Dataset Information

Effects of Physician Volume on Readmission and Mortality in Elderly Patients with Heart Failure: Nationwide Cohort Study.

ABSTRACT:

Purpose

Readmission and mortality rates of patients with heart failure are good indicators of care quality. To determine whether hospital resources are associated with care quality for cardiac patients, we analyzed the effect of number of physicians and the combined effects of number of physicians and beds on 30-day readmission and 1-year mortality.

Materials and methods

We used national cohort sample data of the National Health Insurance Service (NHIS) claims in 2002-2013. Subjects comprised 2345 inpatients (age: >65 years) admitted to acute-care hospitals for heart failure. A multivariate Cox regression was used.

Results

Of the 2345 patients hospitalized with heart failure, 812 inpatients (34.6%) were readmitted within 30 days and 190 (8.1%) had died within a year. Heart-failure patients treated at hospitals with low physician volumes had higher readmission and mortality rates than high physician volumes [30-day readmission: hazard ratio (HR)=1.291, 95% confidence interval (CI)=1.020-1.633; 1-year mortality: HR=2.168, 95% CI=1.415-3.321]. Patients admitted to hospitals with low or middle bed and physician volume had higher 30-day readmission and 1-year mortality rates than those admitted to hospitals with high volume (30-day readmission: HR=2.812, 95% CI=1.561-5.066 for middle-volume beds & low-volume physicians, 1-year mortality: HR=8.638, 95% CI=2.072-36.02 for middle-volume beds & low-volume physicians).

Conclusion

Physician volume is related to lower readmission and mortality for heart failure. Of interest, 30-day readmission and 1-year mortality were significantly associated with the combined effects of physician and institution bed volume.

SUBMITTER: Lee JE

PROVIDER: S-EPMC5823826 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effects of Physician Volume on Readmission and Mortality in Elderly Patients with Heart Failure: Nationwide Cohort Study.

Lee Joo Eun JE Park Eun Cheol EC Jang Suk Yong SY Lee Sang Ah SA Choy Yoon Soo YS Kim Tae Hyun TH

Yonsei medical journal 20180301 2

<h4>Purpose</h4>Readmission and mortality rates of patients with heart failure are good indicators of care quality. To determine whether hospital resources are associated with care quality for cardiac patients, we analyzed the effect of number of physicians and the combined effects of number of physicians and beds on 30-day readmission and 1-year mortality.<h4>Materials and methods</h4>We used national cohort sample data of the National Health Insurance Service (NHIS) claims in 2002-2013. Subjec ...[more]

PMID: 29436192

Similar Datasets

Project description:This study intends to predict in-hospital and 6-month mortality, as well as 30-day and 90-day hospital readmission, using Machine Learning (ML) approach via conventional features. A total of 737 patients remained after applying the exclusion criteria to 1101 heart failure patients. Thirty-four conventional features were collected for each patient. First, the data were divided into train and test cohorts with a 70-30% ratio. Then train data were normalized using the Z-score method, and its mean and standard deviation were applied to the test data. Subsequently, Boruta, RFE, and MRMR feature selection methods were utilized to select more important features in the training set. In the next step, eight ML approaches were used for modeling. Next, hyperparameters were optimized using tenfold cross-validation and grid search in the train dataset. All model development steps (normalization, feature selection, and hyperparameter optimization) were performed on a train set without touching the hold-out test set. Then, bootstrapping was done 1000 times on the hold-out test data. Finally, the obtained results were evaluated using four metrics: area under the ROC curve (AUC), accuracy (ACC), specificity (SPE), and sensitivity (SEN). The RFE-LR (AUC: 0.91, ACC: 0.84, SPE: 0.84, SEN: 0.83) and Boruta-LR (AUC: 0.90, ACC: 0.85, SPE: 0.85, SEN: 0.83) models generated the best results in terms of in-hospital mortality. In terms of 30-day rehospitalization, Boruta-SVM (AUC: 0.73, ACC: 0.81, SPE: 0.85, SEN: 0.50) and MRMR-LR (AUC: 0.71, ACC: 0.68, SPE: 0.69, SEN: 0.63) models performed the best. The best model for 3-month rehospitalization was MRMR-KNN (AUC: 0.60, ACC: 0.63, SPE: 0.66, SEN: 0.53) and regarding 6-month mortality, the MRMR-LR (AUC: 0.61, ACC: 0.63, SPE: 0.44, SEN: 0.66) and MRMR-NB (AUC: 0.59, ACC: 0.61, SPE: 0.48, SEN: 0.63) models outperformed the others. Reliable models were developed in 30-day rehospitalization and in-hospital mortality using conventional features and ML techniques. Such models can effectively personalize treatment, decision-making, and wiser budget allocation. Obtained results in 3-month rehospitalization and 6-month mortality endpoints were not astonishing and further experiments with additional information are needed to fetch promising results in these endpoints.

Project description:BackgroundAn association between increased aortic root dimensions (ARD) and elevated risk of cardiovascular mortality has been reported in the general population. However, evidence regarding the association between ARD and mortality in patients with acute heart failure (AHF) is limited.MethodsIn a nationwide prospective cohort of the China Patient-Centered Evaluative Assessment of Cardiac Events Prospective Heart Failure Study, ARD was measured during diastole using echocardiography and indexed to body mass index (BMI). Cox proportional hazard models were used to validate the association between BMI-indexed ARD and mortality. Additionally, the relationship between BMI-indexed ARD and mortality was presented using restricted cubic spline in all populations, and both sexes.ResultsA total of 2125 participants with ARD were included in the final analysis, among of 38.4 % were women, with a median age of 67 years. Over a median follow-up period of 54.4 (interquartile range: 30.1 to 59.7) months, 895 deaths occurred, with 750 attributed to cardiovascular causes and 145 to non-cardiovascular causes. Compared to the highest tertile group of BMI-indexed ARD, the lowest tertile group had a lower risk of cardiovascular mortality (hazard ratio [HR], 0.71; 95 % confidence interval [CI], 0.58 to 0.87; P < 0.001) and all-cause mortality (HR, 0.68; 95 % CI, 0.56 to 0.81; P < 0.001). Similarly, the middle tertile group also had a lower risk of cardiovascular mortality (HR, 0.78; 95 % CI, 0.65 to 0.93; P = 0.007) and all-cause mortality (HR, 0.75; 95 % CI, 0.63 to 0.89; P < 0.001). Considering the competing risks, the lowest BMI-indexed ARD groups showed a significant mortality risk of cardiovascular mortality in all populations, and both sexes. Moreover, the relationship between BMI-indexed ARD and mortality was linear in males, while a "J" shaped relationship was observed in females.ConclusionLower BMI-indexed ARD was associated with a decreased risk of all-cause and cardiovascular mortality than those with higher BMI-indexed ARD in AHF. Additionally, a discrepancy was observed between the sexes in the relationship between BMI-indexed ARD and mortality. These findings contribute to the prompt identification of potential mortality risks in patients with AHF.

Project description:AimsThis study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.Methods and resultsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full-text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time-point comparisons, ML-derived c-indices were higher than CSM-derived c-indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in-hospital mortality to 1 year survival; of these nine, seven reported higher c-indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c-indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c-indices 0.913 vs. 0.835).ConclusionsML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML-based studies of prediction modelling. We suggest that ML-based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867.

Dataset Information

Effects of Physician Volume on Readmission and Mortality in Elderly Patients with Heart Failure: Nationwide Cohort Study.

Purpose

Materials and methods

Results

Conclusion

Publications

Effects of Physician Volume on Readmission and Mortality in Elderly Patients with Heart Failure: Nationwide Cohort Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets