Table 4.
Illustration ofmortality prediction scores obtained from the SAPS II, APACHE II and SICULA algorithms for three different patient profiles.
| Patient 1: Haemorrhagic shock | Patient 2: Medical Sepsis | Patient 3: Scheduled high-risk surgery | |
|---|---|---|---|
|
| |||
| Age | 40 | 80 | 80 |
| Heart rate | 120 | 100 | 100 |
| Systolic BP | 95 | 85 | 100 |
| GCS | 8 | 14 | 15 |
| Temperature | 35.5 | 38 | 35 |
| Urine output | 700 | 700 | 1200 |
| PaO2/FiO2 | 300 | 200 | 300 |
| Serum urea | 7 | 10 | 7 |
| WBC | 9 | 19 | 14 |
| Potassium | 4.0 | 4.8 | 4.0 |
| Sodium | 142 | 142 | 142 |
| Bicarbonates | 18 | 18 | 22 |
| Hematocrit | 25% | 35% | 35% |
| Bilirubin | 0.8 | 0.8 | 0.8 |
| Chronic diseases | None | None | Metastatic cancer |
| Type of admission | Unscheduled surgery – Trauma | Medical | Scheduled surgery |
|
| |||
| Mortality prediction: | |||
| SAPS II | 46.1% | 41.5% | 21.3% |
| APACHE II | 32.2% | 23.5% | 26.2% |
| SICULA | 29.4% | 29.9% | 28.7% |