Zahra Rahmatinejad1, Fariba Tohidinezhad1, Hamidreza Reihani2, Fatemeh Rahmatinejad3, Ali Pourmand4, Ameen Abu-Hanna5, Saeid Eslami6. 1. Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. 2. Department of Emergency Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. 3. Department of Health Information Technology, Faculty of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran. 4. Department of Emergency Medicine, The George Washington University, School of Medicine and Health Sciences, Washington, DC, United States. 5. Department of Medical Informatics, Amsterdam UMC - Location AMC, University of Amsterdam, the Netherlands. 6. Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Informatics, Amsterdam UMC - Location AMC, University of Amsterdam, the Netherlands.; Pharmaceutical Research Center, Pharmaceutical Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran. Electronic address: EslamiS@mums.ac.ir.
Abstract
BACKGROUND: This study was designed to evaluate and compare the prognostic value of the APACHE II, APACHE IV, and SAPSII scores for predicting in-hospital mortality in the ED on a large sample of patients. Earlier studies in the ED setting have either used a small sample or focused on specific diagnoses. METHODS: A prospective study was conducted to include patients with higher risk of mortality from March 2016 to March 2017 in the ED of Emam Reza Hospital, northeast of Iran. Logistic regression was used to develop three models. Evaluation was performed in terms of the overall performance (Brier Score, BS, and Brier Skill Score, BSS), discrimination (Area Under the Curve, AUC), and calibration (calibration graph). RESULTS: A total of 2205 patients met the study criteria (53% male and median age of 64, IQR: 50-77). In-hospital mortality amounted to 19%. For APACHE II, APACHE IV, and SAPS II the BS was 0.132, 0.125 and 0.133 and the BSS was 0.156, 0.2, and 0.144, respectively. The AUC was 0.755 (0.74 to 0.779) for APACHE II, 0.794 (0.775 to 0.818) for APACHE IV, and 0.751 (0.727 to 0.776) for SAPS II. The APACHE IV showed significantly greater AUC in comparison to the APACHE II and SAPS II. The graphical evaluation revealed good calibration of the APACHE IV model. CONCLUSION: APACHEIV outperformed APACHEII and SAPSII in terms of discrimination and calibration. More validation is needed for using these models for decision-making about individual patients, although they would perform best at a cohort level.
BACKGROUND: This study was designed to evaluate and compare the prognostic value of the APACHE II, APACHE IV, and SAPSII scores for predicting in-hospital mortality in the ED on a large sample of patients. Earlier studies in the ED setting have either used a small sample or focused on specific diagnoses. METHODS: A prospective study was conducted to include patients with higher risk of mortality from March 2016 to March 2017 in the ED of Emam Reza Hospital, northeast of Iran. Logistic regression was used to develop three models. Evaluation was performed in terms of the overall performance (Brier Score, BS, and Brier Skill Score, BSS), discrimination (Area Under the Curve, AUC), and calibration (calibration graph). RESULTS: A total of 2205 patients met the study criteria (53% male and median age of 64, IQR: 50-77). In-hospital mortality amounted to 19%. For APACHE II, APACHE IV, and SAPS II the BS was 0.132, 0.125 and 0.133 and the BSS was 0.156, 0.2, and 0.144, respectively. The AUC was 0.755 (0.74 to 0.779) for APACHE II, 0.794 (0.775 to 0.818) for APACHE IV, and 0.751 (0.727 to 0.776) for SAPS II. The APACHE IV showed significantly greater AUC in comparison to the APACHE II and SAPS II. The graphical evaluation revealed good calibration of the APACHE IV model. CONCLUSION: APACHEIV outperformed APACHEII and SAPSII in terms of discrimination and calibration. More validation is needed for using these models for decision-making about individual patients, although they would perform best at a cohort level.
Authors: Brian J Douthit; Rachel L Walden; Kenrick Cato; Cynthia P Coviak; Christopher Cruz; Fabio D'Agostino; Thompson Forbes; Grace Gao; Theresa A Kapetanovic; Mikyoung A Lee; Lisiane Pruinelli; Mary A Schultz; Ann Wieben; Alvin D Jeffery Journal: Appl Clin Inform Date: 2022-02-09 Impact factor: 2.342
Authors: Raúl López-Izquierdo; Pablo Del Brio-Ibañez; Francisco Martín-Rodríguez; Alicia Mohedano-Moriano; Begoña Polonio-López; Clara Maestre-Miquel; Antonio Viñuela; Carlos Durantez-Fernández; Miguel Á Castro Villamor; José L Martín-Conty Journal: Int J Environ Res Public Health Date: 2020-11-12 Impact factor: 3.390
Authors: GianLuca Colussi; Giacomo Perrotta; Pierpaolo Pillinini; Alessia G Dibenedetto; Andrea Da Porto; Cristiana Catena; Leonardo A Sechi Journal: BMC Emerg Med Date: 2021-12-07