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Apples and Oranges: Four Definitions of Multiple Chronic Conditions and their Relationship to 30-Day Hospital Readmission.

ABSTRACT: To determine the extent of agreement between four commonly used definitions of multiple chronic conditions (MCCs) and compare each definition's ability to predict 30-day hospital readmissions.Retrospective cohort study.National Medicare claims data.Random sample of Medicare beneficiaries discharged from the hospital from 2005 to 2009 (n = 710,609).Baseline chronic conditions were determined for each participant using four definitions of MCC. The primary outcome was all-cause 30-day hospital readmission. Agreement between MCC definitions was measured, and sensitivities and specificities for each definition's ability to identify patients experiencing a future readmission were calculated. Logistic regression was used to assess the ability of each MCC definition to predict 30-day hospital readmission.The sample prevalence of hospitalized Medicare beneficiaries with two or more chronic conditions ranged from 18.6% (Johns Hopkins Adjusted Clinical Groups (ACG) Case-Mix System software) to 92.9% (Medicare Chronic Condition Warehouse (CCW)). There was slight to moderate agreement (kappa = 0.03-0.44) between pair-wise combinations of MCC definitions. CCW-defined MCC was the most sensitive (sensitivity 95.4%, specificity 7.4%), and ACG-defined MCC was the most specific (sensitivity 32.7%, specificity 83.2%) predictor of being readmitted. In the fully adjusted model, the risk of readmission was higher for those with chronic condition Special Needs Plan (c-SNP)-defined MCCs (odds ratio (OR) = 1.50, 95% confidence interval (CI) = 1.47-1.52), Charlson Comorbidity Index-defined MCCs (OR = 1.45, 95% CI = 1.42-1.47), ACG-defined MCCs (OR = 1.22, 95% CI = 1.19-1.25), and CCW-defined MCCs (OR = 1.15, 95% CI = 1.11-1.19) than for those without MCCs.MCC definitions demonstrate poor agreement and should not be used interchangeably. The two definitions with the greatest agreement (CCI, c-SNP) were also the best predictors of 30-day hospital readmissions.

SUBMITTER: Dattalo M

PROVIDER: S-EPMC5397355 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Apples and Oranges: Four Definitions of Multiple Chronic Conditions and their Relationship to 30-Day Hospital Readmission.

Dattalo Melissa M DuGoff Eva E Ronk Katie K Kennelty Korey K Gilmore-Bykovskyi Andrea A Kind Amy J AJ

Journal of the American Geriatrics Society 20170215 4

<h4>Objectives</h4>To determine the extent of agreement between four commonly used definitions of multiple chronic conditions (MCCs) and compare each definition's ability to predict 30-day hospital readmissions.<h4>Design</h4>Retrospective cohort study.<h4>Setting</h4>National Medicare claims data.<h4>Participants</h4>Random sample of Medicare beneficiaries discharged from the hospital from 2005 to 2009 (n = 710,609).<h4>Measurements</h4>Baseline chronic conditions were determined for each parti ...[more]

PMID: 28205206

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Project description:BackgroundLowering hospital readmission rates has become a primary target for the Centers for Medicare & Medicaid Services, but studies of the relationship between adherence to the recommended hospital care processes and readmission rates have provided inconsistent and inconclusive results.ObjectiveTo examine the association between hospital performance on Medicare's Hospital Compare process quality measures and 30-day readmission rates for patients with acute myocardial infarction (AMI), heart failure and pneumonia, and for those undergoing major surgery.Design, setting and participantsWe assessed hospital performance on process measures using the 2007 Hospital Inpatient Quality Reporting Program. The process measures for each condition were aggregated in two separate measures: Overall Measure (OM) and Appropriate Care Measure (ACM) scores. Readmission rates were calculated using Medicare claims.Main outcome measureRisk-standardized 30-day all-cause readmission rate was calculated as the ratio of predicted to expected rate standardized by the overall mean readmission rate. We calculated predicted readmission rate using hierarchical generalized linear models and adjusting for patient-level factors.ResultsAmong patients aged ≥ 66 years, the median OM score ranged from 79.4 % for abdominal surgery to 95.7 % for AMI, and the median ACM scores ranged from 45.8 % for abdominal surgery to 87.9 % for AMI. We observed a statistically significant, but weak, correlation between performance scores and readmission rates for pneumonia (correlation coefficient R = 0.07), AMI (R = 0.10), and orthopedic surgery (R = 0.06). The difference in the mean readmission rate between hospitals in the 1st and 4th quartiles of process measure performance was statistically significant only for AMI (0.25 percentage points) and pneumonia (0.31 percentage points). Performance on process measures explained less than 1 % of hospital-level variation in readmission rates.ConclusionsHospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Project description:BackgroundExisting readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available.ObjectiveThe objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors.MethodsWe extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors.ResultsWe developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported.ConclusionsThe performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

