Linking IL-6 and hsCRP among Indian patients with myocardial infarction.

The association between serum interleukin-6 (IL-6) and highly sensitive C - reactive protein (hsCRP) as predictors of the risk factors for Myocardial Infarction. The study included a total of 50 patients with Myocardial Infarction, aged between 25 to 74 years. The levels of hsCRP were measured using the immunoturbidimetry method, while Interleukin 6 was estimated using the sandwich ELISA method. Statistical analysis was conducted using SPSS version 21.0, with p values calculated using Quartile ratio, ANOVA unpaired t-test, and Kaplan-Meier Curve Method. A p-value of less than 0.05 was considered statistically significant. All participants underwent a questionnaire, physical examination, medical history assessment, and laboratory tests. The results of the study showed that there was a significant correlation between IL-6 and hsCRP levels in the Quartile groups, as well as with lipid profiles. The Kaplan-Meier method also demonstrated a significant association between IL-6 and hsCRP levels in participants. The comparison of biomarkers further supported these findings. Thus, data shows that elevated levels of hsCRP and IL-6 could serve as valuable diagnostic markers for predicting Acute Myocardial Infarction. Our study strongly suggests that IL-6 could be a powerful marker in evaluating the Myocardial Infarction.

ICD-10 anaphylaxis algorithm and the estimate of vaccine-attributable anaphylaxis incidence in Medicare.

BACKGROUND: Anaphylaxis is a rare, serious allergic reaction. Its identification in large healthcare databases can help better characterize this risk. OBJECTIVE: To create an ICD-10 anaphylaxis algorithm, estimate its positive predictive values (PPVs) in a post-vaccination risk window, and estimate vaccination-attributable anaphylaxis rates in the Medicare Fee For Service (FFS) population. METHODS: An anaphylaxis algorithm with core and extended portions was constructed analyzing ICD-10 anaphylaxis claims data in Medicare FFS from 2015 to 2017. Cases of post-vaccination anaphylaxis among Medicare FFS beneficiaries were then identified from October 1, 2015 to February 28, 2019 utilizing vaccine relevant anaphylaxis ICD-10 codes. Information from medical records was used to determine true anaphylaxis cases based on the Brighton Collaboration's anaphylaxis case definition. PPVs were estimated for incident anaphylaxis and the subset of vaccine-attributable anaphylaxis within a 2-day post-vaccination risk window. Vaccine-attributable anaphylaxis rates in Medicare FFS were also estimated. RESULTS: The study recorded 66,572,128 vaccinations among 21,685,119 unique Medicare FFS beneficiaries. The algorithm identified a total of 190 suspected anaphylaxis cases within the 2-day post-vaccination window; of these 117 (62%) satisfied the core algorithm, and 73 (38%) additional cases satisfied the extended algorithm. The core algorithm's PPV was 66% (95% CI [56%, 76%]) for identifying incident anaphylaxis and 44% (95% CI [34%, 56%]) for vaccine-attributable anaphylaxis. The vaccine-attributable anaphylaxis incidence rate after any vaccination was 0.88 per million doses (95% CI [0.67, 1.16]). CONCLUSION: The ICD-10 claims algorithm for anaphylaxis allows the assessment of anaphylaxis risk in real-world data. The algorithm revealed vaccine-attributable anaphylaxis is rare among vaccinated Medicare FFS beneficiaries.

Guillain-Barré Syndrome After High-Dose Influenza Vaccine Administration in the United States, 2018-2019 Season.

BACKGROUND: The Vaccine Safety Datalink (VSD) identified a statistical signal for an increased risk of Guillain-Barré syndrome (GBS) in days 1-42 after 2018-2019 high-dose influenza vaccine (IIV3-HD) administration. We evaluated the signal using Medicare. METHODS: We conducted early- and end-of-season claims-based self-controlled risk interval analyses among Medicare beneficiaries ages ≥65 years, using days 8-21 and 1-42 postvaccination as risk windows and days 43-84 as control window. The VSD conducted chart-confirmed analyses. RESULTS: Among 7 453 690 IIV3-HD vaccinations, we did not detect a statistically significant increased GBS risk for either the 8- to 21-day (odds ratio [OR], 1.85; 95% confidence interval [CI], 0.99-3.44) or 1- to 42-day (OR, 1.31; 95% CI, 0.78-2.18) risk windows. The findings from the end-of-season analyses were fully consistent with the early-season analyses for both the 8- to 21-day (OR, 1.64; 95% CI, 0.92-2.91) and 1- to 42-day (OR, 1.12; 95% CI, 0.70-1.79) risk windows. The VSD's chart-confirmed analysis, involving 646 996 IIV3-HD vaccinations, with 1 case each in the risk and control windows, yielded a relative risk of 1.00 (95% CI, 0.06-15.99). CONCLUSIONS: The Medicare analyses did not exclude an association between IIV3-HD and GBS, but it determined that, if such a risk existed, it was similar in magnitude to prior seasons. Chart-confirmed VSD results did not confirm an increased risk of GBS.

