[Decision tree diagnostic model of Gan-dan damp-heat syndrome and Gan-stagnancy pi-deficiency syndrome in patients with chronic hepatitis B].

OBJECTIVE

To establish a clinical data based practical diagnostic model for Chinese medicine syndrome differentiation in patients with chronic hepatitis B (CHB), by way of seeking a combination of symptoms, signs and laboratory criteria associated with the typical syndromes of the disease.

METHODS

A syndromatologic and laboratory investigation on 88 symptoms, 20 signs, and 14 laboratory indexes, was conducted in 1004 CHB patients. The clinical data of typical syndromes were selected and analyzed using Logistic regression analysis, decision tree and Bayesian network analysis in combination to establish a diagnostic model for effectively identifying the typical syndromes in CHB.

RESULTS

The most typical syndromes revealed in the 1004 CHB patients were the Gan-dan damp-heat syndrome (340 patients, accounting for 33.86%) and the Gan-stagnancy with Pi-deficiency syndrome (366 patients, 36.45%). Based on the clinical data from patients with the two syndromes, 16 symptoms/signs and 3 laboratory indicators, provided with statistical significance, were selected using Logistic regression analysis. Then, a diagnostic model for differential the two syndromes, which was proved to have an accurate diagnosic rate of 74.36%, was formed by decision tree method. It was found that white tongue coating, light red tongue, yellow sclera, eye dryness, blood levels of alanine aminotransferase and HBeAg appeared to be the effective combination of indexes that may be helpful to differentiate the two syndromes. And the significance of the above-mentioned indexes was also verified by a Bayesian network approach.

CONCLUSION

A decision tree model for diagnosing the two typical syndromes in CHB patients, the Gan-dan damp-heat syndrome and the Gan-stagnancy with Pi-deficiency syndrome, was established, which could be helpful for shifting the diagnosis of syndrome from experience-based to the data-model-based form, to make the syndrome diagnosis more objectively.