Development of a Risk Score to Predict 90-Day Readmission After Coronary Artery Bypass Graft

Zhang, Qianzi; Amin, Arsalan; Shah, Rohan; Chatterjee, Subhasis; Wall, Matthew J.; Rosengart, Todd K.; Ghanta, Ravi K.

doi:10.1016/j.athoracsur.2020.04.142

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…The primary purpose of our study was to evaluate which factors could predict the one-year medical expenses after discharge of CABG patients, and build an expense prediction model. Most research discusses mortality, readmission, and the relationship between diseases and surgery [1,4,5,15,28,[32][33][34]. However, only a few studies explored medical expenses, even forecasting.…”

Section: Discussionmentioning

confidence: 99%

“…This research used 44 variables [4,5,7,[26][27][28][29][30][31], which depended on the physician's clinical experience and literature review. Moreover, it used five different machine learning methods to predict after filtering factors, the highest score (10 points) was the most crucial factor, which will be the first on the rank; on the other hand, the lowest predictor was ranked the last (1 point).…”

Section: The Ranking Number Of Feature Selection On Cabgmentioning

confidence: 99%

“…After CABG, the patient will have the following two different situations: one is gradual recovery, the other is due to the complications that lead the patient to rehospitalization again [1]. Therefore, readmission is an essential outcome of CABG surgery, and it has a high incidence in 30 and 90 days [2][3][4]. Furthermore, it is a severe problem because it is directly related to the medical expenses that patients and hospitals must incur, substantially increasing healthcare costs and bringing a vast economic budget.…”

Section: Introductionmentioning

confidence: 99%

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The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

Huang

Chen

et al. 2021

Healthcare

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Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.

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Section: Discussionmentioning

confidence: 99%

Section: The Ranking Number Of Feature Selection On Cabgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

Huang

Chen

et al. 2021

Healthcare

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“…The frail senior patient with comorbidities has a higher risk of post-CABG mortality compared to young adults [ 63 , 64 ]. Moreover, data show that age over 65, female sex and the presence of comorbidities are associated with higher rates of rehospitalization after CABG [ 65 , 66 ]. CABG should be used cautiously in the frail senior patient.…”

Section: Treatment Characteristics In the Fragile Senior Patient With...mentioning

confidence: 99%

Chronic Coronary Syndrome in Frail Old Population

Ilie

Țăranu

Ștefăniu

et al. 2022

Life

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The demographic trend of aging is associated with an increased prevalence of comorbidities among the elderly. Physical, immunological, emotional and cognitive impairment, in the context of the advanced biological age segment, leads to the maintenance and precipitation of cardiovascular diseases. Thus, more and more data are focused on understanding the pathophysiological mechanisms underlying each fragility phenotype and how they potentiate each other. The implications of inflammation, sarcopenia, vitamin D deficiency and albumin, as dimensions inherent in fragility, in the development and setting of chronic coronary syndromes (CCSs) have proven their patent significance but are still open to research. At the same time, the literature speculates on the interdependent relationship between frailty and CCSs, revealing the role of the first one in the development of the second. In this sense, depression, disabilities, polypharmacy and even cognitive disorders in the elderly with ischemic cardiovascular disease mean a gradual and complex progression of frailty. The battery of tests necessary for the evaluation of the elderly with CCSs requires a permanent update, according to the latest guidelines, but also an individualized approach related to the degree of frailty and the conditions imposed by it. By summation, the knowledge of frailty screening methods, through the use of sensitive and individualized tools, is the foundation of secondary prevention and prognosis in the elderly with CCSs. Moreover, a comprehensive geriatric assessment remains the gold standard of the medical approach of these patients. The management of the frail elderly, with CCSs, brings new challenges, also from the perspective of the treatment particularities. Sometimes the risk–benefit balance is difficult to achieve. Therefore, the holistic, individualized and updated approach of these patients remains a desired objective, by understanding and permanently acquiring knowledge on the complexity of the frailty syndrome.

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“…Because IFI helps to intraoperatively detect deficit grafts which might otherwise be overlooked and not revised at the initial CABG procedure 2 , 3 , its use during CABG may help to reduce subsequent resource utilization postindex hospitalization. While most quality metric systems are interested in 30-day postoperative outcomes, there is growing interest in 90-day postoperative outcomes as well 18 , 19 . The aim of this study is to determine if intraoperative assessment with IFI may be associated with 90-day all-cause ER use following CABG surgery.…”

Section: Introductionmentioning

confidence: 99%

Ninety-day all-cause emergency room use among coronary artery bypass grafting patients associated with near-infrared fluorescence imaging: a retrospective cohort study

Sosa

McNicholas

Bebla

et al. 2023

Annals of Medicine &Amp; Surgery

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Background: Reducing emergency room (ER) use may indicate the improved quality of patient care at index hospitalization. The aim of this study is to determine whether the use of near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG) during coronary artery bypass grafting (CABG) surgery is associated with a lowered 90-day all-cause ER use. Materials and Methods: This retrospective cohort study included adult patients with inpatient hospitalizations between January 2016 and June 2020 for an isolated CABG procedure at a US hospital. Propensity score matching was used to create matched cohorts to address the differences in patient, payer type, hospital, and clinical characteristics. A multivariable regression analysis was conducted to determine the association of NIRF imaging with ICG on ER use within 90 days of discharge after controlling for patient, payer type, hospital, and clinical covariates. Results: In total, 230 506 adult patients underwent an isolated CABG procedure. Less than 1% (n=1965) were assessed with NIRF imaging using ICG. There were differences in patient demographic and hospital characteristics between the treatment group (i.e. NIRF with ICG) and the comparison group (i.e. no NIRF with ICG). After controlling for covariates, a statistically significant lower 90-day all-cause ER use was documented among the treatment group (adjusted odds ratio=0.84, 95% confidence interval=0.73–0.96, P<0.009). Reasons associated with ER use were similar between the two groups. Conclusion: Routine intraoperative graft patency assessment with NIRF imaging using ICG may help to improve a patient’s care experience and reduce subsequent resource utilization. Intraoperative graft patency assessment with NIRF imaging using ICG is associated with a 90-day all-cause ER use reduction among CABG patients. Further studies are needed to compare the ER usage among centers that used this technique versus those that did not to determine if associated reductions in ER use are a center or technique-specific phenomenon.

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Development of a Risk Score to Predict 90-Day Readmission After Coronary Artery Bypass Graft

Cited by 7 publications

References 24 publications

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

Chronic Coronary Syndrome in Frail Old Population

Ninety-day all-cause emergency room use among coronary artery bypass grafting patients associated with near-infrared fluorescence imaging: a retrospective cohort study

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