Using mortality risk scores for long-term prognosis of nursing home residents: Caution is recommended

Robin L. Kruse, Debra Parker Oliver, David R. Mehr, Gregory F. Petroski, Denise L. Swenson, Steven C. Zweig

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Background. Determining prognosis for nursing home residents is important for care planning, but reliable prediction is difficult. We compared performance of four long-term mortality risk indices for nursing home residents - the Minimum Data Set Mortality Risk Index (MMRI), a recent revision to this index (MMRI-R), and the original and revised Flacker-Kiely models. Methods. We conducted a prospective cohort study in one 92-bed facility in Missouri. Participants were 130 residents who received a Minimum Data Set assessment from May through October, 2007. We collected the Minimum Data Set variables needed to calculate the mortality risk scores. We determined 6- and 12-month mortality for included residents. Using each mortality risk score as the sole independent predictor in logistic models predicting mortality, we determined discrimination (c-statistic) and calibration (Hosmer-Lemeshow goodness-of-fit statistic) for each model. Results. In our sample, discrimination was 0.59 for both the MMRI and the MMRI-R. Discrimination of the original Flacker-Kiely model was 0.69 for both 6 months and 1 year and 0.71 and 0.70, respectively, for the revised model. Model calibration was adequate for all models. Conclusions. Performance of four models that predict long-term mortality of nursing home residents was fair. In our population, the Flacker-Kiely models had similar and markedly better discrimination than either the MMRI or the MMRI-R.

Original languageEnglish
Pages (from-to)1235-1241
Number of pages7
JournalJournals of Gerontology - Series A Biological Sciences and Medical Sciences
Volume65 A
Issue number11
DOIs
StatePublished - Nov 2010

Keywords

  • Logistic models
  • Mortality
  • Prognosis
  • Risk assessment

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