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Pham MD, Tran TT, Duong TV, Do BN, Dang LT, Nguyen DH, Hoang TA, Nguyen HC, Le LTH, Pham LV, Nguyen LTH, Nguyen HT, Trieu NT, Do TV, Trinh MV, Ha TH, Phan DT, Nguyen TTP, Nguyen KT. Associations of COVID-19-related fear with kidney disease quality of life and its subscales among hemodialysis patients as modified by health literacy: a multi-hospital survey. Health Psychol Behav Med 2024; 12:2376585. [PMID: 39010868 PMCID: PMC11249155 DOI: 10.1080/21642850.2024.2376585] [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: 02/04/2024] [Accepted: 06/26/2024] [Indexed: 07/17/2024] Open
Abstract
Background Receiving hemodialysis treatment makes end-stage renal disease (ESRD) patients highly vulnerable amidst the COVID-19 pandemic. Hence, their kidney disease quality of life (KDQOL) is affected. We aimed to examine the association between fear of COVID-19 (FCoV-19) and KDQOL, and the effect modification of Health literacy (HL) on this association. Material and Methods A survey was conducted at 8 hospitals from July 2020 to March 2021 on 972 patients. Data collection includes socio-demographic factors, clinical parameters, HL, digital healthy diet literacy (DDL), hemodialysis diet knowledge (HDK), FCoV-19, suspected COVID-19 symptoms (S-COVID-19-S), and KDQOL. Results Higher HL scores B = 0.13 (95% CI = 0.06-0.21, p = 0.001) and HDK scores B = 0.58 (95% CI = 0.31-0.85, p = 0.001) were associated with higher KDQOL scores. Whereas, S-COVID-19-S B = -6.12 (95% CI = -7.66 to - 4.58, p = 0.001) and FCoV-19 B = -0.91 (95% CI = -1.03 to - 0.80, p = 0.001) were associated with lower KDQOL scores. Notably, higher HL scores significantly attenuate the negative impact of FCoV-19 on overall KDQOL and the kidney disease component summary. Conclusions In hemodialysis patients, FCoV-19 and S-COVID-19-S were associated with a lower KDQOL. Health literacy significantly mitigates the negative impact of FCoV-19 on KDQOL. Strategic public health interventions to improve HL are suggested to protect patient's KDQOL during the pandemic.
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Affiliation(s)
- Minh D Pham
- Department of Nutrition, Military Hospital 103, Hanoi, Vietnam
- Department of Nutrition, Vietnam Military Medical University, Hanoi, Vietnam
| | - Tu T Tran
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Internal Medicine, Thai Nguyen University of Medicine and Pharmacy, Thai Nguyen, Vietnam
- Department of Nephro-Urology and Dialysis, Thai Nguyen National Hospital, Thai Nguyen City, Vietnam
| | - Tuyen Van Duong
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- School of Nutrition and Health Sciences, Taipei Medical University, Taipei, Taiwan
| | - Binh N Do
- Department of Military Science, Vietnam Military Medical University, Hanoi, Vietnam
- Department of Infectious Diseases, Vietnam Military Medical University, Hanoi, Vietnam
| | - Loan T Dang
- Faculty of Nursing and Midwifery, Hanoi Medical University, Hanoi, Vietnam
- School of Nursing, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
- Thoracic and Vascular surgery department, Bach Mai Hospital, Hanoi, Vietnam
| | - Dung H Nguyen
- Hemodialysis Department, Nephro-Urology-Dialysis Center, Bach Mai Hospital, Hanoi, Vietnam
| | - Trung A Hoang
- Hemodialysis Department, Nephro-Urology-Dialysis Center, Bach Mai Hospital, Hanoi, Vietnam
| | - Hoang C Nguyen
- Director Office, Thai Nguyen National Hospital, Thai Nguyen City, Vietnam
- President Office, Thai Nguyen University of Medicine and Pharmacy, Thai Nguyen City, Vietnam
| | - Lan T H Le
- Director Office, Thai Nguyen National Hospital, Thai Nguyen City, Vietnam
- Training and Direction of Healthcare Activity Center, Thai Nguyen National Hospital, Thai Nguyen City, Vietnam
- Biochemistry Department, Thai Nguyen National Hospital, Thai Nguyen City, Vietnam
| | - Linh V Pham
- Department of Pulmonary & Cardiovascular Diseases, Hai Phong University of Medicine and Pharmacy Hospital, Hai Phong, Vietnam
- President Office, Hai Phong University of Medicine and Pharmacy, Hai Phong, Vietnam
| | - Lien T H Nguyen
- Department of Pulmonary & Cardiovascular Diseases, Hai Phong University of Medicine and Pharmacy Hospital, Hai Phong, Vietnam
| | - Hoi T Nguyen
- Director Office, Hai Phong International Hospital, Hai Phong, Vietnam
| | - Nga T Trieu
- Hemodialysis Division, Hai Phong International Hospital, Hai Phong, Vietnam
| | - Thinh V Do
- Director Office, Bai Chay Hospital, Ha Long, Vietnam
| | - Manh V Trinh
- Director Office, Quang Ninh General Hospital, Ha Long, Vietnam
| | - Tung H Ha
- Director Office, General Hospital of Agricultural, Hanoi, Vietnam
| | - Dung T Phan
