Objectives: to compare changes over time in the in-hospital mortality and the mortality from discharge to 30 days post-discharge for six highly prevalent discharge diagnoses in acutely admitted older patients as well as to assess the effect of separately analysing the in-hospital mortality and the mortality from discharge to 30 days post-discharge.Study design and setting: retrospective analysis of Dutch hospital and mortality data collected between 2000 and 2010.Subjects: the participants included 263,746 people, aged 65 years and above, who were acutely admitted for acute myocardial infarction (AMI), heart failure (HF), stroke, chronic obstructive pulmonary disease, pneumonia or hip fracture.Methods: we compared changes in the in-hospital mortality and mortality from discharge to 30 days post-discharge in the Netherlands using a logistic- and a multinomial regression model.Results: for all six diagnoses, the mortality from admission to 30 days post-discharge declined between 2000 and 2009. The decline ranged from a relative risk ratio (RRR) of 0.41 [95% confidence interval (CI) 0.38–0.45] for AMI to 0.77 [0.73–0.82] for HF. In separate analyses, the in-hospital mortality decreased for all six diagnoses. The mortality from discharge to 30 days post-discharge in 2009 compared to 2000 depended on the diagnosis, and either declined, remained unchanged or increased.Conclusions: the decline in hospital mortality in acutely admitted older patients was largely attributable to the lower in-hospital mortality, while the change in the mortality from discharge to 30 days post-discharge depended on the diagnosis. Separately reporting the two rate estimates might be more informative than providing an overall hospital mortality rate.
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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.
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OBJECTIVE: Depression among older adults predicts mortality after acute hospitalization. Depression is highly heterogeneous in its presentation of symptoms, whereas individual symptoms may differ in predictive value. This study aimed to investigate the prevalence of individual cognitive-affective depressive symptoms during acute hospitalization and investigate the predictive value of both overall and individual cognitive-affective depressive symptoms for mortality between admission up to 3-month postdischarge among older patients.METHODS: A prospective multicenter cohort study enrolled 401 acutely hospitalized patients 70 years and older (Hospitalization-Associated Disability and impact on daily Life Study). The predictive value of depressive symptoms, assessed using the Geriatric Depression Scale 15, during acute hospitalization on mortality was analyzed with multiple logistic regression.RESULTS: The analytic sample included 398 patients (M (SD) = 79.6 (6.6) years; 51% men). Results showed that 9.3% of participants died within 3 months, with symptoms of apathy being most frequently reported. The depression total score during hospitalization was associated with increased mortality risk (admission: odds ratio [OR] = 1.2, 95% confidence interval [CI] = 1.2-1.3; discharge: OR = 1.2, 95% CI = 1.2-1.4). Stepwise multiple logistic regression analyses yielded the finding that feelings of hopelessness during acute hospitalization were a strong unique predictor of mortality (admission: OR = 3.6, 95% CI = 1.8-7.4; discharge: OR = 5.7, 95% CI = 2.5-13.1). These associations were robust to adjustment for demographic factors, somatic symptoms, and medical comorbidities.CONCLUSIONS: Symptoms of apathy were most frequently reported in response to acute hospitalization. However, feelings of hopelessness about their situation were the strongest cognitive-affective predictor of mortality. These results imply that this item is important in identifying patients who are in the last phase of their lives and for whom palliative care may be important.
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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.
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Objective: To predict mortality with the Tilburg Frailty Indicator (TFI) in a sample of community-dwelling older people, using a follow-up of 7 years. Setting and Participants: 479 Dutch community-dwelling people aged 75 years or older. Measurements: The TFI, a self-report questionnaire, was used to collect data about total, physical, psychological, and social frailty. The municipality of Roosendaal (a town in the Netherlands) provided the mortality dates. Conclusions and Implications: This study has shown the predictive validity of the TFI for mortality in community-dwelling older people. Our study demonstrated that physical and psychological frailty predicted mortality. Of the individual TFI components, difficulty in walking consistently predicted mortality. For identifying frailty, using the integral instrument is recommended because total, physical, psychological, and social frailty and its components have proven their value in predicting adverse outcomes of frailty, for example, increase in health care use and a lower quality of life.
MULTIFILE
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.
