34646-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 11/02/2022] [Indexed: 11/12/2022] Open
Abstract
Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.
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Affiliation(s)
- Todd J Levy
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
| | - Kevin Coppa
- Clinical Digital Solutions, Northwell Health, New Hyde Park, NY, 11042, USA
| | - Jinxuan Cang
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
| | - Douglas P Barnaby
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, 11549, USA
| | - Marc D Paradis
- Northwell Holdings, Northwell Health, Manhasset, NY, 11030, USA
| | - Stuart L Cohen
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, 11549, USA
| | - Alex Makhnevich
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, 11549, USA
| | - David van Klaveren
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA, USA
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA, USA
| | - Karina W Davidson
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, 11549, USA
| | - Jamie S Hirsch
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA
- Clinical Digital Solutions, Northwell Health, New Hyde Park, NY, 11042, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, 11549, USA
| | - Theodoros P Zanos
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA.
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA.
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, 11549, USA.
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11
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Navar AM, Cosmatos I, Purinton S, Ramsey JL, Taylor RJ, Sobel RE, Barlow G, Dieck GS, Bulgrein ML, Peterson ED. Using EHR data to identify coronavirus infections in hospitalized patients: Impact of case definitions on disease surveillance. Int J Med Inform 2022; 166:104842. [PMID: 35988510 PMCID: PMC9359535 DOI: 10.1016/j.ijmedinf.2022.104842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/16/2022] [Accepted: 08/04/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE To evaluate the number, characteristics, and outcomes of patients identified hospitalized with coronavirus disease 2019 (COVID-19) using two different case definitions. PROCEDURES Electronic Health Record data were evaluated from patients hospitalized with COVID-19 through May 2020 at 52 health systems across the United States. Characteristics of inpatients with positive laboratory tests for SARS-CoV-2 were compared with those with clinical diagnosis of COVID-19 but without a confirmatory lab result. FINDINGS Of 14,371 inpatients with COVID-19, 6623 (46.1 %) had a positive laboratory result, and n = 7748 (52.9 %) had only a clinical diagnosis of COVID-19. Compared with clinically diagnosed cases, those with laboratory-confirmed COVID were similar in age and sex, but differed by race, ethnicity, and insurance status. Laboratory-confirmed cases were more likely to receive certain COVID-19 therapies including hydroxychloroquine, anti-IL6 agents and antivirals (p < 0.001). Those with laboratory-confirmed COVID-19 had lower rates of most complications such as myocardial infarction, but higher overall mortality (p < 0.001). CONCLUSION We observed a two-fold difference in the number of patients hospitalized with COVID-19 depending on whether the case definition required laboratory confirmation. Variations in case definitions also led to differences in cohort characteristics, treatments, and outcomes.
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Affiliation(s)
- Ann Marie Navar
- University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | | | | | | | | | | | | | | | | | - Eric D Peterson
- University of Texas Southwestern Medical Center, Dallas, TX, United States
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12
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Major VJ, Jones SA, Razavian N, Bagheri A, Mendoza F, Stadelman J, Horwitz LI, Austrian J, Aphinyanaphongs Y. Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial. Appl Clin Inform 2022; 13:632-640. [PMID: 35896506 PMCID: PMC9329139 DOI: 10.1055/s-0042-1750416] [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] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. OBJECTIVES The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). METHODS We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. RESULTS Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. CONCLUSION An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT04570488.
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Affiliation(s)
- Vincent J. Major
- Center for Healthcare Innovation & Delivery Science, Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States,Address for correspondence Vincent J. Major, PhD NYU Grossman School of MedicineNew York, NY 10016United States
| | - Simon A. Jones
- Center for Healthcare Innovation & Delivery Science, Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States
| | - Narges Razavian
- Center for Healthcare Innovation & Delivery Science, Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States
| | - Ashley Bagheri
- Center for Healthcare Innovation & Delivery Science, Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States
| | - Felicia Mendoza
- Center for Healthcare Innovation & Delivery Science, Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States
| | - Jay Stadelman
- Center for Healthcare Innovation & Delivery Science, Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States
| | - Leora I. Horwitz
- Center for Healthcare Innovation & Delivery Science, Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States,Department of Medicine, NYU Grossman School of Medicine, New York, New York, United States
| | - Jonathan Austrian
- Department of Medicine, NYU Grossman School of Medicine, New York, New York, United States
| | - Yindalon Aphinyanaphongs
- Center for Healthcare Innovation & Delivery Science, Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States
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13
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Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction. Sci Rep 2022; 12:10748. [PMID: 35750878 PMCID: PMC9232529 DOI: 10.1038/s41598-022-13072-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 05/20/2022] [Indexed: 11/14/2022] Open
Abstract
Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model (\documentclass[12pt]{minimal}
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\begin{document}$${\textsc {TransMED}}$$\end{document}TRANSMED) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of \documentclass[12pt]{minimal}
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\begin{document}$${\textsc {TransMED}}$$\end{document}TRANSMED’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. \documentclass[12pt]{minimal}
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\begin{document}$${\textsc {TransMED}}$$\end{document}TRANSMED’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.
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14
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Machine learning decision support model for radical cystectomy discharge planning. Urol Oncol 2022; 40:453.e9-453.e18. [PMID: 35750561 DOI: 10.1016/j.urolonc.2022.05.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 05/05/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022]
Abstract
PURPOSE Timely and appropriate discharge placement for patients who have undergone radical cystectomy (RC) remains challenging. Our objective was to improve the discharge planning process by creating a machine learning model that helps to predict the need for non-home hospital discharge to a higher level of care. MATERIALS AND METHODS Patients undergoing elective radical cystectomy for bladder cancer from 2014-2019 were identified in the ACS-NSQIP database. A gradient boosted decision tree was trained on selected predischarge variables to predict discharge location, dichotomized into home and non-home. We used threshold-moving to calibrate model predictions and evaluated model performance on a testing set using receiver operating characteristic and precision recall curves. Model performance was further examined in subgroups of interest. RESULTS AND CONCLUSIONS A total of 11,881 patients met inclusion criteria with a mean age of 68.6 years. 10.6% of patients undergoing RC had non-home discharges. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.80 and an average precision of 0.33. After threshold-moving, our model had a recall of 0.757 and a precision of 0.211. Top variables by importance were septic shock occurrence, ventilator-use greater than 48 hours, organ space surgical site infection and unplanned intubation. Our model shows strong performance in identifying patients who required non-home discharge to higher levels of care, outperforming commonly used clinical indices and prior work. Modern machine learning techniques may be applied to support more timely and appropriate clinical decision making.