- Faculty of Nursing, Hanoi University of Business and Technology, Hanoi, Vietnam
- Nursing Office, Thien An Obstetrics and Gynecology Hospital, Hanoi, Vietnam
| | - Thao T P Nguyen
- Institute for Community Health Research, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Kien T Nguyen
- Department of Health Promotion, Faculty of Social and Behavioral Sciences, Hanoi University of Public Health, Hanoi, Vietnam
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Sundrani S, Chen J, Jin BT, Abad ZSH, Rajpurkar P, Kim D. Predicting patient decompensation from continuous physiologic monitoring in the emergency department. NPJ Digit Med 2023; 6:60. [PMID: 37016152 PMCID: PMC10073111 DOI: 10.1038/s41746-023-00803-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 03/10/2023] [Indexed: 04/06/2023] Open
Abstract
Anticipation of clinical decompensation is essential for effective emergency and critical care. In this study, we develop a multimodal machine learning approach to predict the onset of new vital sign abnormalities (tachycardia, hypotension, hypoxia) in ED patients with normal initial vital signs. Our method combines standard triage data (vital signs, demographics, chief complaint) with features derived from a brief period of continuous physiologic monitoring, extracted via both conventional signal processing and transformer-based deep learning on ECG and PPG waveforms. We study 19,847 adult ED visits, divided into training (75%), validation (12.5%), and a chronologically sequential held-out test set (12.5%). The best-performing models use a combination of engineered and transformer-derived features, predicting in a 90-minute window new tachycardia with AUROC of 0.836 (95% CI, 0.800-0.870), new hypotension with AUROC 0.802 (95% CI, 0.747-0.856), and new hypoxia with AUROC 0.713 (95% CI, 0.680-0.745), in all cases significantly outperforming models using only standard triage data. Salient features include vital sign trends, PPG perfusion index, and ECG waveforms. This approach could improve the triage of apparently stable patients and be applied continuously for the prediction of near-term clinical deterioration.
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Affiliation(s)
- Sameer Sundrani
- School of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Julie Chen
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Boyang Tom Jin
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - David Kim
- Department of Emergency Medicine, Stanford University, Stanford, CA, USA.
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Young DR, Sallis JF, Baecker A, Cohen DA, Nau CL, Smith GN, Sallis RE. Associations of Physical Inactivity and COVID-19 Outcomes Among Subgroups. Am J Prev Med 2023; 64:492-502. [PMID: 36528452 PMCID: PMC9750896 DOI: 10.1016/j.amepre.2022.10.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/19/2022] [Accepted: 10/13/2022] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Physical activity before COVID-19 infection is associated with less severe outcomes. The study determined whether a dose‒response association was observed and whether the associations were consistent across demographic subgroups and chronic conditions. METHODS A retrospective cohort study of Kaiser Permanente Southern California adult patients who had a positive COVID-19 diagnosis between January 1, 2020 and May 31, 2021 was created. The exposure was the median of at least 3 physical activity self-reports before diagnosis. Patients were categorized as follows: always inactive, all assessments at 10 minutes/week or less; mostly inactive, median of 0-60 minutes per week; some activity, median of 60-150 minutes per week; consistently active, median>150 minutes per week; and always active, all assessments>150 minutes per week. Outcomes were hospitalization, deterioration event, or death 90 days after a COVID-19 diagnosis. Data were analyzed in 2022. RESULTS Of 194,191 adults with COVID-19 infection, 6.3% were hospitalized, 3.1% experienced a deterioration event, and 2.8% died within 90 days. Dose‒response effects were strong; for example, patients in the some activity category had higher odds of hospitalization (OR=1.43; 95% CI=1.26, 1.63), deterioration (OR=1.83; 95% CI=1.49, 2.25), and death (OR=1.92; 95% CI=1.48, 2.49) than those in the always active category. Results were generally consistent across sex, race and ethnicity, age, and BMI categories and for patients with cardiovascular disease or hypertension. CONCLUSIONS There were protective associations of physical activity for adverse COVID-19 outcomes across demographic and clinical characteristics. Public health leaders should add physical activity to pandemic control strategies.
