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Идёт набор NCT07714863

ML-Based ABPA Recurrence Prediction and Clinical Utility

— Наблюдательное ABPA Allergic Bronchopulmonary Aspergillosis Allergic Bronchopulmonary Aspergillosis (ABPA) Acute Exacerbation

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
Это наблюдательное исследование: исследуемое лечение участникам по протоколу не назначают.
Кому может быть актуально
Состояния в реестре: ABPA, Allergic Bronchopulmonary Aspergillosis, Allergic Bronchopulmonary Aspergillosis (ABPA), Acute Exacerbation. Базовые параметры: от 18 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
Китай
Следующий шаг
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Официальное название

Machine Learning-Based Prediction of Recurrence Risk in Allergic Bronchopulmonary Aspergillosis and Its Clinical Decision-Making Value: A Multicenter Study With External Validation

Обзор

This multicenter bidirectional cohort study aims to develop and externally validate a machine learning model for predicting the risk of acute exacerbation within 1 year in patients with allergic bronchopulmonary aspergillosis (ABPA) during the stable phase, and further to evaluate the model's practical value in risk stratification and clinical decision-making. All patients diagnosed with ABPA according to the ISHAM 2024 criteria will be assigned to either the acute exacerbation group or the non-exacerbation group based on whether they experience an acute exacerbation within 1 year. Enrolled participants will be randomly divided into a training set and an internal validation set. During the feature selection phase, univariate analysis, collinearity diagnostics, feature importance ranking derived from nine machine learning algorithms, and expert consensus are comprehensively applied, ultimately leading to the development of 12 independent machine learning models. Model performance is assessed using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), sensitivity, specificity, F1-score, calibration curve, and decision curve analysis. In addition, external validation further enhances the credibility of the model. To improve clinical interpretability, the SHAP method is employed to quantify the contribution of each feature, and an interactive nomogram is constructed to facilitate clinical application. All participants will be followed up for 12 months, during which regular clinical and laboratory evaluations will be performed.

Первичные конечные точки

  • The occurrence of ABPA exacerbation within one year of enrollment. [Срок оценки: 1 year]
Вторичные конечные точки (12)
  • Time to first acute exacerbation [Срок оценки: 1 year]
  • Total serum IgE [Срок оценки: 1 year]
  • FEV1 (% predicted) [Срок оценки: 1 year]
  • Changes in chest CT features including scores for bronchiectasis severity [Срок оценки: 1 year]
  • Aspergillus-specific IgE [Срок оценки: 1 year]
  • Aspergillus-specific IgG [Срок оценки: 1 year]
  • forced vital capacity (FVC) [Срок оценки: 1 year]
  • FEV1/FVC ratio [Срок оценки: 1 year]
  • diffusing capacity for carbon monoxide (DLCO) [Срок оценки: 1 year]
  • extent of bronchiectasis of chest CT [Срок оценки: 1 year]
  • mucus plugging on chest CT [Срок оценки: 1 year]
  • high-attenuation (HAM) on chest CT [Срок оценки: 1 year]

Критерии участия

Критерии включения

  • Aged between 18 and 80 years old.
  • Consistent with the diagnostic consensus criteria for ABPA proposed by the ISHAM-ABPA Working Group.
  • Patients in stable phase of ABPA: newly diagnosed treatment-naive patients or those with prior ABPA exacerbation who achieved at least 50% improvement in symptoms assessed by Likert scale or visual analogue scale (VAS) following initial therapy, accompanied by marked radiological improvement (≥50% reduction in pulmonary opacities) or a minimum 20% decline in serum total IgE level.

Критерии исключения

  • Concurrent malignant tumors or severe organ dysfunction involving the heart, brain, kidney and other vital organs.
  • Complicated with severe underlying diseases, including active pulmonary tuberculosis, lung cancer, chronic heart failure (NYHA class Ⅳ), chronic kidney disease stage 5 (CKD 5), decompensated liver cirrhosis, etc.
  • Immunocompromised status, such as human immunodeficiency virus (HIV) infection, long-term oral administration of glucocorticoids or immunosuppressive agents.
  • Pregnant or breastfeeding women.
  • Patients with missing core clinical data or incomplete medical records.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения
Когортное

Центры проведения

Китай · 1 центр
  • Department of Respiratory, The First Affiliated Hospital of Shandong First Medical Univers — Цзинань

Идентификаторы

NCT: NCT07714863 · ABPA-006

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