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Azimi H, Rezapour-Nasrabad R, Borhani F, Sadat-Hoseini AS, Momeni M. The model of solving ethical challenges with nursing based on faith in God: a new model for nurses to care during epidemics. BMC Nurs 2024; 23:538. [PMID: 39112997 PMCID: PMC11304786 DOI: 10.1186/s12912-024-02207-1] [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: 05/25/2024] [Accepted: 07/26/2024] [Indexed: 08/11/2024] Open
Abstract
BACKGROUND The existence of various ethical challenges, the inability to resolve ethical conflicts, and, as a result, the low quality of care and the occurrence of dissatisfaction in patients and nurses have been discussed for years. By creating new ethical challenges, the Covid-19pandemic has played an important role in making the process of care for these patients more difficult and complicated. This study was conducted with the aim of designing a prescriptive model to help provide ethical-care and resolve ethical conflicts during the Covid-19pandemic. METHODS In this two-stage qualitative study, a grounded theory research method was used in the first stage, and data were collected through semi-structured interviews. Sampling started purposefully and continued theoretically. In the second step, the appropriate model was designed using the three-step method proposed by Walker and Avant. RESULTS The core concept was "behavior based on faith in God", based on which the grounded theory of "faithful nursing" and then "model of solving ethical challenges with nursing based on faith in God" were presented. The strategies of the model in three parts are strengthening the beliefs of nurse, strengthening environmental facilitators to help nurse, and strengthening situational analysis in duty diagnosis in nurse were presented. CONCLUSIONS According to this model, nurses' beliefs play a key role, and the strengthening of environmental factors play a secondary role in ethical-care.
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Affiliation(s)
- Hamideh Azimi
- Student Research Committee, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Rafat Rezapour-Nasrabad
- Psychiatric Nursing and Management, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Fariba Borhani
- Medical Surgical Nursing, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Akram Sadat Sadat-Hoseini
- Pediatric and NICU Nursing, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Momeni
- Social Determinants of Health Research Center, Research Institute for Prevention of Non-communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
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Endo T, Khoujah D, Motohashi T, Shinozaki T, Tsushima K, Fujitani S, Shiga T. The National Early Warning Score on admission predicts severe disease and in-hospital mortality of the coronavirus disease 2019 Delta variant: A retrospective cohort study. Acute Med Surg 2023; 10:e851. [PMID: 37261374 PMCID: PMC10227991 DOI: 10.1002/ams2.851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/04/2023] [Accepted: 04/20/2023] [Indexed: 06/02/2023] Open
Abstract
Background Clinical risk scores are widely used in emergency medicine, and some studies have evaluated their use in patients with coronavirus disease 2019 (COVID-19). However, no studies have evaluated their use in patients with the COVID-19 Delta variant. We aimed to study the performance of four different clinical scores (National Early Warning Score [NEWS], quick Sequential Organ Failure Assessment [qSOFA], Confusion, Respiratory rate, Blood pressure, and Age ≥65 [CRB-65], and Kanagawa score) in predicting the risk of severe disease (defined as the need for intubation and in-hospital mortality) in patients with the COVID-19 Delta variant. Methods This was a retrospective cohort study of patients hospitalized with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant infection between June 1 and December 31, 2021. The primary outcomes were the sensitivity and specificity of the aforementioned clinical risk scores at admission to predict severe disease. Areas under the receiver operating characteristic curves (AUROCs) were compared between the clinical risk scores and we identified new cut-off points for all four scores. Results A total of 249 adult patients were included, of whom 18 developed severe disease. A NEWS ≥7 at admission predicted severe disease with 72.2% sensitivity and 86.2% specificity. The NEWS (AUROC 0.88) was superior to both the qSOFA (AUROC 0.74) and the CRB-65 (AUROC 0.67), and there was no significant difference between the NEWS and Kanagawa score (AUROC 0.86). Conclusion The NEWS at hospital admission predicted the severity of the COVID-19 Delta variant with high accuracy.
