{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:56:25Z","timestamp":1781585785441,"version":"3.54.5"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study aimed to develop a machine learning-based model for the early identification of elderly inpatients at high risk of Gram-negative bloodstream infection immediately following the first detection of Gram-negative bacteria in non-blood specimens (e.g., urine, sputum, wound secretions), during the 48\u201372\u202fh window before blood culture results become available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective cohort study was conducted across three centers of Sichuan Provincial People\u2019s Hospital (Qingyang, Caotang, and Chengdong Campuses). It enrolled 9,646 elderly inpatients with Gram-negative bacteria positivity in any specimen during their hospitalization between January 2017 and December 2022. Predictor variables were screened using LASSO regression and Boruta algorithm, and their clinical significance was validated via the Delphi expert consultation method. Six machine learning models\u2014random forest, XGBoost, logistic regression, artificial neural network, k-nearest neighbors, and decision tree\u2014were developed. The discriminatory performance of the models was evaluated using a comprehensive set of metrics, including the area under the receiver operating characteristic curve along with other key indicators such as accuracy and recall.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The XGBoost model demonstrated the optimal performance among the developed models, achieving a test set AUC of 0.816 (95% CI: 0.766\u20130.861), accuracy of 0.733, recall of 0.760, while logistic regression offered a simpler alternative with acceptable accuracy. Seven predictive variables were identified: maximum procalcitonin level, max neutrophil percentage, maximum C-reactive protein level, minimum white blood cell count, venous catheter, age, and length of hospital stay. SHAP variable importance analysis identified maximum procalcitonin level, length of hospital stays, and max_neutrophil_percentage as the top three most important predictors. External validation in an independent MIMIC-IV database confirmed acceptable generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The predictive model developed for Gram-negative bloodstream infections in elderly patients demonstrates promising predictive ability. It overcomes the temporal limitations of traditional blood culture, enabling the identification of high-risk elderly patients at the earliest stage of infection. This approach provides clinicians with a valuable intervention window and represents a novel pathway toward precision infection control.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1819369","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:28:41Z","timestamp":1781584121000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Gram-negative bloodstream infection in elderly patients after isolation of GNB from non-blood specimens: a machine learning-based tool"],"prefix":"10.3389","volume":"13","author":[{"given":"Xinran","family":"Lin","sequence":"first","affiliation":[{"name":"School of Medicine, University of Electronic Science and Technology of China","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daoming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Electronic Science and Technology of China","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Electronic Science and Technology of China","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongping","family":"Yao","sequence":"additional","affiliation":[{"name":"Sichuan Nursing Vocational College","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Lv","sequence":"additional","affiliation":[{"name":"Public Health Department, Sichuan Academy of Medical Sciences, Sichuan People\u2019s Hospital, School of Medicine, University of Electronic Science and Technology of China","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e209","DOI":"10.1097\/CCM.0000000000004155","article-title":"The epidemiology of Sepsis in Chinese ICUs: a National Cross-Sectional Survey","volume":"48","author":"Xie","year":"2020","journal-title":"Crit Care Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1111\/1469-0691.12195","article-title":"Overall burden of bloodstream infection and nosocomial bloodstream infection in North America and Europe","volume":"19","author":"Goto","year":"2013","journal-title":"Clin Microbiol Infect"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1007\/s10096-021-04212-7","article-title":"Bloodstream infections in older population: epidemiology, outcome, and impact of multidrug resistance","volume":"40","author":"de Laroche","year":"2021","journal-title":"Eur J Clin Microbiol Infect Dis"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.1016\/S0140-6736(22)01546-X","article-title":"The path to healthy ageing in China: a Peking University-lancet commission","volume":"400","author":"Chen","year":"2022","journal-title":"Lancet"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.jinf.2023.12.008","article-title":"Economic burden attributable to healthcare-associated infections at western China hospitals: 6 year, prospective cohort study","volume":"88","author":"Lv","year":"2024","journal-title":"J Infect"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s13613-023-01233-7","article-title":"The critically ill older patient with sepsis: a narrative review","volume":"14","author":"Ibarz","year":"2024","journal-title":"Ann Intensive Care"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1016\/j.idc.2007.07.006","article-title":"Infection in the elderly","volume":"21","author":"Htwe","year":"2007","journal-title":"Infect Dis Clin N Am"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1080\/21505594.2015.1132142","article-title":"Bloodstream infections in older patients","volume":"7","author":"Yahav","year":"2016","journal-title":"Virulence"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"e39","DOI":"10.1016\/j.ejim.2011.02.004","article-title":"Risk factors and mortality for nosocomial