A logistic regression-based model to predict ICU mortality: problems and solutions - A. S. Luchinin; A. V. Lyanguzov (Izdatelstvo OKI, 2022)
The ICU department's mortality rate is one of the most important indicators of quality of care. Based on real clinical data, we attempted to build a prognostic model for patients with blood diseases in the ICU with using of the logistic regression method. The study included 202 patients in total. The median age was 57 (19-82) years. There were 112 (55 %) males and 90 (45 %) females. The statistical analysis was performed by using R software, version 3.4.2. The absolute risk of death (mortality rate) was 67 from 202 (33 %), odds - 0.496. The odds of death in ICU grow up to ~20 times if the patient has a Glasgow score of less than 15. Also, the odds of death increase by 1.3 and 11 times of PLT, or serum total protein level decreases by 2 times accordingly. Our model for "high-risk of death" detection classified patients in the test dataset with 0.816 accuracy (95 % CI 0.679-0.912), with sensitivity 0.823, and specificity 0.80. Despite the simple method for data analysis, we got a pretty accurate model of mortality prognosis with efficacy more than qSOFA and MEWS scales. Research in this area should continue.