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Ho M, Levy TJ, Koulas I, Founta K, Coppa K, Hirsch JS, Davidson KW, Spyropoulos AC, Zanos TP. Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York. Int J Med Inform 2024; 181:105286. [PMID: 37956643 PMCID: PMC10843635 DOI: 10.1016/j.ijmedinf.2023.105286] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/20/2023] [Accepted: 11/03/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND COVID-19 is a challenging disease to characterize given its wide-ranging heterogeneous symptomatology. Several studies have attempted to extract clinical phenotypes but often relied on data from small patient cohorts, usually limited to only one viral variant and utilizing a static snapshot of patient data. OBJECTIVE This study aimed to identify clinical phenotypes of hospitalized COVID-19 patients and investigate their longitudinal dynamics throughout the pandemic, with the goal to relate these phenotypes to clinical outcomes and treatment strategies. METHODS We utilized routinely collected demographic and clinical data throughout the hospitalization of 38,077 patients admitted between 3/2020 to 5/2022, in 12 New York hospitals. Uniform Manifold Approximation and Projection and agglomerative hierarchical clustering were used to derive the clusters, followed by exploratory data analysis to compare the prevalence of comorbidities and treatments per cluster. RESULTS 4 distinct clinical phenotypes remained robust in multi-site validation and were associated with different mortality rates. The temporal progression of these phenotypes throughout the COVID-19 pandemic demonstrated increased variability across the waves of the three dominant viral variants (alpha, delta, omicron). Longitudinal analysis evaluating changes in clinical phenotypes of each patient throughout the course of a 4-week hospital stay exemplified the dynamic nature of the disease progression. Factors such as sex, race/ethnicity and specific treatment modalities revealed significant and clinically relevant differences between the observed phenotypes. CONCLUSIONS Our proposed methodology has the potential of enabling clinicians and policy makers to draw evidence-based conclusions for guiding treatment modalities in a dynamic fashion.
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Affiliation(s)
- Matthew Ho
- Institute of Health Systems Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030; Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY 11549
| | - Todd J Levy
- Institute of Health Systems Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030; Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030
| | - Ioannis Koulas
- Institute of Health Systems Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030
| | - Kyriaki Founta
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY 11549
| | - Kevin Coppa
- Department of Clinical Digital Solutions, Northwell Health, New Hyde Park, NY 11042
| | - Jamie S Hirsch
- Institute of Health Systems Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY 11549; Department of Clinical Digital Solutions, Northwell Health, New Hyde Park, NY 11042
| | - Karina W Davidson
- Institute of Health Systems Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY 11549
| | - Alex C Spyropoulos
- Institute of Health Systems Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY 11549
| | - Theodoros P Zanos
- Institute of Health Systems Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030; Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY 11549.
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Zhang Y, Zhu YJ, Zhu DJ, Yu BY, Liu TT, Wang LY, Zhang LL. Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19. Front Public Health 2023; 11:1227935. [PMID: 37522004 PMCID: PMC10375294 DOI: 10.3389/fpubh.2023.1227935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Background Timely recognition of respiratory failure and the need for mechanical ventilation is crucial in managing patients with coronavirus disease 2019 (COVID-19) and reducing hospital mortality rate. A risk stratification tool could assist to avoid clinical deterioration of patients with COVID-19 and optimize allocation of scarce resources. Therefore, we aimed to develop a prediction model for early identification of patients with COVID-19 who may require mechanical ventilation. Methods We included patients with COVID-19 hospitalized in United States. Demographic and clinical data were extracted from the records of the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Model construction involved the use of the least absolute shrinkage and selection operator and multivariable logistic regression. The model's performance was evaluated based on discrimination, calibration, and clinical utility. Results The training set comprised 73,957 patients (5,971 requiring mechanical ventilation), whereas the validation set included 10,428 (887 requiring mechanical ventilation). The prediction model incorporating age, sex, and 11 other comorbidities (deficiency anemias, congestive heart failure, coagulopathy, dementia, diabetes with chronic complications, complicated hypertension, neurological disorders unaffecting movement, obesity, pulmonary circulation disease, severe renal failure, and weight loss) demonstrated moderate discrimination (area under the curve, 0.715; 95% confidence interval, 0.709-0.722), good calibration (Brier score = 0.070, slope = 1, intercept = 0) and a clinical net benefit with a threshold probability ranged from 2 to 34% in the training set. Similar model's performances were observed in the validation set. Conclusion A robust prognostic model utilizing readily available predictors at hospital admission was developed for the early identification of patients with COVID-19 who may require mechanical ventilation. Application of this model could support clinical decision-making to optimize patient management and resource allocation.
