{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:00:52Z","timestamp":1773154852335,"version":"3.50.1"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"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. Artif. Intell."],"abstract":"<jats:p>Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), significantly affecting patient prognosis and increasing healthcare burden. AIS patients are often accompanied by basic diseases, and atrial fibrillation (AF) is one of the common basic diseases. Despite the high prevalence of AF in AIS patients, few studies have specifically addressed SAP prediction in this comorbid population. We aimed to analyze the factors influencing the occurrence of SAP in patients with AIS and AF and to assess the risk of SAP development through an optimal predictive model. We performed a case-control study. This study included 4,496 hospitalized patients with AIS and AF in China between January 2020 and September 2023. The primary outcome was SAP during hospitalization. Univariate analysis and LASSO regression analysis methods were used to screen predictors. The patients with AIS and AF were randomly divided into a training set, validation set, and test set. Then, we established logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models. The accuracy, sensitivity, specificity, area under the curve, Youden index and <jats:italic>F<\/jats:italic><jats:sub>1<\/jats:sub> score were adopted to evaluate the predictive value of each model. The optimal prediction model was visualized using a nomogram. In this study, SAP was identified in 10.16% of cases. The variables screened by univariate analysis and LASSO regression, variables such as coronary artery disease, hypertension, and dysphagia, identified by univariate and LASSO regression analyses (<jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.05), were included in the LR, RF, and SVM. The LR model outperformed other models, achieving an AUC of 0.866, accuracy of 90.13%, sensitivity of 79.49%, specificity of 86.11%, <jats:italic>F<\/jats:italic><jats:sub>1<\/jats:sub> score of 0.80. A nomogram based on the LR model was developed to predict SAP risk, providing a practical tool for early identification of high-risk patients, and enabling targeted interventions to reduce SAP incidence and improve outcomes.<\/jats:p>","DOI":"10.3389\/frai.2025.1595101","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T10:07:49Z","timestamp":1747735669000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models"],"prefix":"10.3389","volume":"8","author":[{"given":"Tai","family":"Su","sequence":"first","affiliation":[]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bingyin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zihao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zexing","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Xiaomei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jixiang","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Xin","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"591874","DOI":"10.3389\/fncel.2020.591874","article-title":"Ferroptosis, a regulated neuronal cell death type after intracerebral hemorrhage","volume":"14","author":"Bai","year":"2020","journal-title":"Front. Cell. Neurosci."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"218","DOI":"10.2174\/15701611113116660168","article-title":"Imaging neuroinflammation in ischemic stroke and in the atherosclerotic vascular disease","volume":"13","author":"Cerami","year":"2015","journal-title":"Curr. Vasc. Pharmacol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"834240","DOI":"10.3389\/fneur.2022.834240","article-title":"The relationship between dysphagia and pneumonia in acute stroke patients: a systematic review and meta-analysis","volume":"13","author":"Chang","year":"2022","journal-title":"Front. Neurol."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1093\/milmed\/usaa302","article-title":"Red cell distribution width, unlike neutrophil lymphocyte ratio is unable to accurately gauge enhanced systemic inflammation associated with panoramic imaged carotid plaque","volume":"186","author":"Chang","year":"2021","journal-title":"Mil. 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Epidemiol."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1016\/S0140-6736(18)30479-3","article-title":"Mortality and morbidity in acutely ill adults treated with liberal versus conservative oxygen therapy (IOTA): a systematic review and meta-analysis","volume":"391","author":"Chu","year":"2018","journal-title":"Lancet"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1001\/jamaneurol.2018.4858","article-title":"Development and validation of a prognostic model of swallowing recovery and enteral tube feeding after ischemic stroke","volume":"76","author":"Galovic","year":"2019","journal-title":"JAMA Neurol."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/S1474-4422(21)00252-0","article-title":"Global, regional, and national burden of stroke and its risk factors, 1990\u20132019: a systematic analysis for the Global Burden of Disease Study 2019","volume":"20","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1177\/17474930231177881","article-title":"Does stroke-associated pneumonia play an important role on risk of in-hospital mortality associated with severe stroke? A four-way decomposition analysis of a national cohort of stroke patients","volume":"18","author":"Gittins","year":"2023","journal-title":"Int. J. Stroke"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s10654-016-0149-3","article-title":"Statistical tests, p values, confidence intervals, and power: a guide to misinterpretations","volume":"31","author":"Greenland","year":"2016","journal-title":"Eur. J. Epidemiol."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"e692","DOI":"10.1212\/NXI.0000000000000692","article-title":"Inflammatory and stress markers predicting pneumonia, outcome, and etiology in patients with stroke: biomarkers for predicting pneumonia, functional outcome, and death after stroke","volume":"7","author":"Hotter","year":"2020","journal-title":"Neurol. Neuroimmunol. Neuroinflamm."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"bbad002","DOI":"10.1093\/bib\/bbad002","article-title":"A review on longitudinal data analysis with random forest","volume":"24","author":"Hu","year":"2023","journal-title":"Brief. Bioinform."