{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T15:44:15Z","timestamp":1789055055015,"version":"build-2803163510"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"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. Pharmacol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Acute kidney injury (AKI) is a significant concern among hospitalized patients receiving aminoglycosides. Identifying the risk factors associated with aminoglycoside-induced AKI and developing machine learning models are imperative in clinical practice.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aims to identify the risk factors associated with AKI in hospitalized patients receiving aminoglycosides, and develop machine learning models for evaluation of the AKI risk in these patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study retrospectively analyzed 7,028 hospitalized patients who received treatment with amikacin or etimicin between 2018 and 2020. According to the type of medication used, patients were divided into amikacin group (n = 307) and etimicin group (n = 6,901). Univariate analyses and the least absolute shrinkage and selection operator algorithm were used to screen risk factors and construct the model. The machine learning models were developed using five different algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting model (XGBoost), and light gradient boosting machine (Light GBM).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The XGBoost model exhibited the most superior performance in predicting amikacin-associated AKI among the developed machine learning models. For the training set, the area under the receiver-operator characteristic curve (AUC) was 0.916, and for the test set, it was 0.841. The model can be accessed online. Regarding AKI risk in etimicin-treated patients, the GBM model demonstrated the best overall performance, with AUC values of 0.886 for the training set and 0.900 for the test set. The model was also made available online.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>These predictive models may offer a valuable tool for estimating the risk of AKI in patients receiving amikacin or etimicin, facilitating clinical decision-making and aiding in the prevention of AKI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Trial Registration<\/jats:title><jats:p><jats:ext-link>ClinicalTrials.gov<\/jats:ext-link> NCT05533593.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2025.1538074","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T05:25:44Z","timestamp":1747891544000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin"],"prefix":"10.3389","volume":"16","author":[{"given":"Pei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Lao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"B1","unstructured":"Machine learning predicted models: for the risk of kidney injury(AKI) in inpatients receiving amikacin\n          \n          \n          2024"},{"key":"B2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/a:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"B3","doi-asserted-by":"publisher","first-page":"4278579","DOI":"10.1155\/2016\/4278579","article-title":"Hospital mortality in the United States following acute kidney injury","volume":"2016","author":"Brown","year":"2016","journal-title":"Biomed. Res. Int."},{"key":"B4","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1007\/s11274-012-1148-5","article-title":"Comparative antibacterial activity of a novel semisynthetic antibiotic: etimicin sulphate and other aminoglycosides","volume":"28","author":"Chaudhary","year":"2012","journal-title":"World J. Microbiol. Biotechnol."},{"key":"B5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/0002-9343(88)90501-3","article-title":"Aminoglycoside nephrotoxicity in obstructive jaundice","volume":"85","author":"Desai","year":"1988","journal-title":"Am. J. Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1053\/ajkd.2002.34487","article-title":"Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis","volume":"40","author":"Dharnidharka","year":"2002","journal-title":"Am. J. Kidney Dis."},{"key":"B7","doi-asserted-by":"publisher","first-page":"736","DOI":"10.3390\/ph15060736","article-title":"Tigecycline and gentamicin-combined treatment enhances renal damage: oxidative stress, inflammatory reaction, and apoptosis interplay","volume":"15","author":"Elgazzar","year":"2022","journal-title":"Pharm. (Basel)"},{"key":"B8","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1080\/1120009X.2022.2153316","article-title":"Amikacin-induced acute kidney injury in mechanically ventilated critically ill patients with sepsis","volume":"35","author":"Ergun","year":"2022","journal-title":"J. Chemother."},{"key":"B9","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: a scalable tree boosting system","volume":"785","author":"Ester","year":"2022","journal-title":"Geogr. Anal."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1055137","DOI":"10.3389\/fmed.2023.1055137","article-title":"Development and validation of a dynamic online nomogram for predicting acute kidney injury in cirrhotic patients upon ICU admission","volume":"10","author":"Feng","year":"2023","journal-title":"Front. Med. (Lausanne)"},{"key":"B11","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1002\/bimj.200410135","article-title":"Estimation of the Youden Index and its associated cutoff point","volume":"47","author":"Fluss","year":"2005","journal-title":"Biom J."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Statistics"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1111\/j.1523-1755.2004.00861.x","article-title":"Early detection of acute renal failure by serum cystatin C","volume":"66","author":"Herget-Rosenthal","year":"2004","journal-title":"Kidney Int."