{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T06:08:16Z","timestamp":1788761296346,"version":"build-2803163510"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"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. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acute kidney injury (AKI) frequently complicates sepsis in patients with multiple myeloma (MM), yet no validated predictive tool integrates the inflammatory and nutritional disturbances specific to this dual-disease population. We aimed to develop and externally validate an interpretable machine learning (ML) model\u2014based on composite inflammatory-nutritional indices\u2014to predict in-hospital AKI in septic patients with MM, and to deploy it as a web-based point-of-care tool.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this retrospective cohort study, 362 septic MM patients (development cohort: 253 training, 109 internal test) and 71 temporal validation patients were enrolled from Shaanxi Provincial People\u2019s Hospital (2020\u20132025). Moreover, an independent external validation cohort from a separate tertiary hospital (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f118, Xi\u2019an No. 3 Hospital). Candidate predictors included 8 composite indices (e.g., NLR, SII, LAR, AAPR) alongside routine clinical and laboratory variables. Boruta and recursive feature elimination (RFE) identified a parsimonious predictor set. Six ML algorithms (logistic regression, SVM, MLP, LightGBM, XGBoost, Random Forest) were compared using AUC, calibration, and decision curve analysis. The final model was interpreted with SHAP and implemented as a public Streamlit application.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      AKI occurred in 206\/362 (56.9%) patients. Eight predictors were selected: NLR, LAR, creatinine, SII, PTA, cystatin C, CRP, and AAPR. Random Forest demonstrated the most robust and generalizable performance, achieving a temporal validation AUC of 0.8574 (accuracy 0.7465, sensitivity 0.8205, specificity 0.6562). Moreover, Random Forest also achieved an external validation AUC of 0.9650 (accuracy 0.8983, sensitivity 0.9000, specificity 0.8971), with favorable calibration and consistent net clinical benefit. SHAP analysis identified higher NLR, LAR, SII, cystatin C, creatinine, and CRP, together with lower PTA and AAPR, as the most influential predictors in the model. The model is freely available at (\n                      <jats:uri>https:\/\/gftcs94ast7omb44cg2izq.streamlit.app\/<\/jats:uri>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An interpretable Random Forest model employing eight routinely available indicators accurately predicts AKI in septic MM patients, bridging algorithmic complexity and bedside applicability. This tool may facilitate early risk stratification and personalized renal protection in this high-risk group.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2026.1874801","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:33:50Z","timestamp":1788759230000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine-learning-derived online prediction model based on inflammatory and nutritional composite indicators for acute kidney injury in sepsis patients with multiple myeloma"],"prefix":"10.3389","volume":"13","author":[{"given":"Qin","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Hematology, Xi\u2019an No. 3 Hospital, The Affiliated Hospital of Northwest University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Xin","sequence":"additional","affiliation":[{"name":"Department of Emergency Surgery, Shaanxi Provincial People\u2019s Hospital","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huayan","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Hematology, Xi\u2019an No. 3 Hospital, The Affiliated Hospital of Northwest University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1097\/CCM.0000000000006284","article-title":"Quantifying the impact of alternative definitions of sepsis-associated acute kidney injury on its incidence and outcomes: a systematic review and Meta-analysis","volume":"52","author":"Donaldson","year":"2024","journal-title":"Crit Care Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e22585","DOI":"10.7759\/cureus.22585","article-title":"Multiple myeloma and renal failure: mechanisms, diagnosis, and management","volume":"14","author":"Kundu","year":"2022","journal-title":"Cureus"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"104264","DOI":"10.1016\/j.lpm.2024.104264","article-title":"Kidney disease in multiple myeloma","volume":"54","author":"Bridoux","year":"2025","journal-title":"Presse Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"2380748","DOI":"10.1080\/0886022X.2024.2380748","article-title":"Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review","volume":"46","author":"Li","year":"2024","journal-title":"Ren Fail"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1680180","DOI":"10.3389\/fmed.2025.1680180","article-title":"Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review","volume":"12","author":"Li","year":"2025","journal-title":"Front Med (Lausanne)"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"e24297","DOI":"10.1002\/jcla.24297","article-title":"Prognostic nutrition index is associated with the all-cause mortality in sepsis patients: a retrospective cohort study","volume":"36","author":"Wu","year":"2022","journal-title":"J Clin Lab Anal"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"690","DOI":"10.5811\/westjem.18466","article-title":"Neutrophil-to-lymphocyte ratio predicts sepsis in adult patients meeting two or more systemic inflammatory response