Investigation of the relationship between microbiological features and mortality in patients with hematological malignancies who developed febrile neutropenia using machine learning models
Abstract
Background: This study aimed to examine the relationship between microbiological features and mortality in hematological malignancy patients who develop febrile neutropenia using machine learning algorithms.
Methods: Patients with hematological malignancies who developed febrile neutropenia between 2011 and 2015 in a training and research hospital were included. The PyCaret low-code Python library was used to streamline the machine-learning workflow. Two separate models were developed to predict early and late mortality. The following machine learning algorithms were evaluated during the modeling process: Ridge Classifier, Random Forest Classifier, Linear Discriminant Analysis, Light Gradient Boosting Machine, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Accuracy and area under the receiver operating characteristic curve (AUC-ROC) metrics were calculated to evaluate the models’ predictive capability for both early and late mortality predictions. All analyses were performed using Python 3.12 and the PyCaret 3.0 library.
Results: The dataset used in this study consisted of 159 patients. For early mortality prediction, the Ridge Classifier demonstrated the best performance with a test set accuracy of 0.92 and an AUC of 0.94. For late mortality prediction, the Random Forest Classifier achieved the highest accuracy of 0.94 and an AUC of 0.98. For both models, ICU admission was identified as the most important feature, with a relative importance of 23.6% for early mortality prediction and 25.3% for late mortality prediction. Other key variables included pneumonia, renal function, and the duration of neutropenia.
Conclusion: Machine learning models can be applied and improved on more patient data, helping traditional statistical methods in medical research.
Keywords
Febrile neutropenia, machine learning, mortality, risk factors
Ethical Statement
The study protocol was approved by the Institutional Ethics Committee of the Bozyaka Training and Research Hospital Ethics Committee (03.09.2024/ 137) and has been performed in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments.
References
- Stohs EJ, Abbas A, Freifeld A. Approach to febrile neutropenia in patients undergoing treatments for hematologic malignancies. Transpl Infect Dis. 2024 Apr; 26(2): e14236. doi: 10.1111/tid.14236.
- Contejean A, Abbara S, Chentouh R, Alviset S, Grignano E, Gastli N, Casetta A, Willems L, Canouï E, Charlier C, Pène F, Charpentier J, Reboul-Marty J, Batista R, Bouscary D, Kernéis S. Antimicrobial stewardship in high-risk febrile neutropenia patients. Antimicrob Resist Infect Control. 2022 Mar; 11(1) :52. doi: 10.1186/s13756-022-01084-0.
- Contejean A, Maillard A, Canouï E, Kernéis S, Fantin B, Bouscary D, Parize P, Garcia-Vidal C, Charlier C. Advances in antibacterial treatment of adults with high-risk febrile neutropenia. J Antimicrob Chemother. 2023 Sep;78(9):2109-2120. doi: 10.1093/jac/dkad166.4.
- Jiang Y, Luo J, Huang D, Liu Y, Li DD. Machine learning advances in microbiology: a review of methods and applications. Front Microbiol 2022 May; 13: 925454. https://doi. org/10.3389/fmicb.2022.925454
- Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Method. 2019 Mar; 19(1): 64. https://doi.org/10.1186/s12874- 019-0681-4.
- Calik S, Ari A, Bilgir O, Cetintepe T, Yis R, Sonmez U, Tosun S. The relationship between mortality and microbiological parameters in febrile neutropenic patients with hematological malignancies. Saudi Med J 2018 Sep; 39(9): 878-885. doi: 10.15537/smj.2018.9.22824.
- Ali M. PyCaret: An open source, low-code machine learning library in Python. Available at: https://www.pycaret.org. Accessed: February 18, 2024.
- Bachlitzanaki M, Aletras G, Bachlitzanaki E, Messaritakis I, Koukias S, Koulouridi A, Bachlitzanakis E, Kaloeidi E, Vakonaki E, Kontopodis E, Androulakis N, Chamilos G, Mavroudis D, Ioannou P, Kofteridis D. Evalution of febrile neutropenia in hospitalized patients with neoplasia undergoing chemotherapy. Microorganisms. 2023 Oct; 11(10): 2547. doi: 10.3390/microorganisms11102547.
- Gedik H, Simşek F, Kantürk A, Yildirmak T, Arica D, Aydin D, Demirel N, Yokuş O. Bloodstream infections in patients with hematological malignancies: which is more fatal - cancer or resistant pathogens? Ther Clin Risk Manag. 2014 Sep; 10: 743–752. doi: 10.2147/TCRM.S68450.
- Kang CI, Chung DR, Ko KS, Peck KR, Song JH, Korean Network for Study of Infectious Diseases. Risk factors for infection and treatment outcome of extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae bacteremia in patients with hematologic malignancy. Ann Hematol. 2012 Jan; 91: 115–121. doi: 10.1007/s00277-011-1247-7.