SYmptom-Based STratification of DiabEtes Mellitus by Renal Function Decline (SYSTEM): A Retrospective Cohort Study and Modeling Assessment

Frontiers in Medicine

article

Kam Wa Chan¹  Tak Yee Chow²  Kam Yan Yu¹  Yulong Xu³  Nevin Lianwen Zhang⁴  Vivian Taam Wong⁵  Saimei Li⁶  Sydney Chi Wai Tang¹ 

[1] Department of Medicine, The University of Hong Kong;Hong Kong Association for Integration of Chinese-Western Medicine;School of Information Technology, Henan University of Traditional Chinese Medicine;Department of Computer Science and Engineering, The Hong Kong University of Science and Technology;School of Chinese Medicine, The University of Hong Kong;The First Affiliated Hospital, Guangzhou University of Chinese Medicine

关键词: diabetes;    diabetic kidney disease;    renal medicine;    epidemiology;    integrative medicine;    symptom;    diagnosis;    traditional chinese medicine;   

DOI  :  10.3389/fmed.2021.682090

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Background: Previous UK Biobank studies showed that symptoms and physical measurements had excellent prediction on long-term clinical outcomes in general population. Symptoms and signs could intuitively and non-invasively predict and monitor disease progression, especially for telemedicine, but related research is limited in diabetes and renal medicine. Methods: This retrospective cohort study aimed to evaluate the predictive power of a symptom-based stratification framework and individual symptoms for diabetes. Three hundred two adult diabetic patients were consecutively sampled from outpatient clinics in Hong Kong for prospective symptom assessment. Demographics and longitudinal measures of biochemical parameters were retrospectively extracted from linked medical records. The association between estimated glomerular filtration rate (GFR) (independent variable) and biochemistry, epidemiological factors, and individual symptoms was assessed by mixed regression analyses. A symptom-based stratification framework of diabetes using symptom clusters was formulated by Delphi consensus method. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were compared between statistical models with different combinations of biochemical, epidemiological, and symptom variables. Results: In the 4.2-year follow-up period, baseline presentation of edema (−1.8 ml/min/1.73m 2 , 95%CI: −2.5 to −1.2, p < 0.001 ), epigastric bloating (−0.8 ml/min/1.73m 2 , 95%CI: −1.4 to −0.2, p = 0.014) and alternating dry and loose stool (−1.1 ml/min/1.73m 2 , 95%CI: −1.9 to −0.4, p = 0.004) were independently associated with faster annual GFR decline. Eleven symptom clusters were identified from literature, stratifying diabetes predominantly by gastrointestinal phenotypes. Using symptom clusters synchronized by Delphi consensus as the independent variable in statistical models reduced complexity and improved explanatory power when compared to using individual symptoms. Symptom-biologic-epidemiologic combined model had the lowest AIC (4,478 vs. 5,824 vs. 4,966 vs. 7,926) and BIC (4,597 vs. 5,870 vs. 5,065 vs. 8,026) compared to the symptom, symptom-epidemiologic and biologic-epidemiologic models, respectively. Patients co-presenting with a constellation of fatigue, malaise, dry mouth, and dry throat were independently associated with faster annual GFR decline (−1.1 ml/min/1.73m 2 , 95%CI: −1.9 to −0.2, p = 0.011). Conclusions: Add-on symptom-based diagnosis improves the predictive power on renal function decline among diabetic patients based on key biochemical and epidemiological factors. Dynamic change of symptoms should be considered in clinical practice and research design.

【 授权许可】

CC BY