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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

 
  1. *: each predictor marked with * means that it is a time-stamped variable, and its corresponding minimum and maximum values within the first 24 h in the ICU were used as inputs in model development