Comparing the prognostic value of geriatric health indicators: a population-based study

Abstract

Background The identification of individuals at increased risk of poor health-related outcomes is a priority. Geriatric research has proposed several indicators shown to be associated with these outcomes, but a head-to-head comparison of their predictive accuracy is still lacking. We therefore aimed to compare the accuracy of five geriatric health indicators in predicting different outcomes among older persons: frailty index (FI), frailty phenotype (FP), walking speed (WS), multimorbidity, and a summary score including clinical diagnoses, functioning, and disability (the Health Assessment Tool; HAT). Methods Data were retrieved from the Swedish National Study on Aging and Care in Kungsholmen, an ongoing longitudinal study including 3363 people aged 60+. To inspect the accuracy of geriatric health indicators, we employed areas under the receiver operating characteristic curve (AUC) for the prediction of 3-year and 5-year mortality, 1-year and 3-year unplanned hospitalizations (1+), and contacts with healthcare providers in the 6 months before and after baseline evaluation (2+). Results FI, WS, and HAT showed the best accuracy in the prediction of mortality [AUC(95%CI) for 3-year mortality 0.84 (0.82–0.86), 0.85 (0.83–0.87), 0.87 (0.85–0.88) and AUC(95%CI) for 5-year mortality 0.84 (0.82–0.86), 0.85 (0.83–0.86), 0.86 (0.85–0.88), respectively]. Unplanned hospitalizations were better predicted by the FI [AUC(95%CI) 1-year 0.73 (0.71–0.76); 3-year 0.72 (0.70–0.73)] and HAT [AUC(95%CI) 1-year 0.73 (0.71–0.75); 3-year 0.71 (0.69–0.73)]. The most accurate predictor of multiple contacts with healthcare providers was multimorbidity [AUC(95%CI) 0.67 (0.65–0.68)]. Predictions were generally less accurate among younger individuals (< 78 years old). Conclusion Specific geriatric health indicators predict clinical outcomes with different accuracy. Comprehensive indicators (HAT, FI, WS) perform better in predicting mortality and hospitalization. Multimorbidity exhibits the best accuracy in the prediction of multiple contacts with providers.

Frailty index predicts poor outcome in COVID-19 patients

“…3 Bicocca Center of Bioinformatics, Biostatistics and Bioimaging, School of Medicine and Surgery, University of Milano -Bicocca, Monza, Italy. 4 Infectious Diseases Unit, San Gerardo Hospital, Monza, Italy.…”

“…Overall, we assessed 43 variables, which provided our index with a sufficient amount of robustness [3]. Importantly, one study has shown that a FI constructed with a similar number of variables was superior to other frailty tools in predicting mortality [4]. The variables included in the 43-item FI are listed in the Electronic Supplementary Material along with the graphical distribution of the scores.…”

Course of frailty stratified by physical and mental multimorbidity patterns: a 5-year follow-up of 92,640 participants of the LifeLines cohort study

“…Many questions remain unanswered to advocate routine assessment of frailty starting among middle-aged people as recommended for older people, especially issues regarding treatment and reversibility of frailty at middle age and cost-effectiveness of case-finding. While multimorbidity may better predict demand for healthrelated services [29], measurement of frailty in middleaged and older adults might provide a tool to estimate mortality risk better than single-disease states or multimorbidity. Since frailty is considered a dynamic condition, more knowledge on prevention is also warranted [30].…”

Predictors and Prognostic Models for Early Discharge Planning of Hospitalized Acute Geriatric Patients, A Retrospective Study

“…ROC-curves are frequently used [7,29,32] for a systematic evaluation of model quality. We see that for both cohorts the three predictor-model has the largest area under the curve and hence gives the best classification.…”