Dataset Information

Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

ABSTRACT:

Objectives

Manual clinical scoring systems are the current standard used for acute asthma clinical care pathways. No automated system exists that assesses disease severity, time course, and treatment impact in pediatric acute severe asthma exacerbations.

Working hypothesis

machine learning applied to continuous vital sign data could provide a novel pediatric-automated asthma respiratory score (pARS) by using the manual pediatric asthma score (PAS) as the clinical care standard.

Methods

Continuous vital sign monitoring data (heart rate, respiratory rate, and pulse oximetry) were merged with the health record data including a provider-determined PAS in children between 2 and 18 years of age admitted to the pediatric intensive care unit (PICU) for status asthmaticus. A cascaded artificial neural network (ANN) was applied to create an automated respiratory score and validated by two approaches. The ANN was compared with the Normal and Poisson regression models.

Results

Out of an initial group of 186 patients, 128 patients met inclusion criteria. Merging physiologic data with clinical data yielded >37 000 data points for model training. The pARS score had good predictive accuracy, with 80% of the pARS values within ±2 points of the provider-determined PAS, especially over the mid-range of PASs (6-9). The Poisson and Normal distribution regressions yielded a smaller overall median absolute error.

Conclusions

The pARS reproduced the manually recorded PAS. Once validated and studied prospectively as a tool for research and for physician decision support, this methodology can be implemented in the PICU to objectively guide treatment decisions.

SUBMITTER: Messinger AI

PROVIDER: S-EPMC6641986 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

Messinger Amanda I AI Bui Nam N Wagner Brandie D BD Szefler Stanley J SJ Vu Tam T Deterding Robin R RR

Pediatric pulmonology 20190421 8

<h4>Objectives</h4>Manual clinical scoring systems are the current standard used for acute asthma clinical care pathways. No automated system exists that assesses disease severity, time course, and treatment impact in pediatric acute severe asthma exacerbations.<h4>Working hypothesis</h4>machine learning applied to continuous vital sign data could provide a novel pediatric-automated asthma respiratory score (pARS) by using the manual pediatric asthma score (PAS) as the clinical care standard.<h4 ...[more]

PMID: 31006993

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Project description:RationaleMore targeted management of severe acute pediatric asthma could improve clinical outcomes.ObjectivesTo identify distinct clinical phenotypes of severe acute pediatric asthma using variables obtained in the first 12 h of hospitalization.MethodsWe conducted a retrospective cohort study in a quaternary care children's hospital from 2014 to 2022. Encounters for children ages 2-18 years admitted to the hospital for asthma were included. We used consensus k means clustering with patient demographics, vital signs, diagnostics, and laboratory data obtained in the first 12 h of hospitalization.Measurements and main resultsThe study population included 683 encounters divided into derivation (80%) and validation (20%) sets, and two distinct clusters were identified. Compared to Cluster 1 in the derivation set, Cluster 2 encounters (177 [32%]) were older (11 years [8; 14] vs. 5 years [3; 8]; p < .01) and more commonly males (63% vs. 53%; p = .03) of Black race (51% vs. 40%; p = .03) with non-Hispanic ethnicity (96% vs. 84%; p < .01). Cluster 2 encounters had smaller improvements in vital signs at 12-h including percent change in heart rate (-1.7 [-11.7; 12.7] vs. -7.8 [-18.5; 1.7]; p < .01), and respiratory rate (0.0 [-20.0; 22.2] vs. -11.4 [-27.3; 9.0]; p < .01). Encounters in Cluster 2 had lower percentages of neutrophils (70.0 [55.0; 83.0] vs. 85.0 [77.0; 90.0]; p < .01) and higher percentages of lymphocytes (17.0 [8.0; 32.0] vs. 9.0 [5.3; 14.0]; p < .01). Cluster 2 encounters had higher rates of invasive mechanical ventilation (23% vs. 5%; p < .01), longer hospital length of stay (4.5 [2.6; 8.8] vs. 2.9 [2.0; 4.3]; p < .01), and a higher mortality rate (7.3% vs. 0.0%; p < .01). The predicted cluster assignments in the validation set shared the same ratio (~2:1), and many of the same characteristics.ConclusionsWe identified two clinical phenotypes of severe acute pediatric asthma which exhibited distinct clinical features and outcomes.

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Project description:BackgroundPediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient representations by leveraging longitudinal information captured in electronic health records (EHRs) while considering future outcomes. However, the traditional approach for subtype analysis requires large amounts of EHR data, which may contain protected health information causing potential concerns regarding patient privacy. Federated learning is the key technology to address privacy concerns while preserving the accuracy and performance of ML algorithms. Federated learning could enable multisite development and implementation of ML algorithms to facilitate the translation of artificial intelligence into clinical practice.ObjectiveThe aim of this study is to develop a research protocol for implementation of federated ML across a large clinical research network to identify and discover pediatric asthma subtypes and their progression over time.MethodsThis mixed methods study uses data and clinicians from the OneFlorida+ clinical research network, which is a large regional network covering linked and longitudinal patient-level real-world data (RWD) of over 20 million patients from Florida, Georgia, and Alabama in the United States. To characterize the subtypes, we will use OneFlorida+ data from 2011 to 2023 and develop a research-grade pediatric asthma computable phenotype and clinical natural language processing pipeline to identify pediatric patients with asthma aged 2-18 years. We will then apply federated learning to characterize pediatric asthma subtypes and their temporal progression. Using the Promoting Action on Research Implementation in Health Services framework, we will conduct focus groups with practicing pediatric asthma clinicians within the OneFlorida+ network to investigate the clinical utility of the subtypes. With a user-centered design, we will create prototypes to visualize the subtypes in the EHR to best assist with the clinical management of children with asthma.ResultsOneFlorida+ data from 2011 to 2023 have been collected for 411,628 patients aged 2-18 years along with 11,156,148 clinical notes. We expect to complete the computable phenotyping within the first year of the project, followed by subtyping during the second and third years, and then will perform the focus groups and establish the user-centered design in the fourth and fifth years of the project.ConclusionsPediatric asthma subtypes incorporating RWD from diverse populations could improve patient outcomes by moving the field closer to precision pediatric asthma care. Our privacy-preserving federated learning methodology and qualitative implementation work will address several challenges of applying ML to large, multicenter RWD data.International registered report identifier (irrid)DERR1-10.2196/57981.

Dataset Information

Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

Objectives

Working hypothesis

Methods

Results

Conclusions

Publications

Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

Similar Datasets

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