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Rasmy L, Nigo M, Kannadath BS, Xie Z, Mao B, Patel K, Zhou Y, Zhang W, Ross A, Xu H, Zhi D. Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data. Lancet Digit Health 2022; 4:e415-e425. [PMID: 35466079 PMCID: PMC9023005 DOI: 10.1016/s2589-7500(22)00049-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 01/11/2022] [Accepted: 03/07/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND Predicting outcomes of patients with COVID-19 at an early stage is crucial for optimised clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, because of their requirements for extensive data preprocessing and feature engineering, they have not been validated or implemented outside of their original study site. Therefore, we aimed to develop accurate and transferrable predictive models of outcomes on hospital admission for patients with COVID-19. METHODS In this study, we developed recurrent neural network-based models (CovRNN) to predict the outcomes of patients with COVID-19 by use of available electronic health record data on admission to hospital, without the need for specific feature selection or missing data imputation. CovRNN was designed to predict three outcomes: in-hospital mortality, need for mechanical ventilation, and prolonged hospital stay (>7 days). For in-hospital mortality and mechanical ventilation, CovRNN produced time-to-event risk scores (survival prediction; evaluated by the concordance index) and all-time risk scores (binary prediction; area under the receiver operating characteristic curve [AUROC] was the main metric); we only trained a binary classification model for prolonged hospital stay. For binary classification tasks, we compared CovRNN against traditional machine learning algorithms: logistic regression and light gradient boost machine. Our models were trained and validated on the heterogeneous, deidentified data of 247 960 patients with COVID-19 from 87 US health-care systems derived from the Cerner Real-World COVID-19 Q3 Dataset up to September 2020. We held out the data of 4175 patients from two hospitals for external validation. The remaining 243 785 patients from the 85 health systems were grouped into training (n=170 626), validation (n=24 378), and multi-hospital test (n=48 781) sets. Model performance was evaluated in the multi-hospital test set. The transferability of CovRNN was externally validated by use of deidentified data from 36 140 patients derived from the US-based Optum deidentified COVID-19 electronic health record dataset (version 1015; from January, 2007, to Oct 15, 2020). Exact dates of data extraction were masked by the databases to ensure patient data safety. FINDINGS CovRNN binary models achieved AUROCs of 93·0% (95% CI 92·6-93·4) for the prediction of in-hospital mortality, 92·9% (92·6-93·2) for the prediction of mechanical ventilation, and 86·5% (86·2-86·9) for the prediction of a prolonged hospital stay, outperforming light gradient boost machine and logistic regression algorithms. External validation confirmed AUROCs in similar ranges (91·3-97·0% for in-hospital mortality prediction, 91·5-96·0% for the prediction of mechanical ventilation, and 81·0-88·3% for the prediction of prolonged hospital stay). For survival prediction, CovRNN achieved a concordance index of 86·0% (95% CI 85·1-86·9) for in-hospital mortality and 92·6% (92·2-93·0) for mechanical ventilation. INTERPRETATION Trained on a large, heterogeneous, real-world dataset, our CovRNN models showed high prediction accuracy and transferability through consistently good performances on multiple external datasets. Our results show the feasibility of a COVID-19 predictive model that delivers high accuracy without the need for complex feature engineering. FUNDING Cancer Prevention and Research Institute of Texas.
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Affiliation(s)
- Laila Rasmy
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Masayuki Nigo
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Ziqian Xie
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bingyu Mao
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Khush Patel
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yujia Zhou
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Wanheng Zhang
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Angela Ross
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hua Xu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Degui Zhi
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA,Correspondence to: Dr Degui Zhi, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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16
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Wang H, Jia S, Li Z, Duan Y, Tao G, Zhao Z. A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic. Front Genet 2022; 13:845305. [PMID: 35559010 PMCID: PMC9086537 DOI: 10.3389/fgene.2022.845305] [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: 12/29/2021] [Accepted: 03/30/2022] [Indexed: 11/13/2022] Open
Abstract
The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.
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Affiliation(s)
- Haishuai Wang
- College of Computer Science, Zhejiang University, Hangzhou, China
| | - Shangru Jia
- Department of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Zhao Li
- Alibaba-ZJU Joint Research Institute of Frontier Technologies, Zhejiang University, Hangzhou, China
| | - Yucong Duan
- College of Computer Science and Technology, Hainan University, Haikou, China
| | - Guangyu Tao
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Ziping Zhao
- Department of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
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17
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Föll S, Lison A, Maritsch M, Klingberg K, Lehmann V, Züger T, Srivastava D, Jegerlehner S, Feuerriegel S, Fleisch E, Exadaktylos A, Wortmann F. A Scalable Risk Scoring System for COVID-19 Inpatients Based on Consumer-grade Wearables: Statistical Analysis and Model Development. JMIR Form Res 2022; 6:e35717. [PMID: 35613417 PMCID: PMC9217156 DOI: 10.2196/35717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/06/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Background To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards. Objective In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches). Methods Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland. Results First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation. Conclusions Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system. Trial Registration Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834
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Affiliation(s)
- Simon Föll
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
| | - Adrian Lison
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
| | - Martin Maritsch
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
| | - Karsten Klingberg
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, Bern, CH
| | - Vera Lehmann
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, CH
| | - Thomas Züger
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, CH.,Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
| | - David Srivastava
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, Bern, CH
| | - Sabrina Jegerlehner
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, Bern, CH
| | - Stefan Feuerriegel
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH.,Institute of AI in Management, LMU Munich, Munich, DE
| | - Elgar Fleisch
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH.,Institute of Technology Management, University of St. Gallen, St. Gallen, CH
| | - Aristomenis Exadaktylos
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, Bern, CH
| | - Felix Wortmann
- Institute of Technology Management, University of St. Gallen, St. Gallen, CH.,Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
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18
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Carobene A, Milella F, Famiglini L, Cabitza F. How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data. Clin Chem Lab Med 2022; 60:1887-1901. [PMID: 35508417 DOI: 10.1515/cclm-2022-0182] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/22/2022] [Indexed: 12/13/2022]
Abstract
The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.
