Prediction of multidrug-resistant gram-negative bacterial infections in community hospitals using the eXtreme Gradient Boosting algorithm
Article Sidebar
Main Article Content
Abstract
Multidrug-resistant Gram-negative bacteria (MDR-GNB) constitute a pressing global public health concern.
The primary objective of this study was to develop a predictive model for identifying multidrug-resistant Gram-negative bacterial (MDR-GNB) infections using blood samples at Thatphanom Crown Prince Hospital, Nakhon Phanom Province, between January 2016 and December 2020, using the eXtreme Gradient Boosting algorithm. This retrospective study included a total of 624 samples. The findings revealed that the majority of patients with MDR-GNB infections were over 70 years old, accounting for 38%, with an average age of 65 years (S.D. = 18.42). The most frequently identified bacteria were Escherichia coli (63.02%) and Klebsiella pneumoniae (11.46%). The machine learning model developed using the eXtreme Gradient Boosting method demonstrated an ability to accurately classify patients with MDR-GNB infections with 71% accuracy and 66% of AUC. Additionally, logistic regression analysis identified that patients infected with Escherichia coli had a higher risk of MDR-GNB infection (adjusted OR 3.44, 95% CI 2.41 - 4.91, p-value < 0.001), with a significantly higher risk observed in cases involving Enterobacteriaceae producing extended-spectrum beta-lactamases (ESBL) (adjusted OR 19.88, 95% CI 5.70 - 69.27, p-value < 0.001). Based on the study results, the overall performance of the XGBoost model showed Sensitivity, Specificity, and AUC values higher than 60%, reflecting its superior discriminatory ability compared to the logistic regression model. The results suggest that the developed model can be effectively used for surveillance among patients in the hospital setting, thereby improving the control and prevention of the spread of MDR-GNB infections.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Petersen E, Shan Lee S, Blumberg L, Levison ME. Antimicrobial resistance - A global problem in need of global solutions. IJID Reg. 2023;9:102-3.
Sumpradit N, Wongkongkathep S, Poonpolsup S, Janejai N, Paveenkittiporn W, Boonyarit P, et al. New chapter in tackling antimicrobial resistance in Thailand. BMJ. 2017;358:j3415. (in Thai)
Khoka A. Antibiotics and antibiotic resistance. Journal of Medicine and Health Sciences. 2020;27(2):125-39. (in Thai)
Samreen, Ahmad I, Malak HA, Abulreesh HH. Environmental antimicrobial resistance and its drivers: a potential threat to public health. Journal of Global Antimicrobial Resistance. 2021;27:101-11.
Yosboonruang A, Jitsatthra C, Tarawet L. Incidence of Resistant Bacteria Isolated from Medical Devices in the Intensive Care Unit of a Hospital. Journal of Public Health. 2017;47:222-34. (in Thai)
Boonrod P, Pornsiriwatanakul A. Prevalence of Carbapenem Resistant Enterobacteriaceae and Carbapenemase Genes in Lerdsin Hospital. Journal of The Department of Medical Services. 2023;48(2):13-20. (in Thai)
Molton JS, Tambyah PA, Ang BSP, Ling ML, Fisher DA. The Global Spread of Healthcare-Associated Multidrug-Resistant Bacteria: A Perspective From Asia. Clinical Infectious Diseases. 2013;56(9):1310-8.
Naghavi M, Vollset SE, Ikuta KS, Swetschinski LR, Gray AP, Wool EE, et al. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. The Lancet. 2024;404(10459):1199-226.
Cooperation OfE, Development. Stemming the superbug tide: just a few dollars more. OECD Paris; 2018.
Phumart P, Phodha T, Thamlikitkul V, Riewpaiboon A, Prakongsai P, Limwattananon S. Health and Economic Impacts of Antimicrobial Resistant Infections in Thailand : A Preliminary Study. Journal of Health Systems Research. 2555;6(3):352–60. (in Thai)
Baramee J, Unahalekhaka A, Klunklin P. Implementation and Barriers in Prevention of Multidrug-Resistant Organisms Transmission Among Community Hospitals. Nursing Journal CMU. 2021;48(2):95-106. (in Thai)
Ponyon J, Ungcharoen R. Incidence of Multidrug-resistant Gram-Negative Bacteria in Blood Specimens in a Community Hospital in Northeast. Pharmacy Practice. 2566;15(1):96-109. (in Thai)
Olivares J, Bernardini A, Garcia-Leon G, Corona F, M BS, Martinez JL. The intrinsic resistome of bacterial pathogens. Front Microbiol. 2013;4:103.
