Practical Analysis of Target Trial Emulation for Cardiorenal Outcomes in the Multimorbid Population

Yafei Luo

doi:10.62051/ijphmr.v6n6.01

Authors

Yafei Luo

DOI:

https://doi.org/10.62051/ijphmr.v6n6.01

Keywords:

Multimorbidity, Cardiorenal Outcomes, Target trial emulation, Clinical trial design, Real-World Research

Abstract

The comorbidity of cardiovascular and metabolic diseases is particularly common among middle-aged and elderly people. Traditional randomized controlled trials have strict inclusion criteria and often exclude subjects with multiple comorbidities, making it difficult for the research conclusions to reflect real clinical practice. This study performed target trial emulation, and selected chronic kidney disease, hypertension, Type 2 diabetes mellitus (T2DM) and heart failure comorbidity groups as the research object. Referring to the domestic CK-NET kidney disease monitoring database, combined with open data from two international cardiorenal trials (CREDENCE, DAPA-CKD), we compared the performance of traditional parallel control, basket test and platform test, and also analyzed the application of single endpoint, composite endpoint and hierarchical outcome indicators in this group of people. A clinically adaptable trial design reduces the required sample size and improves the detection of cardiorenal adverse events. The hierarchical composite endpoint is more suitable for evaluating the prognosis of patients with multimorbidity. This study can provide practical reference for the design of clinical trials and screening of indicators for cardiorenal multimorbidity.

References

[1] Bikbov, B., Purcell, C. A., Levey, A. S., Smith, M., Abdoli, A., Abebe, M., ... & Owolabi, M. O. (2020). Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 395(10225), 709–733. https://doi.org/10.1016/S0140-6736(19)32970-9

[2] Rangaswami, J., Bhalla, V., Blair, J. E., Chang, T. I., Costa, S., Lentine, K. L., ... & American Heart Association Council on the Kidney in Cardiovascular Disease and Council on Clinical Cardiology. (2019). Cardiorenal syndrome: classification, pathophysiology, diagnosis, and treatment strategies: a scientific statement from the American Heart Association. Circulation, 139(16), e840–e878. https://doi.org/10.1161/CIR.0000000000000664

[3] Hernán, M. A., & Robins, J. M. (2016). Using big data to emulate a target trial when a randomized trial is not available. American Journal of Epidemiology, 183(8), 758–764. https://doi.org/10.1093/aje/kwv254

[4] Perkovic, V., Jardine, M. J., Neal, B., Bompoint, S., Heerspink, H. J., Charytan, D. M., ... & Mahaffey, K. W. (2019). Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. New England Journal of Medicine, 380(24), 2295–2306. https://doi.org/10.1056/NEJMoa1811744

[5] Putter, H., Fiocco, M., & Geskus, R. B. (2007). Tutorial in biostatistics: competing risks and multi‐state models. Statistics in Medicine, 26(11), 2389–2430. https://doi.org/10.1002/sim.2762

[6] Matthews, A. A., Danaei, G., Islam, N., & Kurth, T. (2022). Target trial emulation: applying principles of randomised trials to observational studies. BMJ, 378. https://doi.org/10.1136/bmj-2021-068090

[7] Lu, C., Li, X., Broglio, K., Bycott, P., Jiang, Q., Li, X., ... & Ye, J. (2021). Practical considerations and recommendations for master protocol framework: basket, umbrella and platform trials. Therapeutic Innovation & Regulatory Science, 55(6), 1145–1154. https://doi.org/10.1007/s43441-021-00312-2

[8] Matsushita, K., Ballew, S. H., Wang, A. Y. M., Kalyesubula, R., Schaeffner, E., & Agarwal, R. (2022). Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease. Nature Reviews Nephrology, 18(11), 696–707. https://doi.org/10.1038/s41581-022-00616-6

[9] Heerspink, H. J., Jongs, N., Schloemer, P., Little, D. J., Brinker, M., Tasto, C., ... & Gasparyan, S. B. (2023). Development and validation of a new hierarchical composite end point for clinical trials of kidney disease progression. Journal of the American Society of Nephrology, 34(12), 2025–2038. https://doi.org/10.1681/ASN.2023020146

[10] Duan, X. P., Qin, B. D., Jiao, X. D., Liu, K., Wang, Z., & Zang, Y. S. (2024). New clinical trial design in precision medicine: discovery, development and direction. Signal Transduction and Targeted Therapy, 9(1), 57. https://doi.org/10.1038/s41392-024-01789-5

[11] Stevens, P. E., Ahmed, S. B., Carrero, J. J., Foster, B., Francis, A., Hall, R. K., ... & Levin, A. (2024). KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney International, 105(4), S117–S314. https://doi.org/10.1016/j.kint.2024.01.012