Date of Award

1-1-2020

Document Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Shiyi Wang

Second Advisor

Scott Huntington

Abstract

Purpose

Immunotherapy (IT) has been shown to improve cancer survival but there is limited evidence regarding use in patients with advanced cancer and pre-existing autoimmune disease (AI). We sought to address these knowledge gaps and determine IT utilization patterns and overall survival (OS) for these patients.

Patients and Methods

This retrospective cohort study used data from Flatiron Health’s nationwide oncology database. Patients diagnosed with advanced cancer where there was ≥ 1 FDA approved IT were included. Baseline demographics were analyzed using χ2 and t-tests. Patterns of IT use were assessed using logistic regression. OS was estimated using Kaplan-Meier and Cox proportional hazards models.

Results

Of the 70,964 patients included, 1,801 had AI. Controlling for demographic and clinical variables, AI was not associated with IT use in general (Odds Ratio [OR]=0.90, 95% CI 0.79-1.03), but was associated with lower odds of receiving first-line IT (OR=0.72, 95% CI 0.61-0.84). IT usage differed by cancer type. Overall, on multivariate analysis, patients with AI had better survival than those without AI. This remained true for overall IT (Hazard Ratio [HR]=0.86, 95% CI 0.81-0.91) and for first-line IT (HR=0.79, 95% CI 0.71-0.88). Interaction between IT and AI was significant, with IT having stronger association in patients with AI compared to those without (HR=0.57, 95% CI 0.50-0.64 vs HR=0.66, 95% CI 0.64-0.68). Among patients treated with IT, interaction between first-line IT and AI was not significant. Among patients with AI, receiving first-line IT had no survival benefit (HR=0.95, 95% CI 0.77-1.17).

Conclusion

Receipt of IT had greater survival benefit for patients with AI compared to those without AI; first-line IT did not result in further survival benefit. Patients with advanced cancer and pre-existing AI should receive IT, potentially as second or third-line treatment.

Comments

This thesis is restricted to Yale network users only. It will be made publicly available on 05/27/2021

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