bloodstream infections in elderly patients","volume":"22","author":"Reunes","year":"2011","journal-title":"Eur J Intern Med"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1186\/s44280-025-00092-0","article-title":"Antimicrobial resistance profile of clinical isolates in hospitals across China: report from the CHINET antimicrobial resistance surveillance program, 2024","volume":"25","author":"Guo","year":"2025","journal-title":"Chin J Infect Chemother"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.ijantimicag.2011.01.009","article-title":"High burden of antimicrobial resistance in Asia","volume":"37","author":"Jean","year":"2011","journal-title":"Int J Antimicrob Agents"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/S0140-6736(21)02724-0","article-title":"Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis","volume":"399","year":"2022","journal-title":"Lancet"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"102841","DOI":"10.1016\/j.pmedr.2024.102841","article-title":"An early sepsis prediction model utilizing machine learning and unbalanced data processing in a clinical context","volume":"45","author":"Zhou","year":"2024","journal-title":"Prev Med Rep"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"2560","DOI":"10.3201\/eid2710.204826","article-title":"Population-based study of bloodstream infection incidence and mortality rates, Finland, 2004-2018","volume":"27","author":"Kontula","year":"2021","journal-title":"Emerg Infect Dis"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3760\/cma.j.cn114452-20211109-00695","article-title":"Society of Clinical Microbiology and Infection of China international exchange and promotion Association for Medical and Healthcare. Chinese expert consensus on the clinical practice of blood culture in the diagnosis of bloodstream infection. Chin","volume":"45","year":"2022","journal-title":"J Lab Med"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"697","DOI":"10.3389\/fmicb.2016.00697","article-title":"How to optimize the use of blood cultures for the diagnosis of bloodstream infections? A State-of-the Art","volume":"7","author":"Lamy","year":"2016","journal-title":"Front Microbiol"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"2221","DOI":"10.1016\/S0140-6736(22)02185-7","article-title":"Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the global burden of disease study 2019","volume":"400","year":"2022","journal-title":"Lancet"},{"key":"ref18","first-page":"3","article-title":"Ministry of Health of the People's Republic of China. Diagnostic criteria for nosocomial infections (trial)","volume":"11","year":"2001","journal-title":"Chin J Nosocomiol"},{"key":"ref19","volume-title":"Performance Standards for Antimicrobial Susceptibility Testing","year":"2023"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3969\/j.issn.1674-9081.2020.05.011","article-title":"Expert consensus on polymyxin antimicrobial susceptibility testing and clinical interpretation","volume":"11","author":"Yang","year":"2020","journal-title":"Med J Peking Union Med Coll Hosp"},{"key":"ref21","volume-title":"FDA-identified interpretive criteria","year":"2023"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1016\/j.eswa.2012.09.015","article-title":"A novel Likert scale based on fuzzy sets theory","volume":"40","author":"Li","year":"2013","journal-title":"Expert Syst Appl"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"2860","DOI":"10.34172\/ijhpm.2022.6360","article-title":"Establishment and application of an index system for the risk of drug shortages in China: based on Delphi method and analytic hierarchy process","volume":"11","author":"Shi","year":"2022","journal-title":"Int J Health Policy Manag"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"103603","DOI":"10.1016\/j.midw.2023.103603","article-title":"Which mothers' breastfeeding behaviours within six weeks postpartum do health workers need to notice? Developing an indicator system based on the Delphi method and analytic hierarchy process","volume":"119","author":"Wu","year":"2023","journal-title":"Midwifery"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s41256-024-00367-w","article-title":"The development of a performance evaluation index system for Chinese Centers for Disease Control and Prevention: a Delphi consensus study","volume":"9","author":"Sun","year":"2024","journal-title":"Glob Health Res Policy"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1186\/s12879-023-08602-4","article-title":"Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters","volume":"23","author":"Zhang","year":"2023","journal-title":"BMC Infect Dis"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.jointm.2023.10.001","article-title":"Optimizing artificial intelligence in sepsis management: opportunities in the present and looking closely to the future","volume":"4","author":"O'Reilly","year":"2024","journal-title":"J Intensive Med"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s12877-020-01984-z","article-title":"Burden of bloodstream infection in older persons: a population-based study","volume":"21","author":"Laupland","year":"2021","journal-title":"BMC Geriatr"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1038\/s41591-020-0789-4","article-title":"Early prediction of circulatory failure in the intensive care unit using machine learning","volume":"26","author":"Hyland","year":"2020","journal-title":"Nat Med"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"5333","DOI":"10.3390\/ijerph18105333","article-title":"Epidemiology and prevention of healthcare-associated infections in geriatric patients: a narrative review","volume":"18","author":"Cristina","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"S6","DOI":"10.1016\/j.ijantimicag.2010.11.010","article-title":"Conventional and molecular techniques for the early diagnosis of bacteraemia","volume":"36","author":"Paolucci","year":"2010","journal-title":"Int J Antimicrob Agents"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1515\/CCLM.1999.060","article-title":"Outcome prediction by traditional and new markers of