Dataset Information

"Phenotyping" hospital value of care for patients with heart failure.

ABSTRACT:

Objective

To characterize hospitals based on patterns of their combined financial and clinical outcomes for heart failure hospitalizations longitudinally.

Data source

Detailed cost and administrative data on hospitalizations for heart failure from 424 hospitals in the 2005-2011 Premier database.

Study design

Using a mixture modeling approach, we identified groups of hospitals with distinct joint trajectories of risk-standardized cost (RSC) per hospitalization and risk-standardized in-hospital mortality rate (RSMR), and assessed hospital characteristics associated with the distinct patterns using multinomial logistic regression.

Principal findings

During 2005-2011, mean hospital RSC decreased from $12,003 to $10,782, while mean hospital RSMR declined from 3.9 to 3.2 percent. We identified five distinct hospital patterns: highest cost and low mortality (3.2 percent of the hospitals), high cost and low mortality (20.4 percent), medium cost and low mortality (34.6 percent), medium cost and high mortality (6.2 percent), and low cost and low mortality (35.6 percent). Longer hospital stay and greater use of intensive care unit and surgical procedures were associated with phenotypes with higher costs or greater mortality.

Conclusions

Hospitals vary substantially in the joint longitudinal patterns of cost and mortality, suggesting marked difference in value of care. Understanding determinants of the variation will inform strategies for improving the value of hospital care.

SUBMITTER: Xu X

PROVIDER: S-EPMC4254136 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Publications

"Phenotyping" hospital value of care for patients with heart failure.

Xu Xiao X Li Shu-Xia SX Lin Haiqun H Normand Sharon-Lise T SL Kim Nancy N Ott Lesli S LS Lagu Tara T Duan Michael M Kroch Eugene A EA Krumholz Harlan M HM

Health services research 20140628 6

<h4>Objective</h4>To characterize hospitals based on patterns of their combined financial and clinical outcomes for heart failure hospitalizations longitudinally.<h4>Data source</h4>Detailed cost and administrative data on hospitalizations for heart failure from 424 hospitals in the 2005-2011 Premier database.<h4>Study design</h4>Using a mixture modeling approach, we identified groups of hospitals with distinct joint trajectories of risk-standardized cost (RSC) per hospitalization and risk-stand ...[more]

PMID: 24974769

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Project description:BackgroundPrimary health care is essential for an appropriate management of heart failure (HF), a disease which is a major clinical and public health issue and a leading cause of hospitalization. The aim of this study was to evaluate the impact of different organizational factors on readmissions of patients with HF.MethodsThe study population included elderly resident in the Local Health Authority of Bologna (Northern Italy) and discharged with a diagnosis of HF from January to December 2010. Unplanned hospital readmissions were measured in four timeframes: 30 (short-term), 90 (medium-term), 180 (mid-long-term), and 365 days (long-term). Using multivariable multilevel Poisson regression analyses, we investigated the association between readmissions and organizational factors (discharge from a cardiology department, general practitioners' monodisciplinary organizational arrangement, and implementation of a specific HF care pathway).ResultsThe 1873 study patients had a median age of 83 years (interquartile range 77-87) and 55.5% were females; 52.0% were readmitted to the hospital for any reason after a year, while 20.1% were readmitted for HF. The presence of a HF care pathway was the only factor significantly associated with a lower risk of readmission for HF in the short-, medium-, mid-long- and long-term period (short-term: IRR [incidence rate ratio]=0.57, 95%CI [confidence interval]=0.35-0.92; medium-term: IRR=0.70, 95%CI=0.51-0.96; mid-long-term: IRR=0.79, 95%CI=0.64-0.98; long-term: IRR=0.82, 95%CI=0.67-0.99), and with a lower risk of all-cause readmission in the short-term period (IRR=0.73, 95%CI=0.57-0.94).ConclusionOur study shows that the HF care specific pathway implemented at the primary care level was associated with lower readmission rate for HF in each timeframe, and also with lower readmission rate for all causes in the short-term period. Our results suggest that the engagement of primary care professionals starting from the early post-discharge period may be relevant in the management of patients with HF.