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Affiliation(s)
- Deborah Rohm Young
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California.
| | - James F Sallis
- University of California San Diego, San Diego, California; Australian Catholic University, Melbourne, Australia
| | - Aileen Baecker
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California
| | - Deborah A Cohen
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California
| | - Claudia L Nau
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California
| | - Gary N Smith
- Economics Department, Pomona College, Claremont, California
| | - Robert E Sallis
- Department of Family and Sports Medicine, Kaiser Permanente Medical Center, Fontana, California
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Ortíz-Barrios M, Jaramillo-Rueda N, Gul M, Yucesan M, Jiménez-Delgado G, Alfaro-Saíz JJ. A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4591. [PMID: 36901601 PMCID: PMC10001734 DOI: 10.3390/ijerph20054591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/15/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.
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Affiliation(s)
- Miguel Ortíz-Barrios
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia
| | - Natalia Jaramillo-Rueda
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia
| | - Muhammet Gul
- School of Transportation and Logistics, Istanbul University, Istanbul 34320, Turkey
| | - Melih Yucesan
- Department of Emergency Aid and Disaster Management, Munzur University, Tunceli 62000, Turkey
| | - Genett Jiménez-Delgado
- Department of Industrial Engineering, Institución Universitaria de Barranquilla IUB, Barranquilla 080002, Colombia
| | - Juan-José Alfaro-Saíz
- Research Centre on Production Management and Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
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5
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Brooks SC, Rosychuk RJ, Perry JJ, Morrison LJ, Wiemer H, Fok P, Rowe BH, Daoust R, Vatanpour S, Turner J, Landes M, Ohle R, Hayward J, Scheuermeyer F, Welsford M, Hohl C. Derivation and validation of a clinical decision rule to risk-stratify COVID-19 patients discharged from the emergency department: The CCEDRRN COVID discharge score. J Am Coll Emerg Physicians Open 2022; 3:e12868. [PMID: 36579029 PMCID: PMC9780419 DOI: 10.1002/emp2.12868] [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: 06/15/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 12/24/2022] Open
Abstract
Objective To risk-stratify COVID-19 patients being considered for discharge from the emergency department (ED). Methods We conducted an observational study to derive and validate a clinical decision rule to identify COVID-19 patients at risk for hospital admission or death within 72 hours of ED discharge. We used data from 49 sites in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) between March 1, 2020, and September 8, 2021. We randomly assigned hospitals to derivation or validation and prespecified clinical variables as candidate predictors. We used logistic regression to develop the score in a derivation cohort and examined its performance in predicting short-term adverse outcomes in a validation cohort. Results Of 15,305 eligible patient visits, 535 (3.6%) experienced the outcome. The score included age, sex, pregnancy status, temperature, arrival mode, respiratory rate, and respiratory distress. The area under the curve was 0.70 (95% confidence interval [CI] 0.68-0.73) in derivation and 0.71 (95% CI 0.68-0.73) in combined derivation and validation cohorts. Among those with a score of 3 or less, the risk for the primary outcome was 1.9% or less, and the sensitivity of using 3 as a rule-out score was 89.3% (95% CI 82.7-94.0). Among those with a score of ≥9, the risk for the primary outcome was as high as 12.2% and the specificity of using 9 as a rule-in score was 95.6% (95% CI 94.9-96.2). Conclusion The CCEDRRN COVID discharge score can identify patients at risk of short-term adverse outcomes after ED discharge with variables that are readily available on patient arrival.
