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.

Feature selection and prediction of treatment failure in tuberculosis.

BACKGROUND: Tuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control. OBJECTIVE: To identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure. METHODS: On a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone. RESULTS: The complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75) age of 40 (30-51) years. Treatment failure occurred in approximately one fourth of the patients. The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status. The most predictive model was forward stepwise selection (AUC: 0.74), although most models performed at or above AUC 0.7. A sensitivity analysis using the 643 original patients filling the missing values with multiple imputation showed similar predictive features and generally increased predictive performance. CONCLUSION: Machine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries.