tetano
Editor, Senior Moderator
J Clin Med
. 2022 Jul 26;11(15):4342.
doi: 10.3390/jcm11154342.
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
Donna M Wolk[SUP] 1 2 [/SUP], Alon Lanyado[SUP] 3 [/SUP], Ann Marie Tice[SUP] 1 [/SUP], Maheen Shermohammed[SUP] 4 [/SUP], Yaron Kinar[SUP] 3 [/SUP], Amir Goren[SUP] 4 [/SUP], Christopher F Chabris[SUP] 4 [/SUP], Michelle N Meyer[SUP] 4 [/SUP], Avi Shoshan[SUP] 3 [/SUP], Vida Abedi[SUP] 5 [/SUP]
Affiliations
Abstract
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.
Keywords: Clinical Lab 2.0; EHR; RT-PCR; decision support; electronic medical records; influenza; machine learning; precision medicine; risk stratification; vaccine.
. 2022 Jul 26;11(15):4342.
doi: 10.3390/jcm11154342.
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
Donna M Wolk[SUP] 1 2 [/SUP], Alon Lanyado[SUP] 3 [/SUP], Ann Marie Tice[SUP] 1 [/SUP], Maheen Shermohammed[SUP] 4 [/SUP], Yaron Kinar[SUP] 3 [/SUP], Amir Goren[SUP] 4 [/SUP], Christopher F Chabris[SUP] 4 [/SUP], Michelle N Meyer[SUP] 4 [/SUP], Avi Shoshan[SUP] 3 [/SUP], Vida Abedi[SUP] 5 [/SUP]
Affiliations
- PMID: 35893436
- PMCID: PMC9332321
- DOI: 10.3390/jcm11154342
Abstract
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.
Keywords: Clinical Lab 2.0; EHR; RT-PCR; decision support; electronic medical records; influenza; machine learning; precision medicine; risk stratification; vaccine.