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 |  |