Determinants of Prolonged Length of Hospital Stay of Patients with Atrial Fibrillation.
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ABSTRACT:
SUBMITTER: Vijan AE
PROVIDER: S-EPMC8396858 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
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Determinants of Prolonged Length of Hospital Stay of Patients with Atrial Fibrillation.
Vîjan Ancuța Elena AE Daha Ioana Cristina IC Delcea Caterina C Dan Gheorghe-Andrei GA
Journal of clinical medicine 20210820 16
<h4>Background and aim</h4>The increasing prevalence and high hospitalization rates make atrial fibrillation (AF) a significant healthcare strain. However, there are limited data regarding the length of hospital stay (LOS) of AF patients. Our purpose was to determine the main drivers of extended LOS of AF patients.<h4>Methods</h4>All AF patients, hospitalized consecutively in a tertiary cardiology center, from January 2018 to February 2020 were included in this retrospective cohort study. Readmi ...[more]
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Project description:BackgroundThe hospital admission rate is high in patients treated with peritoneal dialysis (PD), and the length of stay (LOS) in the hospital is a key indicator of medical resource allocation. This study aimed to develop a scoring tool for predicting prolonged LOS (pLOS) in PD patients by combining machine learning and traditional logistic regression (LR).MethodsThis study was based on patient data collected using the Hospital Quality Monitoring System (HQMS) in China. Three machine learning methods, classification and regression tree (CART), random forest (RF), and gradient boosting decision tree (GBDT), were used to develop models to predict pLOS, which is longer than the average LOS, in PD patients. The model with the best prediction performance was used to identify predictive factors contributing to the outcome. A multivariate LR model based on the identified predictors was then built to derive the score assigned to each predictor. Finally, a scoring tool was developed, and it was tested by stratifying PD patients into different pLOS risk groups.ResultsA total of 22,859 PD patients were included in our study, with 25.2% having pLOS. Among the three machine learning models, the RF model achieved the best prediction performance and thus was used to identify the 10 most predictive variables for building the scoring system. The multivariate LR model based on the identified predictors showed good discrimination power with an AUROC of 0.721 in the test dataset, and its coefficients were used as a basis for scoring tool development. On the basis of the developed scoring tool, PD patients were divided into three groups: low risk (≤5), median risk [5-10], and high risk (>10). The observed pLOS proportions in the low-risk, median-risk, and high-risk groups in the test dataset were 11.4%, 29.5%, and 54.7%, respectively.ConclusionsThis study developed a scoring tool to predict pLOS in PD patients. The scoring tool can effectively discriminate patients with different pLOS risks and be easily implemented in clinical practice. The pLOS scoring tool has a great potential to help physicians allocate medical resources optimally and achieve improved clinical outcomes.
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Project description:BackgroundThe increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients.ObjectiveThis study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data.MethodsPatient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model.ResultsThe final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided t tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy.ConclusionsThis study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.
| S-EPMC8173398 | biostudies-literature
Project description:IntroductionLeft atrial (LA) enlargement is an important risk factor for incident stroke and a key determinant for the success of rhythm control strategies in patients with atrial fibrillation (AF). However, factors associated with LA volume in AF patients remain poorly understood.MethodsPatients with paroxysmal or persistent AF were enrolled in this study. Real time 3-D echocardiography was performed in all participants and analyzed offline in a standardized manner. We performed stepwise backward linear regression analyses using a broad set of clinical parameters to determine independent correlates for 3-D LA volume.ResultsWe included 210 patients (70.9% male, mean age 61±11years). Paroxysmal and persistent AF were present in 95 (45%) and 115 (55%) patients, respectively. Overall, 115 (55%) had hypertension, 11 (5%) had diabetes, and 18 (9%) had ischemic heart disease. Mean indexed LA volume was 36±12ml/m2. In multivariable models, significant associations were found for female sex (β coefficient -10.51 (95% confidence interval (CI) -17.85;-3.16), p = 0.0053), undergoing cardioversion (β 11.95 (CI 5.15; 18.74), p = 0.0006), diabetes (β 14.23 (CI 2.36; 26.10), p = 0.019), body surface area (BSA) (β 34.21 (CI 19.30; 49.12), p<0.0001), glomerular filtration rate (β -0.21 (CI -0.36; -0.06), p = 0.0064) and plasma levels of NT-pro brain natriuretic peptide (NT-proBNP) (β 6.79 (CI 4.05; 9.52), p<0.0001), but not age (p = 0.59) or hypertension (p = 0.42). Our final model explained 52% of the LA volume variability.ConclusionsIn patients with AF, the most important correlates with LA volume are sex, BSA, diabetes, renal function and NT-proBNP, but not age or hypertension. These results may help to refine rhythm control strategies in AF patients.
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