Baseline Platelet Count and Clinical Outcome in Acute Coronary Syndrome

Wu, Yihua; Wu, Han; Mueller, Christian; Gibson, C. Michael; Murphy, Sabina A.; Shi, Yu; Xu, Geng; Yang, Jun Mo

doi:10.1253/circj.cj-11-0707

Cited by 24 publications

(20 citation statements)

References 50 publications

(18 reference statements)

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“…It leads to reactive thrombocytosis and elevated platelet count resulting in more recurrent events. High platelet numbers have been independently associated with higher mortality and MACE in ACS patients [21,31].…”

Section: The "Fiction Of the Perpetually Platelet Inactivation"mentioning

confidence: 99%

“…Platelet number and immature platelets, probably due to their higher reactivity [28], have been shown to be of prognostic value in different clinical scenarios such as ST-Elevation Myocardial Infarction [31], non STEMI, unstable angina [21] and stable coronary artery disease [36]. Moreover, significantly elevated levels of platelet-monocyte aggregates have been found in patients with acute myocardial Infarction and unstable angina; observation again compatible with a hyper-reactive state in both cells [20].…”

Section: Observations Supporting the Hypothesis And Strategies Tryingmentioning

confidence: 99%

“…However, some patients develop recurrent CV events besides the antiplatelet treatment [11][12][13]. Different hypothesis tried to explain this results, including genetic differences [14][15][16][17] but more recently the relationship between platelet turn-over and an hyper-inflammatory status are emerging [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Antiplatelet agents: Is twice a day a better strategy?

Arazi¹,

Badimón²

2017

Med Clin Arch

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Section: The "Fiction Of the Perpetually Platelet Inactivation"mentioning

confidence: 99%

Section: Observations Supporting the Hypothesis And Strategies Tryingmentioning

confidence: 99%

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Antiplatelet agents: Is twice a day a better strategy?

Arazi¹,

Badimón²

2017

Med Clin Arch

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“…6 By meta-analysis of 8 studies comprising 39,324 patients with ACS, these authors show that a higher platelet count at baseline increases the relative risks of mortality and major cardiac adverse events (MACE) during the short and long term. Moreover, they found a U-shaped relationship between platelet count and the risk of mortality and MACE.…”

Section: Article P 704mentioning

confidence: 99%

“…6 A lower platelet count might correlate with adverse outcomes. McClure et al found that among 10,984 patients with ACS, 7% developed thrombocytopenia during hospitalization.…”

Section: Article P 704mentioning

confidence: 99%

Platelet Count as a Prognostic Marker in Patients With Acute Coronary Syndromes

Katoh

Ikeda

2012

Circ J

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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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Baseline Platelet Count and Clinical Outcome in Acute Coronary Syndrome

Cited by 24 publications

References 50 publications

Antiplatelet agents: Is twice a day a better strategy?

Antiplatelet agents: Is twice a day a better strategy?

Platelet Count as a Prognostic Marker in Patients With Acute Coronary Syndromes

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

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