Clinically useful prediction of hospital admissions in an older population

Abstract

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.

Costs and effects of comprehensive geriatric assessment in primary care for older adults with high risk for hospitalisation

“…Our design allowed us to access a big, high-risk sample that we could follow for 24 months and collect data from all participants. The prediction model that we used to select participants is based on healthcare needs, age and selected diagnoses [ 23 ]. The high mortality, co-morbidities and rate of hospitalisation among our participants support that this model concords with electronic frailty indexes [ 22 ] and other frailty measures [ 6 ].…”

“…We identified 1604 individuals aged 75 years or older at the participating practices in March 2017. We used a recently developed and validated prediction model that contains 38 variables identified with multivariable logistic regression [ 23 ]. Age and healthcare use are the principal predictors, together with diagnoses from inpatient care and outpatient visits.…”

“…Recently, studies using electronic administrative and medical record health care data to detect frailty and predict risk for hospitalisation have provided new tools for this. Using algorithms is a way to detect and grade frailty that does not require manual and time-consuming contacts with patients [ 21 – 23 ]. A high electronic frailty index corresponds to high risk for hospitalisation [ 22 ].…”

“…Therefore, we decided to use an algorithm for prediction of hospitalisations to identify a target group for CGA [ 23 ]. As previous studies have identified difficulties in implementing complex interventions in primary care [ 24 ], we designed a CGA intervention that was person-centred and differentiated according to individual needs.…”

Health status and health care utilization after discharge from geriatric in-hospital stay – description of a register-based study

“…Lack of follow-up after discharge has been shown to increase the risk of readmissions [ 7 ] and discharge planning and care management has been shown to reduce readmissions [ 8 , 9 ]. Identifying people with a high risk for hospital admissions are of importance [ 10 ].…”