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.

Introduction: The quantitative effect of underlying non-communicable diseases on acute kidney injury (AKI) incidence and the factors affecting the odds of death among coronavirus disease 2019 (COVID-19) AKI patients were unclear at population level. This study aimed to assess the association between AKI, mortality, underlying non-communicable diseases, and clinical risk factors. Methods: A systematic search of six databases was performed from January 1, 2020, until October 5, 2020. Peer-reviewed observational studies containing quantitative data on risk factors and incidence of renal manifestations of COVID-19 were included. Location, institution, and time period were matched to avoid duplicated data source. Incidence, prevalence, and odds ratio of outcomes were extracted and pooled by random-effects meta-analysis. History of renal replacement therapy (RRT) and age group were stratified for analysis. Univariable meta-regression models were built using AKI incidence as dependent variable, with underlying comorbidities and clinical presentations at admission as independent variables. Results: Global incidence rates of AKI and RRT in COVID-19 patients were 20.40% [95% confidence interval (CI) = 12.07-28.74] and 2.97% (95% CI = 1.91-4.04), respectively, among patients without RRT history. Patients who developed AKI during hospitalization were associated with 8 times (pooled OR = 9.03, 95% CI = 5.45-14.94) and 16.6 times (pooled OR = 17.58, 95% CI = 10.51-29.38) increased odds of death or being critical. At population level, each percentage increase in the underlying prevalence of diabetes, hypertension, chronic kidney disease, and tumor history was associated with 0.82% (95% CI = 0.40-1.24), 0.48% (95% CI = 0.18-0.78), 0.99% (95% CI = 0.18-1.79), and 2.85% (95% CI = 0.93-4.76) increased incidence of AKI across different settings, respectively. Although patients who had a kidney transplant presented with a higher incidence of AKI and RRT, their odds of mortality was lower. A positive trend of increased odds of death among AKI patients against the interval between symptom onset and hospital admission was observed. Conclusion: Underlying prevalence of non-communicable diseases partly explained the heterogeneity in the AKI incidence at population level. Delay in admission after symptom onset could be associated with higher mortality among patients who developed AKI and warrants further research.

Renal involvement in COVID-19 is less well characterized in settings with vigilant public health surveillance, including mass screening and early hospitalization. We assessed kidney complications among COVID-19 patients in Hong Kong, including the association with risk factors, length of hospitalization, critical presentation, and mortality.
Linked electronic records of all patients with confirmed COVID-19 from 5 major designated hospitals were extracted. Duplicated records due to interhospital transferal were removed. Primary outcome was the incidence of in-hospital acute kidney injury (AKI). Secondary outcomes were AKI-associated mortality, incident renal replacement therapy (RRT), intensive care admission, prolonged hospitalization and disease course (defined as >90th percentile of hospitalization duration [35 days] and duration from symptom onset to discharge [43 days], respectively), and change of estimated glomerular filtration rate (GFR). Patients were further stratified into being symptomatic or asymptomatic.
Patients were characterized by young age (median: 38.4, IQR: 28.4-55.8 years) and short time (median: 5, IQR: 2-9 days) from symptom onset to admission. Among the 591 patients, 22 (3.72%) developed AKI and 4 (0.68%) required RRT. The median time from symptom onset to in-hospital AKI was 15 days. AKI increased the odds of prolonged hospitalization and disease course by 2.0- and 3.5-folds, respectively. Estimated GFR 24 weeks post-discharge reduced by 7.51 and 1.06 mL/min/1.73 m2 versus baseline (upon admission) in the AKI and non-AKI groups, respectively. The incidence of AKI was comparable between asymptomatic (4.8%, n = 3/62) and symptomatic (3.7%, n = 19/519) patients.
The overall rate of AKI among COVID-19 patients in Hong Kong is low, which could be attributable to a vigilant screening program and early hospitalization. Among patients who developed in-hospital AKI, the duration of hospitalization is prolonged and kidney function impairment can persist for up to 6 months post-discharge. Mass surveillance for COVID-19 is warranted in identifying asymptomatic subjects for earlier AKI management.