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Affiliation(s)
- Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | | | - Federico Cabitza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,DISCo, Università Degli Studi di Milano-Bicocca, Milan, Italy
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19
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Khera R, Mortazavi BJ, Sangha V, Warner F, Patrick Young H, Ross JS, Shah ND, Theel ES, Jenkinson WG, Knepper C, Wang K, Peaper D, Martinello RA, Brandt CA, Lin Z, Ko AI, Krumholz HM, Pollock BD, Schulz WL. A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations. NPJ Digit Med 2022; 5:27. [PMID: 35260762 PMCID: PMC8904579 DOI: 10.1038/s41746-022-00570-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 02/04/2022] [Indexed: 01/20/2023] Open
Abstract
Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020-March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Bobak J Mortazavi
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Computer Science & Engineering, Texas A&M University, College Station, TX, USA
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Frederick Warner
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - H Patrick Young
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Joseph S Ross
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Section of General Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Nilay D Shah
- Division of Health Care Delivery Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Elitza S Theel
- Division of Clinical Microbiology, Mayo Clinic Rochester, Rochester, MN, USA
| | - William G Jenkinson
- Department of Quality, Experience, and Affordability, Mayo Clinic, Rochester, MN, USA
| | - Camille Knepper
- Division of Health Care Delivery Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Karen Wang
- Equity Research and Innovation Center, General Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA
| | - David Peaper
- Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Richard A Martinello
- Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Cynthia A Brandt
- Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Zhenqiu Lin
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Albert I Ko
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
- Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, BA, Brazil
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Benjamin D Pollock
- Division of Health Care Delivery Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
- Department of Quality, Experience, and Affordability, Mayo Clinic, Rochester, MN, USA
| | - Wade L Schulz
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
- Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA.
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
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Kamran F, Tang S, Otles E, McEvoy DS, Saleh SN, Gong J, Li BY, Dutta S, Liu X, Medford RJ, Valley TS, West LR, Singh K, Blumberg S, Donnelly JP, Shenoy ES, Ayanian JZ, Nallamothu BK, Sjoding MW, Wiens J. Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study. BMJ 2022; 376:e068576. [PMID: 35177406 PMCID: PMC8850910 DOI: 10.1136/bmj-2021-068576] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/12/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing. DESIGN Retrospective cohort study. SETTING One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21. PARTICIPANTS 33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19. MAIN OUTCOME MEASURES An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early. RESULTS 9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge. CONCLUSION A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.
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Affiliation(s)
- Fahad Kamran
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Joint first authors
| | - Shengpu Tang
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Joint first authors
| | - Erkin Otles
- Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, MI, USA
- Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Dustin S McEvoy
- Mass General Brigham Digital Health eCare, Somerville, MA, USA
| | - Sameh N Saleh
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jen Gong
- Center for Clinical Informatics and Improvement Research, University of California, San Francisco, CA, USA
| | - Benjamin Y Li
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Sayon Dutta
- Mass General Brigham Digital Health eCare, Somerville, MA, USA
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Xinran Liu
- Division of Hospital Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Richard J Medford
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Thomas S Valley
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Lauren R West
- Infection Control Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Karandeep Singh
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Seth Blumberg
- Division of Hospital Medicine, University of California, San Francisco, San Francisco, CA, USA
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA
| | - John P Donnelly
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Erica S Shenoy
- Infection Control Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
| | - John Z Ayanian
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Brahmajee K Nallamothu
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Michael W Sjoding
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
- Joint senior authors
| | - Jenna Wiens
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Joint senior authors
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21
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A machine learning model for predicting deterioration of COVID-19 inpatients. Sci Rep 2022; 12:2630. [PMID: 35173197 PMCID: PMC8850417 DOI: 10.1038/s41598-022-05822-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/19/2022] [Indexed: 01/22/2023] Open
Abstract
The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding of disease progression and of the risk factors for the disease, it is a clinical challenge to predict which hospitalized patients will deteriorate. Moreover, several studies suggested that taking early measures for treating patients at risk of deterioration could prevent or lessen condition worsening and the need for mechanical ventilation. We developed a predictive model for early identification of patients at risk for clinical deterioration by retrospective analysis of electronic health records of COVID-19 inpatients at the two largest medical centers in Israel. Our model employs machine learning methods and uses routine clinical features such as vital signs, lab measurements, demographics, and background disease. Deterioration was defined as a high NEWS2 score adjusted to COVID-19. In the prediction of deterioration within the next 7–30 h, the model achieved an area under the ROC curve of 0.84 and an area under the precision-recall curve of 0.74. In external validation on data from a different hospital, it achieved values of 0.76 and 0.7, respectively.
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22
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Miller JL, Tada M, Goto M, Chen H, Dang E, Mohr NM, Lee S. Prediction models for severe manifestations and mortality due to COVID-19: A systematic review. Acad Emerg Med 2022; 29:206-216. [PMID: 35064988 DOI: 10.1111/acem.14447] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. OBJECTIVE This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19. METHODS Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized. RESULTS A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability. CONCLUSION Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
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Affiliation(s)
- Jamie L. Miller
- University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Masafumi Tada
- Department of Health Promotion and Human Behavior School of Public Health, Kyoto University Graduate School of Medicine Kyoto Japan
| | - Michihiko Goto
- Division of Infectious Diseases, Department of Internal Medicine University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Hao Chen
- University of Iowa Iowa City Iowa USA
| | | | - Nicholas M. Mohr
- Department of Emergency Medicine, Department of Anesthesia, Department of Epidemiology University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Sangil Lee
- Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA
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23
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He F, Page JH, Weinberg KR, Mishra A. The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study. J Med Internet Res 2022; 24:e31549. [PMID: 34951865 PMCID: PMC8785956 DOI: 10.2196/31549] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/26/2021] [Accepted: 12/19/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The current COVID-19 pandemic is unprecedented; under resource-constrained settings, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients; however, there are only few risk scores derived from a substantially large electronic health record (EHR) data set, using simplified predictors as input. OBJECTIVE The objectives of this study were to develop and validate simplified machine learning algorithms that predict COVID-19 adverse outcomes; to evaluate the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration of the algorithms; and to derive clinically meaningful thresholds. METHODS We performed machine learning model development and validation via a cohort study using multicenter, patient-level, longitudinal EHRs from the Optum COVID-19 database that provides anonymized, longitudinal EHR from across the United States. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, intensive care unit (ICU) admission, respiratory failure, and mechanical ventilator usages at inpatient setting. Data from patients who were admitted from February 1, 2020, to September 7, 2020, were randomly sampled into development, validation, and test data sets; data collected from September 7, 2020, to November 15, 2020, were reserved as the postdevelopment prospective test data set. RESULTS Of the 3.7 million patients in the analysis, 585,867 patients were diagnosed or tested positive for SARS-CoV-2, and 50,703 adult patients were hospitalized with COVID-19 between February 1 and November 15, 2020. Among the study cohort (n=50,703), there were 6204 deaths, 9564 ICU admissions, 6478 mechanically ventilated or EMCO patients, and 25,169 patients developed acute respiratory distress syndrome or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC 0.89, 95% CI 0.89-0.89 on the test data set [n=10,752]), consistent prediction through the second wave of the pandemic from September to November (AUC 0.85, 95% CI 0.85-0.86) on the postdevelopment prospective test data set [n=14,863], great clinical relevance, and utility. Besides, a comprehensive set of 386 input covariates from baseline or at admission were included in the analysis; the end-to-end pipeline automates feature selection and model development. The parsimonious model with only 10 input predictors produced comparably accurate predictions; these 10 predictors (age, blood urea nitrogen, SpO2, systolic and diastolic blood pressures, respiration rate, pulse, temperature, albumin, and major cognitive disorder excluding stroke) are commonly measured and concordant with recognized risk factors for COVID-19. CONCLUSIONS The systematic approach and rigorous validation demonstrate consistent model performance to predict even beyond the period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated, and reliable prediction model based on only 10 clinical features as a prognostic tool to stratifying patients with COVID-19 into intermediate-, high-, and very high-risk groups. This simple predictive tool is shared with a wider health care community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize health care resources.
