Abstract: BACKGROUND: The combination of traditional Chinese medicine syndrome and semen quality-related parameters can jointly predict the occurrence of abnormal increase in sperm DNA fragmentation index (DFI) and draw a column chart, which can significantly improve clinical practicality and application efficiency, provide a basis for comprehensive evaluation of semen quality in clinical practice, take active intervention measures to improve clinical outcomes, and formulate personalized medical plans.
OBJECTIVE: To explore the prediction model and verification of sperm DNA fragments based on traditional Chinese medicine syndrome and semen quality-related parameters.  
METHODS: Retrospective analysis was made on 420 infertile patients who received traditional Chinese medicine syndrome diagnosis and sperm DNA fragment rate examination in the Department of Traditional Chinese Medicine Andrology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region from July 2019 to July 2021. According to the Manual of Human Semen Examination and Treatment Laboratories (6th Edition), 137 patients with sperm DFI>30% were included in the group of abnormally high sperm DFI, and 283 patients with sperm DFI ≤ 30% were taken as the control group. First, univariate analysis was used to screen the influencing factors of the abnormal increase of sperm DFI. Then, the best matching factor was selected by using the collinearity problem of LASSO correction factors. Then, it was included in the multifactor forward stepwise logistic regression to find out its independent influencing factors and draw a nomogram. Finally, the receiver operating characteristic curve, calibration curve, decision curve analysis and clinical impact curve were used to verify the differentiation and accuracy of the prediction model and its clinical application effectiveness. 
RESULTS AND CONCLUSION: (1) The results of the univariate analysis showed that age, body mass index, forward motion rate, total sperm motility, sperm concentration, sperm morphology, kidney yang deficiency syndrome, damp heat downpour syndrome, and kidney sperm deficiency syndrome were the influencing factors for the abnormal increase of sperm DFI (P < 0.05). (2) The best matching factors further screened by LASSO regression were age, body mass index, total sperm motility, sperm concentration, sperm morphology, kidney yang deficiency syndrome, damp heat downpour syndrome, and kidney essence deficiency syndrome (P < 0.05). (3) Multifactor forward stepwise Logistic regression showed that age, body mass index, sperm concentration, total sperm motility, damp heat downpour syndrome, and kidney yang deficiency syndrome were six independent factors that caused the abnormal increase in sperm DFI. (4) Receiver operating characteristic curve showed that the area under the curve of the model group was 0.760(0.713,0.806), and the area under the curve of the validation group was 0.745(0.714,0.776). It showed that the prediction model had good discrimination. (5) The average absolute error of the calibration curve was 0.040, and the Hosmer Lemeshow test (P > 0.05), suggesting that there was no significant statistical difference between the probability of the abnormal increase in DFI of spermatozoa predicted by the model and the probability of the abnormal increase in DFI of spermatozoa actually occurred, which confirmed that the model had good accuracy. (6) Decision curve analysis and clinical impact curve showed that the model group and validation group had the maximum clinical net benefit when the threshold probability values were (0.08-0.84) and (0.09-0.78) respectively, and had good clinical application efficiency within the threshold probability range. (7) These findings conclude that age, body mass index, sperm concentration, total sperm viability, damp heat downpour syndrome and kidney yang deficiency syndrome are independent factors that cause the abnormal increase in sperm DFI. The nomogram of the clinical prediction model constructed by them has good clinical prediction value and clinical application efficiency, and can provide the basis for comprehensive clinical evaluation of semen quality and individualized medical service.

Key words: sperm DNA fragment, sperm DNA integrity, traditional Chinese medicine syndromes, sperm DNA fragmentation index, prediction model