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PS-054 Predictive model of hospital mortality risk of complex chronic patients or patients with advanced disease in a geriatric centre
  1. S Ortonobes Roig,
  2. M De Castro Julve,
  3. M Gómez-Valent,
  4. MQGorgas Torner
  1. Parc Taulí Hospital Universitari. Institut d’Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Pharmacy Department, Sabadell, Spain


Background Identifying the risk factors in patients that are more susceptible to drug related problems (DRPs) promotes closer pharmacotherapy monitoring that prevents morbidity–mortality in these patients.

Purpose To develop a predictive model of hospital mortality risk in older patients.

Material and methods We included patients >64 years admitted to a geriatric centre with 233 beds in a university hospital from January to September 2016. We determined the relationship between mortality and number of DRPs detected during admission, adjusted to these variables: age, sex, admission unit (acute geriatric unit (AGU), convalescence, psychogeriatric), Barthel Index and Pfeiffer test before admission, length of stay, number of chronic drugs/patient, DRP type (indication, efficacy, safety, other) and number of potentially inappropriate prescriptions (PIP, according to STOPP-START 2015, Beers 2015 and Priscus criteria) with pharmacist intervention. We used a predictive model of multivariate logistic regression, including significant variables in the bivariate analysis by using the χ2 test for binary qualitative data, the Kruskal–Wallis test for >2 categories and the Mann–Whitney U test for quantitative data. In the bivariate model, p≤0.1 was considered statistically significant and in multivariate analysis, p<0.05 was considered statistically significant. Statistical analysis was performed with Stata13.

Results 523 patients were included. Admission unit: AGU 359 (68.6%) patients; convalescence 103 (19.7%); and psychogeriatrics 61 (11.6%). Median age 86 (82–89) years. Women 292 (55.8%). Discharged 488 (93.3%). Died 102 (19.5%). Of 13 potential predictors, 8 were statistically significant in the bivariate analysis and 3 in the multivariate analysis. Protective factors: Barthel Index (OR=0.99; 95% CI 0.98–1.00); length of stay (OR=0.97; 95% CI 0.95–0.99); number of drugs (OR=0.97, 95% CI 0.91–1.04); intervention of PIPs (OR=0.91; 95% CI 0.69–1.20); and PRM security (OR=0.33, 95% CI 0.08–1.47). Risk factors: age (OR=1.04; 95% CI 1.00–1.09); Pfeiffer test (OR=1.02; 95% CI 0.93–1.13); and psychogeriatrics (OR=2.58; 95% CI 1.19–5.58). The model likelihood ratio test was significant (χ2=37.46, df=10, p<0.001). Regarding the goodness of fit test, the model explained 13.0% of data uncertainty (Nagelkerke index). It correctly classified mortality in 82.21% of patients. Sensitivity: 8.33%; specificity: 99.4%; positive predictive value: 77.78%; and negative predictive value: 82.3%. The AUC of the ROC curve for the mortality and mortality predicted variable was 0.69 (95% CI 0.653–0.741).

Conclusion The results indicate that this logistic model acceptably classifies patients with an increased risk of mortality, and helps us to identify which patients should undergo pharmacotherapy monitoring.

References and/or acknowledgements PMID: 27194906.

No conflict of interest

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