Analysis of influencing factors and construction of risk prediction model for postoperative thrombocytopenia in critically ill patients with heart disease

Abstract

Abstract Objective To analyze the influencing factors of postoperative thrombocytopenia in critically ill patients with heart disease and construct a nomogram prediction model. Methods From October 2022 to October 2023, 319 critically ill patients with heart disease who visited our hospital were collected and separated into postoperative thrombocytopenia group (n = 142) and no postoperative thrombocytopenia group (n = 177) based on their postoperative thrombocytopenia, Logistic regression analysis was applied to screen risk factors for postoperative thrombocytopenia in critically ill patients with heart disease; R software was applied to construct a nomogram for predicting postoperative thrombocytopenia in critically ill patients with heart disease, and ROC curves, calibration curves, and Hosmer-Lemeshow goodness of fit tests were applied to evaluate nomogram. Results A total of 142 out of 319 critically ill patients had postoperative thrombocytopenia, accounting for 44.51%. Logistic regression analysis showed that gender (95% CI 1.607–4.402, P = 0.000), age ≥ 60 years (95% CI 1.380–3.697, P = 0.001), preoperative antiplatelet therapy (95% CI 1.254–3.420, P = 0.004), and extracorporeal circulation time > 120 min (95% CI 1.681–4.652, P = 0.000) were independent risk factors for postoperative thrombocytopenia in critically ill patients with heart disease. The area under the ROC curve was 0.719 (95% CI: 0.663–0.774). The slope of the calibration curve was close to 1, and the Hosmer-Lemeshow goodness of fit test was χ 2 = 6.422, P = 0.491. Conclusion Postoperative thrombocytopenia in critically ill patients with heart disease is influenced by gender, age ≥ 60 years, preoperative antiplatelet therapy, and extracorporeal circulation time > 120 min. A nomogram established based on above multiple independent risk factors provides a method for clinical prediction of the risk of postoperative thrombocytopenia in critically ill patients with heart disease.

Keywords

• Heart
• Critical illness
• Thrombocytopenia
• Influencing factors
• Risk prediction model