2514 Background: Genomic findings have diagnostic, prognostic, and predictive utility in clinical oncology. Population studies have been limited by reliance on trials, registries, or institutional chart review, which are costly and represent narrow populations. Integrating electronic health record (EHR) and genomic data collected as part of routine clinical practice may overcome these hurdles. Methods: Patients in the Flatiron Health Database with non-small cell lung cancer (NSCLC) who underwent comprehensive genomic profiling (CGP) by Foundation Medicine were included. EHR processing included structured data harmonization and abstraction of variables from unstructured documents. EHR and CGP data were de-identified and linked in a HIPAA-compliant process. Data included clinical characteristics, alterations across > 300 genes, tumor mutation burden (TMB), therapies and associated real-world responses, progression, and overall survival (OS). Results: The cohort (n = 1619) had expected clinical (mean age 66; 75% with smoking hx; 80% non-squamous) and genomic (18% EGFR; 4% ALK; 1% ROS1) properties of NSCLC. Presence of a driver mutation (EGFR, ALK, ROS1, MET, BRAF, RET, or ERBB2; n = 576) was associated with younger age, female gender, non-smoking, improved OS (35 vs 19 mo, LR p < 0.0001), and prolonged survival when treated with NCCN-recommended therapy (42 vs 28 mo, LR p = 0.001). CGP identified false negative results in up to 30% of single-biomarker tests for EGFR, ALK, and ROS1. CGP accuracy was supported by clinical outcomes. For example, 5 patients with prior negative ALK-fusion testing began ALK-directed therapy after positive CGP results. All 5 exhibited at least a partial response as recorded in the EHR by treating clinicians. Immunotherapy was used in 22% of patients (n = 353). TMB predicted response to nivolumab, including in PD-L1 negative populations. We recapitulated known associations with smoking, histology, and driver mutations. Conclusions: We present and validate a new paradigm for rapidly generating large, research-grade, longitudinal clinico-genomic databases by linking genomic data with EHR clinical annotation. This method offers a powerful tool for understanding cancer genomics and advancing precision medicine.

Objective: To describe how the Asian National Cancer Centers Alliance (ANCCA) members preserve high standards of care for cancer patients while battling the COVID-19 pandemic and to propose new strategies in the Asian Cancer Centers’ preparedness to future pandemics. Methods: A 41-question-based survey was developed using an online survey tool and conducted among 15 major Asian National Cancer Centers, including 13 ANCCA members. Direct interviews of several specialists were conducted subsequently to obtain additional answers to key questions that emerged during the survey analysis. Result: Institution/country-specific results provided a strong insight on the diverse ways of managing the pandemic around Asia, while maintaining well-balanced cancer care. Pragmatic strategies were put in place in each NCC hospital, including zoning and intensive triage depending on the pandemic impact. Distancing strategies and telemedicine were implemented in different capacity depending on the national healthcare system. In addition, there was a diverse impact on the manpower and financial aspect of cancer care across surveyed NCCs relating to magnitude of the pandemic impact on the country. Conclusion: The priorities nevertheless remain on maintaining cancer care delivery while protecting both patients and health care workers from the risk of COVID-19 infection. The role of a think-tank such as ANCCA to help share experiences in a timely manner can enhance preparedness in future pandemic scenarios.

Background: This study aims to establish a predictive risk model for deep vein thrombosis (DVT) in patients with acute exacerbation chronic obstructive pulmonary disease (AECOPD) based on serum angiopoietin 2 (Ang-2) levels.Methods: The research sample consisted of 650 patients with AECOPD admitted to the First Affiliated Hospital of Chengdu Medical College from January 2019 to January 2021, who were subsequently divided into a modeling group and a verification group. A univariate analysis was performed on the identified risk factors for DVT in AECOPD, and the significant factors were then incorporated into a multivariate logistic regression model to screen for the independent predictors of DVT. A nomogram was constructed, and a receiver operating characteristic curve (ROC), Hosmer-Lemeshow test, decision curve, and clinical impact curve in the modeling and validation cohort were used to analyze the discrimination power, calibration, and clinical validity of the predictive risk nomogram model of AECOPD with comorbid DVT.Results: Univariate and multivariate logistic regression analyses showed that lower limb edema, BMI, diabetes, respiratory failure, D-dimer, and serum Ang-2 were risk factors for DVT in AECOPD. A nomogram model for predicting AECOPD with comorbid DVT was successfully established. The AUC values for the modeling group and the verification group were 0.844 (95% CI: 0.808-0.932) and 0.755 (95% CI: 0.679-0.861), respectively. According to the Hosmer-Lemeshow test, the P values of the nomogram in the modeling group and the verification group were 0.124 and 0.086, respectively. The decision curve and clinical impact curve suggested that most patients can benefit from this prediction model, and the predicted probability of the model was essentially the same as the actual clinical probability of DVT. Conclusions:The predictive risk nomogram model of AECOPD with comorbid DVT based on serum Ang-2 levels has good discrimination power, calibration, and clinical influence. The model is a good fit and has a high predictive value, which helps clinicians identify AECOPD patients at high risk of DTV and formulate corresponding prevention and treatment measures.

