Applying PRIM (Patient Rule Induction Method) and logistic regression for selecting high-risk subgroups in very elderly ICU patients

Int J Med Inform. 2008 Apr;77(4):272-9. doi: 10.1016/j.ijmedinf.2007.06.007. Epub 2007 Jul 23.


Purpose: To apply the Patient Rule Induction Method (PRIM) to identify very elderly Intensive Care (IC) patients at high risk of mortality, and compare the results with those of a conventional logistic regression model.

Methods: A database containing all 12,993 consecutive admissions of patients aged at least 80 between January 1997 and October 2005 from intensive care units (n=33) of mixed type taking part in the National Intensive Care Evaluation (NICE) registry. Demographic, diagnostic, physiologic, laboratory, discharge and prognostic score data were collected. After application of the SAPS II inclusion criteria 6617 patients remained. In these data we searched PRIM subgroups requiring at least 85% mortality and coverage of at least 3% of the patients. Equally sized subgroups were derived from a recalibrated (second level customization) Simplified Acute Physiology Score II model, where new coefficients were fitted. Subgroups were compared on an independent validation set using the positive predictive value (PPV), here equaling the subgroup mortality.

Results: We identified four subgroups with a positive predictive value (PPV) of 92%, 90%, 87% and 87%, covering, respectively, 3%, 3.5%, 7% and 10% of the patients in the validation set. Urine production, lowest pH, lowest systolic blood pressure, mechanical ventilation, all measured within 24 h after admission, and admission type and Glasgow Coma Score were used to define these subgroups. SAPS and PRIM subgroups had equal PPVs.

Conclusions: PRIM successfully identified high-risk subgroups. The subgroups compare in performance to SAPS II, but require less data to collect, result in more homogenous groups and are likely to be more useful for decision makers.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Female
  • Hospital Mortality*
  • Humans
  • Intensive Care Units / statistics & numerical data*
  • Logistic Models*
  • Male
  • Patient Admission
  • Prognosis
  • Risk Assessment
  • Severity of Illness Index*