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Affiliation(s)
- Yi Zhang
- Department of Gastroenterology, Changzheng Hospital, Naval Medical University, Shanghai, China
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yang-Jie Zhu
- Department of Military Health Management, College of Health Service, Naval Medical University, Shanghai, China
| | - Dao-Jun Zhu
- Operating Room, West China Hospital, Sichuan University, Chengdu, China
- West China School of Nursing, Sichuan University, Chengdu, China
| | - Bo-Yang Yu
- Department of Military Health Management, College of Health Service, Naval Medical University, Shanghai, China
| | - Tong-Tong Liu
- Department of Military Health Management, College of Health Service, Naval Medical University, Shanghai, China
| | - Lu-Yao Wang
- Department of Military Health Management, College of Health Service, Naval Medical University, Shanghai, China
| | - Lu-Lu Zhang
- Department of Military Health Management, College of Health Service, Naval Medical University, Shanghai, China
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Ji Y, Gao Y, Bao R, Li Q, Liu D, Sun Y, Ye Y. Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS 2023; 2023:138-144. [PMID: 38486663 PMCID: PMC10939709 DOI: 10.1109/ichi57859.2023.00028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn't consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informative for developing a prediction model to identify COVID-19 patients who are very likely to revisit an ER within 7 days after discharge.
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Affiliation(s)
- Yuelyu Ji
- Department of Information Science, School of Computing and Information, University of Pittsburgh, Pittsburgh,USA
| | - Yuhe Gao
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, USA
| | - Runxue Bao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, USA
| | - Qi Li
- School of Business, State University of New York at New Paltz, New Paltz, USA
| | - Disheng Liu
- Department of Information Science, School of Computing and Information, University of Pittsburgh Pittsburgh, USA
| | - Yiming Sun
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh Pittsburgh, USA
| | - Ye Ye
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, USA
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Rennert-May E, Crocker A, D'Souza AG, Zhang Z, Chew D, Beall R, Vickers DM, Leal J. Healthcare utilization and adverse outcomes stratified by sex, age and long-term care residency using the Alberta COVID-19 Analytics and Research Database (ACARD): a population-based descriptive study. BMC Infect Dis 2023; 23:337. [PMID: 37208609 DOI: 10.1186/s12879-023-08326-5] [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: 12/08/2022] [Accepted: 05/12/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Understanding the epidemiology of Coronavirus Disease of 2019 (COVID-19) in a local context is valuable for both future pandemic preparedness and potential increases in COVID-19 case volume, particularly due to variant strains. METHODS Our work allowed us to complete a population-based study on patients who tested positive for COVID-19 in Alberta from March 1, 2020 to December 15, 2021. We completed a multi-centre, retrospective population-based descriptive study using secondary data sources in Alberta, Canada. We identified all adult patients (≥ 18 years of age) tested and subsequently positive for COVID-19 (including only the first incident case of COVID-19) on a laboratory test. We determined positive COVID-19 tests, gender, age, comorbidities, residency in a long-term care (LTC) facility, time to hospitalization, length of stay (LOS) in hospital, and mortality. Patients were followed for 60 days from a COVID-19 positive test. RESULTS Between March 1, 2020 and December 15, 2021, 255,037 adults were identified with COVID-19 in Alberta. Most confirmed cases occurred among those less than 60 years of age (84.3%); however, most deaths (89.3%) occurred among those older than 60 years. Overall hospitalization rate among those who tested positive was 5.9%. Being a resident of LTC was associated with substantial mortality of 24.6% within 60 days of a positive COVID-19 test. The most common comorbidity among those with COVID-19 was depression. Across all patients 17.3% of males and 18.6% of females had an unplanned ambulatory visit subsequent to their positive COVID-19 test. CONCLUSIONS COVID-19 is associated with extensive healthcare utilization. Residents of LTC were substantially impacted during the COVID-19 pandemic with high associated mortality. Further work should be done to better understand the economic burden associated with related healthcare utilization following a COVID-19 infection to inform healthcare system resource allocation, planning, and forecasting.