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Affiliation(s)
- Steven C. Brooks
- Departments of Emergency Medicine and Public Health SciencesQueen's UniversityKingstonOntarioCanada
| | | | - Jeffrey J. Perry
- Department of Emergency MedicineUniversity of OttawaOttawaOntarioCanada
| | - Laurie J. Morrison
- Department of Emergency Services, Sunnybrook Health Sciences Centre, Department of Medicine, Division of Emergency MedicineUniversity of TorontoTorontoOntarioCanada
| | - Hana Wiemer
- Department of Emergency MedicineDalhousie UniversityHalifaxNova ScotiaCanada
| | - Patrick Fok
- Department of Emergency MedicineDalhousie UniversityHalifaxNova ScotiaCanada
| | - Brian H. Rowe
- Department of Emergency MedicineUniversity of AlbertaEdmontonAlbertaCanada
| | - Raoul Daoust
- Departement de Médecine de Famille et Médecine d'urgenceUniversité de MontréalMontréalQuébecCanada
| | - Shabnam Vatanpour
- Department of Emergency MedicineUniversity of CalgaryCalgaryAlbertaCanada
| | - Joel Turner
- Department of Emergency MedicineMcGill UniversityMontréalQuébecCanada
| | - Megan Landes
- Department of Family and Community MedicineDivision of Emergency MedicineUniversity of TorontoTorontoOntarioCanada
| | - Robert Ohle
- Department of Emergency Medicine, Health Science North Research InstituteNorthern Ontario School of MedicineSudburyOntarioCanada
| | - Jake Hayward
- Department of Emergency MedicineUniversity of AlbertaEdmontonAlbertaCanada
| | - Frank Scheuermeyer
- Department of Emergency MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Michelle Welsford
- Department of Medicine, Division of Emergency MedicineMcMaster UniversityHamiltonOntarioCanada
| | - Corinne Hohl
- Department of Emergency MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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Beiser DG, Jarou ZJ, Kassir AA, Puskarich MA, Vrablik MC, Rosenman ED, McDonald SA, Meltzer AC, Courtney DM, Kabrhel C, Kline JA. Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study. J Am Coll Emerg Physicians Open 2021; 2:e12595. [PMID: 35005705 PMCID: PMC8716570 DOI: 10.1002/emp2.12595] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/09/2021] [Accepted: 10/15/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge. METHODS We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches. RESULTS Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71-0.78), with a sensitivity of 0.46 (95% CI, 0.39-0.54) and a specificity of 0.84 (95% CI, 0.82-0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models. CONCLUSIONS A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources.
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Affiliation(s)
- David G. Beiser
- Section of Emergency MedicineUniversity of ChicagoChicagoIllinoisUSA
| | - Zachary J. Jarou
- Department of Emergency MedicineSt. Joseph Mercy Ann Arbor HospitalUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Alaa A. Kassir
- Section of Emergency MedicineUniversity of ChicagoChicagoIllinoisUSA
| | - Michael A. Puskarich
- Department of Emergency MedicineHennepin County Medical CenterMinneapolisMinnesotaUSA
| | - Marie C. Vrablik
- Department of Emergency MedicineUniversity of WashingtonSeattleWashingtonUSA
| | | | - Samuel A. McDonald
- Department of Emergency MedicineUT Southwestern Medical CenterDallasTexasUSA
| | - Andrew C. Meltzer
- Department of Emergency MedicineGeorge Washington UniversityWashingtonDistrict of ColumbiaUSA
| | - D. Mark Courtney
- Department of Emergency MedicineUT Southwestern Medical CenterDallasTexasUSA
| | - Christopher Kabrhel
- Department of Emergency MedicineMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Jeffrey A. Kline
- Department of Emergency MedicineIndiana UniversityIndianapolisIndianaUSA
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Maves RC, Richard SA, Lindholm DA, Epsi N, Larson DT, Conlon C, Everson K, Lis S, Blair PW, Chi S, Ganesan A, Pollett S, Burgess TH, Agan BK, Colombo RE, Colombo CJ. Predictive Value of an Age-Based Modification of the National Early Warning System in Hospitalized Patients With COVID-19. Open Forum Infect Dis 2021; 8:ofab421. [PMID: 34877361 PMCID: PMC8643671 DOI: 10.1093/ofid/ofab421] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 08/06/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Early recognition of high-risk patients with coronavirus disease 2019 (COVID-19) may improve outcomes. Although many predictive scoring systems exist, their complexity may limit utility in COVID-19. We assessed the prognostic performance of the National Early Warning Score (NEWS) and an age-based modification (NEWS+age) among hospitalized COVID-19 patients enrolled in a prospective, multicenter US Military Health System (MHS) observational cohort study. METHODS Hospitalized adults with confirmed COVID-19 not requiring invasive mechanical ventilation at admission and with a baseline NEWS were included. We analyzed each scoring system's ability to predict key clinical outcomes, including progression to invasive ventilation or death, stratified by baseline severity (low [0-3], medium [4-6], and high [≥7]). RESULTS Among 184 included participants, those with