Spyridon Fortis1, Amy M J O'Shea2, Brice F Beck Mae3, Rajeshwari Nair2, Michihiko Goto4, Gregory A Schmidt5, Peter J Kaboli2, Eli N Perencevich2, Heather Schacht Reisinger2, Mary Vaughan Sarrazin2. 1. Center for Access & Delivery Research & Evaluation (CADRE), Iowa City VA Health Care System, Iowa City, IA, USA; Department of Internal Medicine, Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, USA. Electronic address: spyridon-fortis@uiowa.edu. 2. Center for Access & Delivery Research & Evaluation (CADRE), Iowa City VA Health Care System, Iowa City, IA, USA; Department of Internal Medicine, Division of General Internal Medicine, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, USA. 3. Center for Access & Delivery Research & Evaluation (CADRE), Iowa City VA Health Care System, Iowa City, IA, USA. 4. Center for Access & Delivery Research & Evaluation (CADRE), Iowa City VA Health Care System, Iowa City, IA, USA; Department of Internal Medicine, Division of Infectious Diseases, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, USA. 5. Department of Internal Medicine, Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, USA.
Abstract
PURPOSE: To create a simplified critical illness severity scoring system with high prediction accuracy for 30-day mortality using only commonly available variables. MATERIALS AND METHODS: This is a retrospective cohort study of ICU admissions 2010-2015 in 306 ICUs in 117 Veterans Affairs (VA) hospitals. We randomly divided our cohort into a training dataset (75%) and a validation dataset (25%). We created a critical illness severity scoring system (CISSS) using age, comorbidities, heart rate, mean arterial blood pressure, temperature, respiratory rate, hematocrit, white blood cell count, creatinine, sodium, glucose, albumin, bilirubin, bicarbonate, use of invasive mechanical ventilation, and whether the admission was surgical or not. We validated the performance of CISSS to predict 30-day mortality internally. RESULTS: After excluding 31,743 re-admissions, we divided our sample (n = 534,001) into a training (n = 400,613) and a validation dataset (n = 133,388). In the training dataset, the area under the curve (AUC) of CISSS was 0.847(95%CI = 0.845-0.850). In the validation dataset, the AUC was 0.848 (95%CI = 0.844-0.852), the standardized mortality ratio (SMR) was 1.00 (95%CI = 0.98-1.02), and Brier's score for 30-day mortality was 0.058 (95%CI = 0.057-0.059). CISSS calibration was acceptable. CONCLUSIONS: CISSS has very good performance and requires only commonly used variables that can be easily extracted by electronic health records. Published by Elsevier Inc.
PURPOSE: To create a simplified critical illness severity scoring system with high prediction accuracy for 30-day mortality using only commonly available variables. MATERIALS AND METHODS: This is a retrospective cohort study of ICU admissions 2010-2015 in 306 ICUs in 117 Veterans Affairs (VA) hospitals. We randomly divided our cohort into a training dataset (75%) and a validation dataset (25%). We created a critical illness severity scoring system (CISSS) using age, comorbidities, heart rate, mean arterial blood pressure, temperature, respiratory rate, hematocrit, white blood cell count, creatinine, sodium, glucose, albumin, bilirubin, bicarbonate, use of invasive mechanical ventilation, and whether the admission was surgical or not. We validated the performance of CISSS to predict 30-day mortality internally. RESULTS: After excluding 31,743 re-admissions, we divided our sample (n = 534,001) into a training (n = 400,613) and a validation dataset (n = 133,388). In the training dataset, the area under the curve (AUC) of CISSS was 0.847(95%CI = 0.845-0.850). In the validation dataset, the AUC was 0.848 (95%CI = 0.844-0.852), the standardized mortality ratio (SMR) was 1.00 (95%CI = 0.98-1.02), and Brier's score for 30-day mortality was 0.058 (95%CI = 0.057-0.059). CISSS calibration was acceptable. CONCLUSIONS: CISSS has very good performance and requires only commonly used variables that can be easily extracted by electronic health records. Published by Elsevier Inc.
Authors: Spyridon Fortis; Alejandro P Comellas; Surya P Bhatt; Eric A Hoffman; MeiLan K Han; Nirav R Bhakta; Robert Paine; Bonnie Ronish; Richard E Kanner; Mark Dransfield; Daniel Hoesterey; Russell G Buhr; R Graham Barr; Brett Dolezal; Victor E Ortega; M Bradley Drummond; Mehrdad Arjomandi; Robert J Kaner; Victor Kim; Jeffrey L Curtis; Russell P Bowler; Fernando Martinez; Wassim W Labaki; Christopher B Cooper; Wanda K O'Neal; Gerald Criner; Nadia N Hansel; Jerry A Krishnan; Prescott Woodruff; David Couper; Donald Tashkin; Igor Barjaktarevic Journal: Chest Date: 2021-02-01 Impact factor: 10.262
Authors: Amol A Verma; Tejasvi Hora; Hae Young Jung; Michael Fralick; Sarah L Malecki; Lauren Lapointe-Shaw; Adina Weinerman; Terence Tang; Janice L Kwan; Jessica J Liu; Shail Rawal; Timothy C Y Chan; Angela M Cheung; Laura C Rosella; Marzyeh Ghassemi; Margaret Herridge; Muhammad Mamdani; Fahad Razak Journal: CMAJ Date: 2021-02-10 Impact factor: 8.262
Authors: Spyridon Fortis; Emily S Wan; Ken Kunisaki; Patrick Tel Eyck; Zuhair K Ballas; Russell P Bowler; James D Crapo; John E Hokanson; Chris Wendt; Edwin K Silverman; Alejandro P Comellas Journal: Respir Med X Date: 2020-12-29
Authors: Amol A Verma; Tejasvi Hora; Hae Young Jung; Michael Fralick; Sarah L Malecki; Lauren Lapointe-Shaw; Adina Weinerman; Terence Tang; Janice L Kwan; Jessica J Liu; Shail Rawal; Timothy C Y Chan; Angela M Cheung; Laura C Rosella; Marzyeh Ghassemi; Margaret Herridge; Muhammad Mamdani; Fahad Razak Journal: CMAJ Date: 2021-06-07 Impact factor: 8.262