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Clinically Useful Prediction of Hospital Admissions in an Aged Population
24 Pages Posted: 27 Jun 2019
More...Abstract
Background: The health care for the elderly in many countries is insufficient, not designed to meet the health-care needs of the aged population and is often described as disorganized and reactive. Prediction of old 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.
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 hospital admission. Model fitting was accomplished using an iterative process, selecting in each step the candidate variable to add to the model, using a statistical significance level of <.05. The accuracy of the prediction model is expressed as area under the receiver operating characteristic (ROC) curve, AUC.
Findings: The prediction model exhibited good discriminative accuracy for hospital admissions over the following 3 months (AUC 0.71 [95% confidence interval, CI 0.69-0.72]). Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 68 and 73%, respectively. When the outcome period was prolonged from 3 to 12 months, the positive predicted value (PPV) increased from 12 to 33%.
Interpretation: A prediction model based on routine 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.
Funding Statement: This work was supported by the County Council of Östergötland and Linköping University from the strategic research fund for 'Health Care and Welfare' [Grant number 2016186-14].
Declaration of Interests: The authors of this manuscript have no conflict of interest whatsoever to other parties.
Ethics Approval Statement: The study has been subject to ethical evaluation and was approved by the regional ethical review board in Linköping (Dnr 2016/347-31).
Keywords: Prediction, Elderly, Frail, Hospital care, Hospitalization
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