Prediction Model for Extended Hospital Stay Among Medicare Beneficiaries After Percutaneous Coronary Intervention

Burton, Brittany N.; Abudu, Boya; Danforth, Dennis; Patell, Saatchi; Martinez, Lizett Wilkins y; Fergerson, Byron; Elsharydah, Ahmad; Gabriel, Rodney A.

doi:10.1053/j.jvca.2019.04.022

Cited by 3 publications

(6 citation statements)

References 13 publications

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“…The modelling attempt ( T#10 ), which considered the following input features PAG, PGD, ADC, ADT, PCC, PRG, DTC, SES and CCI produced the best accuracy of 89.3%. This prediction accuracy is comparably higher than some of the prediction models for ELOHS carried out previously as shown in the following references [ 16 , 20 , 21 ].…”

Section: Resultsmentioning

confidence: 56%

“…This is because some of the patients who may have been classified as likely to exceed their expected length of stay in the hospital because they spent 3, 4, 5, 9, or 11 days based on the proposition of the models may have not exceed their expected length of stay in the hospital following the assessment of their DRG per the technique described in this study. Even though most of the studies reported on specific disease conditions [ 9 , 21 , 35 ], the current study painted a better picture of ELOHS by taking a comprehensive look at patients in the acute care setting. This approach gives the hospital a better tool for an immediate decision on requisite patients’ management plans to forestall complications that will result in ELOHS.…”

Section: Discussionmentioning

confidence: 99%

“…There are numerous studies on ELOHS, and their associated risk factors. Burton et al [ 21 ] predicted ELOHS for patients of percutaneous coronary intervention using multivariate Logistic Regression (LR) by taking Normal Length of Hospital Stay (NLOHS) as patients staying < 5 days and those staying ≥ 5 days as ELOHS and obtained a prediction accuracy (AUC) of 79.9–81.9%. Staziak et al [ 16 ] obtained an accuracy of 80—81% for ELOHS prediction of torso trauma patients using clinical and image data with Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies on ELOHS have described ELOHS as a specific number of days in the hospital that corresponds to the 75th percentile of the studied cohorts [ 9 , 16 , 21 ] while others chose a particular number of days in the hospital as the limit for NLOHS for a combination of DRGs [ 30 , 31 , 35 ]. Unfortunately, the variability in the severity of health conditions with various DRGs makes it imperative to consider ELOHS as a DRG-specific definition requiring specific durations.…”

Section: Introductionmentioning

confidence: 99%

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Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data

2022

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Background Patients who exceed their expected length of stay in the hospital come at a cost to stakeholders in the healthcare sector as bed spaces are limited for new patients, nosocomial infections increase and the outcome for many patients is hampered due to multimorbidity after hospitalization. Objectives This paper develops a technique for predicting Extended Length of Hospital Stay (ELOHS) at preadmission and their risk factors using hospital data. Methods A total of 91,468 records of patient’s hospital information from a private acute teaching hospital were used for developing a machine learning algorithm relaying on Recursive Feature Elimination with Cross-Validation and Extra Tree Classifier (RFECV-ETC). The study implemented Synthetic Minority Oversampling Technique (SMOTE) and tenfold cross-validation to determine the optimal features for predicting ELOHS while relying on multivariate Logistic Regression (LR) for computing the risk factors and the Relative Risk (RR) of ELOHS at a 95% confidence level. Results An estimated 11.54% of the patients have ELOHS, which increases with patient age as patients < 18 years, 18–40 years, 40–65 years and ≥ 65 years, respectively, have 2.57%, 4.33%, 8.1%, and 15.18% ELOHS rates. The RFECV-ETC algorithm predicted preadmission ELOHS to an accuracy of 89.3%. Age is a predominant risk factors of ELOHS with patients who are > 90 years—PAG (> 90) {RR: 1.85 (1.34–2.56), P: < 0.001} having 6.23% and 23.3%, respectively, higher likelihood of ELOHS than patient 80–90 years old—PAG (80–90) {RR: 1.74 (1.34–2.38), P: < 0.001} and those 70–80 years old—PAG (70–80) {RR: 1.5 (1.1–2.05), P: 0.011}. Those from admission category—ADC (US1) {RR: 3.64 (3.09–4.28, P: < 0.001} are 14.8% and 70.5%, respectively, more prone to ELOHS compared to ADC (UC1) {RR: 3.17 (2.82–3.55), P: < 0.001} and ADC (EMG) {RR: 2.11 (1.93–2.31), P: < 0.001}. Patients from SES (low) {RR: 1.45 (1.24–1.71), P: < 0.001)} are 13.3% and 45% more susceptible to those from SES (middle) and SES (high). Admission type (ADT) such as AS2, M2, NEWS, S2 and others {RR: 1.37–2.77 (1.25–6.19), P: < 0.001} also have a high likelihood of contributing to ELOHS while the distance to hospital (DTH) {RR: 0.64–0.75 (0.56–0.82), P: < 0.001}, Charlson Score (CCI) {RR: 0.31–0.68 (0.22–0.99), P: < 0.001–0.043} and some VMO specialties {RR: 0.08–0.69 (0.03–0.98), P: < 0.001–0.035} have limited influence on ELOHS. Conclusions Relying on the preadmission assessment of ELOHS helps identify those patients who are susceptible to exceeding their expected length of stay on admission, thus, making it possible to improve patients’ management and outcomes.

