Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.

PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records. METHODS: In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact-myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS-were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity. RESULTS: For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time. CONCLUSION: Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs.

Liver disease progression in patients with alpha-1 antitrypsin deficiency and protease inhibitor ZZ genotype with or without lung disease.

BACKGROUND: Alpha-1 antitrypsin deficiency (AATD) is caused by mutations in SERPINA1, which encodes alpha-1 antitrypsin, a protease inhibitor (Pi). Individuals with AATD and the homozygous Pi*ZZ genotype have variable risk of progressive liver disease but the influence of comorbid lung disease is poorly understood. AIMS: To characterise patients with AATD Pi*ZZ and liver disease (AATD-LD-Pi*ZZ) with or without lung disease and describe liver disease-related clinical events longitudinally. METHODS: This was an observational cohort study of patients in the Mayo Clinic Healthcare System (January 2000-September 2021). Patients were identified using diagnosis codes and natural language processing. Fibrosis stage (F0-F4) was assessed using a hierarchical approach at baseline (90 days before or after the index date) and follow-up. Clinical events associated with liver disease progression were assessed. RESULTS: AATD-LD-Pi*ZZ patients with lung disease had a longer median time from AATD diagnosis to liver disease diagnosis versus those without lung disease (2.2 vs. 0.2 years, respectively). Compared to those without lung disease, patients with lung disease had a longer time to liver disease-related clinical events (8.5 years and not reached, respectively). AATD-LD-Pi*ZZ patients without lung disease were more likely to undergo liver transplantation compared with those with lung disease. CONCLUSION: In patients with AATD and lung disease, there is a delay in the diagnosis of comorbid liver disease. Our findings suggest that liver disease may progress more rapidly in patients without comorbid lung disease.

Retrospective study of propionic acidemia using natural language processing in Mayo Clinic electronic health record data.

BACKGROUND: Propionic acidemia (PA) is a rare autosomal recessive organic acidemia that classically presents within the first days of life with a metabolic crisis or via newborn screening and is confirmed with laboratory tests. Limited data exist on the natural history of patients with PA describing presentation, treatments, and clinical outcomes. OBJECTIVE: To retrospectively describe the natural history of patients with PA in a clinical setting from a real-world database using both structured and unstructured electronic health record (EHR) data using novel data extraction techniques in a unique care setting. DESIGN/METHODS: This retrospective study used EHR data to identify patients with PA seen at the Mayo Clinic. Unstructured clinical text (medical notes, pathology reports) were analyzed using augmented curation natural language processing models to enhance analysis of data extracted by structured data fields (International Classification of Diseases 9th or 10th revision [ICD-9/-10] codes, Current Procedural Terminology [CPT] codes, and medication orders). De-identified health records were also manually reviewed by clinical scientists to ensure data accuracy and completeness. The index date was defined as the patient's date of PA diagnosis at the Mayo Clinic. Results were reported as aggregate descriptive statistics relative to patients' index dates. Complications, therapeutic interventions, laboratory tests, procedures, and hospitalization encounters related to PA were described at and within 6 months of the patient's index date, and from medical history available before the index date. RESULTS: In total, 13 patients with PA were identified, with visits occurring from 1998 to 2022. Age at diagnosis ranged from birth to 3 years; age at initial evaluation at the Mayo Clinic ranged from 3 days to 28 years. The mean number of Mayo Clinic outpatient visits was 31 (median duration of care, 2 years). PA-related complications were documented in 85% of patients and included nutritional difficulties (46%), metabolic decompensation events (MDEs; 38%), neurologic abnormalities (38%), and cardiomyopathy (7%). One pair of affected siblings had mild symptoms and no complications or MDEs. All 5 patients with a history of MDEs presented with developmental delays. Among patients with MDEs, the mean frequency of outpatient clinical care visits was 10 per year, and 3 patients required inpatient hospitalization (mean duration, 16 days). The incidence of severe complications was higher among patients with MDEs than those without MDEs. Of the patients with MDEs, 2 experienced crises while receiving treatment at the Mayo Clinic, with 9 total MDEs occurring between the 2 patients. Symptoms at presentation included hyperammonemia (78%), fever and/or decreased nutritional intake (67%), hyperglycemia/hypoglycemia (56%), intercurrent upper respiratory infection and/or lethargy (44%), constipation (33%), altered mental status (33%), and cough (33%). CONCLUSIONS: This study highlights the range and frequency of clinical outcomes experienced by patients with PA and demonstrates the clinical burden of MDEs.