The estimated glomerular filtration rate predicts pacemaker-induced cardiomyopathy

Figures

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  Figure 1. Flowchart of data collection for the study population. Patients who underwent de novo pacemaker implantation were studied between December 2006 and November 2018. Among them, 113 patients who matched the criteria were enrolled. TTE transthoracic echocardiogram, PICM pacing-induced cardiomyopathy.

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  Figure 2. Incidence of PICM in patients with chronic kidney disease. Kaplan–Meier curves are shown for the development of PICM over 3 years of follow-up. The eGFR was categorized into four groups: eGFR ≥ 90 mL/min/1.73 m2, eGFR ≥ 60 mL/min/1.73 m2 and < 90 mL/min/1.73 m2, eGFR > 30 mL/min/1.73 m2 and < 60 mL/min/1.73 m2, and eGFR ≤ 30 mL/min/1.73 m2. A significant difference in the incidence of PICM among the four groups was detected. PICM pacing-induced cardiomyopathy, eGFR estimated glomerular filtration rate (mL/min/1.73 m2).

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  Figure 3. Incidence of PICM in patients with CKD with an eGFR < 60 mL/min/1.73 m2. Kaplan–Meier curves are shown for the development of PICM. The log-rank test was used for comparing patients with and without CKD. There was no significant difference in the incidence of developing PICM between the two groups. PICM pacing-induced cardiomyopathy, CKD chronic kidney disease.

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  Figure 4. Forest plot of hazard ratios for PICM according to a multivariate analysis by the Cox model. After adjusting for the QRS duration, eGFR, and CAD, a multivariate Cox regression analysis showed that an eGFR ≤ 30 mL/min/1.73 m2 and CAD were independent risk factors for developing PICM. CI confidence interval, HR hazard ratio, eGFR estimated glomerular filtration rate, CAD coronary artery disease.