low baseline NEWS had significantly shorter hospitalizations (P < .01) and lower maximum illness severity (P < .001). Most (80.2%) of low NEWS vs 15.8% of high NEWS participants required no or at most low-flow oxygen supplementation. Low NEWS (≤3) had a negative predictive value of 97.2% for progression to invasive ventilation or death; a high NEWS (≥7) had high specificity (93.1%) but low positive predictive value (42.1%) for such progression. NEWS+age performed similarly to NEWS at predicting invasive ventilation or death (NEWS+age: area under the receiver operating characteristics curve [AUROC], 0.69; 95% CI, 0.65-0.73; NEWS: AUROC, 0.70; 95% CI, 0.66-0.75). CONCLUSIONS NEWS and NEWS+age showed similar test characteristics in an MHS COVID-19 cohort. Notably, low baseline scores had an excellent negative predictive value. Given their easy applicability, these scoring systems may be useful in resource-limited settings to identify COVID-19 patients who are unlikely to progress to critical illness.
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Affiliation(s)
- Ryan C Maves
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Naval Medical Center San Diego, San Diego, California, USA
- Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Stephanie A Richard
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
| | - David A Lindholm
- Brooke Army Medical Center, Joint Base San Antonio, Fort Sam Houston, Texas, USA
- Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Nusrat Epsi
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
| | - Derek T Larson
- Fort Belvoir Community Hospital, Fort Belvoir, Virginia, USA
| | - Christian Conlon
- Madigan Army Medical Center, Joint Base Lewis-McChord, Washington, USA
| | - Kyle Everson
- Madigan Army Medical Center, Joint Base Lewis-McChord, Washington, USA
| | - Steffen Lis
- Madigan Army Medical Center, Joint Base Lewis-McChord, Washington, USA
| | - Paul W Blair
- Austere Environments Consortium for Enhanced Sepsis Outcomes, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
- Department of Pathology, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Sharon Chi
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
- Tripler Army Medical Center, Honolulu, Hawaii, USA
| | - Anuradha Ganesan
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
- Walter Reed National Military Medical Center, Bethesda, Maryland, USA
| | - Simon Pollett
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
| | - Timothy H Burgess
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Brian K Agan
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
| | - Rhonda E Colombo
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
- Madigan Army Medical Center, Joint Base Lewis-McChord, Washington, USA
| | - Christopher J Colombo
- Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Madigan Army Medical Center, Joint Base Lewis-McChord, Washington, USA
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8
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Xu J, Xiao W, Liang X, Shi L, Zhang P, Wang Y, Wang Y, Yang H. A meta-analysis on the risk factors adjusted association between cardiovascular disease and COVID-19 severity. BMC Public Health 2021; 21:1533. [PMID: 34380456 PMCID: PMC8355578 DOI: 10.1186/s12889-021-11051-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 05/12/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Cardiovascular disease (CVD), one of the most common comorbidities of coronavirus disease 2019 (COVID-19), has been suspected to be associated with adverse outcomes in COVID-19 patients, but their correlation remains controversial. METHOD This is a quantitative meta-analysis on the basis of adjusted effect estimates. PubMed, Web of Science, MedRxiv, Scopus, Elsevier ScienceDirect, Cochrane Library and EMBASE were searched comprehensively to obtain a complete data source up to January 7, 2021. Pooled effects (hazard ratio (HR), odds ratio (OR)) and the 95% confidence intervals (CIs) were estimated to evaluate the risk of the adverse outcomes in COVID-19 patients with CVD. Heterogeneity was assessed by Cochran's Q-statistic, I2test, and meta-regression. In addition, we also provided the prediction interval, which was helpful for assessing whether the variation across studies was clinically significant. The robustness of the results was evaluated by sensitivity analysis. Publication bias was assessed by Begg's test, Egger's test, and trim-and-fill method. RESULT Our results revealed that COVID-19 patients with pre-existing CVD tended more to adverse outcomes on the basis of 203 eligible studies with 24,032,712 cases (pooled ORs = 1.41, 95% CIs: 1.32-1.51, prediction interval: 0.84-2.39; pooled HRs = 1.34, 95% CIs: 1.23-1.46, prediction interval: 0.82-2.21). Further subgroup analyses stratified by age, the proportion of males, study design, disease types, sample size, region and disease outcomes also showed that pre-existing CVD was significantly associated with adverse outcomes among COVID-19 patients. CONCLUSION Our findings demonstrated that pre-existing CVD was an independent risk factor associated with adverse outcomes among COVID-19 patients.