Surveillance for Guillain-Barré syndrome after 2015-2016 and 2016-2017 influenza vaccination of Medicare beneficiaries.

BACKGROUND: Guillain-Barré syndrome (GBS) is a serious acute demyelinating disease, an increased risk of which was found after the 1976 swine flu vaccinations. The U.S. Food and Drug Administration, in collaboration with the Centers for Medicare & Medicaid Services, has been conducting active surveillance for GBS after influenza vaccinations of Medicare Fee-For-Service beneficiaries since 2009. METHODS: We conducted active surveillance for GBS claims in the 2015-2016 and 2016-2017 influenza seasons using the Updating Sequential Probability Ratio Test (USPRT) to monitor for signals of GBS risk. We performed self-controlled risk interval (SCRI) analyses at the end of both seasons, including chart confirmation in the 2015-2016 season, to estimate the odds ratio of GBS risk. We used 1-42 and 8-21 days post-vaccination as primary and secondary risk windows, respectively, and 43-84 days post-vaccination as the control window. RESULTS: Over 13 million beneficiaries were vaccinated in each season. USPRT found a low magnitude signal for GBS in both seasons. SCRI analyses did not find excess GBS risk following any influenza vaccine for days 1-42 post-vaccination in either season. In the 2015-2016 season, for the 8-21 day window, our chart-confirmation showed an attributable GBS risk of 0.87 (95% CI: 0.16, 1.49) and 1.68 (95% CI: 0.69, 2.41) cases per million vaccinees after all seasonal and high dose (HD) vaccines, respectively, an elevated GBS risk for beneficiaries aged ≥75 years following all seasonal vaccines (OR: 2.25; 95% CI: 1.15, 4.39) and HD vaccine (OR: 3.67, 95% CI: 1.52, 8.85), and an elevated GBS risk for males who received seasonal vaccines (OR: 2.18; 95% CI: 1.15, 4.15) and HD vaccine (OR: 3.33; 95% CI: 1.35, 8.20). The finding of elevated GBS risk with advancing age and in males is consistent with literature; however, a distinction between HD and SD was a new finding. In the 2016-17 season, for the 8-21 day window, attributed cases showed an attributable GBS risk of 0.87 (95% CI: 0.03, 1.61) and 1.11 (95% CI: 0.00, 2.01) cases per million vaccinees after all seasonal and HD vaccines, respectively. We found no excess GBS risk for standard dose vaccines in the 8-21 day window in either season. CONCLUSIONS: Our primary analysis finding of no excess GBS risk during both seasons was reassuring. The slightly elevated GBS risk, although in the expected range, in the 8-21 day window after all seasonal and high dose vaccines, but not after standard dose vaccines is hypothesis-generating because the difference may be due to vaccine factors such as antigen amount or strains in various seasons or due to host factors.

A simulation study of the statistical power and signaling characteristics of an early season sequential test for influenza vaccine safety.

PURPOSE: The US Food and Drug Administration monitors the risk of Guillain-Barré syndrome (GBS) following influenza vaccination using several data sources including Medicare. In the 2017 to 2018 season, we transitioned our near real-time surveillance in Medicare to more effectively detect large GBS risk increases early in the season while avoiding false positives. METHODS: We conducted a simulation study examining the ability of the updating sequential probability ratio test (USPRT) to detect substantially elevated GBS risk in the 8- to 21-day postvaccination versus 5× to 30× the historical rate. We varied the first testing week (weeks 5-8) and the null rate (1×-3×) and evaluated power. We estimated signal probability and the risk ratio (RR) after signaling when high-risk seasons were rare. RESULTS: Applying fixed alternatives, we found >80% power to detect a risk 30× the historical rate in week 5 for the 1× null and in week 6 for the 1.5× to 3× nulls. Nearly all testing schedules had >80% power for a 5× risk by week 11. To test the robustness of USPRT, we further simulated seasons where 1% were true high-risk seasons. Using a 1× null led to 10% of seasons signaling by week 11 (median RR approximately 1.4), which decreased to approximately 1% with the ≥2.5× null (median RR approximately 16.0). CONCLUSIONS: On the basis of the results from this simulation and subsequent consultations with experts and stakeholders, we specified USPRT to test continuously from weeks 7 to 11 using the null hypothesis that the observed GBS rate was 2.5× the historical rate. This helped improve the ability of USPRT to provide early detection of GBS risk following influenza vaccination as part of a multilayered system of surveillance.

