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

Che-Yi Chou; Charles C N Wang; Hsiu-Yin Chiang; Chien-Fong Huang; Ya-Luan Hsiao; Chuan-Hu Sun; Chun-Sheng Hu; Min-Yen Wu; Sheng-Hsuan Chen; Chun-Min Chang; Yu-Ting Lin; Jie-Sian Wang; Yu-Cuyan Hong; I-Wen Ting; Hung-Chieh Yeh; Chin-Chi Kuo

doi:10.1038/s43856-023-00241-9

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

Commun Med (Lond). 2023 Feb 7;3(1):19. doi: 10.1038/s43856-023-00241-9.

Authors

Che-Yi Chou^{1

2

3}, Charles C N Wang⁴, Hsiu-Yin Chiang⁵, Chien-Fong Huang⁶, Ya-Luan Hsiao⁷, Chuan-Hu Sun⁶, Chun-Sheng Hu⁶, Min-Yen Wu⁶, Sheng-Hsuan Chen⁶, Chun-Min Chang⁸, Yu-Ting Lin⁶, Jie-Sian Wang³, Yu-Cuyan Hong³, I-Wen Ting^{3

9}, Hung-Chieh Yeh^{3

9}, Chin-Chi Kuo^{10

11

12

13}

Affiliations

¹ Division of Nephrology, Department of Internal Medicine, Asia University Hospital, Wufeng, Taichung, Taiwan.
² Department of Post-baccalaureate Veterinary Medicine, Asia University, Wufeng, Taichung, Taiwan.
³ Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
⁴ Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
⁵ Big Data Center, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan. T32031@mail.cmuh.org.tw.
⁶ Big Data Center, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
⁷ Department of Health Administration, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
⁸ Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.
⁹ AKI-CARE (Clinical Advancement, Research and Education) Center, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
¹⁰ Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan. chinchik@gmail.com.
¹¹ Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan. chinchik@gmail.com.
¹² Big Data Center, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan. chinchik@gmail.com.
¹³ AKI-CARE (Clinical Advancement, Research and Education) Center, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan. chinchik@gmail.com.

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.

Plain language summary

An enlarged heart occurs during various medical conditions and can result in early death. However, it is unclear whether this is also the case in patients with chronic kidney disease (CKD). Although the size of the heart can be measured on chest X-rays, this process is time consuming. We used artificial intelligence to quantify the heart size of 3117 CKD patients based on their chest X-rays within hours. We found that CKD patients with an enlarged heart were more likely to develop end-stage kidney disease or die. This could improve monitoring of CKD patients with an enlarged heart and improve their care.