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Affiliation(s)
- Jie Xu
- Department of Epidemiology, School of Public Health, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou, 450001, China
| | - Wenwei Xiao
- Department of Epidemiology, School of Public Health, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou, 450001, China
| | - Xuan Liang
- Department of Epidemiology, School of Public Health, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou, 450001, China
| | - Li Shi
- Department of Epidemiology, School of Public Health, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou, 450001, China
| | - Peihua Zhang
- Department of Epidemiology, School of Public Health, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou, 450001, China
| | - Ying Wang
- Department of Epidemiology, School of Public Health, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou, 450001, China
| | - Yadong Wang
- Department of Toxicology, Henan Center for Disease Control and Prevention, Zhengzhou, 450016, China
| | - Haiyan Yang
- Department of Epidemiology, School of Public Health, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou, 450001, China.
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9
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Cotton DM, Liu L, Vinson DR, Ballard DW, Sax DR, Hofmann ER, Lin JS, Durant EJ, Kene MV, Casey SD, Ghiya M, Shan J, Bouvet SC, McLachlan ID, Rauchwerger AS, Mark DG, Reed ME. Clinical characteristics of COVID-19 patients evaluated in the emergency department: A retrospective cohort study of 801 cases. J Am Coll Emerg Physicians Open 2021; 2:e12538. [PMID: 34467264 PMCID: PMC8382683 DOI: 10.1002/emp2.12538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 07/21/2021] [Accepted: 07/28/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has extracted devastating tolls. Despite its pervasiveness, robust information on disease characteristics in the emergency department (ED) and how that information predicts clinical course remain limited. METHODS We conducted a retrospective cohort study of the first ED visit from SARS-CoV-2-positive patients in our health system, from February 21, 2020 to April 5, 2020. We reviewed each patient's ED visit(s) and included the first visit with symptoms consistent with COVID-19. We collected demographic, clinical, and treatment variables from electronic health records and structured manual chart review. We used multivariable logistic regression to examine the association between patient characteristics and 2 primary outcomes: a critical outcome and hospitalization from index visit. Our critical outcome was defined as death or advanced respiratory support (high flow nasal cannula or greater) within 21 days. RESULTS Of the first 1030 encounters, 801 met our inclusion criteria: 15% were over age 75 years, 47% were female, and 24% were non-Hispanic white. We found 161 (20%) had a critical outcome and 393 (49%) were hospitalized. Independent predictors of a critical outcome included a history of hypertension, abnormal chest x-ray, elevated neutrophil to lymphocyte ratio, elevated blood urea nitrogen (BUN), measured fever, and abnormal respiratory vital signs (respiratory rate, oxygen saturation). Independent predictors of hospitalization included abnormal pulmonary auscultation, elevated BUN, measured fever, and abnormal respiratory vital signs. CONCLUSIONS In this large, diverse study of ED patients with COVID-19, we have identified numerous clinical characteristics that have independent associations with critical illness and hospitalization.
