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Longitudinal changes of muscle mass, muscle strength and physical performance in acutely admitted older adults up to three months post-discharge

Post-discharge light physical activity indicates recovery in acutely hospitalized older adults - the Hospital-ADL study

BACKGROUND: Physical activity (PA) levels might be a simple overall physical function indicator of recovery in acutely hospitalized older adults; however it is unknown which amount and level of PA is associated with recovery. Our objective was to evaluate the amount and level of post discharge PA and its optimum cut-off values associated with recovery among acutely hospitalized older adults and stratified for frailty.METHODS: We performed a prospective observational cohort study including acutely hospitalized older adults (≥ 70 years). Frailty was assessed using Fried's criteria. PA was assessed using Fitbit up to one week post discharge and quantified in steps and minutes light, moderate or higher intensity. The primary outcome was recovery at 3-months post discharge. ROC-curve analyses were used to determine cut-off values and area under the curve (AUC), and logistic regression analyses to calculate odds ratios (ORs).RESULTS: The analytic sample included 174 participants with a mean (standard deviation) age of 79.2 (6.7) years of whom 84/174 (48%) were frail. At 3-months, 109/174 participants (63%) had recovered of whom 48 were frail. In all participants, determined cut-off values were 1369 steps/day (OR: 2.7, 95% confidence interval [CI]: 1.3-5.9, AUC 0.7) and 76 min/day of light intensity PA (OR: 3.9, 95% CI: 1.8-8.5, AUC 0.73). In frail participants, cut-off values were 1043 steps/day (OR: 5.0, 95% CI: 1.7-14.8, AUC 0.72) and 72 min/day of light intensity PA (OR: 7.2, 95% CI: 2.2-23.1, AUC 0,74). Determined cut-off values were not significantly associated with recovery in non-frail participants.CONCLUSIONS: Post-discharge PA cut-offs indicate the odds of recovery in older adults, especially in frail individuals, however are not equipped for use as a diagnostic test in daily practice. This is a first step in providing a direction for setting rehabilitation goals in older adults after hospitalization.

The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients

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.

The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients

BackgroundEarly 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.AimTo 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.MethodsAn 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.ResultsThe 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).DiscussionThe 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.

Experiences of frail older cardiac patients with a nurse-coordinated transitional care intervention - a qualitative study

BackgroundOlder cardiac patients are at high risk of readmission and mortality. Transitional care interventions (TCIs) might contribute to the prevention of adverse outcomes. The Cardiac Care Bridge program was a randomized nurse-coordinated TCI combining case management, disease management and home-based rehabilitation for hospitalized frail older cardiac patients. This qualitative study explored the experiences of patients’ participating in this study, as part of a larger process evaluation as this might support interpretation of the neutral study outcomes. In addition, understanding these experiences could contribute to the design and application of future transitional care interventions for frail older cardiac patients.MethodsA generic qualitative approach was used. Semi-structured interviews were performed with 16 patients ≥70 years who participated in the intervention group. Participants were selected by gender, diagnosis, living arrangement and hospital of inclusion. Data were analysed using thematic analysis. In addition, quantitative data about intervention delivery were analysed.ResultsThree themes emerged from the data: 1) appreciation of care continuity; 2) varying experiences with recovery and, 3) the influence of an existing care network. Participants felt supported by the transitional care intervention as they experienced post-discharge support and continuity of care. The perceived contribution of the program in participants’ recovery varied. Some participants reported physical improvements while others felt impeded by comorbidities or frailty. The home visits by the community nurse were appreciated, although some participants did not recognize the added value. Participants with an existing healthcare provider network preferred to consult these providers instead of the providers who were involved in the transitional care intervention.ConclusionOur results contribute to an explanation of the neutral study of a nurse-coordinated transitional care intervention. For future purpose, it is important to identify which patients might benefit most from TCIs. Furthermore, the intensity and content of TCIs could be more personalized by tailoring interventions to older cardiac patients’ needs, considering their frailty, self-management skills and existing formal and informal caregiver networks.

Experiences of frail older cardiac patients with a nurse-coordinated transitional care intervention

Background: Older cardiac patients are at high risk of readmission and mortality. Transitional care interventions (TCIs) might contribute to the prevention of adverse outcomes. The Cardiac Care Bridge program was a randomized nurse-coordinated TCI combining case management, disease management and home-based rehabilitation for hospitalized frail older cardiac patients. This qualitative study explored the experiences of patients’ participating in this study, as part of a larger process evaluation as this might support interpretation of the neutral study outcomes. In addition, understanding these experiences could contribute to the design and application of future transitional care interventions for frail older cardiac patients. Methods: A generic qualitative approach was used. Semi-structured interviews were performed with 16 patients ≥70 years who participated in the intervention group. Participants were selected by gender, diagnosis, living arrangement and hospital of inclusion. Data were analysed using thematic analysis. In addition, quantitative data about intervention delivery were analysed. Results: Three themes emerged from the data: 1) appreciation of care continuity; 2) varying experiences with recovery and, 3) the influence of an existing care network. Participants felt supported by the transitional care intervention as they experienced post-discharge support and continuity of care. The perceived contribution of the program in participants’ recovery varied. Some participants reported physical improvements while others felt impeded by comorbidities or frailty. The home visits by the community nurse were appreciated, although some participants did not recognize the added value. Participants with an existing healthcare provider network preferred to consult these providers instead of the providers who were involved in the transitional care intervention. Conclusion: Our results contribute to an explanation of the neutral study of a nurse-coordinated transitional care intervention. For future purpose, it is important to identify which patients might benefit most from TCIs. Furthermore, the intensity and content of TCIs could be more personalized by tailoring interventions to older cardiac patients’ needs, considering their frailty, self-management skills and existing formal and informal caregiver networks.