Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community

Abstract

551R eadmission to hospital and death are adverse patient outcomes that are serious, common and costly.1,2 Several studies suggest that focused care after discharge can improve post-discharge outcomes.3-7 Being able to accurately predict the risk of poor outcomes after hospital discharge would allow health care workers to focus post-discharge interventions on patients who are at highest risk of poor post-discharge outcomes. Further, policy-makers have expressed interest in either penalizing hospitals with relatively high rates of readmission or rewarding hospitals with relatively low expected rates. 8 To implement this approach, a validated method of standardizing readmission rates is needed. 9Two validated models for predicting risk of readmission after hospital discharge have been published. 10,11 However, these models are impractical to clinicians. Both require arealevel information (e.g., neighbourhood socio-economic status and community-specific rates of admission) that is not readily available. Getting this information requires access to detailed tables, thereby making the model impractical. Second, both models are so complex that risk estimates cannot be attained from them without the aid of special software. Although these models have been used by health-system planners in the United Kingdom, we are unaware of any clinicians who use them when preparing patients for hospital discharge.Our primary objective was to derive and validate a clinically useful index to quantify the risk of early death or unplanned readmission among patients discharged from hospital to the community. Methods Study designWe performed a secondary analysis of a multicentre prospective cohort study conducted between

The Readmission Reduction Program of Kaiser Permanente Southern California—Knowledge Transfer and Performance Improvement

“…When we retrospectively applied the LACE strategy to more than 300,000 KPSC Health Plan discharges over a 12-month period, we found that readmission prediction curves reported by van Walraven et al 8 were almost identical to readmission curves seen in KPSC. Our analysis included the results for all discharges and for discharges that met HEDIS criteria.…”

“…For KPSC, we decided to develop a risk stratification program that we could standardize and integrate into our electronic medical record (EMR). In 2010, van Walraven and colleagues 8 reported that a new risk stratification tool called LACE (Length of stay, Acuity of admission, Comorbidities, and Emergency room visits in last 6 months) could be used to predict patients at risk of an unplanned 30-day readmission and death.…”

“…On the basis of data reported by van Walraven et al, 8 we assigned the 3 groups as follows: low risk, LACE score of 0 to 6; medium risk, LACE 7 to 10; and high risk, LACE 11 to 19. When we looked at the observed-overexpected readmission ratio as defined by the National Committee for Quality Assurance in 2012, 7 we found that the high-risk group for all 13 KPSC medical centers had an observed-over-expected ratio much greater than 1.0.…”

Early Hospital Readmission is a Predictor of One-Year Mortality in Community-Dwelling Older Medicare Beneficiaries

“…Among all hospitalized Medicare beneficiaries (including community-dwelling and institutionalized elders, as well as younger disabled Medicare beneficiaries), nearly one in five were readmitted within 30 days, and over one-third were readmitted within 90 days. 1 These readmitted individuals have been described in some detail; research has identified early readmission risk factors for general 2,3 and geriatric populations, [4][5][6] as well as those with specific diseases such as heart failure, 7 stroke, 8 and chronic obstructive pulmonary disease. 9 Individuals readmitted early are more likely to have multiple medical comorbidities, greater length of stay during the index hospitalization, and additional recent hospitalizations.…”

Regional variation in hospitalisation and mortality in heart failure: comparison of England and Lombardy using multistate modelling

“…In addition, many patients have multiple readmissions, sometimes over a period of several years, so this measure loses information, especially on disease progression [9]. Most studies of readmissions ignore the competing risk of death or account for it by using a combined endpoint of death or readmission [10], which is unsatisfactory, not least because death and readmission are far from being of equal importance. Other modelling options include hurdle models and resource buckets, which can be useful for some questions [11] but are not flexible enough to show progression of the underlying chronic disease.…”