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The prompt identification of frailty in primary care is the first step to offer personalized care to older individuals. We aimed to detect and quantify frailty among primary care older patients, by developing and validating a primary care frailty index (PC-FI) based on routinely collected health records and providing sex-specific frailty charts. The PC-FI was developed using data from 308,280 primary care patients ≥ 60 years old part of the Health Search Database (HSD) in Italy (baseline 2013–2019) and validated in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K; baseline 2001–2004), a well-characterized population-based cohort including 3363 individuals ≥ 60 years old. Potential health deficits part of the PC-FI were identified through ICD-9, ATC, and exemption codes and selected through an optimization algorithm (i.e., genetic algorithm), using all-cause mortality as the main outcome for the PC-FI development. The PC-FI association at 1, 3 and 5 years, and discriminative ability for mortality and hospitalization were tested in Cox models. The convergent validity with frailty-related measures was verified in SNAC-K. The following cut-offs were used to define absent, mild, moderate and severe frailty: < 0.07, 0.07–0.14, 0.14–0.21, and ≥ 0.21. Mean age of HSD and SNAC-K participants was 71.0 years (55.4% females). The PC-FI included 25 health deficits and showed an independent association with mortality (hazard ratio range 2.03–2.27; p < 0.05) and hospitalization (hazard ratio range 1.25–1.64; p < 0.05) and a fair-to-good discriminative ability (c-statistics range 0.74–0.84 for mortality and 0.59–0.69 for hospitalization). In HSD 34.2%, 10.9% and 3.8% were deemed mildly, moderately, and severely frail, respectively. In the SNAC-K cohort, the associations between PC-FI and mortality and hospitalization were stronger than in the HSD and PC-FI scores were associated with physical frailty (odds ratio 4.25 for each 0.1 increase; p < 0.05; area under the curve 0.84), poor physical performance, disability, injurious falls, and dementia. Almost 15% of primary care patients ≥ 60 years old are affected by moderate or severe frailty in Italy. We propose a reliable, automated, and easily implementable frailty index that can be used to screen the primary care population for frailty. [ABSTRACT FROM AUTHOR]

Introduction Delirium is a frequent condition in hospitalized older patients and it usually has a negative prognostic value. A direct effect of SARS-COV-2 on the central nervous system (CNS) has been hypothesized. Objective To evaluate the presence of delirium in older patients admitted for a suspected diagnosis of COVID-19 and its impact on in-hospital mortality. Setting and subjects 91 patients, aged 70-years and older, admitted to an acute geriatric ward in Northern Italy from March 8th to April 17th, 2020. Methods COVID-19 cases were confirmed by reverse transcriptase-polymerase chain reaction assay for SARS-Cov-2 RNA from nasal and pharyngeal swabs. Delirium was diagnosed by two geriatricians according to the Diagnostic and Statistical Manual of Mental Disorders V (DMS V) criteria. The number of chronic diseases was calculated among a pre-defined list of 60. The pre-disease Clinical Frailty Scale (CFS) was assessed at hospital admission. Results Of the total sample, 39 patients died, 49 were discharged and 3 were transferred to ICU. Twenty-five patients (27.5%) had delirium. Seventy-two percent of patients with delirium died during hospitalization compared to 31.8% of those without delirium. In a multivariate logistic regression model adjusted for potential confounders, patients with delirium were four times more likely to die during hospital stay compared to those without delirium (OR = 3.98;95%CI = 1.05–17.28; p = 0.047). Conclusions Delirium is common in older patients with COVID-19 and strongly associated with in-hospital mortality. Regardless of causation, either due to a direct effect of SARS-COV-2 on the CNS or to a multifactorial cause, delirium should be interpreted as an alarming prognostic indicator in older people. [ABSTRACT FROM AUTHOR]

The presence of multiple chronic conditions (i.e., multimorbidity) increases the risk of hospitalisation in older adults. We aimed to examine the association between different multimorbidity patterns and unplanned hospitalisations over 5 years. To that end, 2,250 community-dwelling individuals aged 60 years and older from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) were studied. Participants were grouped into six multimorbidity patterns using a fuzzy c-means cluster analysis. The associations between patterns and outcomes were tested using Cox models and negative binomial models. After 5 years, 937 (41.6%) participants experienced at least one unplanned hospitalisation. Compared to participants in the unspecific multimorbidity pattern, those in the cardiovascular diseases, anaemia and dementia pattern, the psychiatric disorders pattern and the metabolic and sleep disorders pattern presented with a higher hazard of first unplanned hospitalisation (hazard ratio range: 1.49–2.05; p < 0.05 for all), number of unplanned hospitalisations (incidence rate ratio (IRR) range: 1.89–2.44; p < 0.05 for all), in-hospital days (IRR range: 1.91–3.61; p < 0.05 for all), and 30-day unplanned readmissions (IRR range: 2.94–3.65; p < 0.05 for all). Different multimorbidity patterns displayed a differential association with unplanned hospital care utilisation. These findings call for a careful primary care follow-up of older adults with complex multimorbidity patterns. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]