Predicting the occurrence of major adverse cardiac events within 30 days after a patient’s vascular surgery: An individual patient-data meta-analysis

Abstract

<p><strong>Background:</strong> Major adverse cardiac events, MACE – a composite endpoint of cardiac death and nonfatal myocardial infarction (MI) – are severe harmful outcomes that commonly arise after elective vascular surgeries. As current pre-operative risk prediction models are not as effective in predicting post-operative outcomes, this thesis will discuss the key results of an individual patient data meta-analysis that is based on data from six cohort studies of patients undergoing vascular surgery.</p> <p><strong>Objectives:</strong> The purpose of this thesis is to determine optimal thresholds of continuous covariates and create a prediction model for major adverse cardiac events (MACE), within 30 days after a vascular surgery. The goals include exploring the minimum p-value method to dichotomize cutpoints for continuous variables; employing logistic regression analysis to determine a prediction model for MACE; evaluating its validity against other samples; and assessing its sensitivity to clustering effects. The secondary objectives are to determine individual models for predicting all-cause mortality, cardiac death, and nonfatal MI within 30 days of a vascular surgery, using the final covariates assessed for MACE.<strong></strong></p> <p><strong>Methods: </strong>Both B-type naturietic peptide (BNP) and its N-terminal fragment (NTproBNP) are independently associated with cardiovascular complications after noncardiac surgeries, and particularly frequent after noncardiac vascular surgeries. In a previous study, these covariates were dichotomized using the receiver operating characteristic (ROC) curve approach and a simple logistic regression (SLR) model was created for MACE [1]. The first part of this thesis applies the minimum p-value method to determine a threshold for each natriuretic peptide (NP), BNP and NTproBNP. SLR is then used to model the prediction of MACE within 30 days after a patient’s vascular surgery. Comparisons were made with the ROC curve approach to determine the optimal thresholds and create a prediction model. The validity of this model was tested using bootstrap samples and its robustness was assessed using a mixed effects logistic regression (MELR) model and a generalized estimating equation (GEE). Finally, MELR was performed on each of the secondary outcomes.</p> <p><strong>Results:</strong>A variable, ROC_thrshld, was created to represent the cutpoints of Rodseth’s ROC curve approach, which identified 116pg/mL and 277.5pg/mL to be the optimal thresholds for BNP and NTproBNP, respectively [1]. The minimum p-value method dichotomized these NP thresholds as BNP: 115.57pg/mL (p</p> <p><strong>Discussion:</strong> One key limitation to this thesis is the small sample size received for NTproBNP. Also, determining only one cutpoint for each NP concentration may not be sufficient, since dichotomizing continuous factors can lead to loss of information along with other issues. Further research should be performed to explore other possible cutpoints along with performing reclassification to observe improvements in risk stratification. After validating our final model against other samples, we can conclude that MINP_thrshld, the type of surgery, and diabetes are significant covariates for the prediction of MACE. With the simplicity in only requiring a blood test to measure NP concentration levels and easily learning the status of the other two factors, minimal effort is needed in calculating the points and risk estimates for each patient. Further research should also be performed on the secondary outcomes to examine other factors that may be useful in prediction.</p> <p><strong>Conclusions: </strong>The minimum p-value method produced similar results to the ROC curve method in dichotomizing the NP concentration levels. The cutpoints for BNP and NTproBNP were 115.57pg/mL and 241.7 pg/mL, respectively. Further research needs to be performed to determine the optimality of the final prediction model of MACE, with covariates MINP_thrshld, type of surgery, and diabetes mellitus. <strong></strong></p> <p><strong><br /></strong></p>