Reliability of predictive models to support early decision making in the emergency department for patients with confirmed diagnosis of COVID-19: the Pescara Covid Hospital score

Abstract

Background: The hospital management of patients diagnosed with COVID-19 can be hampered by heterogeneous characteristics at entry into the emergency department. We aimed to identify demographic, clinical and laboratory parameters associated with higher risks of hospitalisation, oxygen support, admission to intensive care and death, to build a risk score for clinical decision making at presentation to the emergency department. Methods: We carried out a retrospective study using linked administrative data and laboratory parameters available in the initial phase of the pandemic at the emergency department of the regional reference hospital of Pescara, Abruzzo, Italy, March–June 2020. Logistic regression and Cox modelling were used to identify independent predictors for risk stratification. Validation was carried out collecting data from an extended timeframe covering other variants of concern, including Alpha (December 2020–January 2021) and Delta/Omicron (January–March 2022). Results: Several clinical and laboratory parameters were significantly associated to the outcomes of interest, independently from age and gender. The strongest predictors were: for hospitalisation, monocyte distribution width ≥ 22 (4.09; 2.21–7.72) and diabetes (OR = 3.04; 1.09–9.84); for oxygen support: saturation < 95% (OR = 11.01; 3.75–41.14), lactate dehydrogenase≥237 U/L (OR = 5.93; 2.40–15.39) and lymphocytes< 1.2 × 103/μL (OR = 4.49; 1.84–11.53); for intensive care, end stage renal disease (OR = 59.42; 2.43–2230.60), lactate dehydrogenase≥334 U/L (OR = 5.59; 2.46–13.84), D-dimer≥2.37 mg/L (OR = 5.18; 1.14–26.36), monocyte distribution width ≥ 25 (OR = 3.32; 1.39–8.50); for death, procalcitonin≥0.2 ng/mL (HR = 2.86; 1.95–4.19) and saturation < 96% (HR = 2.74; 1.76–4.28). Risk scores derived from predictive models using optimal thresholds achieved values of the area under the curve between 81 and 91%. Validation of the scoring algorithm for the evolving virus achieved accuracy between 65 and 84%. Conclusions: A set of parameters that are normally available at emergency departments of any hospital can be used to stratify patients with COVID-19 at risk of severe conditions. The method shall be calibrated to support timely clinical decision during the first hours of admission with different variants of concern.