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Affiliation(s)
- Elissa Rennert-May
- Department of Medicine, University of Calgary, Calgary, AB, Canada.
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada.
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada.
- Alberta Health Services, Calgary, AB, Canada.
| | | | - Adam G D'Souza
- Alberta Health Services, Calgary, AB, Canada
- Centre for Health Informatics, University of Calgary, Calgary, AB, Canada
| | - Zuying Zhang
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
| | - Derek Chew
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Reed Beall
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
| | - David M Vickers
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Mozell Core Analysis Lab, Centre for Health Informatics, University of Calgary, Calgary, AB, Canada
| | - Jenine Leal
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
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Ustebay S, Sarmis A, Kaya GK, Sujan M. A comparison of machine learning algorithms in predicting COVID-19 prognostics. Intern Emerg Med 2023; 18:229-239. [PMID: 36116079 PMCID: PMC9483274 DOI: 10.1007/s11739-022-03101-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/05/2022] [Indexed: 02/01/2023]
Abstract
ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data (n = 11,712), and (2) patient demographics, clinical data, and blood test results (n = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care.
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Affiliation(s)
- Serpil Ustebay
- Department of Computer Engineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Abdurrahman Sarmis
- Department of Microbiology Laboratory, Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Istanbul, Turkey
| | - Gulsum Kubra Kaya
- Department of Industrial Engineering, Istanbul Medeniyet University, Istanbul, Turkey.
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford, MK430AL, UK.
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Walston SL, Matsumoto T, Miki Y, Ueda D. Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study. Br J Radiol 2022; 95:20220058. [PMID: 36193755 PMCID: PMC9733620 DOI: 10.1259/bjr.20220058] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES The purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs. METHODS This retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions, 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models, which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHapley Additive exPlanations (SHAP) values. RESULTS The mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72-0.86), 0.74 (0.68-0.79), 0.77 (0.61-0.88), and 0.74 (0.69-0.79) for the clinical data-based model; 0.77 (0.69-0.85), 0.67 (0.61-0.73), 0.81 (0.67-0.92), 0.70 (0.64-0.75) for the image-based model, and 0.86 (0.81-0.91), 0.76 (0.70-0.81), 0.77 (0.61-0.88), 0.76 (0.70-0.81) for the mixed model. The mixed model had the best performance (p value < 0.05). The radiographs ranked fourth for prognostication overall, and first of the inpatient tests assessed. CONCLUSIONS These results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together. ADVANCES IN KNOWLEDGE This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.
