Dataset Information

Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients.

ABSTRACT:

Background

Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients.

Methods

The California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results.

Results

Area under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation.

Discussion

The results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.

SUBMITTER: Povalej Brzan P

PROVIDER: S-EPMC5407280 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients.

Povalej Brzan Petra P Obradovic Zoran Z Stiglic Gregor G

PeerJ 20170425

<h4>Background</h4>Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information ...[more]

PMID: 28462037

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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:RESEARCH OBJECTIVE:Pharmacists are an expensive and limited resource in the hospital and outpatient setting. A pharmacist can spend up to 25% of their day planning. Time spent planning is time not spent delivering an intervention. A readmission risk adjustment model has potential to be used as a universal outcome-based prioritization tool to help pharmacists plan their interventions more efficiently. Pharmacy-specific predictors have not been used in the constructs of current readmission risk models. We assessed the impact of adding pharmacy-specific predictors on performance of readmission risk prediction models. STUDY DESIGN:We used an observational retrospective cohort study design to assess whether pharmacy-specific predictors such as an aggregate pharmacy score and drug classes would improve the prediction of 30-day readmission. A model of age, sex, length of stay, and admission category predictors was used as the reference model. We added predictor variables in sequential models to evaluate the incremental effect of additional predictors on the performance of the reference. We used logistic regression to regress the outcomes on predictors in our derivation dataset. We derived and internally validated our models through a 50:50 split validation of our dataset. POPULATION STUDIED:Our study population (n=350,810) was of adult admissions at hospitals in a large integrated health care delivery system. PRINCIPAL FINDINGS:Individually, the aggregate pharmacy score and drug classes caused a nearly identical but moderate increase in model performance over the reference. As a single predictor, the comorbidity burden score caused the greatest increase in model performance when added to the reference. Adding the severity of illness score, comorbidity burden score and the aggregate pharmacy score to the reference caused a cumulative increase in model performance with good discrimination (c statistic, 0.712; Nagelkerke R, 0.112). The best performing model included all predictors: severity of illness score, comorbidity burden score, aggregate pharmacy score, diagnosis groupings, and drug subgroups. CONCLUSIONS:Adding the aggregate pharmacy score to the reference model significantly increased the c statistic but was out-performed by the comorbidity burden score model in predicting readmission. The need for a universal prioritization tool for pharmacists may therefore be potentially met with the comorbidity burden score model. However, the aggregate pharmacy score and drug class models still out-performed current Medicare readmission risk adjustment models. IMPLICATIONS FOR POLICY OR PRACTICE:Pharmacists have a great role in preventing readmission, and therefore can potentially use one of our models: comorbidity burden score model, aggregate pharmacy score model, drug class model or complex model (a combination of all 5 major predictors) to prioritize their interventions while exceeding Medicare performance measures on readmission. The choice of model to use should be based on the availability of these predictors in the health care system.

Project description:ImportanceReadmission reduction is linked to improved quality of care, saves cost, and is a desirable patient-centered outcome. Nationally representative readmission metrics for patients with stroke are unavailable to date. Such estimates are necessary for benchmarking performance.ObjectivesTo provide US nationwide estimates and a temporal trend for overall, planned, and potentially preventable 30-day hospital readmission among patients with ischemic and hemorrhagic stroke; to investigate the association between hospitals' stroke discharge volume, teaching status, and 30-day readmission; and to highlight reasons for 30-day readmission and explore the association of 30-day readmission in terms of mortality, length of stay, and cost of care among patients with stroke.Design, setting, and participantsCohort, year-wise analysis of the Nationwide Readmissions Database between January 1, 2010, and September 30, 2015. The setting was a population-based cohort study providing national estimates of 30-day readmission. The database represents 50% of all US hospitalizations from 22 geographically dispersed states. Participants were adult (?18 years) patients with a primary discharge diagnosis of intracerebral hemorrhage, acute ischemic stroke, or subarachnoid hemorrhage. Hospitals were categorized by their annual stroke discharge volume and were classified as teaching hospitals if they had an American Medical Association-approved residency program or had a ratio of full-time equivalent interns and residents to beds of 0.25 or higher.Main outcomes and measuresReadmission was defined as any admission within 30 days of index hospitalization discharge. Using Centers for Medicare & Medicaid Services-defined algorithms, events were classified as planned or unplanned and as potentially preventable.ResultsBased on study criteria, 2?078?854 eligible patients were included (mean [SE] age, 70.02 [0.07] years; 51.9% female). Thirty-day readmission was highest for patients with intracerebral hemorrhage (13.70%; 95% CI, 13.40%-13.99%), followed by patients with acute ischemic stroke (12.44%; 95% CI, 12.33%-12.55%) and patients with subarachnoid hemorrhage (11.48%; 95% CI, 11.01%-11.96%). On average, there was a 3.3% annual decline in readmission between 2010 and 2014, which was statistically significant for the period of investigation (odds ratio, 0.96; 95% CI, 0.95-0.97). Patients discharged from nonteaching hospitals with high stroke discharge volume were at a significantly higher risk of 30-day readmission, and the top 2 reasons for readmission were acute cerebrovascular disease and septicemia.Conclusions and relevanceThis study suggests that nationally representative readmission metrics can be used to benchmark hospitals' performance, and a temporal trend of 3.3% may be used to evaluate the effectiveness of readmission reduction strategies.

Project description:Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Dataset Information

Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients.

Background

Methods

Results

Discussion

Publications

Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients.

Similar Datasets

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