Artificial Intelligence Applied to Forced Spirometry in Primary Care.

Introduction: This study aims to create an artificial intelligence (AI) based machine learning (ML) model capable of predicting a spirometric obstructive pattern using variables with the highest predictive power derived from an active case-finding program for COPD in primary care. Material and methods: A total of 1190 smokers, aged 30-80 years old with no prior history of respiratory disease, underwent spirometry with bronchodilation. The sample was analyzed using AI tools. Based on an exploratory data analysis (EDA), independent variables (according to mutual information analysis) were trained using a gradient boosting algorithm (GBT) and validated through cross-validation. Results: With an area under the curve close to unity, the model predicted a spirometric obstructive pattern using variables with the highest predictive power: FEV1_theoretical_pre values. Sensitivity: 93%. Positive predictive value: 94%. Specificity: 97%. Negative predictive value: 96%. Accuracy: 95%. Precision: 94%. Conclusion: An ML model can predict the presence of an obstructive pattern in spirometry in a primary care smoking population with no prior diagnosis of respiratory disease using the FEV1_theoretical_pre values with an accuracy and precision exceeding 90%. Further studies including clinical data and strategies for integrating AI into clinical workflow are needed.

Introducción: Este estudio tiene como objetivo crear un modelo de aprendizaje automático (ML) basado en inteligencia artificial (IA) capaz de predecir un patrón obstructivo espirométrico utilizando variables con el mayor poder predictivo derivado de un programa activo de búsqueda de casos de enfermedad pulmonar obstructiva crónica (EPOC) en Atención Primaria. Materiales y métodos: Un total de 1.190 fumadores, de entre 30 y 80 años, sin antecedentes de enfermedad respiratoria, fueron sometidos a espirometría con IA artificial. Sobre la base de un análisis de datos exploratorio (EDA), las variables independientes (según el análisis de información mutua) se entrenaron utilizando un algoritmo de gradiente de aumento (GBT) y se validaron mediante validación cruzada. Resultados: Con un área bajo la curva cercana a la unidad, el modelo predijo un patrón obstructivo espirométrico utilizando los valores del FEV1 prebroncodilatador. Sensibilidad: 93%. Valor predictivo positivo: 94%. Especificidad: 97%. Valor predictivo negativo: 96%. Precisión: 95%. Precisión: 94%. Conclusión: Un modelo ML puede predecir la presencia de un patrón obstructivo en la espirometría en una población fumadora de atención primaria sin diagnóstico previo de enfermedad respiratoria utilizando los valores FEV1 prebroncodilatadores con una exactitud y precisión superiores al 90%. Se necesitan más estudios que incluyan datos clínicos y estrategias para integrar la IA en el flujo de trabajo clínico.

Severe COVID-19 Illness and α1-Antitrypsin Deficiency: COVID-AATD Study.

BACKGROUND: Epidemiologic studies have reported that the geographical distribution of the prevalence of allelic variants of serine protein inhibitor-A1 (SERPINA1) and severe cases of COVID-19 were similar. METHODS: A multicenter, cross-sectional, observational study to evaluate the frequency of alpha-1 antitrypsin deficiency (AATD) in patients with COVID-19 and whether it was associated with having suffered severe COVID-19. RESULTS: 2022 patients who had laboratory-confirmed SARS-CoV-2 infection. Mutations associated with AATD were more frequent in severe COVID versus non-severe (23% vs. 18.8%, p = 0.022). The frequency of Pi*Z was 37.8/1000 in severe COVID versus 17.5/1000 in non-severe, p = 0.001. Having an A1AT level below 116 was more frequent in severe COVID versus non-severe (29.5% vs. 23.1, p = 0.003). Factors associated with a higher likelihood of severe COVID-19 were being male, older, smoking, age-associated comorbidities, and having an A1AT level below 116 mg/dL [OR 1.398, p = 0.003], and a variant of the SERPINA1 gene that could affect A1AT protein [OR 1.294, p = 0.022]. CONCLUSIONS: These observations suggest that patients with AATD should be considered at a higher risk of developing severe COVID-19. Further studies are needed on the role of A1AT in the prognosis of SARS-CoV-2 infection and its possible therapeutic role.