Simplified Acute Physiology Score 3 Performance in Austrian COVID-19 Patients Admitted to Intensive Care Units with and without Diabetes.
Faisal AzizAlexander Christian ReisingerFelix AbererCaren SourijNorbert TripoltJolanta M Siller-MatulaDirk von-LewinskiPhilipp EllerSusanne KaserHarald Sourijnull On Behalf Of The Covid-In Diabetes In Austria Study GroupPublished in: Viruses (2022)
This study evaluated and compared the performance of simplified acute physiology score 3 (SAPS 3) for predicting in-hospital mortality in COVID-19 patients admitted to intensive care units (ICUs) with and without diabetes in Austria. The Austrian national public health institute (GÖG) data of COVID-19 patients admitted to ICUs ( n = 5850) were analyzed. Three versions of SAPS 3 were used: standard equation, Central European equation, and Austrian equation customized for COVID-19 patients. The observed in-hospital mortality was 38.9%, 42.9%, and 37.3% in all, diabetes, and non-diabetes patients, respectively. The overall C-statistics was 0.69 with an insignificant ( p = 0.193) difference between diabetes (0.70) and non-diabetes (0.68) patients. The Brier score was > 0.20 for all SAPS 3 equations in all cohorts. Calibration was unsatisfactory for both standard and Central European equations in all cohorts, whereas it was satisfactory for the Austrian equation in diabetes patients only. The SAPS 3 score demonstrated low discrimination and accuracy in Austrian COVID-19 patients, with an insignificant difference between diabetes and non-diabetes. All equations were miscalibrated particularly in non-diabetes patients, while the Austrian equation showed satisfactory calibration in diabetes patients only. Both uncalibrated and calibrated versions of SAPS 3 should be used with caution in COVID-19 patients.
Keyphrases
- type diabetes
- cardiovascular disease
- end stage renal disease
- sars cov
- public health
- glycemic control
- ejection fraction
- newly diagnosed
- coronavirus disease
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- patient reported outcomes
- machine learning
- adipose tissue
- quality improvement
- drug induced
- electronic health record
- respiratory failure