Objective To analyze and explore the risk factors of secondary tricuspid regurgitation (TR) after left-sided valve surgery (left cardiac valve replacement or valvuloplasty) using meta-analysis, so as to provide evidence for clinical diagnosis and treatment of secondary TR. Methods We electronically searched databases including PubMed, MEDLINE, CBM, CNKI, VIP, for literature on the risk factors of secondary TR after left-sided valve surgery from 1995 to 2012. According to the inclusion and exclusion criteria, we screened literature, extracted data, and assessed methodological quality. Then, meta-analysis was performed using RevMan 5.0 software. Results A total of 6 case-control studies were included, involving 437 patients and 2 102 controls. The results of meta-analysis showed that, the risk factors of progressive exacerbation of secondary TR after left-sided valve surgery included preoperative atrial fibrillation (OR=3.90, 95%CI 3.00 to 5.07; adjusted OR=3.04, 95%CI 2.21 to 4.16), age (MD=5.36, 95%CI 3.49 to 7.23), huge left atrium (OR=5.17, 95%CI 3.12 to 8.57; adjusted OR=1.91, 95%CI 1.49 to 2.44) or left atrium diameter (MD=4.85, 95%CI 3.18 to 6.53), degradation of left heart function (OR=2.97, 95%CI 1.73 to 5.08), rheumatic pathological change (OR=3.06, 95%CI 1.66 to 4.68), preoperative TR no less than 2+ (OR=3.52, 95%CI 1.26 to 9.89), and mitral valve replacement (MVR) (OR=2.35, 95%CI 1.68 to 3.30). Sex (OR=1.54, 95%CI 0.94 to 2.52) and preoperative pulmonary arterial hypertension (OR=1.28, 95%CI 0.77 to 2.12) were not associated with secondary TR after left-sided valve surgery. Conclusion The risk factors of progressive exacerbation of secondary TR after left-sided valve surgery include preoperative atrial fibrillation, age, huge left atrium or left atrium diameter, degradation of left heart function, rheumatic pathological change, preoperative TR no less than 2+, and MVR. Understanding these risk factors helps us to improve the long-time effectiveness of preventing and treating TR after left-sided valve surgery.
Objective To introduce the multivariate random effects model (MREM) in the meta-analysis of diagnostic tests with multiple thresholds. Methods This paper expanded and extended the bivariate random effects model (BREM) to develop the MREM, and implemented it in the SAS Proc NLMIXED procedure. Results The MREM could obtain the study specific ROC curve for each study through empirical Bayes estimation, and the summary ROC curve located in between all study specific ROC curves evenly, while the BREM couldn’t obtain the study specific ROC curve. In addition, in the aspect of parameters estimation, the MREM didn’t depend on the choice of the diagnosis threshold and the type of SROC. The MREM could get only one SROC curve and its AUC was between the AUC of the 5 types of SROC from BREM, so it could avoid overestimation or underestimation. Conclusion The MREM can fully exploit the data, obtain stable and reliable results, and have a good application value in meta-analysis of diagnostic tests with multiple thresholds.