Cargando…
Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications
Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning mode...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332321/ https://www.ncbi.nlm.nih.gov/pubmed/35893436 http://dx.doi.org/10.3390/jcm11154342 |
_version_ | 1784758617845530624 |
---|---|
author | Wolk, Donna M. Lanyado, Alon Tice, Ann Marie Shermohammed, Maheen Kinar, Yaron Goren, Amir Chabris, Christopher F. Meyer, Michelle N. Shoshan, Avi Abedi, Vida |
author_facet | Wolk, Donna M. Lanyado, Alon Tice, Ann Marie Shermohammed, Maheen Kinar, Yaron Goren, Amir Chabris, Christopher F. Meyer, Michelle N. Shoshan, Avi Abedi, Vida |
author_sort | Wolk, Donna M. |
collection | PubMed |
description | Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model’s correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783–0.789], compared with 0.694 [0.690–0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model’s prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823–0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system. |
format | Online Article Text |
id | pubmed-9332321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93323212022-07-29 Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications Wolk, Donna M. Lanyado, Alon Tice, Ann Marie Shermohammed, Maheen Kinar, Yaron Goren, Amir Chabris, Christopher F. Meyer, Michelle N. Shoshan, Avi Abedi, Vida J Clin Med Article Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model’s correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783–0.789], compared with 0.694 [0.690–0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model’s prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823–0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system. MDPI 2022-07-26 /pmc/articles/PMC9332321/ /pubmed/35893436 http://dx.doi.org/10.3390/jcm11154342 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wolk, Donna M. Lanyado, Alon Tice, Ann Marie Shermohammed, Maheen Kinar, Yaron Goren, Amir Chabris, Christopher F. Meyer, Michelle N. Shoshan, Avi Abedi, Vida Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications |
title | Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications |
title_full | Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications |
title_fullStr | Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications |
title_full_unstemmed | Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications |
title_short | Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications |
title_sort | prediction of influenza complications: development and validation of a machine learning prediction model to improve and expand the identification of vaccine-hesitant patients at risk of severe influenza complications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332321/ https://www.ncbi.nlm.nih.gov/pubmed/35893436 http://dx.doi.org/10.3390/jcm11154342 |
work_keys_str_mv | AT wolkdonnam predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT lanyadoalon predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT ticeannmarie predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT shermohammedmaheen predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT kinaryaron predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT gorenamir predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT chabrischristopherf predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT meyermichellen predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT shoshanavi predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications AT abedivida predictionofinfluenzacomplicationsdevelopmentandvalidationofamachinelearningpredictionmodeltoimproveandexpandtheidentificationofvaccinehesitantpatientsatriskofsevereinfluenzacomplications |