Predicting Adverse Outcomes Using Machine Learning of COPD Patients in Hong Kong
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 100,000
- 试验地点
- 1
- 主要终点
- Early Readmission
研究概览
简要总结
This study aims to develop predictive models for patients with a diagnosis of COPD at discharge of an index admission on these outcomes using machine learning:
Primary outcome: Early admission
Secondary outcomes:
- Frequent readmission
- Composite outcome (Early + Frequent readmissions)
- Mortality
- Longstayers
详细描述
Chronic obstructive pulmonary disease (COPD) is a common, preventable, and treatable disease that is characterised by persistent respiratory symptoms and airflow limitation that is due to airway and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases and influenced by host factors including abnormal lung development. It was estimated 3.2 million people died from COPD worldwide in 2015 and there was an increase of 11.6% compared with 1990. COPD is the third leading cause of death globally in 2019.
In Hong Kong (HK), the prevalence rates of COPD in the elderly population aged ≥60years were 25.9% and 12.4% based on the spirometric definition of forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio <70% and the lower limit of normal of the FEV1/FVC respectively.4 From our recent study on COPD hospital admissions, there are a total of 67,628 COPD admissions Jan 2017 Week 1 to Jan 2020 Week 3 (before the COVID pandemic) and 11,065 admissions from Jan 2020 Week 4 to Dec 2020 Week 4 (during the COVID pandemic). 5 The burden of COPD hospitalizations is significant and it is important to understand the driver of these admissions for developing suitable strategies to solve the problem and improve the health outcomes of patients suffering from COPD.
Early readmission and frequent admissions resulting from COPD are commonly studied hospital outcomes because of the high financial burden to both individual and state and the high usage of public healthcare resources. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), there has been considerable interest on its application to medicine. Recent metaanalysis showed compatibility of these models in predicting COPD outcomes.7 However, few studies have managed to show that AI/ML are superior to traditional statistical modeling methods, AI/ML are interpretable and can be clinically correlated, and AI/ML can have direct clinical application.
This study aims to develop predictive models for patients with a diagnosis of COPD at discharge of an index admission on these outcomes:
Primary outcome: Early admission
研究设计
- 研究类型
- Observational
- 观察模型
- Ecologic Or Community
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 40 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •≥40 years
- •Patients are discharged from 2016 -2022
- •Discharge Diagnosis: Using the Discharge Diagnosis ICD Codes found in the Primary Diagnosis to determine if a patient has COPD
- •Validated against Spirometry results (for patient with a spirometry reading):
- •Spirometry reading taken from anytime point before. Patient should have Post FEV1/FVC ratio of < 0.7 in any one of the spirometry readings. If Post FEV1/FVC is not available, we will check if patients have a Pre FEV1/FVC value, and will also include patients with Pre FEV1/FVC ratio of < 0.7 in any one of the spirometry readings.
排除标准
- •Admission diagnosis due to causes other than COPD
结局指标
主要结局
Early Readmission
时间窗: 30 days
Patients were readmitted to hospital with the primary diagnosis of AECOPD\* within 30 days since the discharge date of the index admission
次要结局
- Frequent Admitters(365 days)
- 1-Year Mortality(365 days)
- Longstayers(365 days)
研究者
Fanny W.S. Ko
Honorary Clinical Associate Professor
Chinese University of Hong Kong