Dataset Information

Hospitalization Risk Among Older Adults with Sensory Impairments: Development of a Prognostic Model.

ABSTRACT:

Objectives

To develop a prognostic model for hospital admissions over a 1-year period among community-dwelling older adults with self-reported hearing and/or vision impairments based on readily obtainable clinical predictors.

Design

Retrospective cohort study.

Setting

Medicare Current Beneficiary Survey from 1999 to 2006.

Participants

Community-dwelling Medicare beneficiaries, aged 65 years and older, with self-reported hearing and/or vision impairment (N = 15,999).

Measurements

The primary outcome was any hospital admission over a predefined 1-year study period. Candidate predictors included demographic factors, prior healthcare utilization, comorbidities, functional impairment, and patient-level factors. We analyzed the association of all candidate predictors with any hospital admission over the 1-year study period using multivariable logistic regression. The final model was created using a penalized regression method known as the least absolute shrinkage and selection operator. Model performance was assessed by discrimination (concordance statistic (c-statistic)) and calibration (evaluated graphically). Internal validation was performed via bootstrapping, and results were adjusted for overoptimism.

Results

Of the 15,999 participants, the mean age was 78 years and 55% were female. A total of 2,567 participants (16.0%) had at least one hospital admission in the 1-year study period. The final model included seven variables independently associated with hospitalization: number of inpatient admissions in the previous year, number of emergency department visits in the previous year, activities of daily living difficulty score, poor self-rated health, and self-reported history of myocardial infarction, stroke, and nonskin cancer. The c-statistic of the final model was 0.717. The optimism-corrected c-statistic after bootstrap internal validation was 0.716. A calibration plot suggested that the model tended to overestimate risk among patients at the highest risk for hospitalization.

Conclusion

This prognostic model can help identify which community-dwelling older adults with sensory impairments are at highest risk for hospitalization and may inform allocation of healthcare resources.

SUBMITTER: Deardorff WJ

PROVIDER: S-EPMC7988218 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:INTRODUCTION:Chronic kidney disease (CKD) risk staging is based on estimated glomerular filtration rate (eGFR) and albumin-creatinine ratio (ACR). However, the relationship between all-cause hospitalization risk and the current CKD staging system has not been well studied among older adults, despite a high prevalence of CKD and a high risk of hospitalization in old age. METHODS:Among 4,766 participants of the Atherosclerosis Risk in Communities study, CKD was staged according to Kidney Disease Improving Global Outcomes (KDIGO) criteria, using creatinine-based eGFR (eGFRcr) and ACR. Incidence rates of all-cause hospitalization associated with each CKD risk group were analyzed using negative binomial regression. Additionally, cause-specific hospitalization risks for cardiovascular, infectious, kidney, and other diseases were estimated. The impacts of using cystatin C-based eGFR (eGFRcys) to estimate the prevalence of CKD and risks of hospitalization were also quantified. RESULTS:Participants experienced 5,548 hospitalizations and 29% had CKD. Hospitalization rates per 1,000 person-years according to KDIGO risk categories were 208-223 ("low risk"), 288-376 ("moderately increased risk"), 363-548 ("high risk"), and 499-1083 ("very high risk"). The increased risk associated with low eGFR and high ACR persisted in adjusted analyses, examinations of cause-specific hospitalizations, and when CKD was staged by eGFRcys or eGFRcr-cys, a combined equation based on both creatinine and cystatin C. In comparison to eGFRcr, staging by eGFRcys increased the prevalence of CKD to 50%, but hospitalization risks remained similarly high. DISCUSSION/CONCLUSION:In older adults, decreased eGFR, increased ACR, and KDIGO risk stages based on a combination of these measures, were strong risk factors for hospitalization. These relationships were consistent, regardless of the marker used to estimate GFR, but the use of cystatin C resulted in a substantially higher prevalence of CKD than the use of creatinine. Older adults in the population with very high risk stages of CKD have hospitalization rates exceeding 500 per 1,000 person-years.

Project description:OBJECTIVES:Reducing potentially preventable hospitalization (PPH) among older adults with dementia is a goal of Healthy People 2020, yet no tools specifically identify patients with dementia at highest risk. The objective was to develop a risk prediction model to identify older adults with dementia at high imminent risk of PPH. DESIGN:A 30-day risk prediction model was developed using multivariable logistic regression. Patients from fiscal years (FY) 2009 to 2011 were split into development and validation cohorts; FY2012 was used for prediction. SETTING:Community-dwelling older adults (?65 years of age) with dementia who received care through the Veterans Health Administration. PARTICIPANTS:There were 1 793 783 participants. MEASUREMENTS:Characteristics associated with hospitalization risk were (1) age and other demographic factors; (2) outpatient, emergency department, and inpatient utilization; (3) medical and psychiatric diagnoses; and (4) prescribed medication use including changes to psychotropic medications (eg, initiation or dosage increase). Model discrimination was determined by the C statistic for each of the three cohorts. Finally, to determine whether predicted 30-day risk strata were stable over time, the observed PPH rate was calculated out to 1 year. RESULTS:In the development cohort, .6% of patients experienced PPH within 30?days. The C statistic for the development cohort was .83 (95% confidence interval [CI] = .83-.84) and .83 in the prediction cohort (95% CI = .82-.84). Patients in the top 10% of predicted 30-day PPH risk accounted for more than 50% of 30-day PPH admissions in all three cohorts. In addition, those predicted to be at elevated 30-day risk remained at higher risk throughout a year of follow-up. CONCLUSION:It is possible to identify older adults with dementia at high risk of imminent PPH, and their risk remains elevated for an entire year. Given the negative outcomes associated with acute hospitalization for those with dementia, healthcare systems and providers may be able to engage these high-risk patients proactively to avoid unnecessary hospitalization. J Am Geriatr Soc 67:2077-2084, 2019.

Project description:BackgroundAnticholinergic burden has been associated with adverse outcomes such as falls. To date, no gold standard measure has been identified to assess anticholinergic burden, and no conclusion has been drawn on which of the different measure algorithms best predicts falls in older patients from general practice. This study compared the ability of five measures of anticholinergic burden to predict falls. To account for patients' individual susceptibility to medications, the added predictive value of typical anticholinergic symptoms was further quantified in this context.Methods and findingsTo predict falls, models were developed and validated based on logistic regression models created using data from two German cluster-randomized controlled trials. The outcome was defined as "≥ 1 fall" vs. "no fall" within a 6-month follow-up period. Data from the RIME study (n = 1,197) were used in model development, and from PRIMUM (n = 502) for external validation. The models were developed step-wise in order to quantify the predictive ability of anticholinergic burden measures, and anticholinergic symptoms. In the development set, 1,015 patients had complete data and 188 (18.5%) experienced ≥ 1 fall within the 6-month follow-up period. The overall predictive value of the five anticholinergic measures was limited, with neither the employed anticholinergic variable (binary / count / burden), nor dose-dependent or dose-independent measures differing significantly in their ability to predict falls. The highest c-statistic was obtained using the German Anticholinergic Burden Score (0.73), whereby the optimism-corrected c-statistic was 0.71 after interval validation using bootstrapping and 0.63 in the external validation. Previous falls and dizziness / vertigo had the strongest prognostic value in all models.ConclusionsThe ability of anticholinergic burden measures to predict falls does not appear to differ significantly, and the added value they contribute to risk classification in fall-prediction models is limited. Previous falls and dizziness / vertigo contributed most to model performance.