{"title":"心肺旁路心脏手术中铁代谢与围手术期心肌损伤之间的剂量依赖关系:回顾性分析","authors":"Qian Li, Hong Lv, Yuye Chen, Jingjia Shen, Jia Shi, Chenghui Zhou","doi":"10.1159/000541213","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>We sought to comprehensively explore the potential linear and nonlinear relationship between preoperative iron metabolism and perioperative myocardial injury (PMI) following cardiac surgery with cardiopulmonary bypass (CPB).</p><p><strong>Methods: </strong>Patients who underwent cardiac surgery with CPB between December 2018 and April 2021 were retrospectively collected. The measurements of iron metabolism included serum iron (SI), serum ferritin (SF), transferrin (TRF), transferrin saturation (TS), and total iron-binding capacity (TIBC). Logistic regression and restricted cubic spline (RCS) models were used for linear and nonlinear analysis. The primary outcome was PMI with a 100× upper reference limit.</p><p><strong>Results: </strong>Of 2,420 patients screened, 744 eligible patients were enrolled for the final analysis. The incidence of PMI was 25.7%. No significant linear relationship was observed. In the RCS models adjusted with age (median: 56), female, and history of diabetes, a statistically significant difference was detected between TRF (p for nonlinear 0.0152) or TIBC (p for nonlinear 0.0477) and PMI. The gentle U-shaped relationship observed between TRF, TIBC, and PMI suggests that when TRF and TIBC increase, the risk decreases, reaching its lowest point when TRF = 2.4 and TIBC = 54. Nevertheless, as TRF and TIBC continue to increase, the risk starts to rise again. Subgroup analyses yielded consistent findings, with a notable emphasis on older patients who were more susceptible to variations in iron metabolism.</p><p><strong>Conclusion: </strong>Iron metabolism, including TRF, and TIBC, exhibited a nonlinear relationship with PMI by the RCS model adjusted by age, gender, and history of diabetes.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dose-Dependent Relationship between Iron Metabolism and Perioperative Myocardial Injury in Cardiac Surgery with Cardiopulmonary Bypass: A Retrospective Analysis.\",\"authors\":\"Qian Li, Hong Lv, Yuye Chen, Jingjia Shen, Jia Shi, Chenghui Zhou\",\"doi\":\"10.1159/000541213\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>We sought to comprehensively explore the potential linear and nonlinear relationship between preoperative iron metabolism and perioperative myocardial injury (PMI) following cardiac surgery with cardiopulmonary bypass (CPB).</p><p><strong>Methods: </strong>Patients who underwent cardiac surgery with CPB between December 2018 and April 2021 were retrospectively collected. The measurements of iron metabolism included serum iron (SI), serum ferritin (SF), transferrin (TRF), transferrin saturation (TS), and total iron-binding capacity (TIBC). Logistic regression and restricted cubic spline (RCS) models were used for linear and nonlinear analysis. The primary outcome was PMI with a 100× upper reference limit.</p><p><strong>Results: </strong>Of 2,420 patients screened, 744 eligible patients were enrolled for the final analysis. The incidence of PMI was 25.7%. No significant linear relationship was observed. In the RCS models adjusted with age (median: 56), female, and history of diabetes, a statistically significant difference was detected between TRF (p for nonlinear 0.0152) or TIBC (p for nonlinear 0.0477) and PMI. The gentle U-shaped relationship observed between TRF, TIBC, and PMI suggests that when TRF and TIBC increase, the risk decreases, reaching its lowest point when TRF = 2.4 and TIBC = 54. Nevertheless, as TRF and TIBC continue to increase, the risk starts to rise again. Subgroup analyses yielded consistent findings, with a notable emphasis on older patients who were more susceptible to variations in iron metabolism.</p><p><strong>Conclusion: </strong>Iron metabolism, including TRF, and TIBC, exhibited a nonlinear relationship with PMI by the RCS model adjusted by age, gender, and history of diabetes.</p>\",\"PeriodicalId\":9391,\"journal\":{\"name\":\"Cardiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cardiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1159/000541213\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cardiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000541213","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Dose-Dependent Relationship between Iron Metabolism and Perioperative Myocardial Injury in Cardiac Surgery with Cardiopulmonary Bypass: A Retrospective Analysis.
Introduction: We sought to comprehensively explore the potential linear and nonlinear relationship between preoperative iron metabolism and perioperative myocardial injury (PMI) following cardiac surgery with cardiopulmonary bypass (CPB).
Methods: Patients who underwent cardiac surgery with CPB between December 2018 and April 2021 were retrospectively collected. The measurements of iron metabolism included serum iron (SI), serum ferritin (SF), transferrin (TRF), transferrin saturation (TS), and total iron-binding capacity (TIBC). Logistic regression and restricted cubic spline (RCS) models were used for linear and nonlinear analysis. The primary outcome was PMI with a 100× upper reference limit.
Results: Of 2,420 patients screened, 744 eligible patients were enrolled for the final analysis. The incidence of PMI was 25.7%. No significant linear relationship was observed. In the RCS models adjusted with age (median: 56), female, and history of diabetes, a statistically significant difference was detected between TRF (p for nonlinear 0.0152) or TIBC (p for nonlinear 0.0477) and PMI. The gentle U-shaped relationship observed between TRF, TIBC, and PMI suggests that when TRF and TIBC increase, the risk decreases, reaching its lowest point when TRF = 2.4 and TIBC = 54. Nevertheless, as TRF and TIBC continue to increase, the risk starts to rise again. Subgroup analyses yielded consistent findings, with a notable emphasis on older patients who were more susceptible to variations in iron metabolism.
Conclusion: Iron metabolism, including TRF, and TIBC, exhibited a nonlinear relationship with PMI by the RCS model adjusted by age, gender, and history of diabetes.
期刊介绍:
''Cardiology'' features first reports on original clinical, preclinical and fundamental research as well as ''Novel Insights from Clinical Experience'' and topical comprehensive reviews in selected areas of cardiovascular disease. ''Editorial Comments'' provide a critical but positive evaluation of a recent article. Papers not only describe but offer critical appraisals of new developments in non-invasive and invasive diagnostic methods and in pharmacologic, nutritional and mechanical/surgical therapies. Readers are thus kept informed of current strategies in the prevention, recognition and treatment of heart disease. Special sections in a variety of subspecialty areas reinforce the journal''s value as a complete record of recent progress for all cardiologists, internists, cardiac surgeons, clinical physiologists, pharmacologists and professionals in other areas of medicine interested in current activity in cardiovascular diseases.