Project description:BackgroundRural communities represent a vulnerable population that would significantly benefit from hospital-based OSA screening given these areas tend to have significant health-care disparities and poor health outcomes. Although inpatient screening has been studied at urban hospitals, no study to date has assessed this approach in rural populations.MethodsThis study utilized the Electronic Medical Record (EMR) to generate a list of potential candidates by employing inclusion/exclusion criteria as screening. Subjects identified were then approached and offered information regarding the study. Screening for OSA entailed a tiered approach utilizing the sleep apnea clinical score (SAC) and portable sleep testing. Individuals identified as high risk (SAC ≥ 15) for OSA underwent evaluation with a portable sleep testing system while hospitalized. All participants with an apnea-hypopnea index (AHI) ≥5 events/h confirmed by a sleep medicine physician were considered screen positive for OSA. If approved/available, subjects screening positive for OSA were provided with an auto-titrating continuous positive airway pressure (PAP). Patient characteristics were analyzed using descriptive statistics. Categorical data were described using contingency tables, including counts and percentages. Continuously scaled measures were summarized by median with range. This study was registered with ClinicalTrials.gov. Identifier: NCT03056443.ResultsNine hundred and fifty-eight potential subjects were identified. The three most common reasons for exclusion included previous OSA diagnosis or exposure to PAP therapy (n = 357), advanced illness (n = 380), and declined participation by the individual (n = 68). The remaining 31 subjects underwent further evaluation for obstructive sleep apnea. Twenty-three subjects had a high sleep apnea clinic score. Per our study protocol, 13 subjects who screened positive for OSA were initiated on APAP therapy. Conclusion: Our study provides important insight into the burden of sleep-disordered breathing (SDB) and unique challenges of hospital-based OSA screening/treatment in a rural setting. Our study identified barriers to successful screening in a rural population that may be well addressed by adapting previous research in hospital sleep medicine.

Project description:Background: Heart failure (HF) is a significant cause of mortality, morbidity and impaired quality of life and is the leading cause of readmissions and hospitalization. This study aims to identify the factors contributing to readmission in patients with HF. Methods: A prospective-observational single-centre study was conducted in Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates. A total of 146 patients with HF are included in the study. Patient's demographics, patient medical characteristics, lab values, medications were collected for each patient, and the factors associated with readmission are identified. The primary outcome is to identify the factors contributing to readmission and reduce readmission rate. SPSS software for windows version 26 is used for data analysis. Results: The number of patients with heart failure admitted to hospital is higher with males (73.3%) than females. 42.1% were readmitted and were not compliant, whereas patients who are not readmitted and were compliant shows a lower percentage. Noncompliance was the most significant factor associated with readmission (p = 0.02, OR = 3.6, 95%CI: 1.57 - 8.28). Other factors that are associated with readmission were low haemoglobin (p = 0.001, OR = 0.96, 95%CI: 0.94 - 0.98), and NYHA class of HF (p = 0.023, OR = 2.22, 95%CI: 1.12 - 4.43). In addition, there are other factors that are linked with the disease but were not associated with readmission in our findings such as hypertension, coronary artery disease, gender, systolic blood pressure on admission, and age. Majority of the readmitted patients were NYHA Class IV 32/57 (56.1%) against 20/89 (22.7%) in non-readmission group. Length of stay is (Median ± IQR, 6 ± 8.5). Conclusion: The study has revealed that noncompliance, low haemoglobin and NYHA Class IV of HF were the main factors associated with readmission. Clinical pharmacist as a team member could help to improve adherence in order to reduce the rate of admission.

Project description:ObjectiveRisk stratification of patients with congestive heart failure (HF) is vital in clinical practice. The aim of this study was to construct a machine learning model to predict the in-hospital all-cause mortality for intensive care unit (ICU) patients with HF.MethodseXtreme Gradient Boosting algorithm (XGBoost) was used to construct a new prediction model (XGBoost model) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) (training set). The eICU Collaborative Research Database dataset (eICU-CRD) was used for the external validation (test set). The XGBoost model performance was compared with a logistic regression model and an existing model (Get with the guideline-Heart Failure model) for mortality in the test set. Area under the receiver operating characteristic cure and Brier score were employed to evaluate the discrimination and the calibration of the three models. The SHapley Additive exPlanations (SHAP) value was applied to explain XGBoost model and calculate the importance of its features.ResultsThe total of 11,156 and 9,837 patients with congestive HF from the training set and test set, respectively, were included in the study. In-hospital all-cause mortality occurred in 13.3% (1,484/11,156) and 13.4% (1,319/9,837) of patients, respectively. In the training set, of 17 features with the highest predictive value were selected into the models with LASSO regression. Acute Physiology Score III (APS III), age and Sequential Organ Failure Assessment (SOFA) were strongest predictors in SHAP. In the external validation, the XGBoost model performance was superior to that of conventional risk predictive methods, with an area under the curve of 0.771 (95% confidence interval, 0.757-0.784) and a Brier score of 0.100. In the evaluation of clinical effectiveness, the machine learning model brought a positive net benefit in the threshold probability of 0%-90%, prompting evident competitiveness compare to the other two models. This model has been translated into an online calculator which is accessible freely to the public (https://nkuwangkai-app-for-mortality-prediction-app-a8mhkf.streamlit.app).ConclusionThis study developed a valuable machine learning risk stratification tool to accurately assess and stratify the risk of in-hospital all-cause mortality in ICU patients with congestive HF. This model was translated into a web-based calculator which access freely.

Dataset Information

"Phenotyping" hospital value of care for patients with heart failure.

Objective

Data source

Study design

Principal findings

Conclusions

Publications

"Phenotyping" hospital value of care for patients with heart failure.

Similar Datasets

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