Objective: This study aimed to evaluate the association between sleep duration and hypertension among adults in southwest China.Methods: Baseline variables were collected from a representative sample of 20,053 adults aged 23-98 years in southwest China who received physical examinations from January 2019 to December 2020. All participants were categorized into either a hypertension group or a non-hypertension group. Sleep duration was classified as short (<6 h/day), normal (6-8 h/day),or long (>8 h/day). Baseline variables were compared between individuals with and without hypertension by rank-sum tests for two independent samples or χ 2 tests for nonparametric data. Multivariate logistic regression analysis was performed to evaluate the association between sleep duration and hypertension. Results:The overall incidence of hypertension was 51.2%. Unadjusted analysis showed that the risk of hypertension was higher in individuals with short (<6h/day) or long (>8h/ day) sleep durations compared with those with a normal (6-8 h/day) sleep duration. The risk of hypertension was significantly increased by 30.1% in participants with a long (>8h/day) sleep duration compared with those with a normal (6-8h/day) sleep duration (OR = 1.301, P < 0.010, 95%CI = 1.149-1.475). The risk of hypertension was also increased by 1.1% in participants with a short (<6h/day) sleep duration compared with participants with a normal (6-8h/day) sleep duration, but the difference was not significant (OR = 1.011, P = 0.849, 95%CI = 0.905-1.129). After fully adjusting for confounding factors (model 4), the risk of hypertension was increased significantly (by 25%) in individuals with a short (<6h/day) sleep duration (OR = 1.25, P = 0.02, 95%CI = 1.036-1.508) but not in those with a long (>8h/day) sleep duration (17.5% increase) compared with participants with a normal (6-8h/day) sleep duration (OR = 1.175, P = 0.144, 95%CI = 0.946-1.460). Conclusion:The results of this study indicate that a short (<6h/day) sleep duration is related to an increased risk of hypertension, suggesting that sleep helps to protect against hypertension.

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ObjectivesTo evaluate the safety of each anti-TNF therapy for patients with rheumatoid arthritis (RA) and then make the best choice in clinical practice.MethodsWe searched PUBMED, EMBASE, and the Cochrane Library. The deadline for retrieval is August 2021. The ORs, Confidence Intervals (CIs), and p values were calculated by STATA.16.0 software for assessment.Result72 RCTs involving 28332 subjects were included. AEs were more common with adalimumab combined disease-modifying anti-rheumatic drugs (DMARDs) compared with placebo (OR = 1.60, 95% CI: 1.06, 2.42), DMARDs (1.28, 95% CI: 1.08, 1.52), etanercept combined DMARDs (1.32, 95% CI: 1.03, 1.67); certolizumab combined DMARDs compared with placebo (1.63, 95% CI: 1.07, 2.46), DMARDs (1.30, 95% CI: 1.10, 1.54), etanercept combined DMARDs (1.34, 95% CI: 1.05, 1.70). In SAEs, comparisons between treatments showed adalimumab (0.20, 95% CI: 0.07, 0.59), etanercept combined DMARDs (0.39, 95% CI: 0.15, 0.96), golimumab (0.19, 95% CI: 0.05, 0.77), infliximab (0.15, 95% CI: 0.03,0.71) decreased the risk of SAEs compared with golimumab combined DMARDs. In infections, comparisons between treatments showed adalimumab combined DMARDs (0.59, 95% CI: 0.37, 0.95), etanercept (0.49, 95% CI: 0.28, 0.88), etanercept combined DMARDs (0.56, 95% CI: 0.35, 0.91), golimumab combined DMARDs (0.51, 95% CI: 0.31, 0.83) decreased the risk of infections compared with infliximab combined DMARDs. No evidence indicated that the use of TNF-α inhibitors influenced the risk of serious infections, malignant tumors.ConclusionIn conclusion, we regard etanercept monotherapy as the optimal choice for RA patients in clinical practice when the efficacy is similar. Conversely, certolizumab + DMARDs therapy is not recommended.Systematic Review Registrationidentifier PROSPERO CRD42021276176.

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