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Affiliation(s)
- Fang He
- Amgen Inc, Center for Observational Research, South San Francisco, CA, United States
- Amgen Inc, Digital Health & Innovation, Thousand Oaks, CA, United States
| | - John H Page
- Amgen Inc, Center for Observational Research, Thousand Oaks, CA, United States
| | - Kerry R Weinberg
- Amgen Inc, Digital Health & Innovation, Thousand Oaks, CA, United States
| | - Anirban Mishra
- Amgen Inc, Digital Health & Innovation, Thousand Oaks, CA, United States
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24
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Laino ME, Generali E, Tommasini T, Angelotti G, Aghemo A, Desai A, Morandini P, Stefanini GG, Lleo A, Voza A, Savevski V. An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study. Arch Med Sci 2022; 18:587-595. [PMID: 35591841 PMCID: PMC9103632 DOI: 10.5114/aoms/144980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/16/2021] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning. MATERIAL AND METHODS We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation. RESULTS 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality. CONCLUSIONS Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.
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Affiliation(s)
- Maria Elena Laino
- Humanitas AI Center, Humanitas Research Hospital IRCCS, Milan, Italy
| | - Elena Generali
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Division of Internal Medicine, Humanitas Research Hospital IRCCS, Milan, Italy
| | - Tobia Tommasini
- Humanitas AI Center, Humanitas Research Hospital IRCCS, Milan, Italy
| | | | - Alessio Aghemo
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Division of Internal Medicine, Humanitas Research Hospital IRCCS, Milan, Italy
| | - Antonio Desai
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Division of Internal Medicine, Humanitas Research Hospital IRCCS, Milan, Italy
| | | | - Giulio G. Stefanini
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Emergency Department, Humanitas Research Hospital IRCCS, Milan, Italy
| | - Ana Lleo
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Division of Internal Medicine, Humanitas Research Hospital IRCCS, Milan, Italy
| | - Antonio Voza
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Cardio Center, Humanitas Research Hospital IRCCS, Milan, Italy
| | - Victor Savevski
- Humanitas AI Center, Humanitas Research Hospital IRCCS, Milan, Italy
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25
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Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients. NPJ Digit Med 2021; 4:155. [PMID: 34750499 PMCID: PMC8576003 DOI: 10.1038/s41746-021-00527-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/07/2021] [Indexed: 12/23/2022] Open
Abstract
The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.
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26
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Khera R, Liu Y, de Lemos JA, Das SR, Pandey A, Omar W, Kumbhani DJ, Girotra S, Yeh RW, Rutan C, Walchok J, Lin Z, Bradley SM, Velazquez EJ, Churchwell KB, Nallamothu BK, Krumholz HM, Curtis JP. Association of COVID-19 Hospitalization Volume and Case Growth at US Hospitals with Patient Outcomes. Am J Med 2021; 134:1380-1388.e3. [PMID: 34343515 PMCID: PMC8325555 DOI: 10.1016/j.amjmed.2021.06.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 12/01/2022]
Abstract
BACKGROUND Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions. METHODS We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR). RESULTS There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03). CONCLUSIONS An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Conn; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Conn.
| | - Yusi Liu
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Conn
| | - James A de Lemos
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas
| | - Sandeep R Das
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas
| | - Ambarish Pandey
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas
| | - Wally Omar
- Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Mass
| | - Dharam J Kumbhani
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas
| | - Saket Girotra
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Iowa, Iowa City
| | - Robert W Yeh
- Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Mass; Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, Mass
| | | | | | - Zhenqiu Lin
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Conn; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Conn
| | - Steven M Bradley
- Healthcare Delivery Innovation Center, Minneapolis Heart Institute, Minn
| | - Eric J Velazquez
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Conn
| | - Keith B Churchwell
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Conn
| | | | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Conn; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Conn; Department of Health Policy and Management, Yale School of Public Health, New Haven, Conn
| | - Jeptha P Curtis
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Conn; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Conn
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27
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Murri R, Lenkowicz J, Masciocchi C, Iacomini C, Fantoni M, Damiani A, Marchetti A, Sergi PDA, Arcuri G, Cesario A, Patarnello S, Antonelli M, Bellantone R, Bernabei R, Boccia S, Calabresi P, Cambieri A, Cauda R, Colosimo C, Crea F, De Maria R, De Stefano V, Franceschi F, Gasbarrini A, Parolini O, Richeldi L, Sanguinetti M, Urbani A, Zega M, Scambia G, Valentini V. A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19. Sci Rep 2021; 11:21136. [PMID: 34707184 PMCID: PMC8551240 DOI: 10.1038/s41598-021-99905-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/02/2021] [Indexed: 02/08/2023] Open
Abstract
The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.
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Affiliation(s)
- Rita Murri
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
| | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Chiara Iacomini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Massimo Fantoni
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | | | | | | | - Giovanni Arcuri
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Alfredo Cesario
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Massimo Antonelli
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Rocco Bellantone
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Roberto Bernabei
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Stefania Boccia
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Paolo Calabresi
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea Cambieri
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Roberto Cauda
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Cesare Colosimo
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Filippo Crea
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Valerio De Stefano
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Francesco Franceschi
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Antonio Gasbarrini
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Luca Richeldi
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Maurizio Sanguinetti
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea Urbani
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Maurizio Zega
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giovanni Scambia
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- Sezione di Malattie Infettive, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
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28
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Mizrahi B, Bivas-Benita M, Kalkstein N, Akiva P, Yanover C, Yehezkelli Y, Kessler Y, Alon SH, Rubin E, Chodick G. Results of an early second PCR test performed on SARS-CoV-2 positive patients may support risk assessment for severe COVID-19. Sci Rep 2021; 11:20463. [PMID: 34650138 PMCID: PMC8516879 DOI: 10.1038/s41598-021-99671-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Identifying patients at increased risk for severe COVID-19 is of high priority during the pandemic as it could affect clinical management and shape public health guidelines. In this study we assessed whether a second PCR test conducted 2–7 days after a SARS-CoV-2 positive test could identify patients at risk for severe illness. Analysis of a nationwide electronic health records data of 1683 SARS-CoV-2 positive individuals indicated that a second negative PCR test result was associated with lower risk for severe illness compared to a positive result. This association was seen across different age groups and clinical settings. More importantly, it was not limited to recovering patients but also observed in patients who still had evidence of COVID-19 as determined by a subsequent positive PCR test. Our study suggests that an early second PCR test may be used as a supportive risk-assessment tool to improve disease management and patient care.
