[Retracted] Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Zhou, Jing; Li, Jun

doi:10.1155/2022/8552358

Cited by 3 publications

(4 citation statements)

References 33 publications

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“…We demonstrated the predictive power of the models, and the LDA model was superior to the other models, with an average AUC of 0. and prognosis of ACS. 13,14 However, only one ML algorithm was used to predict in-hospital mortality, 30-day CHF rehospitalization, and 180-day cardiovascular death.…”

Section: Discussionmentioning

confidence: 99%

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Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

Song,

Li,

Nie

et al. 2023

Clinical Cardiology

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BackgroundUp to 30% of patients with acute coronary syndrome (ACS) die from adverse events, mainly renal failure and myocardial infarction (MI). Accurate prediction of adverse events is therefore essential to improve patient prognosis.HypothesisMachine learning (ML) methods can accurately identify risk factors and predict adverse events.MethodsA total of 5240 patients diagnosed with ACS who underwent PCI were enrolled and followed for 1 year. Support vector machine, extreme gradient boosting, adaptive boosting, K‐nearest neighbors, random forest, decision tree, categorical boosting, and linear discriminant analysis (LDA) were developed with 10‐fold cross‐validation to predict acute kidney injury (AKI), MI during hospitalization, and all‐cause mortality within 1 year. Features with mean Shapley Additive exPlanations score >0.1 were screened by XGBoost method as input for model construction. Accuracy, F1 score, area under curve (AUC), and precision/recall curve were used to evaluate the performance of the models.ResultsOverall, 2.6% of patients died within 1 year, 4.2% had AKI, and 4.7% had MI during hospitalization. The LDA model was superior to the other seven ML models, with an AUC of 0.83, F1 score of 0.90, accuracy of 0.85, recall of 0.85, specificity of 0.68, and precision of 0.99 in predicting all‐cause mortality. For AKI and MI, the LDA model also showed good discriminating capacity with an AUC of 0.74.ConclusionThe LDA model, using easily accessible variables from in‐hospital patients, showed the potential to effectively predict the risk of adverse events and mortality within 1 year in ACS patients after PCI.

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Section: Discussionmentioning

confidence: 99%

“…A previous study verified the usefulness of ML techniques in predicting the diagnosis and prognosis of ACS. 13 , 14 However, only one ML algorithm was used to predict in‐hospital mortality, 30‐day CHF rehospitalization, and 180‐day cardiovascular death.…”

Section: Discussionmentioning

confidence: 99%

Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

Song,

Li,

Nie

et al. 2023

Clinical Cardiology

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

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confidence: 99%

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confidence: 99%