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Affiliation(s)
| | | | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University,1-4-3 Asahi-machi, Abeno-ku, Osaka, Japan
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Finn A, Tanzer JR, Jindal A, Selvaraj V, Collins B, Dapaah-Afriyie K. Readmission Risk after COVID-19 Hospitalization: A Moderation Analysis by Vital Signs. South Med J 2022; 115:842-848. [PMID: 36318952 PMCID: PMC9612414 DOI: 10.14423/smj.0000000000001472] [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] [Indexed: 11/07/2022]
Abstract
Hospital admissions for coronavirus disease 2019 (COVID-19) are a common occurrence during periods of widespread viral transmission as are 30-day readmissions after COVID-19 hospitalization. This article provides an analysis of risk factors for readmission after COVID-19 hospitalization, in particular, vital signs upon discharge and their predictive value. In addition, this article evaluates whether the stabilization of vital signs within 24 hours of discharge can reduce readmission risk attributable to less modifiable primary factors such as underlying pulmonary and cardiac disease. The authors aim to aid the practicing clinician by providing an approach to safely risk stratify hospitalized COVID-19 patients who may be ready for discharge from the hospital. Objective Readmission to the hospital after hospitalization with coronavirus disease 2019 (COVID-19) is associated with significant morbidity and mortality. Hospital clinicians may identify the presence of a patient’s comorbid conditions, overall severity of illness, and clinical status at discharge as risk factors for readmission. Objective data are lacking to support reliance on these factors for discharge decision making. The objective of our study was to examine risk factors for readmission to the hospital after COVID-19 hospitalization and the impact of vital sign abnormalities, within 24 hours of discharge, on readmission rates. Methods In total, 2557 COVID-19-related hospital admissions within the Lifespan Health System, a large multicenter health system (Rhode Island), of 2230 unique patients aged 18 years and older, occurring from April 1, 2020 to December 31, 2020 were analyzed. Risk factors associated with readmission within 30 days were identified and analyzed using Cox regression. A moderation analysis by vital signs at discharge on the risk of readmission was performed. Results Clinical factors associated with readmissions included existing cardiovascular conditions (risk ratio 2.32, 95% confidence interval [CI] 1.10–4.90) and pulmonary disease (risk ratio 3.25, 95% CI 1.62–6.52). The absence of abnormal vital signs within 24 hours of discharge was associated with decreased 30-day readmission rates (risk ratio 0.70, 95% CI 0.52–0.94). Elevated C-reactive protein and d-dimer values and in-hospital complications including stroke, myocardial infarction, acute renal failure, and gastrointestinal bleeding were not associated with an increased risk of readmission. In moderation analysis, the presence of normal vital signs within 24 hours of discharge was associated with decreased readmission risk in patients who had primary risk factors for readmission including pulmonary disease (risk ratio 0.80, 95% CI 0.65–0.99), psychiatric disorders, and substance use (risk ratio 0.70, 95% CI 0.52–0.94). Conclusions Comorbid conditions, including pulmonary and cardiovascular disease, are associated with readmission risk after COVID-19 hospitalization. The normalization of vital signs within 24 hours of discharge during COVID-19 hospitalization may be an indicator of readiness for discharge and may mitigate some readmission risk conferred by comorbid conditions.
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Yi M, Cao Y, Zhou Y, Cao Y, Zheng X, Wang J, Chen W, Wei L, Zhang K. Association between hospital legal constructions and medical disputes: A multi-center analysis of 130 tertiary hospitals in Hunan Province, China. Front Public Health 2022; 10:993946. [PMID: 36159280 PMCID: PMC9490230 DOI: 10.3389/fpubh.2022.993946] [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: 07/14/2022] [Accepted: 08/12/2022] [Indexed: 01/26/2023] Open
Abstract
Background Medical disputes are common in hospitals and a major challenge for the operations of medical institutions. However, few studies have looked into the association between medical disputes and hospital legal constructions. The purpose of the study was to investigate the relationship between hospital legal constructions and medical disputes, and it also aimed to develop a nomogram to estimate the likelihood of medical disputes. Methods Between July and September 2021, 2,716 administrators from 130 hospitals were enrolled for analysis. The study collected seventeen variables for examination. To establish a nomogram, administrators were randomly split into a training group (n = 1,358) and a validation group (n = 1,358) with a 50:50 ratio. The nomogram was developed using data from participants in the training group, and it was validated in the validation group. The nomogram contained significant variables that were linked to medical disputes and were identified by multivariate analysis. The nomogram's predictive performance was assessed utilizing discriminative and calibrating ability. A web calculator was developed to be conducive to model utility. Results Medical disputes were observed in 41.53% (1,128/2,716) of participants. Five characteristics, including male gender, higher professional ranks, longer length of service, worse understanding of the hospital charters, and worse construction status of hospital rule of law, were significantly associated with more medical disputes based on the multivariate analysis. As a result, these variables were included in the nomogram development. The AUROC was 0.67 [95% confident interval (CI): 0.64-0.70] in the training group and 0.68 (95% CI: 0.66-0.71) in the validation group. The corresponding calibration slopes were 1.00 and 1.05, respectively, and intercepts were 0.00 and -0.06, respectively. Three risk groups were created among the participants: Those in the high-risk group experienced medical disputes 2.83 times more frequently than those in the low-risk group (P < 0.001). Conclusion Medical dispute is prevailing among hospital administrators, and it can be reduced by the effective constructions of hospital rule of law. This study proposes a novel nomogram to estimate the likelihood of medical disputes specifically among administrators in tertiary hospitals, and a web calculator can be available at https://ymgarden.shinyapps.io/Predictionofmedicaldisputes/.
