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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Using mortality risk scores for long-term prognosis of nursing home residents: caution is recommended
Robin L Kruse1, Debra Parker Oliver, David R Mehr
1Department of Family & Community Medicine, University of Missouri, Columbia, MO 65212, USA. kruser@health.missouri.edu
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.
Insights
Predicting nursing home resident mortality is challenging. The Flacker-Kiely models demonstrated better accuracy than the Minimum Data Set Mortality Risk Index (MMRI) for long-term prognosis.
Area of Science:
- Gerontology
- Health Services Research
- Biostatistics
Background:
- Accurate prognosis for nursing home residents is crucial for effective care planning.
- Reliable prediction of long-term mortality in this population remains a significant challenge.
- Existing mortality risk indices require ongoing evaluation for clinical utility.
Purpose of the Study:
- To compare the predictive performance of four long-term mortality risk indices in nursing home residents.
- To evaluate the Minimum Data Set Mortality Risk Index (MMRI), its revision (MMRI-R), and the original and revised Flacker-Kiely models.
- To assess the discrimination and calibration of these models for predicting mortality.
Main Methods:
- A prospective cohort study was conducted in a 92-bed nursing home.
- 130 residents with Minimum Data Set assessments were included.
- Six- and 12-month mortality were determined, and model performance was assessed using c-statistics and Hosmer-Lemeshow tests.
Main Results:
- Discrimination for MMRI and MMRI-R was 0.59.
- Discrimination for the original Flacker-Kiely model was 0.69 (6 months) and 0.69 (1 year).
- Discrimination for the revised Flacker-Kiely model was 0.71 (6 months) and 0.70 (1 year); calibration was adequate for all models.
Conclusions:
- All four models demonstrated fair performance in predicting long-term mortality.
- The Flacker-Kiely models exhibited markedly better discrimination compared to the MMRI and MMRI-R in this study population.
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