Predictors of Hospital Readmission within 30 Days after Coronary Artery Bypass Grafting: Data Analysis of 2,272 Brazilian Patients

Chiorino, Camilla do Rosário Nicolino; Santos, Vinícius Batista; Lopes, Juliana de Lima; Lopes, Camila Takáo

doi:10.21470/1678-9741-2020-0266

Cited by 2 publications

(2 citation statements)

References 23 publications

(7 reference statements)

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“…Given the high costs associated with readmission after hospital discharge, the ability to stratify the risk is essential for preventive measures. Improving upon existing conventional LR prediction models solely based on time-independent variables (e.g., 1-point lab values only postoperative) (77)(78)(79), an advanced XGBoost algorithm incorporating time-dependent factors (e.g., lab values at several time-points) demonstrated a better accuracy in predictive ability (74). Another but more complex ML tool called genetic programs performed equally well in accuracy to an advanced LR method.…”

Section: Readmissionmentioning

confidence: 96%

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

Rellum¹,

Schuurmans²,

Ven³

et al. 2021

J Thorac Dis

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Background: Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients. Methods: We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted. Results: Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results. Conclusions: ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.

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Section: Readmissionmentioning

confidence: 96%

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

Rellum¹,

Schuurmans²,

Ven³

et al. 2021

J Thorac Dis

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“…При желании больных отказаться от курения к лечению следует привлекать врачей-наркологов 2 [24]. В послеоперационном периоде должны быть учтены такие факторы, как темный цвет кожи, афроамериканская принадлежность, хроническая болезнь по-чек, хроническая обструктивная болезнь легких, послеоперационное использование компонентов крови, использование ацетилсалициловой кислоты и антибиотиков [25]. Что же касается нарушения метаболизма глюкозы, то в исследовании C. Djupsjo et al [26] было показано, что пациенты с преддиабетом или впервые выявленным диабетом перед КШ имели сходную кривую выживаемости по сравнению с пациентами без нарушения метаболизма глюкозы.…”

Section: участие медицинского персонала взаимодействие его с пациентом вмешательстваunclassified

Patient adherence after coronary bypass grafting: different facets of same matter. A systematic review

Фофанова

Суботников

Агеев

2021

Kuban. nauсh. med. vestnik

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Background. Adherence in cardiological patients is a leading challenge due to a high society burden imposed by cardiovascular diseases (CVDs) through morbidity, reduced life expectancy, disability and high mortality rates in population. Despite the availability of highly effective medicines and high-technology care, the success of CVD treatment remains insufficient. A particular focus should be placed on patients with previous coronary artery bypass grafting (CABG).Objectives. A review of reasoning for non-adherence to medication and non-medication management in post-CABG patients and current methods influencing it.Methods. Russian-language and foreign literature was mined in the eLibrary and PubMed databases with the query keywords “medication adherence” [приверженность терапии], “coronary artery disease” [ишемическая болезнь сердца], “coronary artery bypass surgery” [коронарное шунтирование], “coronary revascularisation” [вторичная профилактика после коронарного шунтирования]. The review included papers published within 2016–2020, as well as selected relevant publications from 2003–2015, to cover 52 sources irrespective of study design and language. Content and descriptive analyses were used as research tools.Results. Coronary heart disease (CHD) poses an important health, social and economic problem worldwide as a leading cause of reduced life expectancy, disability and high mortality. Non-compliance with medication significantly bursts medical expenditures [1]. Coronary artery bypass grafting is used widely to treat multivessel coronary lesions both in stable and acute CHD. Secondary preventive techniques successfully preclude adverse post-CABG events, but adherence to post-CABG treatment is often low. The non-adherence rationale rarely links to a single factor but is rather complex and multifaceted. It includes social and economic reasons, systemic health care or personnel, therapy and patient-related factors. Methods are developing and refining to improve adherence to both non-medication and medication interventions.Conclusion. Clinical trials to improve secondary prevention adherence in patients after coronary artery bypass grafting will allow a wider implementation of relevant methods in outpatient management of this patient category.

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Predictors of Hospital Readmission within 30 Days after Coronary Artery Bypass Grafting: Data Analysis of 2,272 Brazilian Patients

Cited by 2 publications

References 23 publications

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

Patient adherence after coronary bypass grafting: different facets of same matter. A systematic review

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