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Affiliation(s)
- Barak Mizrahi
- KI Research Institute, 11 Hazayit st, Kfar Malal, Israel.
| | | | - Nir Kalkstein
- KI Research Institute, 11 Hazayit st, Kfar Malal, Israel
| | - Pinchas Akiva
- KI Research Institute, 11 Hazayit st, Kfar Malal, Israel
| | - Chen Yanover
- KI Research Institute, 11 Hazayit st, Kfar Malal, Israel
| | - Yoav Yehezkelli
- KI Research Institute, 11 Hazayit st, Kfar Malal, Israel.,School of Public Health, Tel-Aviv University, Tel-Aviv, Israel
| | - Yoav Kessler
- Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | | | - Eitan Rubin
- Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Gabriel Chodick
- Maccabi Institute for Research and Innovation, Tel-Aviv, Israel
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29
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Zoabi Y, Kehat O, Lahav D, Weiss-Meilik A, Adler A, Shomron N. Predicting bloodstream infection outcome using machine learning. Sci Rep 2021; 11:20101. [PMID: 34635696 PMCID: PMC8505419 DOI: 10.1038/s41598-021-99105-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We developed electronic medical record-based machine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our models were trained using electronic medical records that include demographics, blood tests, and the medical and diagnosis history of 7889 hospitalized patients diagnosed with BSI. Among the implications of this work is implementation of the models as a basis for selective rapid microbiological identification, toward earlier administration of appropriate antibiotic therapy. Additionally, our models may help reduce the development of BSI and its associated adverse health outcomes and complications.
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Affiliation(s)
- Yazeed Zoabi
- Sackler Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel.,Edmond J Safra Center for Bioinformatics, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Orli Kehat
- I-Medata AI Center, Tel-Aviv Sourasky Medical Center, 6423906, Tel Aviv, Israel
| | - Dan Lahav
- Sackler Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel.,Edmond J Safra Center for Bioinformatics, Tel Aviv University, 6997801, Tel Aviv, Israel.,The Blavatnik School of Computer Science, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Ahuva Weiss-Meilik
- I-Medata AI Center, Tel-Aviv Sourasky Medical Center, 6423906, Tel Aviv, Israel.
| | - Amos Adler
- Sackler Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel. .,Clinical Microbiology Laboratory, Tel Aviv Sourasky Medical Center, 6423906, Tel Aviv, Israel.
| | - Noam Shomron
- Sackler Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel. .,Edmond J Safra Center for Bioinformatics, Tel Aviv University, 6997801, Tel Aviv, Israel.
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30
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Imputation of missing values for electronic health record laboratory data. NPJ Digit Med 2021; 4:147. [PMID: 34635760 PMCID: PMC8505441 DOI: 10.1038/s41746-021-00518-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/13/2021] [Indexed: 11/08/2022] Open
Abstract
Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients' comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.
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31
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Swearingen D, Boverman G, Tgavalekos K, Noren DP, Ravindranath S, Ghosh E, Xu M, Wondrely L, Thompson P, Cowden JD, Antonescu C. A Retrospective Cohort Study of Clinical Factors Associated with Transitions of Care among COVID-19 Patients. J Clin Med 2021; 10:jcm10194605. [PMID: 34640626 PMCID: PMC8509460 DOI: 10.3390/jcm10194605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 09/24/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Coronavirus Disease 2019 (COVID-19) is an international health crisis. In this article, we report on patient characteristics associated with care transitions of: 1) hospital admission from the emergency department (ED) and 2) escalation to the intensive care unit (ICU). Analysis of data from the electronic medical record (EMR) was performed for patients with COVID-19 seen in the ED of a large Western U.S. Health System from April to August of 2020, totaling 10,079 encounters. Of these, 5172 resulted in admission as an inpatient within 72 h. Inpatient encounters (n = 6079) were also considered for patients with positive COVID-19 test results, of which 970 resulted in a transfer to the ICU or in-hospital mortality. Laboratory results, vital signs, symptoms, and comorbidities were investigated for each of these care transitions. Different top risk factors were found, but two factors common to hospital admission and ICU transfer were respiratory rate and the need for oxygen support. Comorbidities common to both settings were cerebrovascular disease and congestive heart failure. Regarding laboratory results, the neutrophil-to-lymphocyte ratio was associated with transitions to higher levels of care, along with the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT).
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Affiliation(s)
- Dennis Swearingen
- Department of Medical Informatics, Banner Health, Phoenix, AZ 85012, USA; (D.S.); (P.T.); (J.D.C.); (C.A.)
- Department of Biomedical Informatics, University of Arizona College of Medicine, Phoenix, AZ 85004, USA
| | - Gregory Boverman
- Connected Care and Personal Health Department, Philips Research North America, Cambridge, MA 02141, USA; (K.T.); (D.P.N.); (S.R.); (E.G.); (M.X.); (L.W.)
- Correspondence:
| | - Kristen Tgavalekos
- Connected Care and Personal Health Department, Philips Research North America, Cambridge, MA 02141, USA; (K.T.); (D.P.N.); (S.R.); (E.G.); (M.X.); (L.W.)
| | - David P. Noren
- Connected Care and Personal Health Department, Philips Research North America, Cambridge, MA 02141, USA; (K.T.); (D.P.N.); (S.R.); (E.G.); (M.X.); (L.W.)
| | - Shreyas Ravindranath
- Connected Care and Personal Health Department, Philips Research North America, Cambridge, MA 02141, USA; (K.T.); (D.P.N.); (S.R.); (E.G.); (M.X.); (L.W.)
| | - Erina Ghosh
- Connected Care and Personal Health Department, Philips Research North America, Cambridge, MA 02141, USA; (K.T.); (D.P.N.); (S.R.); (E.G.); (M.X.); (L.W.)
| | - Minnan Xu
- Connected Care and Personal Health Department, Philips Research North America, Cambridge, MA 02141, USA; (K.T.); (D.P.N.); (S.R.); (E.G.); (M.X.); (L.W.)
| | - Lisa Wondrely
- Connected Care and Personal Health Department, Philips Research North America, Cambridge, MA 02141, USA; (K.T.); (D.P.N.); (S.R.); (E.G.); (M.X.); (L.W.)
| | - Pam Thompson
- Department of Medical Informatics, Banner Health, Phoenix, AZ 85012, USA; (D.S.); (P.T.); (J.D.C.); (C.A.)
| | - J. David Cowden
- Department of Medical Informatics, Banner Health, Phoenix, AZ 85012, USA; (D.S.); (P.T.); (J.D.C.); (C.A.)
| | - Corneliu Antonescu
- Department of Medical Informatics, Banner Health, Phoenix, AZ 85012, USA; (D.S.); (P.T.); (J.D.C.); (C.A.)
