Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies

Abstract Background Primary immunodeficiency (PI) is a group of heterogeneous disorders resulting from immune system defects. Over 70% of PI is undiagnosed, leading to increased mortality, co-morbidity and healthcare costs. Among PI disorders, combined immunodeficiencies (CID) are characterized by complex immune defects. Common variable immunodeficiency (CVID) is among the most common types of PI. In light of available treatments, it is critical to identify adult patients at risk for CID and CVID, before the development of serious morbidity and mortality. Methods We developed a deep learning-based method (named “TabMLPNet”) to analyze clinical history from nationally representative medical claims from electronic health records (Optum® data, covering all US), evaluated in the setting of identifying CID/CVID in adults. Further, we revealed the most important CID/CVID-associated antecedent phenotype combinations. Four large cohorts were generated: a total of 47,660 PI cases and (1:1 matched) controls. Results The sensitivity/specificity of TabMLPNet modeling ranges from 0.82-0.88/0.82-0.85 across cohorts. Distinctive combinations of antecedent phenotypes associated with CID/CVID are identified, consisting of respiratory infections/conditions, genetic anomalies, cardiac defects, autoimmune diseases, blood disorders and malignancies, which can possibly be useful to systematize the identification of CID and CVID. Conclusions We demonstrated an accurate method in terms of CID and CVID detection evaluated on large-scale medical claims data. Our predictive scheme can potentially lead to the development of new clinical insights and expanded guidelines for identification of adult patients at risk for CID and CVID as well as be used to improve patient outcomes on population level

Inhibition of Prekallikrein for Hereditary Angioedema

BACKGROUND: Hereditary angioedema is characterized by recurrent and unpredictable swellings that are disabling and potentially fatal. Selective inhibition of plasma prekallikrein production by antisense oligonucleotide treatment (donidalorsen) may reduce the frequency of attacks and the burden of disease. METHODS: In this phase 2 trial, we randomly assigned, in a 2:1 ratio, patients with hereditary angioedema with C1 inhibitor deficiency to receive four subcutaneous doses of either donidalorsen (80 mg) or placebo, with one dose administered every 4 weeks. The primary end point was the time-normalized number of investigator-confirmed angioedema attacks per month (attack rate) between week 1 (baseline) and week 17. Secondary end points included quality of life, as measured with the Angioedema Quality of Life Questionnaire (scores range from 0 to 100, with higher scores indicating worse quality of life), and safety. RESULTS: A total of 20 patients were enrolled, of whom 14 were randomly assigned to receive donidalorsen and 6 to receive placebo. The mean monthly rate of investigator-confirmed angioedema attacks was 0.23 (95% confidence interval [CI], 0.08 to 0.39) among patients receiving donidalorsen and 2.21 (95% CI, 0.58 to 3.85) among patients receiving placebo (mean difference, -90%; 95% CI, -96 to -76; P<0.001). The mean change from baseline to week 17 in the Angioedema Quality of Life Questionnaire score was -26.8 points in the donidalorsen group and -6.2 points in the placebo group (mean difference, -20.7 points; 95% CI, -32.7 to -8.7). The incidence of mild-to-moderate adverse events was 71% among patients receiving donidalorsen and 83% among those receiving placebo. CONCLUSIONS: Among patients with hereditary angioedema, donidalorsen treatment resulted in a significantly lower rate of angioedema attacks than placebo in this small, phase 2 trial. (Funded by Ionis Pharmaceuticals; ISIS 721744-CS2 ClinicalTrials.gov number, NCT04030598.)