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Machine-Learning-Photo-with-spacers.jpg Developing a machine learning (ML) algorithm in claims data to identify anaphylaxis, a rare, serious drug or vaccine induced outcome



Traditional analytical methods are no match for anaphylaxis, a rare but serious reaction to certain drugs and vaccines that can be difficult to find in a sea of administrative claims data. An exceptionally accurate algorithm developed by Carelon Research (formerly HealthCore, Inc.) may be able to help identify drug- and vaccine-induced anaphylaxis within the Elevance Health population. 

Enterprise Analytics Core domain(s):  Vaccines, CarelonRx

Summary

Background
Leveraged the power of machine learning to develop an accurate algorithm to identify conditions in administrative claims that are not easily identified.

Methods
A conventional algorithm for anaphylaxis cases was developed via anaphylaxis diagnosis codes or relevant signs and symptoms. This algorithm was applied to adults with Type 2 diabetes (T2D) within the Carelon Research database containing administrative claims from 2016 to 2018. Clinical experts adjudicated anaphylaxis case status from redacted medical records. We used confirmed case status as an outcome for predictive models to identify predictors and estimate the probability of confirmed anaphylaxis.

Results
The model’s algorithm was very accurate and was able to correctly identify anaphylaxis cases and exclude most non-cases.


Key takeaways

  • An accurate algorithm to identify anaphylaxis in T2D patients could be applied in analyses of similar populations to identify drug and vaccine induced anaphylaxis in Elevance Health affiliated health plan insured individuals.
  • Anaphylaxis is a serious safety outcome that often applies to vaccine and other drug safety research. It is a notoriously difficult health outcome to accurately identify using traditional methods (i.e., diagnosis codes alone). CarelonRx’s pharmacovigilance activities may use this algorithm to identify patients with this condition as a result of using certain medications or vaccines.


Publication

Hui-Lee Wong*, Mao Hu*, Cindy Ke Zhou, Patricia C Lloyd, Kandace L Amend, Daniel C Beachler, Alex Secora, Cheryl N McMahill-Walraven, Yun Lu, Yue Wu, Rachel P Ogilvie, Christian Reich, Djeneba Audrey Djibo, Zhiruo Wan, John D Seeger, Sandia Akhtar, Yixin Jiao, Yoganand Chillarige, Rose Do, John Hornberger, Joyce Obidi, Richard Forshee, Azadeh Shoaibi, Steven A Anderson Risk of myocarditis and pericarditis after the COVID-19 mRNA vaccination in the USA: a cohort study in claims databases. Lancet. 202 ; 399:2191–99.

Carelon Research project team: Daniel C. Beachler, Ramya Avula, Shiva Chaudhary, Brian Greenwald, Navyatha Namburu, Ramin Riahi, Priyanka Sagare, Grace Stockbower, Shiva Vojjala
*Carelon Research Associate at the time of the study.



For more information on a specific study or to connect with the Actionable Insights Committee, contact us at [email protected].

This study was conducted by Carelon Research (formerly HealthCore, Inc.), a subsidiary of Elevance Health, and funded by Sanofi. Dissemination and sharing of the Newsletter is limited to Elevance Health and its subsidiaries and included findings and implications are for Elevance Health and its affiliates’ internal use only.


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Major depressive disorder (MDD) patients highly impacted by COVID-19 face daunting challenges

Hub Domain(s): COVID, member experience
Summary: 

  • Background: Disruption to daily life and lockdown measures that were imposed in an attempt to contain the spread of COVID-19 presented new hurdles for at-risk populations, such as patients with major depressive disorder (MDD)
  • Methods: Conducted a 2020 internet survey to understand reasons for antidepressant continuation, switching and discontinuation in patients with MDD; COVID-19 pandemic questions were developed and added to survey.
    • COVID-19 Pandemic Impact (CPI) score was calculated and, based on their CPI scores, patients were categorized as experiencing low (LPI), medium (MPI) or high pandemic impact (HPI).
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      • Receiving samples from healthcare providers
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      • Alcohol, drug, and tobacco use
      • Unhealthy mindless eating
      • Sedentary behavior
  • Key Takeaways: Our findings are consistent with literature showing that traumatic events, e.g. pandemics, can negatively impact health behaviors.
    • More research is needed to explore the COVID-19 impact in other patient populations.
    • Education opportunities exist for health plans to increase awareness among providers and patients of possible short and long-term COVID-19 effects on health behaviors.

Figure 1: Impact of COVID-19 pandemic on accessing healthcare/mental healthcare

  • Publications: Poster presented at the Virtual American Society of Clinical Psychopharmacology (ASCP) Annual Meeting, June 1-4, 2021

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