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Uncovering Diagnostic Correlations Between Traditional Chinese and Western Medicine Using Instrumental Variable
Jingjing Zhou1,2, Shangcheng Zhou3, Huijing Chen1
1School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People's Republic of China.
Background:
Little is known about the relationship between syndromes in Traditional Chinese Medicine (TCM) and chronic diseases coded by Western Medicine (WM). TCM hospitals where both WM and TCM are practiced offer an opportunity to assess this relationship. TCM, based on syndrome differentiation and treatment, may aid in guiding treatment and predicting length of stay and healthcare costs. However, inconsistent coding of TCM syndromes arises due to variations in diagnostic interpretation, subjective assessment, and lack of standardized coding practices. The objective was to assess the correlation between WM diagnoses and TCM syndromes, accounting for diagnostic miscoding in the data.
Methods:
We examined discharge data from 1,218,224 records for patients aged 45 and above, diagnosed with cardiometabolic diseases and admitted to TCM hospitals between 2017 and 2022, stays ranging from 24-hours to 90 days. Latent class analysis (LCA) was used to measure the correlation between TCM syndromes and WM. To address potential diagnostic errors, we applied bivariate probit models with instrumental variable (IV).
Results:
There were 580,698 (47.67%) records for males, while 989,702 (81.24%) records from Tertiary-A hospitals. The LCA and probit models revealed that TCM syndrome diagnoses had a high ratio of noise to signal. After correcting for diagnostic errors, significant associations were found between WM diagnoses and TCM syndromes. Notably, diabetes mellitus was strongly associated with syndrome/pattern of qi and yin deficiency (coefficient = 2.711); cerebrovascular diseases showed strong associations with syndrome/pattern of qi deficiency with blood stasis (coefficient = 2.433) and syndrome/pattern of wind and phlegm blocking collaterals (coefficient = 3.176). All patterns had strong marginal effects (P < 0.001).
Conclusion:
This large-scale study quantitatively maps the relationship between TCM and WM diagnoses. It introduces a new statistical approach to understanding the correlation between these two diagnostic systems, offering insights into integrated medicine for secondary prevention.
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