Development and validation of an asthma exacerbation prediction model using electronic health record (EHR) data

Martin, Alfred; Bauer, Victoria; Datta, Avisek; Masi, Christopher M.; Mosnaim, Giselle; Solomonides, Anthony; Rao, Goutham

doi:10.1080/02770903.2019.1648505

Cited by 22 publications

(20 citation statements)

References 25 publications

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“…In our population of asthmatics who required any hospital readmission, more than 71% were obese or overweight, but this is not associated with the incidence of hospital readmissions. Our results are in agreement with the other authors who did not observe a relationship between obesity and exacerbations or with the control of the asthma [8,16,21,[29][30][31], although it was different from other studies where we showed a poorer control of asthma or increase in the incidence of the exacerbations with obesity [13][14][15]19,20,[32][33][34][35][36].…”

Section: Discussionsupporting

confidence: 80%

“…On analysing the impact of obesity on the incidence of exacerbations, there are still discrepancies between authors. Some do not observe a relationship between the increase in weight and the incidence of exacerbations in some cases [17,18], but in other publications it is mentioned that exacerbations are more common with an increase in weight [15,19,20]. However, obesity does not appear to be associated with the severity of the exacerbations or in the time required to recover from them [21][22][23].…”

Section: Introductionmentioning

confidence: 97%

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Obesity Does Not Increase the Risk of Asthma Readmissions

Barcala

Nieto‐Fontarigo

Lourido-Cebreiro

et al. 2020

JCM

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The relationship between obesity and asthma exacerbations is still under debate. The aim of our work is to analyse the relationship between obesity and hospital re-admissions in asthmatics. A review was retrospectively performed on all hospital admissions of adult patients due to asthma exacerbation occurring in our hospital for 11 years. All those cases with asthma as the first diagnosis in the discharge report were included, or those with asthma as the second diagnosis provided when the first diagnosis was respiratory infection or respiratory failure. Only the first hospital admission of each patient was included in this study. The Odds Ratios of a higher incidence of early/late readmissions due to asthma exacerbation were calculated using a binary logistic regression, using the body mass index (BMI) as independent variable, adjusted for all the variables included in the study. The study included 809 patients with a mean age of 55.6 years, and 65.2% were female. The majority (71.4%) were obese or overweight. No significant relationship was observed in the univariate or multivariate analyses between overweight or obesity and the early or late hospital readmissions due to asthma. Therefore, obesity does not seem to be a determining factor in the risk of asthma exacerbations.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 97%

Obesity Does Not Increase the Risk of Asthma Readmissions

Barcala

Nieto‐Fontarigo

Lourido-Cebreiro

et al. 2020

JCM

View full text Add to dashboard Cite

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“…Additionally, the performance of the final model was also validated on a large test set. The large training data set may also be why, despite using a shorter prediction horizon than most other previously published prediction models, our models achieved a better AUROC in internal validation than the models in the review of Loymans et al [15] and other recent publications [12,36,[44][45][46][47][48].…”

Section: Strengths Of the Studymentioning

confidence: 91%

“…Furthermore, the extraction program used for retrieval of information from EMRs has been validated in a specific study and concluded that it is highly reliable and that appropriate and accurate information is extracted from the EMRs [23]. Although other published models have investigated longer prediction windows, we built our models using a 15 day prediction window, aimed to support future development of a potential model to predict near term risk for exacerbation potentially using novel wearable devices and thus the potential for clinical intervention within this shorter window [12,15,36,[44][45][46][47][48]. We tested different methods and were able to explore the additional value of more recently developed ML models compared with more traditional logistic regression approaches.…”

Section: Strengths Of the Studymentioning

confidence: 99%

Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients’ data using machine learning - Based on the ARCTIC study

Lisspers

Ställberg

Janson

et al. 2021

Respiratory Medicine

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The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization and improve patient outcomes. We aimed to develop models using machine learning to predict risk of exacerbations. Methods: Data from 29,396 asthma patients was collected from electronic medical records and national registers covering clinical and epidemiological factors (e.g. comorbidities, health care contacts), between 2000 and 2013. Machine-learning classifiers were used to create models to predict exacerbations within the next 15 days. Model selection was done using the mean cross validation score of area under precision-recall curve (AUPRC). Results: The most important predictors of exacerbation were comorbidity burden and previous exacerbations. Model validation on test data yielded an AUPRC = 0.007 (95% CI: ± 0.0002), indicating that historic clinical information alone may not be sufficient to predict a near future risk of asthma exacerbation. Conclusions: Supplementation with additional data on environmental triggers, (e.g. weather, pollen count, air quality) and from wearables, might be necessary to improve performance of the short-term predictive model to develop a more clinically useful tool.

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“…Investigations on risk factor analysis or prediction for asthma exacerbation have been respectable, in which the mainstream adopts traditional statistical methods, such as logistic regression [7][8][9][10], proportional hazards regression [11], and generalized linear mixed models [12]. However, most of them have only explored a small group of candidate risk factors and are usually hard to extend to other data sets and make personalized predictions difficult [13,14].…”

Section: Introductionmentioning

confidence: 99%

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study (Preprint)

Xiang¹,

Ji²,

Zhou³

et al. 2019

Preprint

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BACKGROUND Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients’ quality of life. However, efficient methods that can help <i>identify personalized risk factors and make early predictions</i> are lacking. OBJECTIVE This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. METHODS We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. RESULTS The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. CONCLUSIONS The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual’s level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.

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Development and validation of an asthma exacerbation prediction model using electronic health record (EHR) data

Cited by 22 publications

References 25 publications

Obesity Does Not Increase the Risk of Asthma Readmissions

Obesity Does Not Increase the Risk of Asthma Readmissions

Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients’ data using machine learning - Based on the ARCTIC study

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study (Preprint)

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