Acheewakullamas M, Sawasrak K, Wanthana maneekul W, Chaiyabutra S, Thanwiset T. Risk Factors for Multi-Drug Resistant Acinetobacter baumannii Nosocomial Infection, Chaiyaphum Hospital. Chaiyaphum medical journal. 2016;38(1):5-15. (in Thai)
Sripromma N. Nursing care for COVID-19 with ventilator-associatedpneumonia and multidrug resistance. The Academic and Nursing Journal of Boromarajonani College of Nursing,Chakriraj. 2023;3(2):E000518. (in Thai)
Fatima S, Hussain A, Amir SB, Ahmed SH, Aslam SMH. XGBoost and Random Forest Algorithms: An in Depth Analysis. Pakistan Journal of Scientific Research. 2023;3(1):26-31.
Duman E. Implementation of XGBoost Method for Healthcare Fraud Detection. Scientific Journal of Mehmet Akif Ersoy University. 2022;5(2):69-75.
Garcia-Vidal C, Puerta-Alcalde P, Cardozo C, Orellana MA, Besanson G, Lagunas J, et al. Machine Learning to Assess the Risk of Multidrug-Resistant Gram-Negative Bacilli Infections in Febrile Neutropenic Hematological Patients. Infect Dis Ther. 2021;10(2):971-83.
Moran E, Robinson E, Green C, Keeling M, Collyer B. Towards personalized guidelines: using machine-learning algorithms to guide antimicrobial selection. Journal of Antimicrobial Chemotherapy. 2020;75(9):2677-80.
Liu J, Gao Y, Hu F. A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM. Computers & Security. 2021;106:102289.
Humbert-Vidan L, Hansen CR, Patel V, Johansen J, King AP, Guerrero Urbano T. External validation of a multimodality deep-learning normal tissue complication probability model for mandibular osteoradionecrosis trained on 3D radiation distribution maps and clinical variables. Physics and Imaging in Radiation Oncology. 2024;32:100668.
Boonlum S. Risk Factors of the Multidrug-resistant in Surgical II Intensive Care Unit, Suratthani Hospital. Journal of Health Sciences and Pedagogy. 2021;1(1):28-39. (in Thai)
Lee ALH, To CCK, Lee ALS, Chan RCK, Wong JSH, Wong CW, et al. Deep learning model for prediction of extended-spectrum beta-lactamase (ESBL) production in community-onset Enterobacteriaceae bacteraemia from a high ESBL prevalence multi-centre cohort. Eur J Clin Microbiol Infect Dis. 2021;40(5):1049-61.
Sick-Samuels AC, Goodman KE, Rapsinski G, Colantouni E, Milstone AM, Nowalk AJ, et al. A Decision Tree Using Patient Characteristics to Predict Resistance to Commonly Used Broad-Spectrum Antibiotics in Children With Gram-Negative Bloodstream Infections. Journal of the Pediatric Infectious Diseases Society. 2019;9(2):142-9.
Rich SN, Jun I, Bian J, Boucher C, Cherabuddi K, Morris JG, Jr., et al. Development of a Prediction Model for Antibiotic-Resistant Urinary Tract Infections Using Integrated Electronic Health Records from Multiple Clinics in North-Central Florida. Infect Dis Ther. 2022;11(5):1869-82.
Liang Q, Ding S, Chen J, Chen X, Xu Y, Xu Z, et al. Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning. BMC Medical Informatics and Decision Making. 2024;24(1):123.
Ponyon J, Kerdsin A, Preeprem T, Ungcharoen R. Risk Factors of Infections Due to Multidrug-Resistant Gram-Negative Bacteria in a Community Hospital in Rural Thailand. Trop Med Infect Dis. 2022;7(11). (in Thai)