Surveillance for Guillain-Barré syndrome after influenza vaccination among U.S. Medicare beneficiaries during the 2017-2018 season.

BACKGROUND: The U.S. Food and Drug Administration and the Centers for Medicare & Medicaid Services have been actively monitoring the risk of Guillain-Barré syndrome (GBS) following influenza vaccination among Fee-for-Service (FFS) Medicare beneficiaries every season since 2008. We present our evaluation of the GBS risk following influenza vaccinations during the 2017-2018 season. METHODS: We implemented a multilayered approach to active safety surveillance that included near real-time surveillance early in the season, comparing GBS rates post-vaccination during the 2017-2018 season with rates from five prior seasons using the Updating Sequential Probability Ratio Test (USPRT), and end-of-season self-controlled risk interval (SCRI) analyses. RESULTS: We identified approximately 16 million influenza vaccinations. The near real-time surveillance did not signal for a potential 2.5-fold increased GBS risk either in days 8-21 or 1-42 post-influenza vaccination. In the SCRI analyses, we did not detect statistically significant increased GBS risks among influenza-vaccinated Medicare beneficiaries ≥65 years for either the 8-21 or 1-42-day risk windows for all seasonal vaccines combined, high-dose vaccine, or standard-dose vaccines; we did detect an increased GBS risk in days 8-21 post-vaccination for individuals vaccinated with the adjuvanted vaccine (OR: 3.75; 95% CI: 1.01, 13.96), although this finding was not statistically significant after multiplicity adjustment (p = 0.146). CONCLUSIONS: Our multilayered surveillance approach-which allows for early detection of elevated GBS risk and provides reliable end-of-season SCRI estimates of effect size-did not identify an increased GBS risk following 2017-2018 influenza vaccinations. The slightly increased GBS risk with the adjuvanted vaccine, which was not statistically significant following multiplicity adjustment, is consistent with the package inserts of all U.S.-licensed influenza vaccines, which warn of a potential low increased GBS risk. The benefits of influenza vaccines in preventing morbidity and mortality heavily outweigh this potential risk.

Health Status in Adults With Chronic Conditions: Intervention Strategies for Improving Patient-Reported Outcomes.

This literature review analyzes 418 articles from 2 periods (2000-2010 and 2011-2017) to provide interpretative guidelines for the change in physical (PCS) and mental component summaries (MCS) of well-established patient-reported measures (MOS SF-36 V1, HOS SF-12, VR-36, and VR-12). The magnitude of the intervention effects was calculated using baseline and follow-up data. Results were similar across the 2 periods, although the effects of social and behavioral interventions are less consistent and are smaller for PCS. Both single interventions and multicomponent interventions met the moderate to large effect size criterion for PCS and MCS.

Application of Natural Language Processing and Network Analysis Techniques to Post-market Reports for the Evaluation of Dose-related Anti-Thymocyte Globulin Safety Patterns.

OBJECTIVE: To evaluate the feasibility of automated dose and adverse event information retrieval in supporting the identification of safety patterns. METHODS: We extracted all rabbit Anti-Thymocyte Globulin (rATG) reports submitted to the United States Food and Drug Administration Adverse Event Reporting System (FAERS) from the product's initial licensure in April 16, 1984 through February 8, 2016. We processed the narratives using the Medication Extraction (MedEx) and the Event-based Text-mining of Health Electronic Records (ETHER) systems and retrieved the appropriate medication, clinical, and temporal information. When necessary, the extracted information was manually curated. This process resulted in a high quality dataset that was analyzed with the Pattern-based and Advanced Network Analyzer for Clinical Evaluation and Assessment (PANACEA) to explore the association of rATG dosing with post-transplant lymphoproliferative disorder (PTLD). RESULTS: Although manual curation was necessary to improve the data quality, MedEx and ETHER supported the extraction of the appropriate information. We created a final dataset of 1,380 cases with complete information for rATG dosing and date of administration. Analysis in PANACEA found that PTLD was associated with cumulative doses of rATG >8 mg/kg, even in periods where most of the submissions to FAERS reported low doses of rATG. CONCLUSION: We demonstrated the feasibility of investigating a dose-related safety pattern for a particular product in FAERS using a set of automated tools.

