Predictors and Profile of Severe Infectious Complications in Multiple Myeloma Patients Treated with Daratumumab-Based Regimens: A Machine Learning Model for Pneumonia Risk.
Simple Summary: Our research explores the profile and risk factors for infections in multiple myeloma patients undergoing treatment with daratumumab, a key drug in chemotherapy regimens for this disease. The study seeks to identify which patients are at the highest risk of developing severe infectio...
| Publicado en: | Cancers Vol. 16; no. 21; pp. 3709 - 3729 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
MDPI
Nov2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180784743&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180784743 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Nov2024 vid: 16 iid: 21 pid: 97109 pub: MDPI artinfo: ui: 180784743 180784743 180784743 10.3390/cancers16213709 180784743 ppf: 3709 ppct: 20 formats: tig: atl: Predictors and Profile of Severe Infectious Complications in Multiple Myeloma Patients Treated with Daratumumab-Based Regimens: A Machine Learning Model for Pneumonia Risk. aug: au: Mikulski, Damian Kędzior, Marcin Kamil Mirocha, Grzegorz Jerzmanowska-Piechota, Katarzyna Witas, Żaneta Woźniak, Łukasz Pawlak, Magdalena Kościelny, Kacper Kośny, Michał Robak, Paweł Gołos, Aleksandra Robak, Tadeusz Fendler, Wojciech Góra-Tybor, Joanna affil: Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland sug: subj: Multiple Myeloma Drug Therapy Antibodies, Monoclonal Therapeutic Use Antibodies, Monoclonal Adverse Effects Pneumonia Risk Factors Risk Assessment Human Male Female Infection Risk Factors Machine Learning Poland Pneumonia Epidemiology Hemoglobins Blood Prediction Models Algorithms Infection Prevention and Control Pneumonia Prevention and Control Infection Epidemiology Quality of Health Care Quality Improvement Retrospective Design Infection Mortality Hospitalization Odds Ratio Confidence Intervals Decision Trees Boosting Machine Learning Algorithms Random Forest Erythrocyte Indices Pneumonia Mortality Bortezomib Therapeutic Use Thalidomide Therapeutic Use Dexamethasone Therapeutic Use Bortezomib Adverse Effects Thalidomide Adverse Effects Dexamethasone Adverse Effects Multivariate Analysis Male Female ab: Simple Summary: Our research explores the profile and risk factors for infections in multiple myeloma patients undergoing treatment with daratumumab, a key drug in chemotherapy regimens for this disease. The study seeks to identify which patients are at the highest risk of developing severe infections and the factors contributing to this risk, as infections are a major concern for these patients. Analysis of patient data from our facility showed that lower hemoglobin levels and poorer performance status significantly increase the risk of serious infections. Additionally, we developed predictive algorithms to identify individuals at elevated risk of developing pneumonia during treatment. The findings from our study may help healthcare providers identify high-risk patients and implement targeted strategies to prevent infections, ultimately improving patient care. Background: Daratumumab (Dara) is the first monoclonal antibody introduced into clinical practice to treat multiple myeloma (MM). It currently forms the backbone of therapy regimens in both newly diagnosed (ND) and relapsed/refractory (RR) patients. However, previous reports indicated an increased risk of infectious complications (ICs) during Dara-based treatment. In this study, we aimed to determine the profile of ICs in MM patients treated with Dara-based regimens and establish predictors of their occurrence. Methods: This retrospective, real-life study included MM patients treated with Dara-based regimens between July 2019 and March 2024 at our institution. Infectious events were evaluated using the Terminology Criteria for Adverse Events (CTCAE) version 5.0. Results: The study group consisted of a total of 139 patients, including 49 NDMM and 90 RRMM. In the RR setting, the majority (60.0%) of patients received the Dara, bortezomib, and dexamethasone (DVd) regimen, whereas ND patients were predominantly (98%) treated with the Dara, bortezomib, thalidomide, and dexamethasone (DVTd) regimen. Overall, 55 patients (39.6%) experienced ICs. The most common IC was pneumonia (37.5%), followed by upper respiratory tract infections (26.8%). Finally, twenty-five patients had severe ICs (grade ≥ 3) and required hospitalization, and eight patients died due to ICs. In the final multivariable model adjusted for setting (ND/RR) and age, hemoglobin level (OR 0.77, 95% CI: 0.61–0.96, p = 0.0037), and Eastern Cooperative Oncology Group (ECOG) >1 (OR 4.46, 95% CI: 1.63–12.26, p = 0.0037) were significant factors influencing severe IC occurrence. Additionally, we developed predictive models using the J48 decision tree, gradient boosting, and random forest algorithms. After conducting 10-fold cross-validation, these models demonstrated strong performance in predicting the occurrence of pneumonia during treatment with daratumumab-based regimens. Conclusions: Simple clinical and laboratory assessments, including hemoglobin level and ECOG scale, can be valuable in identifying patients vulnerable to infections during Dara-based regimens, facilitating personalized prophylactic strategies. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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