inflammation in patients with sepsis","volume":"37","author":"Oberhoffer","year":"1999","journal-title":"Clin Chem Lab Med"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1080\/078538902321117841","article-title":"Markers of inflammation in sepsis","volume":"34","author":"Takala","year":"2002","journal-title":"Ann Med"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"581","DOI":"10.2174\/092986708783769704","article-title":"Inflammatory markers in SIRS, sepsis and septic shock","volume":"15","author":"Herzum","year":"2008","journal-title":"Curr Med Chem"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"1447523","DOI":"10.3389\/fimmu.2024.1447523","article-title":"Assessing sepsis-induced immunosuppression to predict positive blood cultures","volume":"15","author":"Hern\u00e1ndez-Jim\u00e9nez","year":"2024","journal-title":"Front Immunol"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"1577105","DOI":"10.3389\/fimmu.2025.1577105","article-title":"Sepsis-induced immunosuppression: mechanisms, biomarkers and immunotherapy","volume":"16","author":"Gao","year":"2025","journal-title":"Front Immunol"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1186\/s12877-019-1208-x","article-title":"Risk factors of health care-associated infection in elderly patients: a retrospective cohort study performed at a tertiary hospital in China","volume":"19","author":"Zhao","year":"2019","journal-title":"BMC Geriatr"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.3390\/microorganisms12102052","article-title":"Immunosenescence: how aging increases susceptibility to bacterial infections and virulence factors","volume":"12","author":"Theodorakis","year":"2024","journal-title":"Microorganisms"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1086\/313947","article-title":"Clinical relevance of age-related immune dysfunction","volume":"31","author":"Castle","year":"2000","journal-title":"Clin Infect Dis"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"107290","DOI":"10.1016\/j.ijid.2024.107290","article-title":"Preventing central line-associated bloodstream infections: a position paper of the International Society for Infectious Diseases, 2024 update","volume":"150","author":"Rosenthal","year":"2025","journal-title":"Int J Infect Dis"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1177\/08968608231172740","article-title":"ISPD catheter-related infection recommendations: 2023 update","volume":"43","author":"Chow","year":"2023","journal-title":"Perit Dial Int"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1017\/ice.2022.87","article-title":"Strategies to prevent central line-associated bloodstream infections in acute-care hospitals: 2022 update","volume":"43","author":"Buetti","year":"2022","journal-title":"Infect Control Hosp Epidemiol"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"103","DOI":"10.11816\/cn.ni.2021-211205","article-title":"PCT, IL-6, and IL-10 facilitate early diagnosis and pathogen classifications in bloodstream infection","volume":"22","author":"Yang","year":"2023","journal-title":"Ann Clin Microbiol Antimicrob"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s12879-024-10415-y","article-title":"Predictive value of serum procalcitonin level for the diagnosis of bloodstream infections in hematological patients","volume":"25","author":"Sun","year":"2025","journal-title":"BMC Infect Dis"},{"key":"ref47","first-page":"2788","article-title":"Risk factors for allogeneic blood transfusion-related nosocomial infection","volume":"31","author":"Lai","year":"2021","journal-title":"Chin J Nosocomiol"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1053\/tmrv.2002.31463","article-title":"Possible mechanisms of allogeneic blood transfusion-associated postoperative infection","volume":"16","author":"Vamvakas","year":"2002","journal-title":"Transfus Med Rev"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1097\/00045391-200209000-00005","article-title":"Immunomodulation and blood transfusion","volume":"9","author":"Blajchman","year":"2002","journal-title":"Am J Ther"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"116","DOI":"10.5662\/wjm.v11.i4.116","article-title":"Delphi methodology in healthcare research: how to decide its appropriateness","volume":"11","author":"Nasa","year":"2021","journal-title":"World J Methodol"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1017\/ice.2018.137","article-title":"Centers for medicare and medicaid services hospital-acquired conditions policy for central line-associated bloodstream infection (CLABSI) and catheter-associated urinary tract infection (CAUTI) shows minimal impact on hospital reimbursement","volume":"39","author":"Calderwood","year":"2018","journal-title":"Infect Control Hosp Epidemiol"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"333","DOI":"10.3390\/jpm13020333","article-title":"Biomarkers as prognostic predictors and therapeutic guide in critically ill patients: clinical evidence","volume":"13","author":"M\u00e9ndez Hern\u00e1ndez","year":"2023","journal-title":"J Pers Med"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1038\/s41597-022-01899-x","article-title":"Severe infection antimicrobial therapy: cognitive pitfalls and improvement strategies","volume":"33","author":"Feng","year":"2024","journal-title":"Chin J Emer Med"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"483","DOI":"10.3389\/fcimb.2017.00483","article-title":"Antimicrobial resistance of Hypervirulent Klebsiella pneumoniae: epidemiology, Hypervirulence-associated determinants, and resistance mechanisms","volume":"7","author":"Lee","year":"2017","journal-title":"Front Cell Infect Microbiol"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1819369\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:28:52Z","timestamp":1781584132000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1819369\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":54,"alternative-id":["10.3389\/fmed.2026.1819369"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1819369","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"article-number":"1819369"}}