Project description:ImportanceThirty-day readmission rates have been suggested as a marker for quality of care. By investigating the factors associated with readmissions in all otolaryngology subspecialties we provide data relevant for the development of risk stratification systems to improve outcomes.ObjectiveTo establish the association of surgical and hospital volume and patient characteristics with 30-day readmission rates to guide the development of otolaryngology-specific risk stratification models.Design, setting, and participantsA retrospective cohort study including adult patients who underwent inpatient otolaryngology surgery in New York State between 1995 and 2015 was conducted using the Statewide Planning and Research Cooperative System (SPARCS). Regression techniques were used to describe relationships of patient-level factors, hospital, and surgeon volume to 30-day readmission rates in New York State.Main outcomes and measuresThe main outcome measures were patient-, surgeon-, and hospital-level risk factors for readmission. Secondary outcome measures were rate of readmissions by subspecialty procedure and by diagnosis on readmission.ResultsWe identified 254 257 cases of otolaryngology surgery (147 065 women [58%], mean [SD] age 50 [17] years). The 30-day readmission rate was 6%. In a multivariable model, odds ratios (ORs) identified Medicaid insurance (OR, 1.46; 99% CI, 1.36-1.57), Medicare insurance (OR, 1.32; 99% CI, 1.24-1.42), bottom quartile income (OR, 1.08; 99% CI, 1.01-1.15), patient comorbidities measured by the Charlson Comorbidity Index (CCI) (CCI >1; OR, 2.31; 99% CI, 2.16-2.47), length of stay (LOS) (LOS >10 days; OR, 2.29; 99% CI, 2.00-2.45), rhinology (OR, 1.37; 99% CI, 1.24-1.51), laryngology (OR, 1.98; 99% CI, 1.62-2.43), and head and neck cancer (OR, 1.27; 99% CI, 1.17-1.37) procedures as readmission predictors. High-volume surgeons were protective of 30-day readmission (OR, 0.67; 99% CI, 0.635-0.708) relative to low volume. Hospital volume was not significantly associated to readmissions. The most common causes of readmission included wound- (2682 patients, 18%), respiratory- (1776 patients, 12%), cardiovascular- (1210 patients, 8%), and volume- (1089 patients, 7%) related disorders.Conclusions and relevanceThis study evaluated the combined effects of patient-, surgeon-, and hospital-level factors on 30-day readmission after otolaryngology surgery. Socioeconomic factors, patient comorbidities, surgeon volumes, and procedure were significantly associated with 30-day readmission. Though the cause of 30-day readmission is multifactorial, a large portion is driven by socioeconomic factors. Addressing these disparities at the system level is necessary to address the described readmission disparities. The development of risk-stratification models based on patient-, procedure-, and surgeon-level factors may help facilitate resource distribution.

Project description:Several care transition interventions propose that post-discharge phone calls can reduce adverse events and decrease costly return visits to the hospital. However, given the multi-faceted nature of most care transitions interventions, the true relationship between post-discharge phone calls and readmissions in a real world setting is uncertain.To determine the effect of receiving a post-discharge telephone call on all-cause 30-day readmission in a general medicine population.Retrospective observational study.Patients discharged home from the Medicine Service at a tertiary care academic medical center between November 2010 and May 2012.Patients received two telephone call attempts by a nurse within 72 h of discharge. Nurses followed a standard script to address issues associated with readmission.Billing data captured readmissions. We used logistic regression-adjusted patient and clinical covariates as well as a propensity score representing likelihood of being called to determine the association between call receipt and risk for readmission.There were 5,507 eligible patients. In unadjusted analyses, patients who received a call and completed the intervention were significantly less likely to be readmitted compared to those who did not [155 (5.8 %) vs 123 (8.6 %), p?<?0.01]. In multivariable models adjusting for socio-demographic and clinical covariates alone, completing a post-discharge telephone call intervention was associated with lower odds for readmission (AOR 0.71; 95 % CI: 0.55-0.91). However, when models adjusted for the likelihood of receiving the phone call using the propensity score, no association between call receipt and readmission was observed (AOR 0.91; 95%CI: 0.69-1.20).Effectiveness of post-discharge phone call programs may be more related to whether patients are able to answer a phone call than to the care delivered by the phone call. Programs would benefit from improving their ability to perform phone outreach while simultaneously improving on the care delivered during the calls.

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Apples and Oranges: Four Definitions of Multiple Chronic Conditions and their Relationship to 30-Day Hospital Readmission.

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Apples and Oranges: Four Definitions of Multiple Chronic Conditions and their Relationship to 30-Day Hospital Readmission.

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