Predicting in-hospital mortality for initial survivors of acute respiratory compromise (ARC) events: Development and validation of the ARC Score.

Abstract

Acute respiratory compromise (ARC) is a common and highly morbid event in hospitalized patients. To date, however, few investigators have explored predictors of outcome in initial survivors of ARC events. In the present study, we leveraged the American Heart Association's Get With The Guidelines^®-Resuscitation (GWTG-R) ARC data registry to develop a prognostic score for initial survivors of ARC events. Using GWTG-R ARC data, we identified 13,193 index ARC events. These events were divided into a derivation cohort (9807 patients) and a validation cohort (3386 patients). A score for predicting in-hospital mortality was developed using multivariable modeling with generalized estimating equations. The two cohorts were well balanced in terms of baseline demographics, illness-types, pre-event conditions, event characteristics, and overall mortality. After model optimization, nine variables associated with the outcome of interest were included. Age, hypotension preceding the event, and intubation during the event were the greatest predictors of in-hospital mortality. The final score demonstrated good discrimination in both the derivation and validation cohorts. The score was also very well calibrated in both cohorts. Observed average mortality was <10% in the lowest score category of both cohorts and >70% in the highest category, illustrating a wide range of mortality separated effectively by the scoring system. In the present study, we developed and internally validated a prognostic score for initial survivors of in-hospital ARC events. This tool will be useful for clinical prognostication, selecting cohorts for interventional studies, and for quality improvement initiatives seeking to risk-adjust for hospital-to-hospital comparisons.