Table 2 Population characteristics.
From: Predicting outcomes of acute kidney injury in critically ill patients using machine learning
Characteristics | N=101 |
|---|---|
Demographics | |
Age (years), median (IQR) | 74 (30 - 92) |
Female sex, % | 36.6% |
Body weight, kg | 83 (45 - 150) |
Body mass index, \(kg/m^2\) | 27.7 (17 - 57) |
Scores | |
APACHE II | 25.9 (11 - 43) |
SOFA | 9.2 (4.6 - 20.6) |
SAPS II | 55.2 ( 21.9 - 102.5) |
ICU types | |
MICU, % | 80% |
SICU, % | 17% |
Trauma, % | 2% |
Comorbidities | |
Arterial hypertension, % | 48% |
Chronic Liver Failure | 12% |
Diabetes mellitus | 22% |
Chronic obstructive pulmonary disease | 22% |
Oncological history | 22% |
Suspected infection on admission | 57% |
ICU interventions | |
Invasive ventilation days, median (IQR) | 0.9 (0 - 20) |
Fluid balance, median (IQR) | 1032 (182 - 3470) |
Transfusion, % | 2.2% |
Antibiotics, % | 88% |
Outcomes | |
ICU days, median (IQR) | 14.5 (1 - 160) |
Hospital days, median (IQR) | 26 (3 - 186) |
CKD (after ICU discharge), % | 49% |
Mortality (hospital and follow-up), % | 43% |
Laboratory results | |
Cystatin C (mg/L) at ICU admission | 1.94 (0.67 - 8.06) |
Creatinine (mg/dL) at ICU admission | 1.98 (0.31 - 12.64) |
