Cardiothoracic ratio values and trajectories are associated with risk of requiring dialysis and mortality in chronic kidney disease

Abstract

Background The prognostic role of the cardiothoracic ratio (CTR) in chronic kidney disease (CKD) remains undetermined. Methods We conducted a retrospective cohort study of 3117 patients with CKD aged 18–89 years who participated in an Advanced CKD Care Program in Taiwan between 2003 and 2017 with a median follow up of 1.3(0.7–2.5) and 3.3(1.8–5.3) (IQR) years for outcome of end-stage renal disease (ESRD) and overall death, respectively. We developed a machine learning (ML)–based algorithm to calculate the baseline and serial CTRs, which were then used to classify patients into trajectory groups based on latent class mixed modelling. Association and discrimination were evaluated using multivariable Cox proportional hazards regression analyses and C-statistics, respectively. Results The median (interquartile range) age of 3117 patients is 69.5 (59.2–77.4) years. We create 3 CTR trajectory groups (low [30.1%], medium [48.1%], and high [21.8%]) for the 2474 patients with at least 2 CTR measurements. The adjusted hazard ratios for ESRD, cardiovascular mortality, and all-cause mortality in patients with baseline CTRs ≥0.57 (vs CTRs <0.47) are 1.35 (95% confidence interval, 1.06–1.72), 2.89 (1.78–4.71), and 1.50 (1.22–1.83), respectively. Similarly, greater effect sizes, particularly for cardiovascular mortality, are observed for high (vs low) CTR trajectories. Compared with a reference model, one with CTR as a continuous variable yields significantly higher C-statistics of 0.719 (vs 0.698, P = 0.04) for cardiovascular mortality and 0.697 (vs 0.693, P < 0.001) for all-cause mortality. Conclusions Our findings support the real-world prognostic value of the CTR, as calculated by a ML annotation tool, in CKD. Our research presents a methodological foundation for using machine learning to improve cardioprotection among patients with CKD.

Correlations of sST2 and Gal-3 with Cardiothoracic Ratio in Patients with Chronic Kidney Disease

“…For instance, a comprehensive, prospective cohort study involving nearly 3500 participants over four years demonstrated that among HD patients, an elevated CTR correlates with increased mortality risks, both overall and cardiovascular-specific [37]. Multiple studies demonstrated that the CTR serves as a crucial marker for mortality and declining functional health in patients initiating dialysis and those with ESRD undergoing consistent HD or peritoneal dialysis [32,[37][38][39][40]. Recently, Chou et al used a machine learning algorithm to analyze the CTR in 3117 CKD patients from Taiwan and found that higher CTR values significantly correlated with increased risks of ESRD [40].…”

“…Multiple studies demonstrated that the CTR serves as a crucial marker for mortality and declining functional health in patients initiating dialysis and those with ESRD undergoing consistent HD or peritoneal dialysis [32,[37][38][39][40]. Recently, Chou et al used a machine learning algorithm to analyze the CTR in 3117 CKD patients from Taiwan and found that higher CTR values significantly correlated with increased risks of ESRD [40]. Their findings confirm the practical prognostic value of the CTR, as assessed by a machine learning annotation tool, within the setting of CKD.…”