External Validation of an Acute Respiratory Distress Syndrome Prediction Model Using Radiology Reports.
External Validation of an Acute Respiratory Distress Syndrome Prediction Model Using Radiology Reports.
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DOI：
10.1097/ccm.0000000000004468
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发表时间：
2020-09
影响因子：
8.8
通讯作者：
Afshar M
中科院分区：
文献类型：
--
作者：
Mayampurath A;Churpek MM;Su X;Shah S;Munroe E;Patel B;Dligach D;Afshar M
Acute respiratory distress syndrome (ARDS) is frequently under recognized, and associated with increased mortality. Previously, we developed a model that utilized machine learning and natural language processing (NLP) of text from radiology reports to identify ARDS. The model showed improved performance in diagnosing ARDS when compared to a rule-based method. In this study, our objective was to externally validate the NLP model in patients from an independent hospital setting. Secondary analysis of data across five prospective clinical studies. An urban, tertiary care, academic hospital Adult patients admitted to the medical intensive care unit and at-risk for ARDS None The NLP model was previously derived and internally validated in burn, trauma, and medical patients at Loyola University Medical Center. Two machine learning models were examined with the following text features from qualifying radiology reports: (1) word representations (n-grams); (2) standardized clinical named entity mentions mapped from the National Library of Medicine Unified Medical Language System. The models were externally validated in a cohort of 235 patients at the University of Chicago Medicine, among which 110 (47%) were diagnosed with ARDS by expert annotation. During external validation, the n-gram model demonstrated good discrimination between ARDS and non-ARDS patients (c-statistic 0.78; 95% CI 0.72–0.84). The n-gram model had a higher discrimination for ARDS when compared to the standardized named entity model, although not statistically significant (c-statistic 0.78 vs 0.72, P=0.09). The most important features in the model had good face validity for ARDS characteristics but differences in frequencies did occur between hospital settings. Our computable phenotype for ARDS had good discrimination in external validation and may be used by other health systems for case-identification. Discrepancies in feature representation are likely due to differences in characteristics of the patient cohorts.
