A logistic regression-based model to predict ICU mortality: problems and solutions
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 tota...
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| Main Authors: | , |
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| Format: | Book |
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Izdatelstvo OKI,
2022-08-01T00:00:00Z.
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| Summary: | 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. |
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| Item Description: | 2588-0519 2618-8473 10.37489/2588-0519-2022-2-13-20 |