Table 2 Predictor variables used in this study
From: Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU
| Predictors | |
|---|---|
| Acute physiology (first 24 h in the ICU) | Chronic health status |
| Heart rate* | Elixhauser comorbidity index |
| Systolic blood pressure* | Congestive heart failure |
| Diastolic blood pressure* | Cardiac arrhythmias |
| Mean blood pressure* | Valvular heart disease |
| Respiratory rate* | Pulmonary circulation |
| Temperature* | Peripheral vascular |
| SpO2* (blood oxygen saturation) | Hypertension |
| Total CO2* | Other neurological diseases |
| pCO2* (partial pressure of CO2) | Chronic obstructive pulmonary disease |
| pH* (acidity in the blood) | Diabetes without complications |
| Urine output | Diabetes with complications |
| Glasgow Coma Score (GCS) | Hypothyroidism |
| GCS (eye) | Renal failure |
| GCS (motor) | Liver disease |
| GCS (verbal) | Metastatic cancer |
| Anion gap* | Coagulopathy |
| Bicarbonate* | Obesity |
| Creatinine* | Fluid electrolyte |
| Chloride* | Alcohol abuse |
| Glucose* | Depression |
| Haematocrit* | Renal replacement therapy |
| Haemoglobin* | Other |
| Lactate* | Gender |
| Platelet* | Weight loss |
| Potassium* | Ventilation |
| Partial thromboplastin time* | Age |
| INR* | Weight |
| Prothrombin time* | SAPS II score (first 24 h in the ICU) |
| Sodium* | SOFA score (first 24 h in the ICU) |
| Blood urea nitrogen (BUN)* | |
| WBC* | |
| Acute kidney injury | |