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Affiliation(s)
- Min Yi
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanlin Cao
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,*Correspondence: Yanlin Cao
| | - Yujin Zhou
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuebin Cao
- Health Commission of Hunan Province, Changsha, China
| | - Xueqian Zheng
- Chinese Hospital Association Medical Legality Specialized Committee, Beijing, China
| | | | - Wei Chen
- Beijing Jishuitan Hospital, Beijing, China
| | | | - Ke Zhang
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Figueiredo FDA, Ramos LEF, Silva RT, Ponce D, de Carvalho RLR, Schwarzbold AV, Maurílio ADO, Scotton ALBA, Garbini AF, Farace BL, Garcia BM, da Silva CTCA, Cimini CCR, de Carvalho CA, Dias CDS, Silveira DV, Manenti ERF, Cenci EPDA, Anschau F, Aranha FG, de Aguiar FC, Bartolazzi F, Vietta GG, Nascimento GF, Noal HC, Duani H, Vianna HR, Guimarães HC, de Alvarenga JC, Chatkin JM, de Morais JDP, Machado-Rugolo J, Ruschel KB, Martins KPMP, Menezes LSM, Couto LSF, de Castro LC, Nasi LA, Cabral MADS, Floriani MA, Souza MD, Souza-Silva MVR, Carneiro M, de Godoy MF, Bicalho MAC, Lima MCPB, Aliberti MJR, Nogueira MCA, Martins MFL, Guimarães-Júnior MH, Sampaio NDCS, de Oliveira NR, Ziegelmann PK, Andrade PGS, Assaf PL, Martelli PJDL, Delfino-Pereira P, Martins RC, Menezes RM, Francisco SC, Araújo SF, Oliveira TF, de Oliveira TC, Sales TLS, Avelino-Silva TJ, Ramires YC, Pires MC, Marcolino MS. Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients. BMC Med 2022; 20:324. [PMID: 36056335 PMCID: PMC9438299 DOI: 10.1186/s12916-022-02503-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/28/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. METHODS This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS The median age of the model-derivation cohort was 59 (IQR 47-70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918-0.939) and validation (temporal AUROC 0.927, 95% CI 0.911-0.941; geographic AUROC 0.819, 95% CI 0.792-0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator ( https://www.mmcdscore.com/ ). CONCLUSIONS The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.