syndrome criteria","volume":"25","author":"Balakrishnan","year":"2024","journal-title":"West J Emerg Med"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"e943414","DOI":"10.12659\/MSM.943414","article-title":"Systemic immune-inflammation index (SII) as a predictor of short-term mortality risk in sepsis-associated acute kidney injury: a retrospective cohort study","volume":"30","author":"Sun","year":"2024","journal-title":"Med Sci Monit"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1177\/08850666231214243","article-title":"Development and validation of a predictive model for acute kidney injury in sepsis patients based on recursive partition analysis","volume":"39","author":"Lai","year":"2024","journal-title":"J Intensive Care Med"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"20552076251355669","DOI":"10.1177\/20552076251355669","article-title":"The application of explainable artificial intelligence in the prediction, diagnoses, treatment, and management of chronic diseases: a systematic review","volume":"11","author":"Hoghooghi Esfahani","year":"2025","journal-title":"Digit Health"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"e538","DOI":"10.1016\/S1470-2045(14)70442-5","article-title":"International myeloma working group updated criteria for the diagnosis of multiple myeloma","volume":"15","author":"Rajkumar","year":"2014","journal-title":"Lancet Oncol"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e418","DOI":"10.1200\/EDBK_159009","article-title":"Updated diagnostic criteria and staging system for multiple myeloma","volume":"35","author":"Rajkumar","year":"2016","journal-title":"Am Soc Clin Oncol Educ Book"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.20452\/pamw.15373","article-title":"Acute kidney injury, its definition, and treatment in adults: guidelines and reality","volume":"130","author":"Matuszkiewicz-Rowi\u0144ska","year":"2020","journal-title":"Pol Arch Intern Med"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14090918","article-title":"Early identification of sepsis-induced acute kidney injury by using monocyte distribution width, red-blood-cell distribution, and neutrophil-to-lymphocyte ratio","volume":"14","author":"Pan","year":"2024","journal-title":"Diagnostics (Basel)"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"2525456","DOI":"10.1080\/0886022X.2025.2525456","article-title":"Systemic immune-inflammatory biomarkers assist in differentiating clinical features of different etiologies of acute kidney injury: results from eICU collaborative research database","volume":"47","author":"Liang","year":"2025","journal-title":"Ren Fail"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1487576","DOI":"10.3389\/fimmu.2024.1487576","article-title":"The role of inflammatory response and metabolic reprogramming in sepsis-associated acute kidney injury: mechanistic insights and therapeutic potential","volume":"15","author":"Liu","year":"2024","journal-title":"Front Immunol"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.3390\/diagnostics14171988","article-title":"Kinetics of the lactate to albumin ratio in new onset sepsis: prognostic implications","volume":"14","author":"Karampela","year":"2024","journal-title":"Diagnostics (Basel)"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"3170","DOI":"10.1038\/s41598-024-53384-7","article-title":"Association between alkaline phosphatase to albumin ratio and mortality among patients with sepsis","volume":"14","author":"Liu","year":"2024","journal-title":"Sci Rep"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10157-022-02234-9","article-title":"Prognostic significance of albumin to alkaline phosphatase ratio in critically ill patients with acute kidney injury","volume":"26","author":"Xia","year":"2022","journal-title":"Clin Exp Nephrol"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/nrneph.2011.168","article-title":"The pathogenesis and diagnosis of acute kidney injury in multiple myeloma","volume":"8","author":"Hutchison","year":"2011","journal-title":"Nat Rev Nephrol"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1667488","DOI":"10.3389\/fmed.2025.1667488","article-title":"Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches","volume":"12","author":"Quan","year":"2025","journal-title":"Front Med (Lausanne)"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1186\/s13054-025-05457-w","article-title":"ORAKLE: optimal risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning","volume":"29","author":"Oh","year":"2025","journal-title":"Crit Care"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"31892","DOI":"10.1038\/s41598-025-15867-z","article-title":"Personalized health monitoring using explainable AI: bridging trust in predictive healthcare","volume":"15","author":"Vani","year":"2025","journal-title":"Sci Rep"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1038\/s41746-025-01958-8","article-title":"Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour","volume":"8","author":"Hur","year":"2025","journal-title":"NPJ Digit Med"}],"container-title":["Frontiers in Nutrition"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnut.2026.1874801\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:33:51Z","timestamp":1788759231000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnut.2026.1874801\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,7]]},"references-count":25,"alternative-id":["10.3389\/fnut.2026.1874801"],"URL":"https:\/\/doi.org\/10.3389\/fnut.2026.1874801","relation":{},"ISSN":["2296-861X"],"issn-type":[{"value":"2296-861X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,7]]},"article-number":"1874801"}}