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Affiliation(s)
- Dale M. Cotton
- Kaiser PermanenteSouth Sacramento Medical CenterSacramentoCaliforniaUSA
| | - Liyan Liu
- Kaiser PermanenteDivision of ResearchOaklandCaliforniaUSA
| | - David R. Vinson
- Kaiser PermanenteDivision of ResearchOaklandCaliforniaUSA
- Kaiser PermanenteRoseville Medical CenterRosevilleCaliforniaUSA
| | - Dustin W. Ballard
- Kaiser PermanenteDivision of ResearchOaklandCaliforniaUSA
- Kaiser PermanenteSan Rafael Medical CenterSan RafaelCaliforniaUSA
| | - Dana R. Sax
- Kaiser PermanenteDivision of ResearchOaklandCaliforniaUSA
- Kaiser PermanenteOakland Medical CenterOaklandCaliforniaUSA
| | - Erik R. Hofmann
- Kaiser PermanenteSouth Sacramento Medical CenterSacramentoCaliforniaUSA
| | - James S. Lin
- Kaiser PermanenteSanta Clara Medical CenterSanta ClaraCaliforniaUSA
| | | | - Mamata V. Kene
- Kaiser PermanenteSan Leandro Medical CenterSan LeandroCaliforniaUSA
| | - Scott D. Casey
- University of CaliforniaDavisUniversity of California Davis Medical CenterSacramentoCaliforniaUSA
| | - Meena Ghiya
- Kaiser PermanenteSouth San Francisco Medical CenterSouth San FranciscoCaliforniaUSA
| | - Judy Shan
- Kaiser PermanenteDivision of ResearchOaklandCaliforniaUSA
| | - Sean C. Bouvet
- Kaiser PermanenteWalnut Creek Medical CenterWalnut CreekCaliforniaUSA
| | - Ian D. McLachlan
- Kaiser PermanenteSan Francisco Medical CenterSan FranciscoCaliforniaUSA
| | | | - Dustin G. Mark
- Kaiser PermanenteDivision of ResearchOaklandCaliforniaUSA
- Kaiser PermanenteOakland Medical CenterOaklandCaliforniaUSA
| | - Mary E. Reed
- Kaiser PermanenteDivision of ResearchOaklandCaliforniaUSA
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10
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Lehmann B, Baumgartner C, Brill AK, Günther G, Nüesch S, Ott D, Ruder TD, Thurnheer Zürcher MC, Furrer H. [COVID-19 in the Family Practice - Outpatient Treatment or Hospitalization?]. PRAXIS 2021; 110:517-524. [PMID: 34231383 DOI: 10.1024/1661-8157/a003686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
COVID-19 in the Family Practice - Outpatient Treatment or Hospitalization? Abstract. Switzerland has been severely affected by the COVID-19 pandemic. The clinical spectrum of this disease in terms of its clinical presentation and course is very broad. A correct initial evaluation in the practice or in the emergency department is important and includes history-taking and clinical examination as well as imaging and laboratory tests. Most patients with COVID-19 can be treated as outpatients. Hospitalization may be necessary in patients with a marked COVID-19 pneumonia or further complications, which occur primarily in the second or third phase of the disease. The dynamics of the disease must also be taken into consideration. In outpatients, symptomatic therapy is often sufficient, antibiotics and corticosteroids are not indicated.
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Affiliation(s)
- Beat Lehmann
- Universitäres Notfallzentrum, Inselspital, Universität Bern, Bern
| | - Christine Baumgartner
- Universitätsklinik für Allgemeine Innere Medizin, Inselspital, Universität Bern, Bern
| | | | - Gunar Günther
- Universitätsklinik für Pneumologie, Inselspital, Universität Bern, Bern
| | - Susanne Nüesch
- Universitäres Notfallzentrum, Inselspital, Universität Bern, Bern
| | - Daniel Ott
- Universitätsinstitut für Diagnostische, Interventionelle und Pädiatrische Radiologie, Inselspital, Universität Bern, Bern
| | - Thomas D Ruder
- Universitätsinstitut für Diagnostische, Interventionelle und Pädiatrische Radiologie, Inselspital, Universität Bern, Bern
| | | | - Hansjakob Furrer
- Universitätsklinik für Infektiologie, Inselspital, Universität Bern, Bern
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11
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Colombo CJ, Colombo RE, Maves RC, Branche AR, Cohen SH, Elie MC, George SL, Jang HJ, Kalil AC, Lindholm DA, Mularski RA, Ortiz JR, Tapson V, Liang CJ. Performance Analysis of the National Early Warning Score and Modified Early Warning Score in the Adaptive COVID-19 Treatment Trial Cohort. Crit Care Explor 2021; 3:e0474. [PMID: 34278310 PMCID: PMC8280088 DOI: 10.1097/cce.0000000000000474] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
