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1194 Machine Learning Model Defines Higher Risk of Venous Thromboembolism in Young Adults with Multiple Myeloma

Program: Oral and Poster Abstracts
Session: 332. Thrombosis and Anticoagulation: Clinical and Epidemiological: Poster I
Hematology Disease Topics & Pathways:
Bleeding and Clotting, thromboembolism, Diseases
Saturday, December 10, 2022, 5:30 PM-7:30 PM

Inés Martínez-Alfonzo, MD1*, Diego Velasco2*, Pablo Mínguez Paniagua, PhD3*, Ignacio Mahillo-Fernández4*, Elham Askari, MD5*, Rosa Vidal Laso, MD1*, Cristina Fernández Maqueda, MD6*, Alberto Velasco, MD7*, Ana González-Teomiro, MD8*, Maria Civeira-Marín, MD9*, Elena Prieto-Pareja, MD1*, Sara Martín-Herrero, MD1*, José Manuel Calvo Villas, MD, PhD9*, Isabel Krsnik, MD, PhD10*, Joaquín Sánchez-Garcia, MD11,12,13,14,15, Miguel Angel Alvarez16*, María Pilar Llamas Sillero, MD, PhD17* and Juana Serrano-López, PhD18*

1Hematology, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
2Hospital Universitario fundacion Jimenez Diaz- IISFJD-UAM, Madrid, Spain
3Genetics Department and Bioinformatics Unit, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
4Epidemiology Department, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
5Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
6Hematology, Hospital Universitario Puerta de Hierro, Madrid, Spain
7Hematology, Hospital Universitario Rey Juan Carlos, Madrid, Spain
8Hematology, Hospital Universitario Reina Sofía, Cordoba, Spain
9Hematology, Hospital Universitario Miguel Servet, Zaragoza, Spain
10Hospital Universitario Puerta de Hierro, Madrid, Spain
11IMIBIC, Hematology, Hospital Universitario Reina Sofía, UCO, Córdoba, Spain
12Reina Sofia UniversityHospital, Hematology service, Córdoba, Spain
13Hematology Department and Pathology Department, Hospital Universitario Reina Sofía, Córdoba, Andalucía, Spain
14Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Cordoba, Spain
15Reina Sofia University Hospital, Hematology service, Cordoba, Spain
16Servicio de Hematología, Instituto Maimónides de Investigación Biomédica de Córdoba IMIBIC/Hospital Universitario Reina Sofía, Cordoba, ESP
17Hospital Universitario Fundacion Jimenez Diaz - IISFJD-UAM, Madrid, Spain
18Experimental Haematology Laboratory, Hospital Universitario Fundación Jiménez Díaz, Madrid, South Carolina, Spain

Introduction: Venous thromboembolism (VTE) is a common cause of morbidity and mortality in patients with multiple myeloma (MM). Up to 10% of them may develop a thrombotic complication, being the risk higher during the first year of diagnosis. Currently, the use of thromboprophylaxis is based on risk scales according to logistic regression that generally are not validated and present modest predictive values. Machine learning (ML) algorithms identifies patterns that help us predict risk factors in a given disease. Numerous studies have shown that ML improves outcome prediction over traditional multivariate analysis models. To optimize VTE management in MM patients, thrombotic risk assessment models (RAM) will be generated in order to predict VTE development in MM patients (VTE-MM). To test our objective, we retrospectively analyzed a total of 133 patients with MM from 5 Spanish hospitals between January 2014 and December 2018. MM cohort was enriched with a total of 45 VTE patients in order to help machine learning algorithms and solving the imbalance problem. To build our predictive models, 131 clinical and biological variables were included. Variables with more than 25% of missing data were removed. Feature pre-filtering was carried out using unsupervised Boruta algorithm. Subsequently, the supervised Artifitial Neural Network (ANN) algorithm (80% training and 20% test) was applied on an input layer of 10 dimensions. To evaluate our ML model, we performed 10-fold cross validation. A confusion matrix was also used in the testing sets to evaluate the accuracy of the model. Parallelly, a univariate logistic regression (ULR) analysis was performed to identify significant variables (P<0.05) to entering into a multiple logistic regression (MLR) model. To determine a score for each independent variable, we used beta coefficients. The model was validated and evaluated applying leave-one-out method and ROC curve respectively. All analyses were carried out in R 3.6.3 and SPSS 26.0. Among VTE-MM (median age 67y, RIQ=60-74), 53% were men, mostly with ECOG >1, BMI 28 kg/m2 (RIQ 24-32), 53% had IgG and 45% of VTE-MM had revised international staging system (R-ISS) high at diagnosis. PE was the most frequent event (55%), occurring after diagnosis or 1st line of treatment. (Table 1). Boruta significantly selected 10 features (Fig.1A) and ANN algorithm demonstrated an AUC of 0.82 (Fig.1B and 1C). ML model selected young patients with previous surgeries, CVC before VTE, acute infections and with a high R-ISS as risk predictor factors. Multivariate-derived model were constructed with 8 ULR-derived variables (Fig 1D) and showed an AUC of 0.82 (Fig 1E). MLR model highlighted age (> 65y) as a protective factor (score of -4, CI: 0.003-0.308) (Fig 1D). Deep analysis of this group showed women as the higher risk patients (p=0.02). Conclusion: Both RAM models are good predictors of VTE in MM. They share 4 variables. Among them, young adult MM women seem to have more risk to develop VTE.

Disclosures: No relevant conflicts of interest to declare.

*signifies non-member of ASH