Development and validation of the COVID-19 severity index (CSI): a prognostic tool for early respiratory decompensation

Abstract

Objective: The goal of this study was to create a predictive model of early hospital respiratory decompensation among patients with COVID-19.Design: Observational, retrospective cohort study.Setting: Nine-hospital health system within the Northeastern United States.Populations: Adult patients (≥ 18 years) admitted from the emergency department who tested positive for SARS-CoV-2 (COVID-19) up to 24 hours after initial presentation. Patients meeting criteria for respiratory critical illness within 4 hours of arrival were excluded.Main outcome and performance measures: We used a composite endpoint of critical illness as defined by oxygen requirement (greater than 10 L/min by low-flow device, high-flow device, non-invasive, or invasive ventilation) or death within the first 24 hours of hospitalization. We developed models predicting our composite endpoint using patient demographic and clinical data available within the first four hours of arrival. Eight hospitals (n = 932) were used for model development and one hospital (n = 240) was held out for external validation. Area under receiver operating characteristic (AU-ROC), precision-recall curves (AU-PRC), and calibration metrics were used to compare predictive models to three illness scoring systems: Elixhauser comorbidity index, qSOFA, and CURB-65.

Exacerbation of neurological symptoms and COVID-19 severity in patients with preexisting neurological disorders and COVID-19: A systematic review

“…The full-text screening of these studies led to the exclusion of 286 that did not meet the inclusion criteria. The remaining 26 studies were identified as eligible to be reviewed [ 6 , 7 , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] ]. Fig.…”

“…Tracheostomy 4. Need to remove respiratory secretions 2/4) Severe 5/7 (71) Liao et al [ 24 ] Retro 4 NA NA RT-PCR Cerebrovascular disease 4 Severe 1/4 (25) Bruce et al [ 25 ] Retro 79 NA NA RT-PCR Stroke 79 Severe 19/79 (24) Yin et al [ 26 ] Retro 106 72.7 (±11.8) 60 RT-PCR Cerebral infarction 81, Dementia 20, Acute cerebral infarction 10, Cerebral hemorrhage 5, Intracranial mass lesions 4, Epilepsy 3, Parkinson’s disease 2, and Myelopathy 1 Exacerbation of preexisting neurological symptoms 8/106 (8), Severe 59/106 (56) Ji et al [ 27 ] Retro 967 NA NA RT-PCR Parkinson's disease and movement disorder 209, Dementia and degenerative disease 206, Stroke and Transient cerebral ischemia 349, Epilepsy 97, Systemic atrophy 5, Multiple sclerosis 1 Severe 162/967 (17) Haimovich et al [ 28 ] Retro 356 NA NA PCR Neurologic disorders 356 Severe 36/356 (10) Berekashvili et al [ 29 ] Retro 1 70−80 0 RT-PCR Cerebral vascular accident 1 Severe 1/1 (100) Kim et al [ …”

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

“…Several hospital-based studies [3][4][5][6][7], including a systematic review of literature and meta-analysis [8], focused around the attempt for predicting the progression of the disease towards developing critical manifestations or death. These studies are important for the clinical practice point of view for identifying patients at whom early treatment must be guaranteed.…”

Generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inference

“…In this section we present an experiment that learns a mapping between clinical measurements and FACS measurements from COVID-19 patients [17]. The clinical measurements are taken from the first 24 hours in the ICU, with a patient's record being the most extreme value taken during that period when more than one record is taken.…”