Unplanned admissions and readmissions in older people: a review of recent evidence on identifying and managing high-risk individuals

Older patients acutely admitted and readmitted to the same geriatric department: a descriptive cohort study of primary diagnoses and health characteristics

PURPOSE

Advancing age is associated with increased risk for acute admissions and readmissions. The societal challenges of ageing populations have made the prevention of readmissions come into focus. Readmission may be perceived as the result of inadequate treatment during index admission but may also be caused by the onset of new disease following a generally impaired health of geriatric patients. We aimed at comparing the diagnoses at index and readmission to illuminate this issue.

METHODS

This is a descriptive, retrospective cohort study of patients acutely admitted and readmitted (within 30 days from discharge) to the same geriatric ward (November 1, 2017-April 30, 2018). Electronic medical records were scrutinised manually for discharge diagnoses and patient characteristics.

RESULTS

Readmission rate was 10.7% (98 of 918 unique admissions). Mean age was 85.6 (men 56%). About 75% were readmitted with a new acute disease unrelated to index admission, most commonly pneumonia (27%), other infections (22%), and dehydration (14%). The health characteristics were long index length-of-stay (median 7; IQR 5-11), high Charlson Comorbidity Index (CCI ≥ 3, n = 49 (50%), polypharmacy (≥ 5 prescriptions) (94%), and hospitalisations 12 months prior to index admission (57%).

KEY CONCLUSIONS

The majority of readmitted geriatric patients have contracted a new acute condition. Although being characterised by several adverse health characteristics, prospective studies comparing readmitted and non-readmitted geriatric patients are needed. Still, increasing the awareness of early recognition of acute disease onset in geriatric patients is warranted.

A review of literature on risk prediction tools for hospital readmissions in older adults

PURPOSE

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

DESIGN/METHODOLOGY/APPROACH

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

FINDINGS

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5-0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

RESEARCH LIMITATIONS/IMPLICATIONS

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

ORIGINALITY/VALUE

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

Impact of an anticipatory care planning intervention on unscheduled acute hospital care using difference-in-difference analysis

Unscheduled admissions to hospital place great demands on the use of limited healthcare resources in health systems worldwide. A range of approaches exist to manage demand; however, interventions within hospitals have received less attention, and the evidence base on effectiveness is limited. This study aimed to assess the effectiveness of a novel intervention, implemented in National Health Service Lothian, to reduce the number of unscheduled attendances, and to estimate the impact on hospital admissions, length of hospital stay and overall total acute hospital costs.

METHODS

Before and after observational study of an anticipatory care planning intervention targeted among people identified by a prediction algorithm (Scottish Patients at Risk of Readmission and Admission) as being at high risk of future unscheduled hospital admissions. The statistical significance of the difference in outcomes observed before and after implementation of the intervention between August 2014 and July 2015 was tested using difference-in-difference analysis.

RESULTS

The intervention was estimated to reduce the number of unscheduled hospital admissions and emergency department (ED) visits by approximately 0.36 (95% CI -0.905 to 0.191) per patient per year (based on 954 and 450 patients in the intervention and control groups, respectively). There was also non-significant reductions in length of hospital stay for unscheduled admissions and hospital costs for ED visits and inpatient care. The overall predicted effect of the intervention for the average participant was a saving of around £2912 (95% CI -7347.0 to 1523.9) per patient per year.

CONCLUSION

An anticipatory care planning intervention focused among people judged to be at higher risk of future unscheduled hospital admissions can be effective in reducing the number of unscheduled admissions to hospital and ED visits, and may lead to an overall saving in use of hospital resources.

Predictors of hospital readmission among older adults with cancer

OBJECTIVE

Older adults with cancer are at higher risk for costly and potentially dangerous hospital readmissions. Identifying risk factors for readmission in this population is important for future prevention of readmission.

MATERIALS AND METHODS

Hospital discharges among patients ≥ 65 years with solid tumors on non-surgical services from 2006-2011 were reviewed in this matched case-control study. We abstracted patient/cancer characteristics; functional status; fall risk; chemotherapy line; comorbidities; laboratory values; discharge parameters; and miscellaneous information (Do Not Resuscitate Order, pain scores) from medical records. Conditional logistic regression was used for univariate and multivariable analysis.

RESULTS

This analysis included 184 case-patients readmitted within 30 days after discharge from the index admission and 184 sex- and age-matched control-patients discharged from index admission within three months of the cases with no readmission. Cases and controls had no differences in terms of primary cancer type, treatment, and index admission reason. Cases were more likely to have abnormal hemoglobin, albumin, sodium, and SGOT on discharge. Compared to those with ≤1 abnormal laboratory test, patients with 2 or more abnormal test results were 3 times more likely to be readmitted within 30 days.

CONCLUSION

This study demonstrated that older adults with cancer who had at least 2 abnormal laboratory results (hemoglobin, albumin, sodium, and SGOT) at discharge were 3 times more likely to be readmitted within 30 days compared to those with ≤1 abnormal results. These laboratory values may be predictive of the risk of readmission, and should be monitored before discharge to potentially prevent readmission.