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Affiliation(s)
- Flávio de Azevedo Figueiredo
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil. .,Department of Medicine, Universidade Federal de Lavras, R. Tomas Antonio Gonzaga, 277, Lavras, Brazil.
| | - Lucas Emanuel Ferreira Ramos
- Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, Belo Horizonte, 6627, Brazil
| | - Rafael Tavares Silva
- Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, Belo Horizonte, 6627, Brazil
| | - Daniela Ponce
- Botucatu Medical School, Universidade Estadual Paulista "Júlio de Mesquita Filho", Av. Prof. Mário Rubens Guimarães Montenegro, s/n, Botucatu, Brazil
| | | | | | | | | | - Andresa Fontoura Garbini
- Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Av. Francisco Trein, 326, Porto Alegre, Brazil
| | | | | | | | - Christiane Corrêa Rodrigues Cimini
- Hospital Santa Rosália, R. do Cruzeiro, 01, Teófilo Otoni, Brazil.,Mucuri Medical School, Universidade Federal dos Vales do Jequitinhonha e Mucuri, R. Cruzeiro, 01, Teófilo Otoni, Brazil
| | | | - Cristiane Dos Santos Dias
- Department of Pediatrics, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil
| | | | | | | | - Fernando Anschau
- Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Av. Francisco Trein, 326, Porto Alegre, Brazil
| | | | - Filipe Carrilho de Aguiar
- Hospital das Clínicas da Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, 1235, Recife, Brazil
| | - Frederico Bartolazzi
- Hospital Santo Antônio, Praça Dr. Márcio Carvalho Lopes Filho, 501, Curvelo, Brazil
| | | | | | - Helena Carolina Noal
- Hospital Universitário da Universidade Federal de Santa Maria, Av. Roraima, 1000, Santa Maria, Brazil
| | - Helena Duani
- Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, Belo Horizonte, 190, Brazil
| | | | | | | | | | | | - Juliana Machado-Rugolo
- Botucatu Medical School, Universidade Estadual Paulista "Júlio de Mesquita Filho", Av. Prof. Mário Rubens Guimarães Montenegro, s/n, Botucatu, Brazil
| | - Karen Brasil Ruschel
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Hospital Mãe de Deus, R. José de Alencar, 286, Porto Alegre, Brazil
| | - Karina Paula Medeiros Prado Martins
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, Belo Horizonte, 190, Brazil
| | - Luanna Silva Monteiro Menezes
- Hospital Luxemburgo, R. Gentios, 1350, Belo Horizonte, Brazil.,Hospital Metropolitano Odilon Behrens, R. Formiga, 50, Belo Horizonte, Brazil
| | | | | | - Luiz Antônio Nasi
- Hospital Moinhos de Vento, R. Ramiro Barcelos, 910, Porto Alegre, Brazil
| | - Máderson Alvares de Souza Cabral
- Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, Belo Horizonte, 190, Brazil
| | | | - Maíra Dias Souza
- Hospital Metropolitano Odilon Behrens, R. Formiga, 50, Belo Horizonte, Brazil
| | - Maira Viana Rego Souza-Silva
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil
| | - Marcelo Carneiro
- Hospital Santa Cruz, R. Fernando Abott, 174, Santa Cruz do Sul, Brazil
| | | | - Maria Aparecida Camargos Bicalho
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil.,Hospital Júlia Kubitschek, R. Dr. Cristiano Rezende, 2745, Belo Horizonte, Brazil
| | | | - Márlon Juliano Romero Aliberti
- Laboratorio de Investigacao Medica em Envelhecimento (LIM-66), Serviço de Geriatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil.,Research Institute, Hospital Sirio-Libanes, Sao Paulo, Brazil
| | | | | | | | | | | | - Patricia Klarmann Ziegelmann
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Hospital Tacchini, R. Dr. José Mário Mônaco, 358, Bento Gonçalves, Brazil
| | | | - Pedro Ledic Assaf
- Hospital Metropolitano Doutor Célio de Castro, R. Dona Luiza, 311, Belo Horizonte, Brazil
| | | | - Polianna Delfino-Pereira
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil.,Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil
| | | | | | | | | | | | | | - Thaís Lorenna Souza Sales
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Universidade Federal de São João del-Rei, R. Sebastião Gonçalves Coelho, 400, Divinópolis, Brazil
| | - Thiago Junqueira Avelino-Silva
- Laboratorio de Investigacao Medica em Envelhecimento (LIM-66), Serviço de Geriatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil.,Faculdade Israelita de Ciencias da Saúde Albert Einstein, Hospital Israelita Albert Einstein, Sao Paulo, Brazil
| | | | - Magda Carvalho Pires
- Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, Belo Horizonte, 6627, Brazil
| | - Milena Soriano Marcolino
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil.,Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, Belo Horizonte, 190, Brazil.,Telehealth Center, University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 110, Belo Horizonte, Brazil
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10
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Loo WK, Hasikin K, Suhaimi A, Yee PL, Teo K, Xia K, Qian P, Jiang Y, Zhang Y, Dhanalakshmi S, Azizan MM, Lai KW. Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies. Front Public Health 2022; 10:898254. [PMID: 35677770 PMCID: PMC9168237 DOI: 10.3389/fpubh.2022.898254] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/20/2022] [Indexed: 01/19/2023] Open
Abstract