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Section: Resultsmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data

2022

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“…Previous studies developed LOS prediction tools for conditions such as critical care (10)(11)(12), heart diseases (13)(14)(15)(16)(17), and liver diseases (12,18). Some of these tools were customized from traditional severity scoring tools (14,16), some were developed via logistic regression (LR) with or without severity scores as independent predictors (12,17), and some were developed using machine learning methods (10,15). Meanwhile, some new and specific scoring tools have been developed for LOS prediction (18,19).…”

Section: Introductionmentioning

confidence: 99%

Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining

Kong

Lin

et al. 2020

Ann Transl Med

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Background: The 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).Methods: This 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.Results: A 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][6][7][8][9][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.Conclusions: This 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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Factors Associated with Extended Hospital Stay and its Impact on Subsequent Short-term Readmission with Tuberculosis Patients

Cao,

Xie,

et al. 2024

Arch Iran Med

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<strong data-sider-select-id="72ab8072-c5c2-430e-955c-7d40b15e5789">Background: This study aimed to explore the factors associated with extended length of stay (LOSE) for patients with tuberculosis (TB) in China, and construct a nomogram to predict it. In addition, the impact of extended hospital stay on short-term readmission after discharge was assessed. <strong data-sider-select-id="fae5f176-d9ca-459d-b777-0c4860e05eec">Methods: A retrospective observational study was conducted at Changsha Central Hospital, from January 2018 to December 2020. Patients (≥18 years who were first admitted to hospital for TB treatment) with non-multidrug-resistant TB were selected using the World Health Organization’s International Classification of Diseases, 10th Revision (ICD-10-CM), and the hospital’s electronic medical record system. <strong data-sider-select-id="86fb3d7e-0f65-43d6-80be-478bfa46bbe2">Results: A multivariate logistic regression analysis was used to evaluate the associations between TB and LOSE. The relationship between length of hospital stay and readmission within 31 days after discharge was assessed using a univariate Cox proportional risk model. A total of 14259 patients were included in this study (13629 patients in the development group and 630 in the validation group). The factors associated with extended hospital stays were age, smear positivity, extrapulmonary involvement, surgery, transfer from other medical structures, smoking, chronic liver disease, and drug-induced hepatitis. There was no statistical significance in the 31-day readmission rate of TB between the LOSE and length of stay≤14 days groups (hazards ratio: 0.92, 95% CI: 0.80-1.06, P=0.229). <strong data-sider-select-id="a6f584f7-f578-408e-b36f-226653e345f6">Conclusion: LOSE with TB was influenced by several patient-level factors, which were combined to construct a nomograph. The established nomograph can help hospital administrator and clinicians to identify patients with TB requiring extended hospital stays, and more efficiently plan for treatment programs and resource needs.

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Prediction Model for Extended Hospital Stay Among Medicare Beneficiaries After Percutaneous Coronary Intervention

Cited by 3 publications

References 13 publications

Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data

Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data

Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining

Factors Associated with Extended Hospital Stay and its Impact on Subsequent Short-term Readmission with Tuberculosis Patients

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