We sought to validate prognostic scores in coronavirus disease 2019 including National Early Warning Score, Modified Early Warning Score, and age-based modifications, and define their performance characteristics. DESIGN We analyzed prospectively collected data from the Adaptive COVID-19 Treatment Trial. National Early Warning Score was collected daily during the trial, Modified Early Warning Score was calculated, and age applied to both scores. We assessed prognostic value for the end points of recovery, mechanical ventilation, and death for score at enrollment, average, and slope of score over the first 48 hours. SETTING A multisite international inpatient trial. PATIENTS A total of 1,062 adult nonpregnant inpatients with severe coronavirus disease 2019 pneumonia. INTERVENTIONS Adaptive COVID-19 Treatment Trial 1 randomized participants to receive remdesivir or placebo. The prognostic value of predictive scores was evaluated in both groups separately to assess for differential performance in the setting of remdesivir treatment. MEASUREMENTS AND MAIN RESULTS For mortality, baseline National Early Warning Score and Modified Early Warning Score were weakly to moderately prognostic (c-index, 0.60-0.68), and improved with addition of age (c-index, 0.66-0.74). For recovery, baseline National Early Warning Score and Modified Early Warning Score demonstrated somewhat better prognostic ability (c-index, 0.65-0.69); however, National Early Warning Score+age and Modified Early Warning Score+age further improved performance (c-index, 0.68-0.71). For deterioration, baseline National Early Warning Score and Modified Early Warning Score were weakly to moderately prognostic (c-index, 0.59-0.69) and improved with addition of age (c-index, 0.63-0.70). All prognostic performance improvements due to addition of age were significant (p < 0.05). CONCLUSIONS In the Adaptive COVID-19 Treatment Trial 1 cohort, National Early Warning Score and Modified Early Warning Score demonstrated moderate prognostic performance in patients with severe coronavirus disease 2019, with improvement in predictive ability for National Early Warning Score+age and Modified Early Warning Score+age. Area under receiver operating curve for National Early Warning Score and Modified Early Warning Score improved in patients receiving remdesivir versus placebo early in the pandemic for recovery and mortality. Although these scores are simple and readily obtainable in myriad settings, in our data set, they were insufficiently predictive to completely replace clinical judgment in coronavirus disease 2019 and may serve best as an adjunct to triage, disposition, and resourcing decisions.
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Affiliation(s)
- Christopher J Colombo
- Madigan Army Medical Center, Tacoma, WA
- Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Rhonda E Colombo
- Madigan Army Medical Center, Tacoma, WA
- Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | - Ryan C Maves
- Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD
- Naval Medical Center, San Diego, CA
| | | | | | | | - Sarah L George
- Saint Louis University and St. Louis VA Medical Center, Saint Louis, MO
| | - Hannah J Jang
- Department of Community Health Systems, School of Nursing and Center for Nursing Excellence and Innovation, University of California San Francisco, San Francisco, CA
| | | | - David A Lindholm
- Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD
- Brooke Army Medical Center, San Antonio, TX
| | - Richard A Mularski
- The Center for Health Research, Kaiser Permanente Northwest, Portland, OR
| | - Justin R Ortiz
- University of Maryland School of Medicine, Baltimore, MD
| | | | - C Jason Liang
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD
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12
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Alsunaidi SJ, Almuhaideb AM, Ibrahim NM, Shaikh FS, Alqudaihi KS, Alhaidari FA, Khan IU, Aslam N, Alshahrani MS. Applications of Big Data Analytics to Control COVID-19 Pandemic. SENSORS (BASEL, SWITZERLAND) 2021; 21:2282. [PMID: 33805218 PMCID: PMC8037067 DOI: 10.3390/s21072282] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 03/20/2021] [Accepted: 03/22/2021] [Indexed: 12/29/2022]
Abstract
The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.
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Affiliation(s)
- Shikah J. Alsunaidi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Abdullah M. Almuhaideb
- Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Nehad M. Ibrahim
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Fatema S. Shaikh
- Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Kawther S. Alqudaihi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Fahd A. Alhaidari
- Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Irfan Ullah Khan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Nida Aslam
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Mohammed S. Alshahrani
- Department of Emergency Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
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13
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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: 1649] [Impact Index Per Article: 412.3] [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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