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Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical records have low numbers of confirmed cases and have limited use in training such models. We applied a machine-learning model to a large US claims database to identify undiagnosed symptomatic patients with AATD. Using deidentified data from the Komodo US claims database (April 26, 2016-January 31, 2023), a model was trained to identify symptomatic patients with high probability of AATD. Eighty claims records for high-probability candidates identified by the model were independently reviewed and validated by 2 clinical experts. The experts independently indicated that of the 80 high-probability candidate patients, 65 (81%) and 62 (78%) patients, respectively, should be tested for AATD. Feedback from this validation step informed model optimization. The optimized model was applied to claims data to identify symptomatic patients with probable AATD. Eleven and 14 "features" of the claims data were informative in distinguishing patients with AATD from patients with COPD without AATD and from unspecified chronic liver diseases. Moreover, patients with diagnosed AATD and COPD without AATD had unique cadences of similar medical events in their diagnostic journeys. Our work shows that a machine-learning model trained on a large US claims database can accurately identify symptomatic patients with AATD and provides useful insights into the diagnostic journey of patients with AATD.

Aims: Patients with inborn errors of immunity (IEI) are predisposed to severe recurrent/chronic infections, and often require hospitalization, resulting in substantial burden to patients/healthcare systems. While immunoglobulin replacement therapies (IgRTs) are the standard first-line treatment for most forms of IEI, limited real-world data exist regarding clinical characteristics and treatment costs for patients with IEI initiating such treatment. This retrospective analysis examined infection and treatment characteristics in US patients with IEI initiating IgRT with immune globulin infusion (human), 10% (IG10%). Healthcare resource utilization (HCRU) and associated costs before and after treatment initiation were compared. Additionally, the impact of COVID-19 on infection diagnoses was evaluated., Methods: Patients with IEI initiating IG10% between July 2012 and August 2019 were selected from Merative MarketScan Databases using diagnosis/prescription codes. Patients were followed 6 months before and after first IG10% claim date. Demographic and clinical characteristics were described. Treatment characteristics and HCRU before and after IG10% initiation were compared. Infection diagnoses during 2020 and 2019 (March-December) were compared., Results: The study included 1,497 patients with IEI diagnoses (mean age = 43.4 years) initiating IG10%, with frequently reported comorbidities like asthma (32.1%). Following IG10% initiation, fewer severe infection diagnoses (11.6% vs 19.9%), fewer infection-related inpatient (10.8% vs 19.5%) and outpatient services (71.6% vs 79.9%), and lower infection-related total healthcare costs ($7,849 vs $13,995; p  < 0.001)-driven by lower inpatient costs ($2,746 vs $9,900)-were observed than before. Fewer patients had infection diagnoses during COVID-19 (22.8%) than the prior year (31.2%)., Conclusion: Patients with IEI are susceptible to severe infections leading to high disease burden and treatment costs. Following IG10% initiation, we observed fewer infections, lower infection-related treatment costs, and shift in care (inpatient to outpatient) leading to significant cost savings. Among patients with IEI, 27% fewer infection diagnoses were observed during the early COVID-19 lockdown period than the prior year.