Objective: To predict mortality by disability in a sample of 479 Dutch community-dwelling people aged 75 years or older. Methods: A longitudinal study was carried out using a follow-up of seven years. The Groningen Activity Restriction Scale (GARS), a self-reported questionnaire with good psychometric properties, was used for data collection about total disability, disability in activities in daily living (ADL) and disability in instrumental activities in daily living (IADL). The mortality dates were provided by the municipality of Roosendaal (a city in the Netherlands). For analyses of survival, we used Kaplan–Meier analyses and Cox regression analyses to calculate hazard ratios (HR) with 95% confidence intervals (CI). Results: All three disability variables (total, ADL and IADL) predicted mortality, unadjusted and adjusted for age and gender. The unadjusted HRs for total, ADL and IADL disability were 1.054 (95%-CI: [1.039;1.069]), 1.091 (95%-CI: [1.062;1.121]) and 1.106 (95%-CI: [1.077;1.135]) with p-values <0.001, respectively. The AUCs were <0.7, ranging from 0.630 (ADL) to 0.668 (IADL). Multivariate analyses including all 18 disability items revealed that only “Do the shopping” predicted mortality. In addition, multivariate analyses focusing on 11 ADL items and 7 IADL items separately showed that only the ADL item “Get around in the house” and the IADL item “Do the shopping” significantly predicted mortality. Conclusion: Disability predicted mortality in a seven years follow-up among Dutch community-dwelling older people. It is important that healthcare professionals are aware of disability at early stages, so they can intervene swiftly, efficiently and effectively, to maintain or enhance the quality of life of older people.
BackgroundICU patients lose muscle mass rapidly and maintenance of muscle mass may contribute to improved survival rates and quality of life. Protein provision may be beneficial for preservation of muscle mass and other clinical outcomes, including survival. Current protein recommendations are expert-based and range from 1.2 to 2.0 g/kg. Thus, we performed a systematic review and meta-analysis on protein provision and all clinically relevant outcomes recorded in the available literature.MethodsWe conducted a systematic review and meta-analyses, including studies of all designs except case control and case studies, with patients aged ≥18 years with an ICU stay of ≥2 days and a mean protein provision group of ≥1.2 g/kg as compared to <1.2 g/kg with a difference of ≥0.2 g/kg between protein provision groups. All clinically relevant outcomes were studied. Meta-analyses were performed for all clinically relevant outcomes that were recorded in ≥3 included studies.ResultsA total of 29 studies published between 2012 and 2022 were included. Outcomes reported in the included studies were ICU, hospital, 28-day, 30-day, 42-day, 60-day, 90-day and 6-month mortality, ICU and hospital length of stay, duration of mechanical ventilation, vomiting, diarrhea, gastric residual volume, pneumonia, overall infections, nitrogen balance, changes in muscle mass, destination at hospital discharge, physical performance and psychological status. Meta-analyses showed differences between groups in favour of high protein provision for 60-day mortality, nitrogen balance and changes in muscle mass.ConclusionHigh protein provision of more than 1.2 g/kg in critically ill patients seemed to improve nitrogen balance and changes in muscle mass on the short-term and likely 60-day mortality. Data on long-term effects on quality of life are urgently needed.
MULTIFILE
Background: Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. Aim: To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. Methods: An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (PHL) to describe predictive performance in terms of discrimination and calibration. Results: The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56-0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63-0.73; PHL was 0.658). Discussion: The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.