- Department of Biomedical Informatics, University of Arizona College of Medicine, Phoenix, AZ 85004, USA
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32
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Petch J, Di S, Nelson W. Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can J Cardiol 2021; 38:204-213. [PMID: 34534619 DOI: 10.1016/j.cjca.2021.09.004] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/23/2021] [Accepted: 09/08/2021] [Indexed: 11/29/2022] Open
Abstract
Many clinicians remain wary of machine learning due to long-standing concerns about "black box" models. "Black box" is shorthand for models that are sufficiently complex that they are not straightforwardly interpretable to humans. Lack of interpretability in predictive models can undermine trust in those models, especially in health care where so many decisions are literally life and death. There has recently been an explosion of research in the field of explainable machine learning aimed at addressing these concerns. The promise of explainable machine learning is considerable, but it is important for cardiologists who may encounter these techniques in clinical decision support tools or novel research papers to have a critical understanding of both their strengths and their limitations. This paper reviews key concepts and techniques in the field of explainable machine learning as they apply to cardiology. Key concepts reviewed include interpretability versus explainability and global versus local explanations. Techniques demonstrated include permutation importance, surrogate decision trees, local interpretable model-agnostic explanations, and partial dependence plots. We discuss several limitations with explainability techniques, focusing on the how the nature of explanations as approximations may omit important information about how black box models work and why they make certain predictions. We conclude by proposing a rule of thumb about when it is appropriate to use black box models with explanations, rather than interpretable models.
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Affiliation(s)
- Jeremy Petch
- Centre for Data Science and Digital Health, Hamilton Health Sciences; Institute of Health Policy, Management and Evaluation, University of Toronto; Division of Cardiology, Department of Medicine, McMaster University; Population Health Research Institute.
| | - Shuang Di
- Centre for Data Science and Digital Health, Hamilton Health Sciences; Dalla Lana School of Public Health, University of Toronto
| | - Walter Nelson
- Centre for Data Science and Digital Health, Hamilton Health Sciences; Department of Statistical Sciences, University of Toronto
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33
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AlJame M, Imtiaz A, Ahmad I, Mohammed A. Deep forest model for diagnosing COVID-19 from routine blood tests. Sci Rep 2021; 11:16682. [PMID: 34404838 PMCID: PMC8371014 DOI: 10.1038/s41598-021-95957-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/03/2021] [Indexed: 02/07/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.
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Affiliation(s)
- Maryam AlJame
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait.
| | | | - Imtiaz Ahmad
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait
| | - Ameer Mohammed
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait
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34
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Werfel S, Jakob CEM, Borgmann S, Schneider J, Spinner C, Schons M, Hower M, Wille K, Haselberger M, Heuzeroth H, Rüthrich MM, Dolff S, Kessel J, Heemann U, Vehreschild JJ, Rieg S, Schmaderer C. Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients. J Med Virol 2021; 93:6703-6713. [PMID: 34331717 PMCID: PMC8426905 DOI: 10.1002/jmv.27252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/13/2021] [Accepted: 07/29/2021] [Indexed: 11/10/2022]
Abstract
Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77-0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77-0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the "first wave" of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78-0.87]; for full follow-up: 0.82 [95% CI: 0.78-0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.
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Affiliation(s)
- Stanislas Werfel
- Department of Nephrology, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich, Germany
| | - Carolin E M Jakob
- Department I for Internal Medicine, University Hospital of Cologne, University of Cologne, Cologne, Germany.,German Centre for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany
| | - Stefan Borgmann
- Department of Infectious Diseases and Infection Control, Ingolstadt Hospital, Ingolstadt, Germany
| | - Jochen Schneider
- Department of Internal Medicine II, School of Medicine, Technical University of Munich, University Hospital rechts der Isar, Munich, Germany.,German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Christoph Spinner
- Department of Internal Medicine II, School of Medicine, Technical University of Munich, University Hospital rechts der Isar, Munich, Germany.,German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Maximilian Schons
- Department I for Internal Medicine, University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Martin Hower
- Department of Pneumology, Infectious Diseases and Internal Medicine, Klinikum Dortmund gGmbH, Dortmund, Germany
| | - Kai Wille
- University Clinic for Haematology, Oncology, Haemostaseology and Palliative Care, Johannes Wesling Medical Centre Minden UKRUB, University of Bochum, Minden, Germany
| | | | - Hanno Heuzeroth
- Department of Emergency and Intensive Care Medicine, Klinikum Ernst von Bergmann, Potsdam, Germany
| | - Maria M Rüthrich
- Department of Internal Medicine II, Hematology and Medical Oncology, University Hospital Jena, Jena, Germany
| | - Sebastian Dolff
- Department of Infectious Diseases, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Johanna Kessel
- Department of Internal Medicine, Hematology and Oncology, Goethe University Frankfurt, Frankfurt, Germany
| | - Uwe Heemann
- Department of Nephrology, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich, Germany
| | - Jörg J Vehreschild
- Department I for Internal Medicine, University Hospital of Cologne, University of Cologne, Cologne, Germany.,German Centre for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany.,Department of Internal Medicine, Hematology and Oncology, Goethe University Frankfurt, Frankfurt, Germany
| | - Siegbert Rieg
- Department of Medicine II, University of Freiburg, Freiburg, Germany
| | - Christoph Schmaderer
- Department of Nephrology, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich, Germany
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35
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Wang JM, Liu W, Chen X, McRae MP, McDevitt JT, Fenyö D. Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation. J Med Internet Res 2021; 23:e29514. [PMID: 34081611 PMCID: PMC8274681 DOI: 10.2196/29514] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/17/2021] [Accepted: 05/26/2021] [Indexed: 01/13/2023] Open
Abstract
Background The COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients at elevated risk of morbidity and mortality. Objective The goal of this retrospective study of patients who tested positive with COVID-19 and were treated at NYU (New York University) Langone Health was to identify clinical markers predictive of disease severity in order to assist in clinical decision triage and to provide additional biological insights into disease progression. Methods The clinical activity of 3740 patients at NYU Langone Hospital was obtained between January and August 2020; patient data were deidentified. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to the intensive care unit (ICU). Results The XGBoost (eXtreme Gradient Boosting) model that was trained on clinical data from the final 24 hours excelled at predicting mortality (area under the curve [AUC]=0.92; specificity=86%; and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 (peripheral oxygen saturation) and being aged 75 years and over. Performance of this model to predict the deceased outcome extended 5 days prior, with AUC=0.81, specificity=70%, and sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU-admitted outcomes, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers, including diabetic history, age, and temperature, offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen and lactate dehydrogenase (LDH). Features that were predictive of morbidity included LDH, calcium, glucose, and C-reactive protein. Conclusions Together, this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
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Affiliation(s)
- Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, United States.,Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, United States.,Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, United States.,Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, United States
| | - Xiaoshan Chen
- Department of Medicine, NYU Grossman School of Medicine, New York, NY, United States
| | - Michael P McRae
- Department of Biomaterials, Bioengineering Institute, New York University, New York, NY, United States
| | - John T McDevitt
- Department of Biomaterials, Bioengineering Institute, New York University, New York, NY, United States
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, United States.,Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, United States.,NYU Langone Health, New York, NY, United States
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36
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Mauer E, Lee J, Choi J, Zhang H, Hoffman KL, Easthausen IJ, Rajan M, Weiner MG, Kaushal R, Safford MM, Steel PAD, Banerjee S. A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories. J Biomed Inform 2021; 118:103794. [PMID: 33933654 PMCID: PMC8084618 DOI: 10.1016/j.jbi.2021.103794] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 04/13/2021] [Accepted: 04/22/2021] [Indexed: 11/20/2022]
Abstract
From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.
