Background The healthcare system needs effective strategies to identify the most vulnerable group of older patients, assess their needs and plan their care proactively. To evaluate the effectiveness of comprehensive geriatric assessment (CGA) of older adults with a high risk of hospitalisation we conducted a prospective, pragmatic, matched-control multicentre trial at 19 primary care practices in Sweden. Methods We identified 1604 individuals aged 75 years and older using a new, validated algorithm that calculates a risk score for hospitalisation from electronic medical records. After a nine-month run-in period for CGA in the intervention group, 74% of the available 646 participants had accepted and received CGA, and 662 participants remained in the control group. Participants at intervention practices were invited to CGA performed by a nurse together with a physician. The CGA was adapted to the primary care context. The participants thereafter received actions according to individual needs during a two-year follow-up period. Participants at control practices received care as usual. The primary outcome was hospital care days. Secondary outcomes were number of hospital care episodes, number of outpatient visits, health care costs and mortality. Outcomes were analysed according to intention to treat and adjusted for age, gender and risk score. We used generalised linear mixed models to compare the intervention group and control group regarding all outcomes. Results Mean age was 83.2 years, 51% of the 1308 participants were female. Relative risk reduction for hospital care days was − 22% (− 35% to − 4%, p = 0.02) during the two-year follow-up. Relative risk reduction for hospital care episodes was − 17% (− 30% to − 2%, p = 0.03). There were no significant differences in outpatient visits or mortality. Health care costs were significantly lower in the intervention group, adjusted mean difference was € − 4324 (€ − 7962 to − 686, p = 0.02). Conclusions and relevance Our findings indicate that CGA in primary care can reduce the need for hospital care days in a high-risk population of older adults. This could be of great importance in order to manage increasing prevalence of frailty and multimorbidity. Trial registration clinicaltrials.gov Identifier: NCT03180606, first posted 08/06/2017.
Background: The healthcare for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future healthcare system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine healthcare data. Methods: We used the healthcare data on 40,728 persons, 75-109 years of age to predict hospital inward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC. Results: The prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0.69 [95% confidence interval, CI 0.68-0.70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31 and 29%, respectively. Conclusion: A prediction model based on routine administrative healthcare data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for healthcare.
IntroductionThe provision of healthcare services is not dedicated to promoting maintenance of function and does not target frail older persons at high risk of the main causes of morbidity and mortality. The aim of this study is to evaluate the effects of a proactive medical and social intervention in comparison with conventional care on a group of persons aged 75 and older selected by statistical prediction.Methods and analysisIn a pragmatic multicentre primary care setting (n=1600), a prediction model to find elderly (75+) persons at high risk of complex medical care or hospitalisation is used, followed by proactive medical and social care, in comparison with usual care. The study started in April 2017 with a run-in period until December 2017, followed by a 2-year continued intervention phase that will continue until the end of December 2019. The intervention includes several tools (multiprofessional team for rehabilitation, social support, medical care home visits and telephone support). Primary outcome measures are healthcare cost, number of hospital care episodes, hospital care days and mortality. Secondary outcome measures are number of outpatient visits, cost of social care and informal care, number of prescribed drugs, health-related quality of life, cost-effectiveness, sense of security, functional status and ability. We also study the care of elderly persons in a broader sense, by covering the perspectives of the patients, the professional staff and the management, and on a political level, by using semistructured interviews, qualitative methods and a questionnaire.Ethics and disseminationApproved by the regional ethical review board in Linköping (Dnr 2016/347-31). The results will be presented in scientific journals and scientific meetings during 2019–2022 and are planned to be used for the development of future care models.Trial registration number NCT03180606.
Objective: Comprehensive geriatric assessment (CGA) is recommended for the management of frailty. Little is known about professionals' experiences of CGA; therefore we wanted to investigate the experiences of staff in primary care using a new CGA tool: the Primary care Assessment Tool for Elderly (PASTEL). Design: Focus group interviews. Manifest qualitative content analysis. Setting: Nine primary health care centres in Sweden that participated in a CGA intervention. These centres represent urban as well as rural areas. Subjects: Nine nurses, five GPs and one pharmacist were divided into three focus groups. Main outcome measures: Participants' experiences of conducting CGA with PASTEL. Results: The analysis resulted in four main categories. A valuable tool for selected patients:The participants considered the assessment tool to be feasible and valuable. They stated that having enough time for the assessment interview was essential but views about the ideal patient for assessment were divided. Creating conditions for dialogue: The process of adapting the assessment to the individual and create conditions for dialogue was recognised as important. Managing in-depth conversations: In-depth conversations turned out to be an important component of the assessment. Patients were eager to share their stories, but talking about the future or the end of life was demanding. The winding road of actions and teamwork: PASTEL was regarded as a good preparation tool for care planning and a means of support for identifying appropriate actions to manage frailty but there were challenges to implement these actions and to obtain good teamwork. Conclusion:The participants reported that PASTEL, a tool for CGA, gave a holistic picture of the older person and was helpful in care planning. KEY POINTSTo manage frailty using comprehensive geriatric assessment (CGA) in primary care, there is a need for tools that are efficient, user-friendly and which support patient involvement and teamworkThis study found that the Primary care Assessment tool for Elderly (PASTEL) is regarded as both valuable and feasible by primary care professionals Use of carefully selected items in the tool and allowing enough time for dialogue may enhance patient-centeredness The PASTEL tool supports the process of identifying actions to manage frailty in older adults. Teamwork related to the tool and CGA in primary care needs to be further investigated and developed ARTICLE HISTORY
Background The health care for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future health-care system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine health-care data.Methods We used the health-care data on 40,728 persons, 75-109 years of age to predict hospital in-ward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC.Results The prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0·69 [95% confidence interval, CI 0·68–0·70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31% and 29%, respectively.Conclusion A prediction model based on routine administrative health-care data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for health care.
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