Safety signal identification for COVID-19 bivalent booster vaccination using tree-based scan statistics in the Vaccine Safety Datalink

BackgroundTraditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination.MethodsVaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code “tree” and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance.ResultsAnalysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1–3, p = 0.0001–0.0007), influenza (Days 35–56, p = 0.0001), cough (Days 44–55, p = 0.0002), and COVID-19 (Days 52–56, p = 0.0004).ConclusionsFor Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the “tree.”     

Creating Experimental Analogs with Available Clinical Information

SUMMARY

Comparison of the implementation and findings of a “gold standard” evaluation of social work intervention and its experimental analog based on available clinical information illustrates the strengths and weaknesses of each. From a practice-research integration perspective, however, “clinical data-mining” may be a credible alternative to randomized controlled experiments.     

周宜轩辨治高血压病的数据挖掘分析

目的：挖掘与总结周宜轩教授临床辨治高血压病的诊疗规律及学术思想。方法选取2012年12月－2013年12月周教授诊治的原发性高血压病患者149例,208诊次,将其初诊数据录入数据库,进行名词标准化,对其临床证候、治法、方药进行统计分析。结果149例高血压病患者,按照证候出现频次排序,由高到低依次为痰湿中阻证、气虚血瘀证、气阴两虚证、肝阳上亢证、肝肾阴虚证等。方剂多为参苓白术散、补阳还五汤、生脉散、天麻钩藤饮、六味地黄丸等加减。用药频次排序,由高到低依次为炙黄芪、佛手、川芎、丹参、当归、山萸肉、白芍、赤芍、炒白术、酸枣仁、枳壳、薏苡仁、党参、合欢皮、水蛭。结论高血压病是本虚标实证,临床以气虚、气阴两虚、肝肾阴虚为本,肝阳上亢、瘀血、痰浊为标。周教授善于用益气、养阴、理气、化痰、活血、安神法治疗高血压病,体现了周教授的学术思想。     

Stratifying the risk of lymph node metastasis in undifferentiated-type early gastric cancer

AIM:To study how lymph node metastasis(LNM) risk is stratified in undifferentiated-type early gastric cancer(undiff-EGC) dependent on combinations of risk factors.METHODS:Five hundred and sixty-seven cases with undiff-EGC undergoing gastrectomy with lymphadenectomy were examined retrospectively.Using clinicopathological factors of patient age,location,size,an endoscopic macroscopic tumor form,ulceration,depth,histology,lymphatic involvement(LI) and venous involvement(VI),LNM risk was examined and stratified by conventional statistical analysis and datamining analysis.RESULTS:LNM was positive in 44 of 567 cases(7.8%).Univariate analysis revealed > 2 cm,protrusion,submucosal(sm),mixed type,LI and VI as significant prognostic factors and > 2 cm and LI-positive were independent factors by multivariate analysis.In preoperatively evaluable factors excluding LVI,sm and > 2 cm were independent factors.According to the depth and size,cases were categorized into the low-risk group [m and ≤ 2 cm,0%(LNM incidence)],the moderaterisk group(m and > 2 cm,5.6%; and sm and ≤ 2 cm,6.0%),and the high-risk group(sm and > 2 cm,19.3%).On the other hand,LNM occurred in 1.4% in all LI-negative cases,greatly lower than 28.2% in all LI-positive cases,and LNM incidence was low in LInegative cases even in the moderate- and high-risk groups.CONCLUSION:LNM-related factors in undiff-EGC were depth and size preoperatively while those were LI and size postoperatively.Among these factors,LI was the most significantly correlated factor.     

主成分分析在经前期综合症数据处理中的应用

"经前期综合症(PMS)肝气逆证辨证规范与疗效评价标准研究"项目收集了较大数量的数据,应用主成分分析进行降维处理,提取出经前期综合症的主证候要素并探讨证候要素的分布,运用SQLAS(Analysis Services)实现聚集检验,初步构建经前期综合症的辩证及数学模型。     

基于数据挖掘对庄礼兴教授治疗腰痛病用药规律分析

目的：基于数据挖掘发现庄礼兴教授腰痛病用药规律,为中药治疗腰痛病提供参考。方法：收集庄礼兴教授中药治疗腰痛患者病例223例,对符合纳入标准病例的处方中的药物进行统计。运用Microsoft Excel 2010进行中药性味归经及频次统计,运用SPSS Statistics 21.0进行聚类分析和因子分析,利用SPSS modeler 14.1及Gephi 0.92分别进行关联法则分析和复杂网络分析。结果：共纳入处方223张,涉及中药116味,其中用药频次较高的药物依次是鸡血藤、甘草、白芍、当归、牛膝;使用频次＞7的药物,以温、平和甘、苦、辛味为主,入肝、肾、脾经药物居多。对高频药物因子分析后进行聚类分析,分别形成4个聚类组合。通过关联规则分析,共得出药对规则3条。复杂网络分析形成了以鸡血藤、牛膝、当归、白芍、桂枝、桑寄生、细辛、牛大力、千斤拔、甘草为主的核心网络。结论：庄礼兴教授治疗腰痛病时注重活血温经、补肾祛湿,在临床常能获得满意疗效。     

基于数据挖掘技术的小儿腹泻临床证型分布特点研究

目的探讨小儿腹泻病的临床证型分布特点。方法收载古今文献1359篇,应用频数分析、关联规则、频繁模式增长树等数据挖掘方法,对小儿腹泻的常见症状、主要证型及症-证关联等进行研究分析。结果提取了小儿腹泻病临床高频症状及核心症状组团;常见证型17个及其判定要点8个,初步获取了小儿腹泻常见证型的症-证对应关系。结论从症、证、症-证的角度初步揭示了小儿腹泻病的临床证型分布特点,在一定程度上完善了对小儿腹泻中医证治规律的认识。