背景:先前的英国生物银行研究表明,症状和身体测量对一般人群的长期临床结果具有很好的预测作用。症状和体征可以直观和非侵入性地预测和监测疾病进展,尤其是远程医疗,但是相关的研究在糖尿病和肾脏医学方面是有限的。方法:这项回顾性队列研究旨在评估基于症状的分层框架和个体糖尿病症状的预测能力。连续从香港的门诊诊所抽取三百名成年糖尿病患者进行前瞻性症状评估。从链接的医疗记录中回顾性地提取了人口统计学和生化参数的纵向测量。估算的肾小球滤过率(GFR)(自变量)与生物化学之间的关联,流行病学因素,通过混合回归分析评估个体症状.采用德尔菲共识法建立了基于症状聚类的糖尿病症状分层框架。Akaike信息准则(AIC)和贝叶斯信息准则(BIC)在具有不同生化组合的统计模型之间进行了比较,流行病学,和症状变量。结果:在4.2年的随访期内,水肿的基线表现(-1.8ml/min/1.73m2,95CI:-2.5至-1.2,p<0.001),上腹胀(-0.8ml/min/1.73m2,95CI:-1.4至-0.2,p=0.014)和干便和稀便交替(-1.1ml/min/1.73m2,95CI:-1.9至-0.4,p=0.004)与较快的年度GFR下降独立相关。从文献中确定了11个症状群,主要通过胃肠道表型对糖尿病进行分层。与使用单个症状相比,使用Delphi共识同步的症状聚类作为统计模型中的自变量降低了复杂性并提高了解释力。症状-生物-流行病学联合模型的AIC最低(4,478vs.5,824vs.4,966vs.7,926)和BIC(4,597vs.5,870vs.5,065vs.8,026)与症状相比,症状流行病学和生物流行病学模型,分别。患者共同表现出一系列的疲劳,萎靡不振,口干,和干燥的咽喉与较快的年度GFR下降独立相关(-1.1ml/min/1.73m2,95CI:-1.9至-0.2,p=0.011)。结论:基于关键生化和流行病学因素的基于症状的附加诊断提高了糖尿病患者肾功能下降的预测能力。在临床实践和研究设计中应考虑症状的动态变化。
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/1.73m2, 95%CI: -2.5 to -1.2, p < 0.001), epigastric bloating (-0.8 ml/min/1.73m2, 95%CI: -1.4 to -0.2, p = 0.014) and alternating dry and loose stool (-1.1 ml/min/1.73m2, 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/1.73m2, 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.