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Affiliation(s)
- Takuro Endo
- Department of Emergency MedicineInternational University of Health and Welfare, School of Medicine, Narita HospitalChibaJapan
| | - Danya Khoujah
- Department of Emergency MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Takako Motohashi
- Department of Preventive MedicineSt. Marianna University School of MedicineKawasakiJapan
| | - Tomohiro Shinozaki
- Department of Information and Computer Technology, Faculty of EngineeringTokyo University of ScienceTokyoJapan
| | - Kenji Tsushima
- Department of Pulmonary MedicineInternational University of Health and Welfare, School of Medicine, Narita HospitalChibaJapan
| | - Shigeki Fujitani
- Department of Emergency and Critical Care MedicineSt. Marianna University School of MedicineKawasakiJapan
| | - Takashi Shiga
- Department of Emergency MedicineInternational University of Health and Welfare, School of Medicine, Narita HospitalChibaJapan
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Heydari F, Zamani M, Masoumi B, Majidinejad S, Nasr-Esfahani M, Abbasi S, Shirani K, Sheibani Tehrani D, Sadeghi-aliabadi M, Arbab M. Physiologic Scoring Systems in Predicting the COVID-19 Patients' one-month Mortality; a Prognostic Accuracy Study. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2022; 10:e83. [PMID: 36426162 PMCID: PMC9676706 DOI: 10.22037/aaem.v10i1.1728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Introduction : It is critical to quickly and easily identify severe coronavirus disease 2019 (COVID-19) patients and predict their mortality. This study aimed to determine the accuracy of the physiologic scoring systems in predicting the mortality of COVID-19 patients. Methods: This prospective cross-sectional study was performed on COVID-19 patients admitted to the emergency department (ED). The clinical characteristics of the participants were collected by the emergency physicians and the accuracy of the Quick Sequential Failure Assessment (qSOFA), Coronavirus Clinical Characterization Consortium (4C) Mortality, National Early Warning Score-2 (NEWS2), and Pandemic Respiratory Infection Emergency System Triage (PRIEST) scores for mortality prediction was evaluated. Results: Nine hundred and twenty-one subjects were included. Of whom, 745 (80.9%) patients survived after 30 days of admission. The mean age of patients was 59.13 ± 17.52 years, and 550 (61.6%) subjects were male. Non-Survived patients were significantly older (66.02 ± 17.80 vs. 57.45 ± 17.07, P< 0.001) and had more comorbidities (diabetes mellitus, respiratory, cardiovascular, and cerebrovascular disease) in comparison with survived patients. For COVID-19 mortality prediction, the AUROCs of PRIEST, qSOFA, NEWS2, and 4C Mortality score were 0.846 (95% CI [0.821-0.868]), 0.788 (95% CI [0.760-0.814]), 0.843 (95% CI [0.818-0.866]), and 0.804 (95% CI [0.776-0.829]), respectively. All scores were good predictors of COVID-19 mortality. Conclusion: All studied physiologic scores were good predictors of COVID-19 mortality and could be a useful screening tool for identifying high-risk patients. The NEWS2 and PRIEST scores predicted mortality in COVID-19 patients significantly better than qSOFA.
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Affiliation(s)
- Farhad Heydari
- Department of Emergency Medicine, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Majid Zamani
- Department of Emergency Medicine, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Babak Masoumi
- Department of Emergency Medicine, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.,Corresponding author: Babak Masoumi; Alzahra Hospital, Sofeh Ave, Keshvari Blvd., Isfahan, Iran. , ORCID: https://orcid.org/0000-0002-7330-5986, Tel: +989121979028
| | - Saeed Majidinejad
- Department of Emergency Medicine, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Nasr-Esfahani
- Department of Emergency Medicine, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Saeed Abbasi
- Department of Infectious Diseases, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Kiana Shirani
- Department of Infectious Diseases, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Mahsa Sadeghi-aliabadi
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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Martin J, Gaudet-Blavignac C, Lovis C, Stirnemann J, Grosgurin O, Leidi A, Gayet-Ageron A, Iten A, Carballo S, Reny JL, Darbellay-Fahroumand P, Berner A, Marti C. Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study. BMJ Open Respir Res 2022; 9:9/1/e001340. [PMID: 36002181 PMCID: PMC9412043 DOI: 10.1136/bmjresp-2022-001340] [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: 06/21/2022] [Accepted: 08/07/2022] [Indexed: 11/12/2022] Open
Abstract
