Risk Factors for Pediatric Acute Chest Syndrome Utilizing Machine Learning Techniques
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Le résumé fourni par la source
Acute chest syndrome (ACS) causes significant morbidity and mortality in both adult and pediatric patients with sickle cell disease (SCD). Despite known risk factors for developing ACS, this complication is often unexpected. We hypothesized that a machine learning model could predict progression to ACS in children. A random forest model was utilized using the Scikit-Learn Python library. Prediction performance was assessed using the C-statistic, sensitivity, and specificity. The features included are demographics, vital signs, laboratory values, medication orders, and additional comorbidities. The positive cohort developed ACS on diagnosis or during admission and the negative cohort did not. A total of 3223 encounters were included in the model for training and testing. 65 percent of the positive cohort developed ACS 24 hours after admission. The model had a sensitivity of 65.1% and specificity of 77.4% at the cutoff point of 0.6. For diagnosing ACS, the negative predictive value was 89.8% and the positive predictive value was 42.3%. For the correct positive cases the model predicted ACS a median of 18 hours before the diagnosis was determined in the electronic health record. The features that were important included pulse, respiratory rate, presence of hypoxia, temperature, neutrophil count and use of pain medications. Despite the model's limitations, it is the first model utilizing machine learning techniques to predict ACS in children. Detecting patients who are a higher risk for ACS could impact the approach to acute care management.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Risk Factors for Pediatric Acute Chest Syndrome Utilizing Machine Learning Techniques
- Date Crossref
- 02/11/2023
- Éditeur
- American Society of Hematology
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Albert Einstein College of Medicine pays non établi dans la noticeUniversité ou école supérieure
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Children's Hospital at Montefiore Oncology and Cellular Therapy pays non établi dans la noticeÉtablissement de santé
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Montefiore Medical Center pays non établi dans la noticeÉtablissement de santé
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St. Jude Children's Research Hospital pays non établi dans la noticeÉtablissement de santé
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Bronx pays non établi dans la noticeInstitution
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St Jude Children's Research Hospital pays non établi dans la noticeÉtablissement de santé
Albert Einstein College of Medicine, Oncology and Cellular Therapy — Children's Hospital at Montefiore et Montefiore Medical Center, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.