Development of a prediction model for 30-day acute readmissions among older medical patients: the influence of social factors along with other patient-specific and organisational factors

Abstract

BackgroundReadmission rate is one way to measure quality of care for older patients. Knowledge is sparse on how different social factors can contribute to predict readmission. We aimed to develop and internally validate a comprehensive model for prediction of acute 30-day readmission among older medical patients using various social factors along with demographic, organisational and health-related factors.MethodsWe performed an observational prospective study based on a group of 770 medical patients aged 65 years or older, who were consecutively screened for readmission risk factors at an acute care university hospital during the period from February to September 2012. Data on outcome and candidate predictors were obtained from clinical screening and administrative registers. We used multiple logistic regression analyses with backward selection of predictors. Measures of model performance and performed internal validation were calculated.ResultsTwenty percent of patients were readmitted within 30 days from index discharge. The final model showed that low educational level, along with male gender, contact with emergency doctor, specific diagnosis, higher Charlson Comorbidity Index score, longer hospital stay, cognitive problems, and medical treatment for thyroid disease, acid-related disorders, and glaucoma, predicted acute 30-day readmission. Area under the receiver operating characteristic curve (0.70) indicated acceptable discriminative ability of the model. Calibration slope was 0.98 and calibration intercept was 0.01. In internal validation analysis, both discrimination and calibration measures were stable.ConclusionsWe developed a model for prediction of readmission among older medical patients. The model showed that social factors in the form of educational level along with demographic, organisational and health-related factors contributed to prediction of acute 30-day readmissions among older medical patients.

The effect of the employment of consultants in the Emergency Department on quality of care and equity – a quasi-experimental retrospective cohort study

“…Another indicator of effective service delivery is readmission rate, as patients returning to the hospital apply additional pressure to the health care system by increasing patient volume 37 . Our study con rmed the previous ndings of no effect of models of consultant-led ED services on readmission rates 19,20 and the readmission rate which was found is similar to the one found at another Danish Acute Hospital 38 . These ndings are in contrast to a British study that found a signi cant decrease in readmission within 28 days of discharge utilising adjusted analyses 17 .…”

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“…Physical, psychological and social characteristics associated with higher risks of adverse discharge outcomes were selected based on past studies [6][7][8][9][10][11][12][13][14]:…”

“…Furthermore, past studies suggested that some physical, psychological and social characteristics increased the risk of these adverse discharge outcomes in older patients [6,7]. In particular, difficulty to walk, comorbidities, cognitive impairment, living alone and lack of social support have been identified as strong predictors of readmissions within 30 days post-discharge [8][9][10][11][12][13][14]. While not all readmissions are avoidable, a large proportion (27-76%) is thought to be preventable if best-practice discharge preparation and practices are followed [15].…”

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

“…The vast majority of mortality and readmission predictive models focus on maximizing performance of the models rather than real-world impact on care delivery. In doing so, model developers may use predictors that require data collection from additional workflows [ 5 – 6 ], use variables that may not be fully available or have to mature during the hospitalization [ 6 – 9 ], or may not integrate the model into their electronic health record (EHR) either for real-time prospective validation or for calculation to support clinical workflows [ 6 , 10 – 12 ].…”

“…Furthermore, current predictive models are frequently focused on highly specific segments of the population, such as the elderly [ 13 – 14 ], or on specific disease states, such as pneumonia [ 11 , 15 ], chronic obstructive pulmonary disease[ 16 , 17 ] or heart failure [ 11 , 18 – 21 ]. While this approach can lead to improved model performance, it tends to cover only a subset of the population, and may not fully recognize that complex patients have multiple, often related, diagnoses that are together driving their clinical risk.…”