Background The SARS-CoV-2 pandemic led to a steep increase in hospital and intensive care unit (ICU) admissions for acute respiratory failure worldwide. Early identification of patients at risk of clinical deterioration is crucial in terms of appropriate care delivery and resource allocation. We aimed to evaluate and compare the prognostic performance of Sequential Organ Failure Assessment (SOFA), Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Uraemia, Respiratory Rate, Blood Pressure and Age ≥65 (CURB-65), Respiratory Rate and Oxygenation (ROX) index and Coronavirus Clinical Characterisation Consortium (4C) score to predict death and ICU admission among patients admitted to the hospital for acute COVID-19 infection. Methods and analysis Consecutive adult patients admitted to the Geneva University Hospitals during two successive COVID-19 flares in spring and autumn 2020 were included. Discriminative performance of these prediction rules, obtained during the first 24 hours of hospital admission, were computed to predict death or ICU admission. We further exluded patients with therapeutic limitations and reported areas under the curve (AUCs) for 30-day mortality and ICU admission in sensitivity analyses. Results A total of 2122 patients were included. 216 patients (10.2%) required ICU admission and 303 (14.3%) died within 30 days post admission. 4C score had the best discriminatory performance to predict 30-day mortality (AUC 0.82, 95% CI 0.80 to 0.85), compared with SOFA (AUC 0.75, 95% CI 0.72 to 0.78), qSOFA (AUC 0.59, 95% CI 0.56 to 0.62), CURB-65 (AUC 0.75, 95% CI 0.72 to 0.78) and ROX index (AUC 0.68, 95% CI 0.65 to 0.72). ROX index had the greatest discriminatory performance (AUC 0.79, 95% CI 0.76 to 0.83) to predict ICU admission compared with 4C score (AUC 0.62, 95% CI 0.59 to 0.66), CURB-65 (AUC 0.60, 95% CI 0.56 to 0.64), SOFA (AUC 0.74, 95% CI 0.71 to 0.77) and qSOFA (AUC 0.59, 95% CI 0.55 to 0.62). Conclusion Scores including age and/or comorbidities (4C and CURB-65) have the best discriminatory performance to predict mortality among inpatients with COVID-19, while scores including quantitative assessment of hypoxaemia (SOFA and ROX index) perform best to predict ICU admission. Exclusion of patients with therapeutic limitations improved the discriminatory performance of prognostic scores relying on age and/or comorbidities to predict ICU admission.
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Affiliation(s)
- Jeremy Martin
- Faculty of Medicine, University of Geneva, Geneve, Switzerland
| | - Christophe Gaudet-Blavignac
- Department of Medical Imaging and Medical Information Sciences, Geneva University Hospitals, Geneve, Switzerland
| | - Christian Lovis
- Faculty of Medicine, University of Geneva, Geneve, Switzerland.,Department of Medical Imaging and Medical Information Sciences, Geneva University Hospitals, Geneve, Switzerland
| | - Jérôme Stirnemann
- Faculty of Medicine, University of Geneva, Geneve, Switzerland.,Department of Medicine, Geneva University Hospitals, Geneve, Switzerland
| | - Olivier Grosgurin
- Faculty of Medicine, University of Geneva, Geneve, Switzerland.,Department of Medicine, Geneva University Hospitals, Geneve, Switzerland
| | - Antonio Leidi
- Department of Medicine, Geneva University Hospitals, Geneve, Switzerland
| | - Angèle Gayet-Ageron
- Faculty of Medicine, University of Geneva, Geneve, Switzerland.,Division of Clinical Epidemiology, Geneva University Hospitals, Geneve, Switzerland
| | - Anne Iten
- Faculty of Medicine, University of Geneva, Geneve, Switzerland.,Infection Control Program, Geneva University Hospitals, Geneve, Switzerland
| | - Sebastian Carballo
- Faculty of Medicine, University of Geneva, Geneve, Switzerland.,Department of Medicine, Geneva University Hospitals, Geneve, Switzerland
| | - Jean-Luc Reny
- Faculty of Medicine, University of Geneva, Geneve, Switzerland.,Department of Medicine, Geneva University Hospitals, Geneve, Switzerland
| | - Pauline Darbellay-Fahroumand
- Faculty of Medicine, University of Geneva, Geneve, Switzerland.,Department of Medicine, Geneva University Hospitals, Geneve, Switzerland
| | - Amandine Berner
- Department of Medicine, Geneva University Hospitals, Geneve, Switzerland
| | - Christophe Marti
- Faculty of Medicine, University of Geneva, Geneve, Switzerland .,Department of Medicine, Geneva University Hospitals, Geneve, Switzerland
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Khari S, Salimi Akin Abadi A, Pazokian M, Yousefifard M. CURB-65, qSOFA, and SIRS Criteria in Predicting In-Hospital Mortality of Critically Ill COVID-19 Patients; a Prognostic Accuracy Study. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2022; 10:e36. [PMID: 35765619 PMCID: PMC9187131 DOI: 10.22037/aaem.v10i1.1565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
