Add-On Chinese Medicine for Coronavirus Disease 2019 (ACCORD): A Retrospective Cohort Study of Hospital Registries

Shu, Zixin; Chang, Kai Wei; Zhou, Yue; Peng, C.; Li, Xugui; Cai, Wei; Li, Wei; Zheng, Qiguang; Tian, Haoyu; Xia, Jianan; Yang, Kuo; Wang, Ning; Ji-fen, Liu; Min, Xiaojun; Yan, Dengying; Sun, Jing; Wu, Huan; Li, Yongming; Zheng, Yi; Yu, Zecong; Lu, Xi; Yang, Yongjian; Jia, Ting; Ji, Jinghui; Zou, Qunzheng; Wang, Yinyan; Xiao, Minzhong; Zhang, Qing; Xiong, Yajuan; Sun, Feng; Zhu, Qiang; Xi, Jiang; Wang, Guodong; Tang, Sydney C.W.; Zhang, Junhua; Li, Xiuyang; Zhang, Nevin L.; Zhang, Boli; Tong, Xiaolin; Liu, Baoyan; Zhou, Xenzhong; Chan, Kam Wa; Li, Xiaodong

doi:10.1142/s0192415x21500257

Cited by 22 publications

(16 citation statements)

References 44 publications

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“…Cytokine storm and elevated D-dimer levels have been widely reported and suspected to correlate with poorer clinical outcomes (8,(19)(20)(21). Previous renal pathology studies showed that COVID-19 patients with clinical signs of kidney injuries had diffuse proximal tubule injury, loss of brush border, nonisometric vacuolar degeneration, and gross necrosis with possible Abbreviations: AKI, acute kidney injury; COVID-19, coronavirus disease 2019; KDIGO, Kidney Disease Improving Global Outcomes; OR, odds ratio; RRT, renal replacement therapy; SMD, standardized mean difference; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; WMD, weighted mean difference.…”

Section: Introductionmentioning

confidence: 99%

Global REnal Involvement of CORonavirus Disease 2019 (RECORD): A Systematic Review and Meta-Analysis of Incidence, Risk Factors, and Clinical Outcomes

Chan

Lee

et al. 2021

Front. Med.

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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.

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Section: Introductionmentioning

confidence: 99%

Global REnal Involvement of CORonavirus Disease 2019 (RECORD): A Systematic Review and Meta-Analysis of Incidence, Risk Factors, and Clinical Outcomes

Chan

Lee

et al. 2021

Front. Med.

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“…Fever, fatigue, and amnesia have great overlapping pathways and co-occurrence with insomnia, which reflected the high diversity of these insomnia symptom pairs from both phenotype and molecular mechanisms (Table 3 ). For example, there are many reasons for insomnia patients with fever, such as influenza 57 , tuberculosis 58 , pneumonia 59 , tumors 60 , and neurological disorders 61 , which would be involved in various molecular pathways, including the immune system pathway (e.g., intestinal immune network for IgA production and intestinal immune network for IgA production), signal transduction pathway (e.g., cAMP signaling pathway and AMPK signaling pathway), and infectious disease pathway (e.g., Influenza A and Tuberculosis) (Fig. 6 ).…”

Section: Resultsmentioning

confidence: 99%

Diversity and molecular network patterns of symptom phenotypes

et al. 2021

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Symptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein–protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.

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“…Symptom-based research offers a new perspective in assessing correlations in pathogenesis among diseases, accelerating the discovery of secondary application of existing drugs ( 29 ). In this study, edema is a strongly independent cross-sectional and longitudinal predictor of reduced renal function.…”

Section: Discussionmentioning

confidence: 99%

“…Previous big data studies showed that add-on symptom-based CM treatment led to reduced risk of end-stage kidney disease and mortality among chronic kidney disease (CKD) patients with diabetes ( 26 – 28 ). CM subclassifies diseases based on different symptom combinations on top of conventional medicine diagnosis to formulate personalized regimens ( 12 , 17 , 29 ). In each symptom-based subtype (SS) of diabetes, related symptoms are weighed into core and supplementary criteria based on the importance to diagnosis ( 30 ).…”

Section: Introductionmentioning

confidence: 99%

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

Chan

Chow²,

et al. 2021

Front. Med.

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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.

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Add-On Chinese Medicine for Coronavirus Disease 2019 (ACCORD): A Retrospective Cohort Study of Hospital Registries

Cited by 22 publications

References 44 publications

Global REnal Involvement of CORonavirus Disease 2019 (RECORD): A Systematic Review and Meta-Analysis of Incidence, Risk Factors, and Clinical Outcomes

Global REnal Involvement of CORonavirus Disease 2019 (RECORD): A Systematic Review and Meta-Analysis of Incidence, Risk Factors, and Clinical Outcomes

Diversity and molecular network patterns of symptom phenotypes

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

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