Machine learning prediction models for prognosis of critically ill patients after open-heart surgery

Zhong, Zhihua; Yuan, Xin; Liu, Shizhen; Yang, Yuer; Liu, Fanna

doi:10.1038/s41598-021-83020-7

Cited by 59 publications

(38 citation statements)

References 24 publications

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“…In order to improve the nursing quality and make individualized clinical decision, several models of predicting ICU length of stay after CABG have been developed [ 27 , 28 ]. A study built up machine learning models to predict 30-day mortality and three complications in critically ill patients after open-heart surgery (including CABG) from MIMIC III database [ 29 ]. The machine learning model predicted the short-term outcome by window 10 software with more than 30 risk factors.…”

Section: Discussionmentioning

confidence: 99%

“…Age and CHF were acknowledged as the risk factors influencing the prognosis of patients after CABG [ 6 , 7 , 20 , 31 ]. WBC play an essential role in cardiovascular disease, some studies have found elevated WBC was associated with cardiovascular complications and mortality in patients after CABG [ 29 , 32 ]. Creatinine increased during perioperative stage served as independent risk factor for mortality in patients after CABG, and some risk assessment models after cardiac surgery included creatinine [ 6 , 20 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

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A novel nomogram for predicting 3-year mortality in critically ill patients after coronary artery bypass grafting

2021

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Background The long-term outcomes for patients after coronary artery bypass grafting (CABG) have been received more and more concern. The existing prediction models are mostly focused on in-hospital operative mortality after CABG, but there is still little research on long-term mortality prediction model for patients after CABG. Objective To develop and validate a novel nomogram for predicting 3-year mortality in critically ill patients after CABG. Methods Data for developing novel predictive model were extracted from Medical Information Mart for Intensive cart III (MIMIC-III), of which 2929 critically ill patients who underwent CABG at the first admission were enrolled. Results A novel prognostic nomogram for 3-year mortality was constructed with the seven independent prognostic factors, including age, congestive heart failure, white blood cell, creatinine, SpO2, anion gap, and continuous renal replacement treatment derived from the multivariable logistic regression. The nomogram indicated accurate discrimination in primary (AUC: 0.81) and validation cohort (AUC: 0.802), which were better than traditional severity scores. And good consistency between the predictive and observed outcome was showed by the calibration curve for 3-year mortality. The decision curve analysis also showed higher clinical net benefit than traditional severity scores. Conclusion The novel nomogram had well performance to predict 3-year mortality in critically ill patients after CABG. The prediction model provided valuable information for treatment strategy and postdischarge management, which may be helpful in improving the long-term prognosis in critically ill patients after CABG.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A novel nomogram for predicting 3-year mortality in critically ill patients after coronary artery bypass grafting

2021

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“…However, we integrated data from several datasets, the number of patients and operations is relatively small and needs to be extended. We also faced with the imbalance problem during the study, which is usual for medical data [11]. In such situations machine-learning algorithms tend to classify the data into predominant class.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Aortic Aneurysm Postoperative Risks Based on Russian Integrated Data

et al. 2021

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This article describes the results of feature extraction from unstructured medical records and prediction of postoperative complications for patients with thoracic aortic aneurysm operations using machine learning algorithms. The datasets from two different medical centers were integrated. Seventy-two features were extracted from Russian unstructured medical records. We formulated 8 target features: Mortality, Temporary neurological deficit (TND), Permanent neurological deficit (PND), Prolonged (> 7 days) lung ventilation (LV), Renal replacement therapy (RRT), Bleeding, Myocardial infarction (MI), Multiple organ failure (MOF). XGBoost showed the best performance for most target variables (F-measure 0.74–0.95) which is comparable to recent results in cardiovascular postoperative risks prediction.

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“…Addressing these same outcomes, two advanced models (LR and ANN) show even better predictive capability in comparison, with the most significant advantage in predictive power for the ANN model with an AUC of 0.85 (14) (Table 2). In a recent study comparing advanced models reciprocally, an XGBoost model had the upper hand over ANN (18). However, these are outliers in terms of morbidity prediction.…”

Section: Morbidity In the Icumentioning

confidence: 99%

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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Machine learning prediction models for prognosis of critically ill patients after open-heart surgery

Cited by 59 publications

References 24 publications

A novel nomogram for predicting 3-year mortality in critically ill patients after coronary artery bypass grafting

A novel nomogram for predicting 3-year mortality in critically ill patients after coronary artery bypass grafting

Predicting the Aortic Aneurysm Postoperative Risks Based on Russian Integrated Data

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

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