Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.

<h4>Introduction</h4>Previous work had shown that machine learning models can predict inflammatory bowel disease (IBD)-related hospitalizations and outpatient corticosteroid use based on patient demographic and laboratory data in a cohort of United States Veterans. This study aimed to re...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ryan W Gan, Diana Sun, Amanda R Tatro, Shirley Cohen-Mekelburg, Wyndy L Wiitala, Ji Zhu, Akbar K Waljee
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8a3c6b4a2c1f4064b127a5e3d11dc3e5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8a3c6b4a2c1f4064b127a5e3d11dc3e5
record_format	dspace
spelling	oai:doaj.org-article:8a3c6b4a2c1f4064b127a5e3d11dc3e52021-12-02T20:14:27ZReplicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.1932-620310.1371/journal.pone.0257520https://doaj.org/article/8a3c6b4a2c1f4064b127a5e3d11dc3e52021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257520https://doaj.org/toc/1932-6203<h4>Introduction</h4>Previous work had shown that machine learning models can predict inflammatory bowel disease (IBD)-related hospitalizations and outpatient corticosteroid use based on patient demographic and laboratory data in a cohort of United States Veterans. This study aimed to replicate this modeling framework in a nationally representative cohort.<h4>Methods</h4>A retrospective cohort design using Optum Electronic Health Records (EHR) were used to identify IBD patients, with at least 12 months of follow-up between 2007 and 2018. IBD flare was defined as an inpatient/emergency visit with a diagnosis of IBD or an outpatient corticosteroid prescription for IBD. Predictors included demographic and laboratory data. Logistic regression and random forest (RF) models were used to predict IBD flare within 6 months of each visit. A 70% training and 30% validation approach was used.<h4>Results</h4>A total of 95,878 patients across 780,559 visits were identified. Of these, 22,245 (23.2%) patients had at least one IBD flare. Patients were predominantly White (87.7%) and female (57.1%), with a mean age of 48.0 years. The logistic regression model had an area under the receiver operating curve (AuROC) of 0.66 (95% CI: 0.65-0.66), sensitivity of 0.69 (95% CI: 0.68-0.70), and specificity of 0.74 (95% CI: 0.73-0.74) in the validation cohort. The RF model had an AuROC of 0.80 (95% CI: 0.80-0.81), sensitivity of 0.74 (95% CI: 0.73-0.74), and specificity of 0.72 (95% CI: 0.72-0.72) in the validation cohort. Important predictors of IBD flare in the RF model were the number of previous flares, age, potassium, and white blood cell count.<h4>Conclusion</h4>The machine learning modeling framework was replicated and results showed a similar predictive accuracy in a nationally representative cohort of IBD patients. This modeling framework could be embedded in routine practice as a tool to distinguish high-risk patients for disease activity.Ryan W GanDiana SunAmanda R TatroShirley Cohen-MekelburgWyndy L WiitalaJi ZhuAkbar K WaljeePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257520 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ryan W Gan Diana Sun Amanda R Tatro Shirley Cohen-Mekelburg Wyndy L Wiitala Ji Zhu Akbar K Waljee Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.
description	<h4>Introduction</h4>Previous work had shown that machine learning models can predict inflammatory bowel disease (IBD)-related hospitalizations and outpatient corticosteroid use based on patient demographic and laboratory data in a cohort of United States Veterans. This study aimed to replicate this modeling framework in a nationally representative cohort.<h4>Methods</h4>A retrospective cohort design using Optum Electronic Health Records (EHR) were used to identify IBD patients, with at least 12 months of follow-up between 2007 and 2018. IBD flare was defined as an inpatient/emergency visit with a diagnosis of IBD or an outpatient corticosteroid prescription for IBD. Predictors included demographic and laboratory data. Logistic regression and random forest (RF) models were used to predict IBD flare within 6 months of each visit. A 70% training and 30% validation approach was used.<h4>Results</h4>A total of 95,878 patients across 780,559 visits were identified. Of these, 22,245 (23.2%) patients had at least one IBD flare. Patients were predominantly White (87.7%) and female (57.1%), with a mean age of 48.0 years. The logistic regression model had an area under the receiver operating curve (AuROC) of 0.66 (95% CI: 0.65-0.66), sensitivity of 0.69 (95% CI: 0.68-0.70), and specificity of 0.74 (95% CI: 0.73-0.74) in the validation cohort. The RF model had an AuROC of 0.80 (95% CI: 0.80-0.81), sensitivity of 0.74 (95% CI: 0.73-0.74), and specificity of 0.72 (95% CI: 0.72-0.72) in the validation cohort. Important predictors of IBD flare in the RF model were the number of previous flares, age, potassium, and white blood cell count.<h4>Conclusion</h4>The machine learning modeling framework was replicated and results showed a similar predictive accuracy in a nationally representative cohort of IBD patients. This modeling framework could be embedded in routine practice as a tool to distinguish high-risk patients for disease activity.
format	article
author	Ryan W Gan Diana Sun Amanda R Tatro Shirley Cohen-Mekelburg Wyndy L Wiitala Ji Zhu Akbar K Waljee
author_facet	Ryan W Gan Diana Sun Amanda R Tatro Shirley Cohen-Mekelburg Wyndy L Wiitala Ji Zhu Akbar K Waljee
author_sort	Ryan W Gan
title	Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.
title_short	Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.
title_full	Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.
title_fullStr	Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.
title_full_unstemmed	Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.
title_sort	replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8a3c6b4a2c1f4064b127a5e3d11dc3e5
work_keys_str_mv	AT ryanwgan replicatingpredictionalgorithmsforhospitalizationandcorticosteroiduseinpatientswithinflammatoryboweldisease AT dianasun replicatingpredictionalgorithmsforhospitalizationandcorticosteroiduseinpatientswithinflammatoryboweldisease AT amandartatro replicatingpredictionalgorithmsforhospitalizationandcorticosteroiduseinpatientswithinflammatoryboweldisease AT shirleycohenmekelburg replicatingpredictionalgorithmsforhospitalizationandcorticosteroiduseinpatientswithinflammatoryboweldisease AT wyndylwiitala replicatingpredictionalgorithmsforhospitalizationandcorticosteroiduseinpatientswithinflammatoryboweldisease AT jizhu replicatingpredictionalgorithmsforhospitalizationandcorticosteroiduseinpatientswithinflammatoryboweldisease AT akbarkwaljee replicatingpredictionalgorithmsforhospitalizationandcorticosteroiduseinpatientswithinflammatoryboweldisease
_version_	1718374657564868608

Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.

Ejemplares similares