},{"key":"B14","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: a brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav. Ther."},{"key":"B15","article-title":"LightGBM: a highly efficient gradient boosting decision tree","volume-title":"Proceedings of the 31st international conference on neural information processing systems","author":"Ke","year":"2017"},{"key":"B16","doi-asserted-by":"publisher","first-page":"c179","DOI":"10.1159\/000339789","article-title":"KDIGO clinical practice guidelines for acute kidney injury","volume":"120","author":"Khwaja","year":"2012","journal-title":"Nephron Clin. Pract."},{"key":"B17","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1053\/j.ajkd.2014.08.024","article-title":"Hospital-acquired acute kidney injury and hospital readmission: a cohort study","volume":"65","author":"Koulouridis","year":"2015","journal-title":"Am. J. Kidney Dis."},{"key":"B18","doi-asserted-by":"publisher","first-page":"6109","DOI":"10.3390\/ijms22116109","article-title":"The mechanism of drug nephrotoxicity and the methods for preventing kidney damage","volume":"22","author":"Kwiatkowska","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1111\/liv.13693","article-title":"Profile, risk factors and outcome of acute kidney injury in paediatric acute-on-chronic liver failure","volume":"38","author":"Lal","year":"2018","journal-title":"Liver Int."},{"key":"B20","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1053\/j.ajkd.2018.08.008","article-title":"Risk factors for recurrent acute kidney injury in a large population-based cohort","volume":"73","author":"Liu","year":"2019","journal-title":"Am. J. Kidney Dis."},{"key":"B21","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/s13293-021-00373-4","article-title":"Acute kidney injury is more common in men than women after accounting for socioeconomic status, ethnicity, alcohol intake and smoking history","volume":"12","author":"Loutradis","year":"2021","journal-title":"Biol. Sex. Differ."},{"key":"B22","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.lfs.2016.01.007","article-title":"Metabolic acidosis aggravates experimental acute kidney injury","volume":"146","author":"Magalhaes","year":"2016","journal-title":"Life Sci."},{"key":"B23","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1038\/ki.2015.115","article-title":"Phenotype standardization for drug-induced kidney disease","volume":"88","author":"Mehta","year":"","journal-title":"Kidney Int."},{"key":"B24","doi-asserted-by":"publisher","first-page":"2616","DOI":"10.1016\/S0140-6736(15)60126-X","article-title":"International Society of Nephrology's 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): a human rights case for nephrology","volume":"385","author":"Mehta","year":"","journal-title":"Lancet"},{"key":"B25","doi-asserted-by":"publisher","first-page":"183","DOI":"10.5414\/cn108927","article-title":"The effect of gender on aminoglycoside-associated nephrotoxicity","volume":"86","author":"Neugarten","year":"2016","journal-title":"Clin. Nephrol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1128\/AAC.01430-08","article-title":"Prevalence and risk factors for aminoglycoside nephrotoxicity in intensive care units","volume":"53","author":"Oliveira","year":"2009","journal-title":"Antimicrob. Agents Chemother."},{"key":"B27","doi-asserted-by":"publisher","first-page":"3250","DOI":"10.1093\/jac\/dkw296","article-title":"Aminoglycoside-associated acute kidney injury in elderly patients with and without shock","volume":"71","author":"Ong","year":"2016","journal-title":"J. Antimicrob. Chemother."},{"key":"B28","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1111\/cts.12731","article-title":"Development and validation of a risk prediction model of vancomycin-associated nephrotoxicity in elderly patients: a pilot study","volume":"13","author":"Pan","year":"2020","journal-title":"Clin. Transl. Sci."},{"key":"B29","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1159\/000440867","article-title":"Acute kidney injury and renal recovery with the use of aminoglycosides: a large retrospective study","volume":"131","author":"Paquette","year":"2015","journal-title":"Nephron"},{"key":"B30","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s40264-019-00873-8","article-title":"Comparing acute kidney injury reports among antibiotics: a pharmacovigilance study of the fda Adverse event reporting system (faers)","volume":"43","author":"Patek","year":"2020","journal-title":"Drug Saf."},{"key":"B31","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1111\/bcp.13216","article-title":"The drugs that mostly frequently induce acute kidney injury: a case - noncase study of a pharmacovigilance database","volume":"83","author":"Pierson-Marchandise","year":"2017","journal-title":"Br. J. Clin. Pharmacol."},{"key":"B32","doi-asserted-by":"publisher","first-page":"eAO6828","DOI":"10.31744\/einstein_journal\/2022AO6828","article-title":"Relation between red blood cell distribution width and acute kidney injury in patients with sepsis","volume":"20","author":"Ramires","year":"2022","journal-title":"Einstein (Sao Paulo)"},{"key":"B33","doi-asserted-by":"publisher","first-page":"899164","DOI":"10.3389\/fphar.2022.899164","article-title":"Use of the capture-recapture method to estimate the frequency of community- and hospital-acquired drug-induced acute kidney injuries in French databases","volume":"13","author":"Rey","year":"2022","journal-title":"Front. Pharmacol."},{"key":"B34","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s12882-016-0430-4","article-title":"Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions","volume":"18","author":"Sawhney","year":"2017","journal-title":"BMC Nephrol."