In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of (“COVID-19” OR “covid19” OR “covid” OR “coronavirus” OR “Sars-CoV-2”) AND (“readmission” OR “re-admission” OR “rehospitalization” OR “rehospitalization”) were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles. In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (n = 13) uses an inferential statistical approach in their studies, while only one uses a machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from the multi region. The logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor of readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD. A few future research directions has been proposed, including the utilization of machine learning in statistical analysis, investigation on dominant risk factors, experimental design on interventions to curb dominant risk factors and increase the scale of data collection from single centered to multi centered.
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Affiliation(s)
- Wei Kit Loo
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Anwar Suhaimi
- Department of Rehabilitation Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Por Lip Yee
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kareen Teo
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kaijian Xia
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Pengjiang Qian
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Yizhang Jiang
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Yuanpeng Zhang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong, China
| | - Samiappan Dhanalakshmi
- Department of ECE, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
- Samiappan Dhanalakshmi
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Malaysia
- Muhammad Mokhzaini Azizan
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Khin Wee Lai
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11
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Shanbehzadeh M, Yazdani A, Shafiee M, Kazemi-Arpanahi H. Predictive modeling for COVID-19 readmission risk using machine learning algorithms. BMC Med Inform Decis Mak 2022; 22:139. [PMID: 35596167 PMCID: PMC9122247 DOI: 10.1186/s12911-022-01880-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 05/18/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified. Results The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795). Conclusions Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.
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Affiliation(s)
- Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Azita Yazdani
- Clinical Education Research Center, Health Human Resources Research Center, Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohsen Shafiee
- Department of Nursing, Abadan University of Medical Sciences, Abadan, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran. .,Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran.
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12
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Knosp BM, Craven CK, Dorr DA, Bernstam EV, Campion TR. Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing. J Am Med Inform Assoc 2022; 29:671-676. [PMID: 35289370 PMCID: PMC8922193 DOI: 10.1093/jamia/ocab256] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/05/2021] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology. MATERIALS AND METHODS We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs. RESULTS Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities. DISCUSSION Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research. CONCLUSION Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services.
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Affiliation(s)
- Boyd M Knosp
- Roy J. and Lucille A. Carver College of Medicine and the Institute for Clinical & Translational Science, University of Iowa, Iowa City, Iowa, USA
| | - Catherine K Craven
- Division of Clinical Research Informatics, Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, Texas, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
- Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Elmer V Bernstam
- Center for Clinical and Translational Sciences, University of Texas Health Science Center, Houston, Texas, USA
| | - Thomas R Campion
- Clinical & Translational Science Center, Weill Cornell Medicine, New York, New York, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
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13
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Afrash MR, Kazemi-Arpanahi H, Shanbehzadeh M, Nopour R, Mirbagheri E. Predicting hospital readmission risk in patients with COVID-19: A machine learning approach. INFORMATICS IN MEDICINE UNLOCKED 2022; 30:100908. [PMID: 35280933 PMCID: PMC8901230 DOI: 10.1016/j.imu.2022.100908] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/18/2022] [Accepted: 03/06/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features. Material and methods The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics. Results Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7%, specificity of 91.3%, the sensitivity of 91.6%, F-measure of 91.8%, and AUC of 0.91%. Conclusion The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective.
