ObjectiveTo construct a prediction model of diabetics distal symmetric polyneuropathy (DSPN) based on neural network algorithm and the characteristic data of traditional Chinese medicine and Western medicine. MethodsFrom the inpatients with diabetes in the First Affiliated Hospital of Anhui University of Chinese Medicine from 2017 to 2022, 4 071 cases with complete data were selected. The early warning model of DSPN was established by using neural network, and 49 indicators including general epidemiological data, laboratory examination, signs and symptoms of traditional Chinese medicine were included to analyze the potential risk factors of DSPN, and the weight values of variable features were sorted. Validation was performed using ten-fold crossover, and the model was measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC value. ResultsThe mean duration of diabetes in the DSPN group was about 4 years longer than that in the non-DSPN group (P<0.001). Compared with non-DSPN patients, DSPN patients had a significantly higher proportion of Chinese medicine symptoms and signs such as numbness of limb, limb pain, dizziness and palpitations, fatigue, thirst with desire to drink, dry mouth and throat, blurred vision, frequent urination, slow reaction, dull complexion, purple tongue, thready pulse and hesitant pulse (P<0.001). In this study, the DSPN neural network prediction model was established by integrating traditional Chinese and Western medicine feature data. The AUC of the model was 0.945 3, the accuracy was 87.68%, the sensitivity was 73.9%, the specificity was 92.7%, the positive predictive value was 78.7%, and the negative predictive value was 90.72%. ConclusionThe fusion of Chinese and Western medicine characteristic data has great clinical value for early diagnosis, and the established model has high accuracy and diagnostic efficacy, which can provide practical tools for DSPN screening and diagnosis in diabetic population.
ObjectiveTo analyze the quality of evidence and the use of evidence grades in evidence-based integrated Chinese and Western medicine (ICWM) guidelines, especially the recommendations that include human experience evidence, and then provide references for future guideline development and the grading standards of human experience. MethodsThe literature search was conducted on the PubMed、SinoMed、CNKI、VIP、WanFang Data databases from January 1, 2021 to January 31, 2024, to conduct descriptive statistics on the integrated Chinese and Western medicine guidelines included. In addition, the recommendations that include human experience evidence were further analyzed. ResultsA total of 46 integrated Chinese and Western medicine guideline documents were included, of which 35 were evidence-based. A total of 1 189 recommendations were formed (including 492 TCM recommendations, 265 Western medicine recommendations, 338 integrated Chinese and Western medicine recommendations and 94 other recommendations). Among the 1 189 recommendations, 21.36% were not found in modern research evidence, of which 88.58% did not provide clear supporting evidence, 5.12% and 7.48% were based on ancient books and modern masters' experience, respectively. In addition, there were 29 recommendations with evidence from famous masters in 5 guidelines (1 evidence-based guideline), 16 of which only included famous masters' experience as independent evidence support, and 15 guidelines (10 evidence-based guidelines) included 84 recommendations with evidence from ancient books, with only 10 of them including ancient books as independent evidence support. ConclusionThe phenomenon of lack of clinical research evidence in recommendations in integrated Chinese and Western medicine guidelines is common. A few integrated Chinese and Western medicine guidelines include ancient books and masters' experience as the evidence for recommendations. However, the integration of human experience evidence into the evidence-based system is not uniform, and the results of the quality evaluation of the recommendations are also quite different.
