Risk factors for the development of premature ventricular complex-induced cardiomyopathy: a systematic review and meta-analysis

Fig. 1. Study selection chart flow

Fig. 2. Random effects model showing the overall effect of age on the risk of developing PVC-CM. TE, estimate of treatment effect; seTE, standard error of treatment estimate; OR, odds ratio; CI, confidence interval

Fig. 3. Random effects model showing the overall effect of overall PVC burden on the risk of developing PVC-CM. TE, estimate of treatment effect; seTE, standard error of treatment estimate; OR, odds ratio; CI, confidence interval

Fig. 4. Random effects model showing the overall effect of epicardial origin of the PVC on the risk of the developing PVC-CM. TE, estimate of treatment effect; seTE, standard error of treatment estimate; OR, odds ratio; CI, confidence interval

Fig. 5. Random effects model showing the overall effect of interpolated PVCs on the risk for PVC-CM. TE, estimate of treatment effect; seTE, standard error of treatment estimate; OR, odds ratio; CI, confidence interval

Fig. 6. Random effects model showing the overall effect of left ventricular origin of the PVC on the risk of the developing PVC-CM. TE, estimate of treatment effect; seTE, standard error of treatment estimate; OR, odds ratio; CI, confidence interval

Fig. 7. Random effects model showing the overall effect of non-sustained ventricular tachycardia on the risk for PVC-CM. TE, estimate of treatment effect; seTE, standard error of treatment estimate; OR, odds ratio; CI, confidence interval

Fig. 8. Random effects model showing the overall effect of symptoms on the risk for PVC-CM. TE, estimate of treatment effect; seTE, standard error of treatment estimate; OR, odds ratio; CI, confidence interval

Fig. 9. Random effects model showing the effect of PVC duration (per ms increase in QRS PVC duration) on the risk for PVC-CM. TE, estimate of treatment effect; seTE, standard error of treatment estimate; OR, odds ratio; CI, confidence interval

Fig. 10. Dose–response plot of PVC burden and association with PVC-CMP. Based on 7 studies reporting PVC burden with a cutoff, a dose–response analysis was conducted. The black line represents the predicted increase in PVC-CMP risk associated with an increase in PVC burden in %. The gray ribbon represents the confidence interval of the prediction

Fig. 11. Assessment of publication bias using a contour-enhanced funnel plot. The contour-enhanced funnel plot represents the different studies reporting estimated for the association between PVC burden (continuous increase in %) and assess the risk for publication bias. The 7 studies reporting a cutoff of PVC burden were summarized beforehand as the “dose–response analysis.” The dotted line represents the overall estimate using all available studies and the dashed line represents a classical funnel plot with the expected distribution of the studies if no publication bias is present. The contour-enhanced funnel plot is centered at 0 (i.e., the value under the null hypothesis of no relationship) and various levels of statistical significance are indicated by the shaded region. The white region corresponds to non-significant P values. Highly significant P values appear in the light gray region

Fig. 12. Assessment of study quality. Evaluation of study quality according to the QUIPS tool. Five domains of bias (participation, attrition, prognostic factor measurement, outcome measurement, confounding and statistical analysis and reporting) are represented with the associated risk of bias (high in red, moderate in yellow, and low in green). The overall column represents the mean risk of bias from the 6 domains