INTRODUCTION Outcome prediction of intensive care unit (ICU)-admitted patients is one of the important issues for physicians. This study aimed to compare the accuracy of Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Urea, Respiratory Rate, Blood Pressure and Age Above or Below 65 Years (CURB-65), and Systemic Inflammatory Response Syndrome (SIRS) scores in predicting the in-hospital mortality of COVID-19 patients. METHODS This prognostic accuracy study was performed on 225 ICU-admitted patients with a definitive diagnosis of COVID-19 from July to December 2021 in Tehran, Iran. The patients' clinical characteristics were evaluated at the time of ICU admission, and they were followed up until discharge from ICU. The screening performance characteristics of CURB-65, qSOFA, and SIRS in predicting their mortality was compared. RESULTS 225 patients with the mean age of 63.27±14.89 years were studied (56.89% male). The in-hospital mortality rate of this series of patients was 39.10%. The area under the curve (AUC) of SIRS, CURB-65, and qSOFA were 0.62 (95% CI: 0.55 - 0.69), 0.66 (95% CI: 0.59 - 0.73), and 0.61(95% CI: 0.54 - 0.67), respectively (p = 0.508). In cut-off ≥1, the estimated sensitivity values of SIRS, CURB-65, and qSOFA were 85.23%, 96.59%, and 78.41%, respectively. The estimated specificity of scores were 34.31%, 6.57%, and 38.69%, respectively. In cut-off ≥2, the sensitivity values of SIRS, CURB-65, and qSOFA were evaluated as 39.77%, 87.50%, and 15.91%, respectively. Meanwhile, the specificity of scores were 72.99%, 34.31%, and 92.70%. CONCLUSIONS It seems that the performance of SIRS, CURB-65, and qSOFA is similar in predicting the ICU mortality of COVID-19 patients. However, the sensitivity of CURB-65 is higher than qSOFA and SIRS.
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Affiliation(s)
- Sorour Khari
- Student Research Committee, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Atefe Salimi Akin Abadi
- Clinical Research Development Center, Shahid Modarres Educational Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Marzieh Pazokian
- Department of Medical- Surgical Nursing, School of Nursing and Midwifery, Clinical Research Development Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,Corresponding author: Marzieh Pazokian; Department of Medical- Surgical Nursing, School of Nursing and Midwifery, Clinical Research Development Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. , ORCID: 0000-0002-7583-1824, Tel: 0098-21-88202519, Fax: 0098-21-88202518
| | - Mahmoud Yousefifard
- Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran.,Corresponding author: Marzieh Pazokian; Department of Medical- Surgical Nursing, School of Nursing and Midwifery, Clinical Research Development Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. , ORCID: 0000-0002-7583-1824, Tel: 0098-21-88202519, Fax: 0098-21-88202518
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Lombardi Y, Azoyan L, Szychowiak P, Bellamine A, Lemaitre G, Bernaux M, Daniel C, Leblanc J, Riller Q, Steichen O. External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals. Intensive Care Med 2021; 47:1426-1439. [PMID: 34585270 PMCID: PMC8478265 DOI: 10.1007/s00134-021-06524-w] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 08/30/2021] [Indexed: 12/21/2022]
Abstract
Purpose The Coronavirus disease 2019 (COVID-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for COVID-19. Methods We used “COVID-19 Evidence Alerts” (McMaster University) to retrieve high-quality prognostic scores predicting death or intensive care unit (ICU) transfer from routinely collected data. We studied their accuracy in a retrospective multicenter cohort of adult patients hospitalized for COVID-19 from January 2020 to April 2021 in the Greater Paris University Hospitals. Areas under the receiver operating characteristic curves (AUC) were computed for the prediction of the original outcome, 30-day in-hospital mortality and the composite of 30-day in-hospital mortality or ICU transfer. Results We included 14,343 consecutive patients, 2583 (18%) died and 5067 (35%) died or were transferred to the ICU. We examined 274 studies and found 32 scores meeting the inclusion criteria: 19 had a significantly lower AUC in our cohort than in previously published validation studies for the original outcome; 25 performed better to predict in-hospital mortality than the composite of in-hospital mortality or ICU transfer; 7 had an AUC > 0.75 to predict in-hospital mortality; 2 had an AUC > 0.70 to predict the composite outcome. Conclusion Seven prognostic scores were fairly accurate to predict death in hospitalized COVID-19 patients. The 4C Mortality Score and the ABCS stand out because they performed as well in our cohort and their initial validation cohort, during the first epidemic wave and subsequent waves, and in younger and older patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-021-06524-w.