Using Probabilistic Record Linkage of Structured and Unstructured Data to Identify Duplicate Cases in Spontaneous Adverse Event Reporting Systems.

INTRODUCTION: Duplicate case reports in spontaneous adverse event reporting systems pose a challenge for medical reviewers to efficiently perform individual and aggregate safety analyses. Duplicate cases can bias data mining by generating spurious signals of disproportional reporting of product-adverse event pairs. OBJECTIVE: We have developed a probabilistic record linkage algorithm for identifying duplicate cases in the US Vaccine Adverse Event Reporting System (VAERS) and the US Food and Drug Administration Adverse Event Reporting System (FAERS). METHODS: In addition to using structured field data, the algorithm incorporates the non-structured narrative text of adverse event reports by examining clinical and temporal information extracted by the Event-based Text-mining of Health Electronic Records system, a natural language processing tool. The final component of the algorithm is a novel duplicate confidence value that is calculated by a rule-based empirical approach that looks for similarities in a number of criteria between two case reports. RESULTS: For VAERS, the algorithm identified 77% of known duplicate pairs with a precision (or positive predictive value) of 95%. For FAERS, it identified 13% of known duplicate pairs with a precision of 100%. The textual information did not improve the algorithm's automated classification for VAERS or FAERS. The empirical duplicate confidence value increased performance on both VAERS and FAERS, mainly by reducing the occurrence of false-positives. CONCLUSIONS: The algorithm was shown to be effective at identifying pre-linked duplicate VAERS reports. The narrative text was not shown to be a key component in the automated detection evaluation; however, it is essential for supporting the semi-automated approach that is likely to be deployed at the Food and Drug Administration, where medical reviewers will perform some manual review of the most highly ranked reports identified by the algorithm.

Decision support environment for medical product safety surveillance.

We have developed a Decision Support Environment (DSE) for medical experts at the US Food and Drug Administration (FDA). The DSE contains two integrated systems: The Event-based Text-mining of Health Electronic Records (ETHER) and the Pattern-based and Advanced Network Analyzer for Clinical Evaluation and Assessment (PANACEA). These systems assist medical experts in reviewing reports submitted to the Vaccine Adverse Event Reporting System (VAERS) and the FDA Adverse Event Reporting System (FAERS). In this manuscript, we describe the DSE architecture and key functionalities, and examine its potential contributions to the signal management process by focusing on four use cases: the identification of missing cases from a case series, the identification of duplicate case reports, retrieving cases for a case series analysis, and community detection for signal identification and characterization.

Adverse Events After MMR or MMRV Vaccine in Infants Under Nine Months Old.

BACKGROUND: In the United States, measles is resurging, with more than 700 confirmed cases since January 2014. During measles outbreaks, vaccination as early as at 6 months of age is sometimes recommended for infants who are at risk for exposure. METHODS: We searched the Vaccine Adverse Event Reporting System for reports of measles, mumps and rubella vaccine combined or measles, mumps, rubella and varicella vaccine combined vaccination in children less than 9 months of age. We performed a clinical assessment of each report and summarized the frequency, range, onset time and severity of adverse events. RESULTS: After excluding 346 reports because they were duplicates or because they contained insufficient information about the child's age or vaccine(s), we retained 204 reports in the analysis, including 35 (17%) that were serious. Among the 169 nonserious reports, more than half (88; 52%) described a vaccination error without any adverse event per se. Other nonserious reports described fever, injection reactions and gastrointestinal symptoms. Serious adverse events included developmental disorders, fever and fussiness. There were 44 reports of fever, but only 4 cases began 5-12 days after immunization, the peak risk window. The vast majority of fever reports listed concomitant vaccines, such as diphtheria and tetanus toxoids, acellular or whole-cell pertussis vaccine. CONCLUSIONS: This review did not identify any major safety concerns. These findings may facilitate discussions about the risks and benefits of vaccinating infants who are potentially exposed to this life-threatening disease.