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Key Words
- AUC, Area under the curve
- Artificial intelligent
- CDSS, Clinical Decision Support Systems
- COVID-19
- COVID-19, Coronavirus disease 2019
- CRISP, Cross-Industry Standard Process
- Coronavirus
- HGB, Hist Gradient Boosting
- LASSO, Least Absolute Shrinkage and Selection Operator
- ML, Machine learning
- MLP, Multi-Layered Perceptron
- Machine learning
- Readmission
- SVM, Support Vector Machine
- XGBoost, Extreme Gradient Boosting
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Affiliation(s)
- Mohammad Reza Afrash
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran
- Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Raoof Nopour
- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Esmat Mirbagheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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14
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Hao B, Hu Y, Sotudian S, Zad Z, Adams WG, Assoumou SA, Hsu H, Mishuris RG, Paschalidis IC. OUP accepted manuscript. J Am Med Inform Assoc 2022; 29:1253-1262. [PMID: 35441692 PMCID: PMC9129120 DOI: 10.1093/jamia/ocac062] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/13/2022] [Accepted: 04/14/2022] [Indexed: 01/08/2023] Open
Abstract
Objective To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs. Materials and Methods Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models. Results Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively. Discussion The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories. Conclusions This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role.
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Affiliation(s)
- Boran Hao
- Center for Information and Systems Engineering, Boston University, Boston, Massachusetts, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA
| | - Yang Hu
- Center for Information and Systems Engineering, Boston University, Boston, Massachusetts, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA
| | - Shahabeddin Sotudian
- Center for Information and Systems Engineering, Boston University, Boston, Massachusetts, USA
- Division of Systems Engineering, Boston University, Boston, Massachusetts, USA
| | - Zahra Zad
- Center for Information and Systems Engineering, Boston University, Boston, Massachusetts, USA
- Division of Systems Engineering, Boston University, Boston, Massachusetts, USA
| | - William G Adams
- Department of Pediatrics, Boston Medical Center and Boston University School of Medicine, Boston, Massachusetts, USA
| | - Sabrina A Assoumou
- Department of Medicine, Boston Medical Center and Boston University School of Medicine, Boston, Massachusetts, USA
| | - Heather Hsu
- Department of Pediatrics, Boston Medical Center and Boston University School of Medicine, Boston, Massachusetts, USA
| | - Rebecca G Mishuris
- Department of Medicine, Boston Medical Center and Boston University School of Medicine, Boston, Massachusetts, USA
| | - Ioannis C Paschalidis
- Corresponding Author: Ioannis C. Paschalidis, Division of Systems Engineering, Department of Electrical and Computer Engineering, Department of Biomedical Engineering, and Faculty of Computing & Data Sciences, Boston University, 8 Saint Mary’s St., Boston, MA 02215, USA; http://sites.bu.edu/paschalidis
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15
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Campbell TW, Wilson MP, Roder H, MaWhinney S, Georgantas RW, Maguire LK, Roder J, Erlandson KM. Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data. Int J Med Inform 2021; 155:104594. [PMID: 34601240 PMCID: PMC8459591 DOI: 10.1016/j.ijmedinf.2021.104594] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 08/30/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022]
Abstract
Rationale Prognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease. The study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission. Methods Hierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of acute respiratory distress syndrome, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models’ predictions of risk. Main results Test performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, C-reactive protein, lactate dehydrogenase, and D-dimer were often found to be important in the assignment of risk. Conclusions Risk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.
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Affiliation(s)
| | - Melissa P Wilson
- Department of Medicine, Division of Personalized Medicine and Bioinformatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States
| | | | - Samantha MaWhinney
- Department of Biostatistics and Informatics, University of Colorado, Colorado School of Public Health, United States
| | | | | | | | - Kristine M Erlandson
- Department of Medicine, Division of Infectious Diseases, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States
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