PROLOGUE (PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages): Development and validation of a scoring system for early prognostication in unselected adult cardiac arrest patients

Abstract

BackgroundEarly prognostication after cardiac arrest would be useful. We aimed to develop a scoring model for early prognostication in unselected adult cardiac arrest patients. MethodsWe retrospectively analysed data of adult non-traumatic cardiac arrest patients treated at a tertiary hospital between 2014 and 2018. The primary outcome was poor outcome at hospital discharge (cerebral performance category, 3–5). Using multivariable logistic regression analysis, independent predictors were identified among known outcome predictors, that were available at intensive care unit admission, in patients admitted in the first 3 years (derivation set, N = 671), and a scoring system was developed with the variables that were retained in the final model. The scoring model was validated in patients admitted in the last 2 years (validation set, N = 311). ResultsThe poor outcome rates at hospital discharge were similar between the derivation (66.0%) and validation sets (64.3%). Age <59 years, witnessed collapse, shockable rhythm, adrenaline dose <2 mg, low-flow duration <18 min, reactive pupillary light reflex, Glasgow Coma Scale motor score ≥2, and levels of creatinine <1.21 mg dl−1, potassium <4.4 mEq l−1, phosphate <5.8 mg dl−1, haemoglobin ≥13.2 g dl−1, and lactate <8 mmol l−1 were retained in the final multivariable model and used to develop the scoring system. Our model demonstrated excellent discrimination in the validation set (area under the curve of 0.942, 95% confidence interval 0.917–0.968). ConclusionsWe developed a scoring model for early prognostication in unselected adult cardiac arrest patients. Further validations in various cohorts are needed.