Initiation patterns of statin therapy among adult patients undergoing intermediate to high-risk non-cardiac surgery

The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery

BACKGROUND

The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this.

OBJECTIVES

Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery.

SEARCH METHODS

We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period.

SELECTION CRITERIA

We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews.

MAIN RESULTS

We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS₂, CHA₂DS₂-VASc, R₂CHADS₂, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively.

AUTHORS' CONCLUSIONS

Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.

Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: A plasmode simulation

BACKGROUND

Inverse probability of treatment weighting (IPTW) may be biased by influential observations, which can occur from misclassification of strong exposure predictors.

METHODS

We evaluated bias and precision of IPTW estimators in the presence of a misclassified confounder and assessed the effect of propensity score (PS) trimming. We generated 1000 plasmode cohorts of size N = 10 000, sampled with replacement from 6063 NHANES respondents (1999-2014) age 40 to 79 with labs and no statin use. We simulated statin exposure as a function of demographics and CVD risk factors; and outcomes as a function of 10-year CVD risk score and statin exposure (rate ratio [RR] = 0.5). For 5% of the people in selected populations (eg, all patients, exposed, those with outcomes), we randomly misclassified a confounder that strongly predicted exposure. We fit PS models and estimated RRs using IPTW and 1:1 PS matching, with and without asymmetric trimming.

RESULTS

IPTW bias was substantial when misclassification was differential by outcome (RR range: 0.38-0.63) and otherwise minimal (RR range: 0.51-0.53). However, trimming reduced bias for IPTW, nearly eliminating it at 5% trimming (RR range: 0.49-0.52). In one scenario, when the confounder was misclassified for 5% of those with outcomes (0.3% of cohort), untrimmed IPTW was more biased and less precise (RR = 0.37 [SE(logRR) = 0.21]) than matching (RR = 0.50 [SE(logRR) = 0.13]). After 1% trimming, IPTW estimates were unbiased and more precise (RR = 0.49 [SE(logRR) = 0.12]) than matching (RR = 0.51 [SE(logRR) = 0.14]).

CONCLUSIONS

Differential misclassification of a strong predictor of exposure resulted in biased and imprecise IPTW estimates. Asymmetric trimming reduced bias, with more precise estimates than matching.

Statin intensity and postoperative mortality following open repair of intact abdominal aortic aneurysm

Background

There is a lack of evidence for the association between intensive statin therapy and outcomes following vascular surgery. The aim of this study was to evaluate the association between perioperative statin intensity and in-hospital mortality following open abdominal aortic aneurysm (AAA) repair.

Methods

Patients undergoing open AAA repair between 2009 and 2015 were identified from the Premier Healthcare Database. Statin use was classified into low, moderate and high intensity, based on American College of Cardiology/American Heart Association guidelines. Supratherapeutic intensity was defined as doses higher than the recommended guidelines. Multivariable logistic regression analyses were undertaken to assess the association between statin intensity and postoperative major adverse events and in-hospital mortality.

Results

Of 6497 patients undergoing open AAA repair, 3217 (49·5 per cent) received perioperative statin. Statin users were more likely to present with three or more co-morbidities than non-users (26·5 versus 21·8 per cent; P < 0·001). Unadjusted postoperative mortality was significantly lower in statin users (2·6 versus 6·3 per cent; P < 0·001); however, there was no difference in the risk of developing major adverse events. Multivariable analysis showed that statin use was associated with lower odds of death (odds ratio 0·41, 95 per cent c.i. 0·31 to 0·54). Moderate, high and supratherapeutic statin intensities were not associated with lower odds of death or major adverse events compared with low-intensity statin therapy.

Conclusion

Statin use is associated with lower odds of death in hospital following open AAA repair. High-intensity statins were not associated with lower morbidity or mortality.