Scoring system for the selection of high-risk patients in the intensive care unit

Objective. Patients admitted to the intensive care unit greatly differ in severity and intensity of care. We devised a system for selecting high-risk patients that reduces bias by excluding low-risk patients and patients with an early death irrespective of the treatment. Design: A posteriori analysis of a multiple-center prospective observational trial. Setting: A total of 89 units from 12 European countries, with 12,615 patients. Intervention: Demographic and clinical data: severity of illness at admission, daily score of nursing workload, length of stay, and hospital mortality. Methods: We enrolled patients with intensive care unit length of stay of > 24 hrs. Three groups of high-risk patients were created: a) Severity group, those with Simplified Acute Physiology Score (SAPS II) over the median; b) Intensity-of-care group, patients with > 1 day of high level of care (assessed by logistic analysis); and c) MIX group, patients fulfilling both Severity and Intensity-of-care criteria. The groups were included in a logistic regression model (random split-sample design) to identify the characteristics associated with hospital mortality. We compared the outcome prediction of the SAPS II model (unsplit sample) against our model. Main Results. Out of 8,248 patients, the Severity method selected 3,838 patients, Intensity-of-care selected 4,244, and both methods combined selected 2,662 patients. There were 2,828 low-risk patients. Significant associations with hospital mortality were observed for: age, sites of admission, medical/unscheduled surgical admission, acute physiologic score of SAPS II, and the indicator variable "only Severity," "only Intensity-of-care," or MIX (developmental sample: calibration chi-square test, p = .205; area under the receiver operation characteristic curve, 0.814). Calibration and discrimination were better in our model than with the SAPS II model (unsplit sample). Conclusion: All three indicator variables select high-risk patients, the Severity/Intensity-of -care MIX being the most robust. These stratification criteria can improve case-mix selection for clinical and organizational studies.