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Affiliation(s)
- Elizabeth Mauer
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Jihui Lee
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Justin Choi
- Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Hongzhe Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Katherine L Hoffman
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Imaani J Easthausen
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Mangala Rajan
- Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Monika M Safford
- Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Peter A D Steel
- Emergency Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Samprit Banerjee
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
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Greysen SR, Auerbach AD, Mitchell MD, Goldstein JN, Weiss R, Esmaili A, Kuye I, Manjarrez E, Bann M, Schnipper JL. Discharge Practices for COVID-19 Patients: Rapid Review of Published Guidance and Synthesis of Documents and Practices at 22 US Academic Medical Centers. J Gen Intern Med 2021; 36:1715-1721. [PMID: 33835314 PMCID: PMC8034037 DOI: 10.1007/s11606-021-06711-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 03/09/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND There are currently no evidence-based guidelines that provide standardized criteria for the discharge of COVID-19 patients from the hospital. OBJECTIVE To address this gap in practice guidance, we reviewed published guidance and collected discharge protocols and procedures to identify and synthesize common practices. DESIGN Rapid review of existing guidance from US and non-US public health organizations and professional societies and qualitative review using content analysis of discharge documents collected from a national sample of US academic medical centers with follow-up survey of hospital leaders SETTING AND PARTICIPANTS: We reviewed 65 websites for major professional societies and public health organizations and collected documents from 22 Academic Medical Centers (AMCs) in the US participating in the HOspital MEdicine Reengineering Network (HOMERuN). RESULTS We synthesized data regarding common practices around 5 major domains: (1) isolation and transmission mitigation; (2) criteria for discharge to non-home settings including skilled nursing, assisted living, or homeless; (3) clinical criteria for discharge including oxygenation levels, fever, and symptom improvement; (4) social support and ability to perform activities of daily living; (5) post-discharge instructions, monitoring, and follow-up. LIMITATIONS We used streamlined methods for rapid review of published guidance and collected discharge documents only in a focused sample of US academic medical centers. CONCLUSION AMCs studied showed strong consensus on discharge practices for COVID-19 patients related to post-discharge isolation and transmission mitigation for home and non-home settings. There was high concordance among AMCs that discharge practices should address COVID-19-specific factors in clinical, functional, and post-discharge monitoring domains although definitions and details varied.
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Affiliation(s)
- S Ryan Greysen
- Penn Medicine Center for Evidence-based Practice, Section of Hospital Medicine, Division of General Internal Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
- Center for Evidence-based Practice, University of Pennsylvania Health System, Philadelphia, USA.
| | - Andrew D Auerbach
- Division of Hospital Medicine, University of California San Francisco, San Francisco, USA
| | - Matthew D Mitchell
- Center for Evidence-based Practice, University of Pennsylvania Health System, Philadelphia, USA
| | | | - Rachel Weiss
- Division of Hospital Medicine, University of California San Francisco, San Francisco, USA
- University of Virginia, Charlottesville, VA, USA
| | - Armond Esmaili
- Division of Hospital Medicine, University of California San Francisco, San Francisco, USA
| | - Ifedayo Kuye
- Division of Hospital Medicine, University of California San Francisco, San Francisco, USA
- Johns Hopkins University, Baltimore, MD, USA
| | | | - Maralyssa Bann
- Division of General Internal Medicine, University of Washington, Seattle, WA, USA
| | - Jeffrey L Schnipper
- Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Khera R, Mortazavi BJ, Sangha V, Warner F, Young HP, Ross JS, Shah ND, Theel ES, Jenkinson WG, Knepper C, Wang K, Peaper D, Martinello RA, Brandt CA, Lin Z, Ko AI, Krumholz HM, Pollock BD, Schulz WL. Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021. [PMID: 34013299 PMCID: PMC8132274 DOI: 10.1101/2021.03.16.21253770] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Objective: Real-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations. Methods: Electronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations. Results: Of the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19. Conclusions: COVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.
