RESUMEN
CONTEXT: Multimorbidity is highly prevalent among older adults and associated with a high mortality. Prediction of mortality in multimorbid people would be clinically useful but there is no mortality risk index designed for this population. Our objective was therefore to develop and internally validate a 1-year mortality prognostic index for older multimorbid adults. METHODS: We analysed data of the OPERAM cohort study in Bern, Switzerland, including 822 adults aged 70 years or more with multimorbidity (3 or more chronic medical conditions) and polypharmacy (use of 5 drugs or more for >30 days). Time to all-cause mortality was assessed up to 1 year of follow-up. We performed a parametric Weibull regression model with backward stepwise selection to identify mortality risk predictors. The model was internally validated and optimism corrected using bootstrapping techniques. We derived a point-based risk score from the regression coefficients. Calibration and discrimination were assessed by the calibration slope and C statistic. RESULTS: 805 participants were included in the analysis. During 1-year of follow-up, 158 participants (20%) had died. Age, Charlson-Comorbidity-Index, number of drugs, body mass index, number of hospitalizations, Barthel-Index (functional impairment), and nursing home residency were predictors of 1-year mortality in a multivariable model. Using these variables, the 1-year probability of dying could be predicted with an optimism-corrected C statistic of 0.70. The optimism-corrected calibration slope was 0.93. Based on the derived point-based risk score to predict mortality risk, 7% of the patients classified at low-risk of mortality, 19% at moderate-risk, and 37% at high-risk died after one year of follow-up. A simpler mortality score, without the Charlson-Comorbidity-Index and Barthel-Index, showed reduced discriminative power (optimism-corrected C statistic: 0.59) compared to the full score. CONCLUSION: We developed and internally validated a mortality risk index including for the first-time specific predictors for multimorbid adults. This new 1-year mortality prediction point-based score allowed to classify multimorbid older patients into three categories of increasing risk of mortality. Further validation of the score among various populations of multimorbid patients is needed before its implementation into practice.
Asunto(s)
Multimorbilidad , Anciano , Enfermedad Crónica , Estudios de Cohortes , Humanos , Pronóstico , Factores de RiesgoRESUMEN
RESEARCH QUESTION: How much the variability in patients' response during in vitro fertilization (IVF) may add to the initial predicted prognosis based only on patients' basal characteristics? DESIGN: Anonymous data were obtained from the Human Fertilization and Embryology Authority (HFEA). Data involving 114,882 stimulated fresh IVF cycles were retrospectively analyzed. Logistic regression was used to develop the models. RESULTS: Prediction of live birth was feasible with moderate accuracy in all of the three models; discrimination of the model based only on basal patients' characteristics (AUROC 0.61) was markedly improved adding information of number of embryos (AUROC 0.65) and, mostly, number of oocytes (AUROC 0.66). CONCLUSIONS: The addition to prediction models of parameters such as the number of embryos obtained and especially the number of oocytes retrieved can statistically significantly improve the overall prediction of live birth probabilities when based on only basal patients' characteristics. This seems to be particularly true for women after the first IVF cycle. Since ovarian response affects the probability of live birth in IVF, it is highly recommended to add markers of ovarian response to models based on basal characteristics to increase their predictive ability.