Machine Learning Approach for Active Vaccine Safety Monitoring.

BACKGROUND: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active surveillance, we developed and verified a machine learning-based active surveillance system using national claim data. METHODS: We used two databases, one from the Korea Disease Control and Prevention Agency, which contains flu vaccination records for the elderly, and another from the National Health Insurance Service, which contains the claim data of vaccinated people. We developed a case-crossover design based machine learning model to predict the health outcome of interest events (anaphylaxis and agranulocytosis) using a random forest. Feature importance values were evaluated to determine candidate associations with each outcome. We investigated the relationship of the features to each event via a literature review, comparison with the Side Effect Resource, and using the Local Interpretable Model-agnostic Explanation method. RESULTS: The trained model predicted each health outcome of interest with a high accuracy (approximately 70%). We found literature supporting our results, and most of the important drug-related features were listed in the Side Effect Resource database as inducing the health outcome of interest. For anaphylaxis, flu vaccination ranked high in our feature importance analysis and had a positive association in Local Interpretable Model-Agnostic Explanation analysis. Although the feature importance of vaccination was lower for agranulocytosis, it also had a positive relationship in the Local Interpretable Model-Agnostic Explanation analysis. CONCLUSION: We developed a machine learning-based active surveillance system for detecting possible factors that can induce adverse events using health claim and vaccination databases. The results of the study demonstrated a potentially useful application of two linked national health record databases. Our model can contribute to the establishment of a system for conducting active surveillance on vaccination.

Effectiveness of COVID-19 vaccines against severe outcomes in cancer patients: Real-world evidence from self-controlled risk interval and retrospective cohort studies.

BACKGROUND: The effectiveness of COVID-19 vaccines is generally reduced in cancer patients compared to the general population. However, there are only a few studies that compare the relative risk of breakthrough infections and severe COVID-19 outcomes in fully vaccinated cancer patients versus their unvaccinated counterparts. METHODS: To assess the effectiveness of COVID-19 vaccines in cancer patients, we employed (1) a self-controlled risk interval (SCRI) design, and (2) a retrospective matched cohort design. A SCRI design was used to compare the risk of breakthrough infection in vaccinated cancer patients during the period immediately following vaccination ("control window") and the period in which immunity is achieved ("exposure windows"). The retrospective matched cohort design was used to compare the risk of severe COVID-19 outcomes between vaccinated and unvaccinated cancer patients. For both studies, data were extracted from the Korea Disease Control and Prevention Agency-COVID-19-National Health Insurance Service cohort, including demographics, medical history, and vaccination records of all individuals confirmed with COVID-19. We used conditional Poisson regression to calculate the incidence rate ratio (IRR) for breakthrough infection and Cox regression to estimate the hazard ratio (HR) for severe outcomes. RESULTS: Of 14,448 cancer patients diagnosed with COVID-19 between October 2020 and December 2021, a total of 217 and 3996 cancer patients were included in the SCRI and cohort study respectively. While the risk of breakthrough infections, measured by the incidence rate in the control and exposure windows, did not show statistically significant difference in vaccinated cancer patients (IRR=0.88, 95% CI: 0.64-1.22), the risk of severe COVID-19 outcomes was significantly lower in vaccinated cancer patients compared to those unvaccinated (HR=0.27, 95% CI: 0.22-0.34). CONCLUSION: COVID-19 vaccines significantly reduce the risk of severe outcomes in cancer patients, though their efficacy against breakthrough infections is less evident.

Clinical Outcomes of Mucinous Gastric Carcinomas Compared with Non-mucinous and Signet Ring Cell Carcinomas.

Background/Aims: This study examined the clinical features and prognosis of patients with mucinous gastric carcinoma (MGC), non-mucinous gastric carcinoma (NMGC), and signet ring cell gastric carcinoma (SRC). Methods: A retrospective cohort study was performed, enrolling 65 patients with MGC from January 2007 to December 2016. During the same period, 1,814 patients with histologically proven gastric cancers underwent curative or palliative operations. One hundred and ninety-five NMGC patients were selected as the 1:3 age- and sex-matched control groups. In addition, 200 SRC patients were identified. This study evaluated the demographic features of the patients, pathologic features of the tumor, and the predictive factors, such as the recurrence-free survival and overall survival. Results: The recurrence rates were significantly high in MGC than in NMGC or SRC (both p<0.01). The proportion of early gastric cancer was lower in the MGC group than in the other groups (p<0.01). In addition, metastatic lymph nodes were found more frequently in the MGC group (p<0.01), and the proportion of initial pT4, M1 stage, was highest in the MGC group. The recurrence-free survival and overall survival in the MGC group were significantly lower than those in the NMGC or SRC. Subgroup analysis showed that patients with the same American Joint Committee on Cancer (AJCC) stage of each cancer group showed a similar prognosis. Conclusions: MGC frequently presents an advanced stage with an unfavorable prognosis compared to NMGC or SRC. On the other hand, MGC of the same AJCC stage had a similar prognosis to NMGC and SRC.