},{"key":"B35","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1053\/j.ajkd.2022.12.008","article-title":"Readmission and mortality after hospitalization with acute kidney injury","volume":"82","author":"Schulman","year":"2023","journal-title":"Am. J. Kidney Dis."},{"key":"B36","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1093\/qjmed\/hcp143","article-title":"Gentamicin-associated acute kidney injury","volume":"102","author":"Selby","year":"2009","journal-title":"QJM"},{"key":"B37","unstructured":"A real-world based probabilistic prediction model for predicting the risk of AKI after treatment with Etimicin\n          \n          \n          2024"},{"key":"B38","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/0002-9343(87)90498-0","article-title":"Risk factors and outcome of hospital-acquired acute renal failure. Clinical epidemiologic study","volume":"83","author":"Shusterman","year":"1987","journal-title":"Am. J. Med."},{"key":"B39","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.amjmed.2016.09.016","article-title":"30-Day readmissions after an acute kidney injury hospitalization","volume":"130","author":"Silver","year":"2017","journal-title":"Am. J. Med."},{"key":"B40","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.2215\/CJN.00710113","article-title":"World incidence of AKI: a meta-analysis","volume":"8","author":"Susantitaphong","year":"2013","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"B41","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1001\/jama.294.7.813","article-title":"Acute renal failure in critically ill patients: a multinational, multicenter study","volume":"294","author":"Uchino","year":"2005","journal-title":"JAMA"},{"key":"B42","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.21037\/jtd-22-937","article-title":"Serum cystatin C is a potential predictor of short-term mortality and acute kidney injury in acute aortic dissection patients: a retrospective cohort study","volume":"14","author":"Wang","year":"2022","journal-title":"J. Thorac. Dis."},{"key":"B43","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1080\/0886022X.2021.1871919","article-title":"A nomogram based on serum cystatin C for predicting acute kidney injury in patients with traumatic brain injury","volume":"43","author":"Wang","year":"2021","journal-title":"Ren. Fail"},{"key":"B44","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/1354750X.2019.1688865","article-title":"Early serum cystatin C-enhanced risk prediction for acute kidney injury post cardiac surgery: a prospective, observational, cohort study","volume":"25","author":"Wang","year":"2020","journal-title":"Biomarkers"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.2215\/CJN.02140215","article-title":"Epidemiology and clinical correlates of AKI in Chinese hospitalized adults","volume":"10","author":"Xu","year":"2015","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"B46","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1038\/s41401-019-0342-5","article-title":"Mechanisms and pharmacokinetic\/pharmacodynamic profiles underlying the low nephrotoxicity and ototoxicity of etimicin","volume":"41","author":"Yao","year":"2020","journal-title":"Acta Pharmacol. Sin."},{"key":"B47","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1186\/s12967-022-03364-0","article-title":"Machine learning for the prediction of acute kidney injury in patients with sepsis","volume":"20","author":"Yue","year":"2022","journal-title":"J. Transl. Med."},{"key":"B48","doi-asserted-by":"publisher","first-page":"10887","DOI":"10.3389\/ti.2023.10887","article-title":"A novel online calculator predicting acute kidney injury after liver transplantation: a retrospective study","volume":"36","author":"Zeng","year":"2023","journal-title":"Transpl. Int."},{"key":"B49","doi-asserted-by":"publisher","first-page":"924173","DOI":"10.3389\/fphar.2022.924173","article-title":"The incidence and risk factors analysis of acute kidney injury in hospitalized patients received diuretics: a single-center retrospective study","volume":"13","author":"Zhang","year":"2022","journal-title":"Front. Pharmacol."},{"key":"B50","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1053\/j.ajkd.2011.02.389","article-title":"Cystatin C in prediction of acute kidney injury: a systemic review and meta-analysis","volume":"58","author":"Zhang","year":"2011","journal-title":"Am. J. Kidney Dis."},{"key":"B51","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1007\/s40620-019-00625-y","article-title":"A simple risk score for prediction of sepsis associated-acute kidney injury in critically ill patients","volume":"32","author":"Zhou","year":"2019","journal-title":"J. Nephrol."},{"key":"B52","doi-asserted-by":"publisher","first-page":"987684","DOI":"10.3389\/fneur.2022.987684","article-title":"Dynamic nomogram for predicting acute kidney injury in patients with acute ischemic stroke: a retrospective study","volume":"13","author":"Zhu","year":"2022","journal-title":"Front. Neurol."}],"container-title":["Frontiers in Pharmacology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphar.2025.1538074\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T05:25:50Z","timestamp":1747891550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphar.2025.1538074\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,22]]},"references-count":52,"alternative-id":["10.3389\/fphar.2025.1538074"],"URL":"https:\/\/doi.org\/10.3389\/fphar.2025.1538074","relation":{},"ISSN":["1663-9812"],"issn-type":[{"value":"1663-9812","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,22]]},"article-number":"1538074"}}