Bianca Magro1, Valentina Zuccaro2, Luca Novelli3, Lorenzo Zileri4, Ciro Celsa5,6, Federico Raimondi3, Mauro Gori7, Giulia Cammà4, Salvatore Battaglia8, Vincenzo Giuseppe Genova8, Laura Paris9, Matteo Tacelli1, Francesco Antonio Mancarella4, Marco Enea10, Massimo Attanasio8, Michele Senni7, Fabiano Di Marco3, Luca Ferdinando Lorini11, Stefano Fagiuoli1, Raffaele Bruno2,12, Calogero Cammà5, Antonio Gasbarrini4. 1. Gastroenterology Hepatology and Transplantation, ASST Papa Giovanni XXIII-Bergamo, Bergamo, Italy. 2. Department of Infectious Diseases, IRCCS Fondazione Policlinico San Matteo, Pavia, Italy. 3. Pneumology Unit, ASST Papa Giovanni XXIII-Bergamo, Bergamo, Italy. 4. UOC di Medicina Interna e Gastroenterologia, Dipartimento di Scienze Gastroenterologiche, Endocrino-Metaboliche e Nefro-Urologiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy. 5. Section of Gastroenterology and Hepatology, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy. 6. Department of Surgical, Oncological and Oral Sciences (Di.Chir.On.S.), University of Palermo, Palermo, Italy. 7. Cardiovascular Department and Cardiology 1 Unit, ASST Papa Giovanni XXIII-Bergamo, Bergamo, Italy. 8. Department of Economics, Business and Statistics (SEAS), University of Palermo, Palermo, Italy. 9. Hematology and Bone Marrow Transplant Unit, ASST Papa Giovanni XXIII-Bergamo, Bergamo, Italy. 10. Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy. 11. Emergency and Intensive care Department, ASST Papa Giovanni XXIII-Bergamo, Bergamo, Italy. 12. Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
Abstract
BACKGROUNDS: Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. METHODS AND FINDINGS: We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07-1.09), male sex (HR 1.62, 95%CI 1.30-2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39-2.12), diabetes (HR 1.21, 95%CI 1.02-1.45), coronary heart disease (HR 1.40 95% CI 1.09-1.80), chronic liver disease (HR 1.78, 95%CI 1.16-2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002-1.0005). The AUC was 0.822 (95%CI 0.722-0.922) in the derivation cohort and 0.820 (95%CI 0.724-0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). CONCLUSIONS: A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.
BACKGROUNDS: Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. METHODS AND FINDINGS: We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07-1.09), male sex (HR 1.62, 95%CI 1.30-2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39-2.12), diabetes (HR 1.21, 95%CI 1.02-1.45), coronary heart disease (HR 1.40 95% CI 1.09-1.80), chronic liver disease (HR 1.78, 95%CI 1.16-2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002-1.0005). The AUC was 0.822 (95%CI 0.722-0.922) in the derivation cohort and 0.820 (95%CI 0.724-0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). CONCLUSIONS: A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.
Authors: James W Antoon; Carlos G Grijalva; Cary Thurm; Troy Richardson; Alicen B Spaulding; Ronald J Teufel; Mario A Reyes; Samir S Shah; Julianne E Burns; Chén C Kenyon; Adam L Hersh; Derek J Williams Journal: J Hosp Med Date: 2021-10 Impact factor: 2.899
Authors: Luis Gustavo Modelli de Andrade; Tainá Veras de Sandes-Freitas; Lúcio R Requião-Moura; Laila Almeida Viana; Marina Pontello Cristelli; Valter Duro Garcia; Aline Lima Cunha Alcântara; Ronaldo de Matos Esmeraldo; Mario Abbud Filho; Alvaro Pacheco-Silva; Erika Cristina Ribeiro de Lima Carneiro; Roberto Ceratti Manfro; Kellen Micheline Alves Henrique Costa; Denise Rodrigues Simão; Marcos Vinicius de Sousa; Viviane Brandão Bandeira de Mello Santana; Irene L Noronha; Elen Almeida Romão; Juliana Aparecida Zanocco; Gustavo Guilherme Queiroz Arimatea; Deise De Boni Monteiro de Carvalho; Helio Tedesco-Silva; José Medina-Pestana Journal: Am J Transplant Date: 2021-09-02 Impact factor: 9.369
Authors: Alexandra June Gordon; Prasanthi Govindarajan; Christopher L Bennett; Loretta Matheson; Michael A Kohn; Carlos Camargo; Jeffrey Kline Journal: BMJ Open Date: 2022-04-21 Impact factor: 3.006
Authors: Corinne M Hohl; Rhonda J Rosychuk; Patrick M Archambault; Fiona O'Sullivan; Murdoch Leeies; Éric Mercier; Gregory Clark; Grant D Innes; Steven C Brooks; Jake Hayward; Vi Ho; Tomislav Jelic; Michelle Welsford; Marco L A Sivilotti; Laurie J Morrison; Jeffrey J Perry Journal: CMAJ Open Date: 2022-02-08
Authors: Avishek Chatterjee; Guangyao Wu; Sergey Primakov; Cary Oberije; Henry Woodruff; Pieter Kubben; Ronald Henry; Marcel J H Aries; Martijn Beudel; Peter G Noordzij; Tom Dormans; Niels C Gritters van den Oever; Joop P van den Bergh; Caroline E Wyers; Suat Simsek; Renée Douma; Auke C Reidinga; Martijn D de Kruif; Julien Guiot; Anne-Noelle Frix; Renaud Louis; Michel Moutschen; Pierre Lovinfosse; Philippe Lambin Journal: PLoS One Date: 2021-04-15 Impact factor: 3.240