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Affiliation(s)
- Yannis Lombardi
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Loris Azoyan
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Piotr Szychowiak
- Médecine Intensive-Réanimation, Centre Hospitalier Régional Universitaire de Tours, Tours, France.,Université de Tours, Tours, France
| | | | | | - Mélodie Bernaux
- Strategy and Transformation Department, AP-HP, Paris, France
| | | | - Judith Leblanc
- Institut Pierre Louis d'Épidémiologie et de Santé Publique, UMR-S 1136 , Sorbonne Université, INSERM, Paris, France.,Clinical Research Platform, Saint Antoine Hospital, AP-HP, Paris, France
| | - Quentin Riller
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Olivier Steichen
- Institut Pierre Louis d'Épidémiologie et de Santé Publique, UMR-S 1136 , Sorbonne Université, INSERM, Paris, France. .,Internal Medicine Department, Tenon Hospital, AP-HP, Sorbonne Université, Paris, France. .,Service de Médecine Interne, Hôpital Tenon, 4 rue de la Chine, 75020, Paris, France.
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Bourn SS, Crowe RP, Fernandez AR, Matt SE, Brown AL, Hawthorn AB, Myers JB. Initial prehospital Rapid Emergency Medicine Score (REMS) to predict outcomes for COVID-19 patients. J Am Coll Emerg Physicians Open 2021; 2:e12483. [PMID: 34223444 PMCID: PMC8240529 DOI: 10.1002/emp2.12483] [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: 04/08/2020] [Revised: 05/20/2021] [Accepted: 05/28/2021] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE The Rapid Emergency Medicine Score (REMS) has not been widely studied for use in predicting outcomes of COVID-19 patients encountered in the prehospital setting. This study aimed to determine whether the first prehospital REMS could predict emergency department and hospital dispositions for COVID-19 patients transported by emergency medical services. METHODS This retrospective study used linked prehospital and hospital records from the ESO Data Collaborative for all 911-initiated transports of patients with hospital COVID-19 diagnoses from July 1 to December 31, 2020. We calculated REMS with the first recorded prehospital values for each component. We calculated area under the receiver operating curve (AUROC) for emergency department (ED) mortality, ED discharge, hospital mortality, and hospital length of stay (LOS). We determined optimal REMS cut-points using test characteristic curves. RESULTS Among 13,830 included COVID-19 patients, median REMS was 6 (interquartile range [IQR]: 5-9). ED mortality was <1% (n = 80). REMS ≥9 predicted ED death (AUROC 0.79). One-quarter of patients (n = 3,419) were discharged from the ED with an optimal REMS cut-point of ≤5 (AUROC 0.72). Eighteen percent (n = 1,742) of admitted patients died. REMS ≥8 optimally predicted hospital mortality (AUROC 0.72). Median hospital LOS was 8.3 days (IQR: 4.1-14.8 days). REMS ≥7 predicted hospitalizations ≥3 days (AUROC 0.62). CONCLUSION Initial prehospital REMS was modestly predictive of ED and hospital dispositions for patients with COVID-19. Prediction was stronger for outcomes more proximate to the first set of emergency medical services (EMS) vital signs. These findings highlight the potential value of first prehospital REMS for risk stratification of individual patients and system surveillance for resource planning related to COVID-19.
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Affiliation(s)
| | | | - Antonio R. Fernandez
- ESO, Inc.AustinTexasUSA
- Department of Emergency MedicineSchool of MedicineUniversity of North Carolina at Chapel HillNorth CarolinaChapel HillUSA
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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: 1655] [Impact Index Per Article: 413.8] [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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