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Abstract Purpose To determine the predictive value of quality of life for mortality at the domain and item levels. Methods This longitudinal study was carried out in a sample of 479 Dutch people aged 75 years or older living independently, using a follow-up of 7 years. Participants completed a self-report questionnaire. Quality of life was assessed with the WHOQOL-BREF, including four domains: physical health, psychological, social relationships, and environment. The municipality of Roosendaal (a town in the Netherlands) indicated the dates of death of the individuals. Results Based on mean, all quality of life domains predicted mortality adjusted for gender, age, marital status, education, and income. The hazard ratios ranged from 0.811 (psychological) to 0.933 (social relationships). The areas under the curve (AUCs) of the four domains were 0.730 (physical health), 0.723 (psychological), 0.693 (social relationships), and 0.700 (environment). In all quality of life domains, at least one item predicted mortality (adjusted). Conclusion Our study showed that all four quality of life domains belonging to the WHOQOL-BREF predict mortality in a sample of Dutch community-dwelling older people using a follow-up period of 7 years. Two AUCs were above threshold (psychological, physical health). The findings offer health care and welfare professionals evidence for conducting interventions to reduce the risk of premature death.
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BACKGROUND: Prognostic assessments of the mortality of critically ill patients are frequently performed in daily clinical practice and provide prognostic guidance in treatment decisions. In contrast to several sophisticated tools, prognostic estimations made by healthcare providers are always available and accessible, are performed daily, and might have an additive value to guide clinical decision-making. The aim of this study was to evaluate the accuracy of students', nurses', and physicians' estimations and the association of their combined estimations with in-hospital mortality and 6-month follow-up.METHODS: The Simple Observational Critical Care Studies is a prospective observational single-center study in a tertiary teaching hospital in the Netherlands. All patients acutely admitted to the intensive care unit were included. Within 3 h of admission to the intensive care unit, a medical or nursing student, a nurse, and a physician independently predicted in-hospital and 6-month mortality. Logistic regression was used to assess the associations between predictions and the actual outcome; the area under the receiver operating characteristics (AUROC) was calculated to estimate the discriminative accuracy of the students, nurses, and physicians.RESULTS: In 827 out of 1,010 patients, in-hospital mortality rates were predicted to be 11%, 15%, and 17% by medical students, nurses, and physicians, respectively. The estimations of students, nurses, and physicians were all associated with in-hospital mortality (OR 5.8, 95% CI [3.7, 9.2], OR 4.7, 95% CI [3.0, 7.3], and OR 7.7 95% CI [4.7, 12.8], respectively). Discriminative accuracy was moderate for all students, nurses, and physicians (between 0.58 and 0.68). When more estimations were of non-survival, the odds of non-survival increased (OR 2.4 95% CI [1.9, 3.1]) per additional estimate, AUROC 0.70 (0.65, 0.76). For 6-month mortality predictions, similar results were observed.CONCLUSIONS: Based on the initial examination, students, nurses, and physicians can only moderately predict in-hospital and 6-month mortality in critically ill patients. Combined estimations led to more accurate predictions and may serve as an example of the benefit of multidisciplinary clinical care and future research efforts.
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Background: A new selective preventive spinal immobilization (PSI) protocol was introduced in the Netherlands. This may have led to an increase in non-immobilized spinal fractures (NISFs) and consequently adverse patient outcomes. Aim: A pilot study was conducted to describe the adverse patient outcomes in NISF of the PSI protocol change and assess the feasibility of a larger effect study. Methods: Retrospective comparative cohort pilot study including records of trauma patients with a presumed spinal injury who were presented at the emergency department of a level 2 trauma center by the emergency medical service (EMS). The pre-period 2013-2014 (strict PSI protocol), was compared to the post-period 2017-2018 (selective PSI protocol). Primary outcomes were the percentage of records with a NISF who had an adverse patient outcome such as neurological injuries and mortality before and after the protocol change. Secondary outcomes were the sample size calculation for a larger study and the feasibility of data collection. Results: 1,147 records were included; 442 pre-period, and 705 post-period. The NISF-prevalence was 10% (95% CI 7-16, n = 19) and 8% (95% CI 6-11, n = 33), respectively. In both periods, no neurological injuries or mortality due to NISF were found, by which calculating a sample size is impossible. Data collection showed to be feasible. Conclusions: No neurological injuries or mortality due to NISF were found in a strict and a selective PSI protocol. Therefore, a larger study is discouraged. Future studies should focus on which patients really profit from PSI and which patients do not.
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Thirty to sixty per cent of older patients experience functional decline after hospitalisation, associated with an increase in dependence, readmission, nursing home placement and mortality. First step in prevention is the identification of patients at risk. The objective of this study is to develop and validate a prediction model to assess the risk of functional decline in older hospitalised patients.
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