10 August 2024 : Database Analysis
Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis
Łukasz Lewandowski
1ABCDEF, Michał Czapla 234ABDEF*, Izabella Uchmanowicz 5DEG, Grzegorz Kubielas 6E, Stanisław Zieliński 7E, Małgorzata Krzystek-Korpacka 1E, Catherine Ross 8E, Raúl Juárez-Vela 3E, Marzena Zielińska 7BDE
DOI: 10.12659/MSM.944408
Med Sci Monit 2024; 30:e944408
Table 4 The association between selected parameters and the odds of death – multivariate logistic regression.
| MODEL4 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Hosmer-Lemeshow p | β=0 hypothesis p | AIC | BIC | Pseudo-R2 | AUC (learning) | AUC (testing) | ||||
| 0.055 | <0.001 | 132.08 | 156.47 | 0.3964 | 0.835± 0.0403 | 0.762± 0.0466 | ||||
| β intercept | – | 0.004 | 0.451 | 0.00 | −0.88 | 0.89 | 0.993 | 1.00 | 0.42 | 2.43 |
| Sex | Male | 1.737 | 0.645 | 7.26 | 0.47 | 3.00 | 1.61 | 20.09 | ||
| Cardiac arrest mechanism | VF/pVT | 0.430 | 0.867 | 0.25 | −1.27 | 2.13 | 0.620 | 1.54 | 0.28 | 8.40 |
| BMI | – | 0.164 | 0.076 | 4.62 | 0.01 | 0.31 | 1.01 | 1.37 | ||
| Albumin [g/dl] | – | 0.042 | 0.477 | 0.01 | −0.89 | 0.98 | 0.929 | 1.04 | 0.41 | 2.66 |
| hsCRP [mg/l] | – | 0.008 | 0.004 | 3.84 | 0.00 | 0.02 | 1.00 | 1.02 | ||
| Cardiac arrest mechanism*Albumin | – | −1.081 | 0.679 | 2.53 | −2.41 | 0.25 | 0.112 | 0.34 | 0.09 | 1.28 |
| Cardiac arrest mechanism*BMI | – | −0.233 | 0.096 | 5.84 | −0.42 | −0.04 | 0.66 | 0.96 | ||
| Cardiac arrest mechanism*Sex | – | −2.510 | 1.048 | 5.74 | −4.56 | −0.46 | 0.01 | 0.63 | ||
| The ‘Analyzed cat.’ column refers to categories which are compared to reference categories in terms of odds of death. AIC – Akaike Information Criterion; BIC – Bayesian Information Criterion; β – regression coefficient; SE – standard error; OR – odds ratio; CI – confidence interval; BMI – body mass index; PEA – pulseless electrical activity; pVT – pulseless ventricular tachycardia; VF – Ventricular fibrillation; hsCRP – high sensitivity C-reactive protein concentration. The ‘AUC (learning)’ and ‘AUC (testing)’ columns show AUC values from 10-fold cross validation. Information on other models is shown in . | ||||||||||