Abstract

Sepsis-associated thrombocytopenia (SAT) is a common complication in the intensive care unit (ICU), which significantly increases the mortality rate and leads to poor prognosis of diseases. Machine learning (ML) is widely used in disease prediction in critically ill patients. Here, we aimed to establish prediction models for platelet decrease and severe platelet decrease in ICU patients with sepsis based on four common ML algorithms and identify the best prediction model. The research subjects were 1,455 ICU sepsis patients admitted to Dongyang People's Hospital affiliated with Wenzhou Medical University from January 1, 2015, to October 31, 2019. Basic clinical demographic information, biochemical indicators, and clinical outcomes were recorded. The prediction models were based on four ML algorithms: random forest, neural network, gradient boosting machine, and Bayesian algorithms. Thrombocytopenia was found to occur in 732 patients (49.7%). The mechanical ventilation time and length of ICU stay were longer, and the mortality rate was higher for the thrombocytopenia group than for the non-thrombocytopenia group. The models were validated on an online international database (Medical Information Mart for Intensive Care III). The areas under the receiver operating characteristic curves (AUCs) of the four models for the prediction of thrombocytopenia were between 0.54 and 0.72. The AUCs of the models for the prediction of severe thrombocytopenia were between 0.70 and 0.77. The neural network and gradient boosting machine models effectively predicted the occurrence of SAT, and the Bayesian models had the best performance in predicting severe thrombocytopenia. Therefore, these models can be used to identify such high-risk patients at an early stage and guide individualized clinical treatment, to improve the prognosis of diseases.

Highlights

  • Artificial intelligence (AI) has enabled many cutting-edge scientific research achievements in the field of medical care, especially for acute and severe diseases

  • Sepsis-related thrombocytopenia (SAT) is a common complication in the intensive care unit (ICU); in particular, the incidence of thrombocytopenia in patients with septic shock can be as high as 55% [3]

  • The incidence rate of SAT in the three groups of patients was similar, and there was no significant difference in age, SOFA score, and initial platelet count in ICU admission

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Summary

Introduction

Artificial intelligence (AI) has enabled many cutting-edge scientific research achievements in the field of medical care, especially for acute and severe diseases. In fields such as disease risk assessment, early warning of disease deterioration, and early warning of death, AI can alert officials regarding potential risks earlier and more accurately. Patients may have to wait for 2–3 days from the beginning of platelet reservation to the actual infusion of platelets In this process, the patients are at a high risk of bleeding and may even experience hemorrhagic shock, which is life-threatening. Detection of platelet decrease is crucial for critically ill patients

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