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Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021; 19:2833-2850. [PMID: 34025952 PMCID: PMC8123783 DOI: 10.1016/j.csbj.2021.05.010] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/01/2021] [Accepted: 05/02/2021] [Indexed: 12/23/2022] Open
Abstract
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
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Key Words
- ABG, Arterial Blood Gas
- ADA, Adenosine Deaminase
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APTT, Activated Partial Thromboplastin Time
- ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer
- AUC, Area Under the Curve
- Acc, Accuracy
- Adaboost, Adaptive Boosting
- Apol AI, Apolipoprotein AI
- Apol B, Apolipoprotein B
- Artificial intelligence
- BNB, Bernoulli Naïve Bayes
- BUN, Blood Urea Nitrogen
- CI, Confidence Interval
- CK-MB, Creatine Kinase isoenzyme
- CNN, Convolutional Neural Networks
- COVID-19
- CPP, COVID-19 Positive Patients
- CRP, C-Reactive Protein
- CRT, Classification and Regression Decision Tree
- CoxPH, Cox Proportional Hazards
- DCNN, Deep Convolutional Neural Networks
- DL, Deep Learning
- DLC, Density Lipoprotein Cholesterol
- DNN, Deep Neural Networks
- DT, Decision Tree
- Diagnosis
- ED, Emergency Department
- ESR, Erythrocyte Sedimentation Rate
- ET, Extra Trees
- FCV, Fold Cross Validation
- FL, Federated Learning
- FiO2, Fraction of Inspiration O2
- GBDT, Gradient Boost Decision Tree
- GBM light, Gradient Boosting Machine light
- GDCNN, Genetic Deep Learning Convolutional Neural Network
- GFR, Glomerular Filtration Rate
- GFS, Gradient boosted feature selection
- GGT, Glutamyl Transpeptidase
- GNB, Gaussian Naïve Bayes
- HDLC, High Density Lipoprotein Cholesterol
- INR, International Normalized Ratio
- Inception Resnet, Inception Residual Neural Network
- L1LR, L1 Regularized Logistic Regression
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LDH, Lactate Dehydrogenase
- LDLC, Low Density Lipoprotein Cholesterol
- LR, Logistic Regression
- LSTM, Long-Short Term Memory
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean corpuscular volume
- ML, Machine Learning
- MLP, MultiLayer Perceptron
- MPV, Mean Platelet Volume
- MRMR, Maximum Relevance Minimum Redundancy
- Multimodal data
- NB, Naïve Bayes
- NLP, Natural Language Processing
- NPV, Negative Predictive Values
- Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer
- OB, Occult Blood test
- PCT, Thrombocytocrit
- PPV, Positive Predictive Values
- PWD, Platelet Distribution Width
- PaO2, Arterial Oxygen Tension
- Paco2, Arterial Carbondioxide Tension
- Prognosis
- RBC, Red Blood Cell
- RBF, Radial Basis Function
- RBP, Retinol Binding Protein
- RDW, Red blood cell Distribution Width
- RF, Random Forest
- RFE, Recursive Feature Elimination
- RSV, Respiratory Syncytial Virus
- SEN, Sensitivity
- SG, Specific Gravity
- SMOTE, Synthetic Minority Oversampling Technique
- SPE, Specificity
- SRLSR, Sparse Rescaled Linear Square Regression
- SVM, Support Vector Machine
- SaO2, Arterial Oxygen saturation
- Screening
- TBA, Total Bile Acid
- TTS, Training Test Split
- WBC, White Blood Cell count
- XGB, eXtreme Gradient Boost
- k-NN, K-Nearest Neighbor
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Affiliation(s)
- Eleni S. Adamidi
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantinos Mitsis
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantina S. Nikita
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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Gray WK, Navaratnam AV, Day J, Wendon J, Briggs TWR. Changes in COVID-19 in-hospital mortality in hospitalised adults in England over the first seven months of the pandemic: An observational study using administrative data. LANCET REGIONAL HEALTH-EUROPE 2021; 5:100104. [PMID: 33969337 PMCID: PMC8086562 DOI: 10.1016/j.lanepe.2021.100104] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background Previous research by our team identified factors associated with in-hospital mortality in patients with a diagnosis of COVID-19 in England between March and May 2020. The aim of the current paper was to investigate the changing role of demographics and co-morbidity, with a particular focus on ethnicity, as risk factors for in-hospital mortality over an extended period. Methods This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. All patients aged ≥ 18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed (discharged alive or died) between 1st March and 30th September 2020 were included. In-hospital mortality was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between in-hospital mortality with adjustment for the covariates: age, sex, deprivation, ethnicity, date of discharge and a number of comorbidities. Findings Compared to patients in March-May (n = 93,379), patients in June-September (n = 24,059) were younger, more likely to be female and of Asian ethnicity, but less likely to be of Black ethnicity. In-hospital mortality rates, adjusted for covariates, declined from 33–34% in March to 11–12% in September. Compared to the March-May period, Bangladeshi, Indian and Other Asian ethnicity patients had a lower relative odds of death (compared to White ethnicity patients) during June-September. For Pakistani patients, the decline in-hospital mortality rates was more modest across the same time periods with the relative odds of death increasing slightly (odds ratio (95% confidence interval)) 1.24 (1.10 to 1.40) and 1.35 (1.08 to 1.69) respectively. From March-May to June-September the relative odds of death in patients with a diagnosis of metastatic carcinoma increased (1.90 (1.73 to 2.08) vs 3.01 (2.55 to 3.54)) but decreased for male patients (1.44 (1.39 to 1.49) vs 1.27 (1.17 to 1.38)) and patients with obesity (1.42 (1.34 to 1.52) vs 0.97 (0.83 to 1.14)) and diabetes without complications (1.14 (1.10 to 1.19) vs 0.95 (0.87 to 1.05)). Interpretation In-hospital mortality rates for patients with a diagnosis of COVID-19 have fallen substantially and there is evidence that the relative importance of some covariates has changed since the start of the pandemic. These patterns should continue to be tracked as new variants of the virus emerge, vaccination programmes are rolled out and hospital pressures fluctuate. Funding None.
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Affiliation(s)
- William K Gray
- Getting It Right First Time programme, NHS England and NHS Improvement, London, United Kingdom
| | - Annakan V Navaratnam
- Getting It Right First Time programme, NHS England and NHS Improvement, London, United Kingdom
| | - Jamie Day
- Getting It Right First Time programme, NHS England and NHS Improvement, London, United Kingdom
| | | | - Tim W R Briggs
- Getting It Right First Time programme, NHS England and NHS Improvement, London, United Kingdom.,Royal National Orthopaedic Hospital, London, Stanmore, United Kingdom
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Wang JM, Liu W, Chen X, McRae MP, McDevitt JT, Fenyö D. Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021. [PMID: 33300013 PMCID: PMC7724684 DOI: 10.1101/2020.12.02.20235879] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Clinical activity of 3740 de-identified COVID-19 positive patients treated at NYU Langone Health (NYULH) were collected between January and August 2020. XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 and age 75+. Performance of this model to predict the deceased outcome extended 5 days prior with AUC=0.81, specificity=70%, sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU admitted, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers including diabetic history, age and temperature offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). Features predictive of morbidity included LDH, calcium, glucose, and C-reactive protein (CRP). Together this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
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Affiliation(s)
- Joshua M Wang
- Institute for Systems Genetics; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Wenke Liu
- Institute for Systems Genetics; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Xiaoshan Chen
- Department of Medicine, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Michael P McRae
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, United States
| | - John T McDevitt
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, United States
| | - David Fenyö
- Institute for Systems Genetics; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, 10016, USA
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42
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1661] [Impact Index Per Article: 415.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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