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"862164","DOI":"10.3389\/fphys.2022.862164","article-title":"Role of inflammation in the pathogenesis of atrial fibrillation","volume":"13","author":"Ihara","year":"2022","journal-title":"Front. Physiol."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"3436","DOI":"10.1161\/STROKEAHA.113.001931","article-title":"Interrelationship among common medical complications after acute stroke: pneumonia plays an important role","volume":"44","author":"Ji","year":"2013","journal-title":"Stroke"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/S1474-4422(06)70495-1","article-title":"Clinical interpretation and use of stroke scales","volume":"5","author":"Kasner","year":"2006","journal-title":"Lancet Neurol."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1016\/j.hrthm.2019.10.001","article-title":"Impact of atrial fibrillation\/flutter on the in-hospital mortality of ischemic stroke patients","volume":"17","author":"Keller","year":"2020","journal-title":"Heart Rhythm."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.3390\/biomedicines9121835","article-title":"Heart-brain relationship in stroke","volume":"9","author":"Kelley","year":"2021","journal-title":"Biomedicines"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1016\/j.jacep.2018.08.003","article-title":"Asymptomatic cerebral infarction during catheter ablation for atrial fibrillation: comparing uninterrupted rivaroxaban and warfarin (ASCERTAIN)","volume":"4","author":"Kimura","year":"2018","journal-title":"JACC Clin. Electrophysiol."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1212\/WNL.0b013e31822dc795","article-title":"Factors influencing in-hospital mortality and morbidity in patients treated on a stroke unit","volume":"77","author":"Koennecke","year":"2011","journal-title":"Neurology"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1002\/joa3.12077","article-title":"Inflammation and atrial fibrillation: a comprehensive review","volume":"34","author":"Korantzopoulos","year":"2018","journal-title":"J. Arrhythm."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"4469","DOI":"10.1002\/cam4.4800","article-title":"Prediction of lung cancer risk in Chinese population with genetic-environment factor using extreme gradient boosting","volume":"11","author":"Li","year":"2022","journal-title":"Cancer Med."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neuint.2018.01.002","article-title":"Research progress in stroke-induced immunodepression syndrome (SIDS) and stroke-associated pneumonia (SAP)","volume":"114","author":"Liu","year":"2018","journal-title":"Neurochem. Int."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1038\/nrn1765","article-title":"Central nervous system injury-induced immune deficiency syndrome","volume":"6","author":"Meisel","year":"2005","journal-title":"Nat. Rev. Neurosci."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1055\/s-0042-1757692","article-title":"Brazilian practice guidelines for stroke rehabilitation: part II","volume":"80","author":"Minelli","year":"2022","journal-title":"Arq. Neuropsiquiatr."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/joa3.12473","article-title":"Role of inflammation in atrial fibrillation: a comprehensive review of current knowledge","volume":"37","author":"Nso","year":"2020","journal-title":"J. Arrhythm."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"e25823","DOI":"10.1097\/MD.0000000000025823","article-title":"Brain activation in response to visual sexual stimuli in male patients with right middle cerebral artery infarction: the first case-control functional magnetic resonance imaging study","volume":"100","author":"Park","year":"2021","journal-title":"Medicine"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1097\/NRL.0000000000000269","article-title":"Stroke-associated pneumonia: a retrospective study of risk factors and outcomes","volume":"25","author":"Patel","year":"2020","journal-title":"Neurologist"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.rce.2019.04.001","article-title":"Sarcopenia, frailty, cognitive impairment and mortality in elderly patients with non-valvular atrial fibrillation","volume":"219","author":"Requena Calleja","year":"2019","journal-title":"Rev. Clin. Esp."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1177\/096777200501300405","article-title":"George E Holtzapple (1862\u20131946) and oxygen therapy for lobar pneumonia: the first reported case (1887) and a review of the contemporary literature to 1899","volume":"13","author":"Shultz","year":"2005","journal-title":"J. Med. Biogr."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"6958","DOI":"10.1109\/JBHI.2024.3448238","article-title":"Multimodal machine learning for stroke prognosis and diagnosis: a systematic review","volume":"28","author":"Shurrab","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.neunet.2023.08.055","article-title":"Symmetric LINEX loss twin support vector machine for robust classification and its fast iterative algorithm","volume":"168","author":"Si","year":"2023","journal-title":"Neural Netw."},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-16399-0","volume-title":"Clinical prediction models: a practical approach to development, validation, and updating","author":"Steyerberg","year":"2019"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1111\/ane.12956","article-title":"Impact of stroke-associated pneumonia on mortality, length of hospitalization, and functional outcome","volume":"138","author":"Teh","year":"2018","journal-title":"Acta Neurol. Scand."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"91","DOI":"10.11622\/smedj.2021054","article-title":"Machine learning in medicine: what clinicians should know","volume":"64","author":"Ting Sim","year":"2023","journal-title":"Singapore Med. J."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s13760-020-01542-9","article-title":"Predictors of mortality and disability in stroke-associated pneumonia","volume":"121","author":"Tinker","year":"2021","journal-title":"Acta Neurol. Belg."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"1115031","DOI":"10.3389\/fimmu.2023.1115031","article-title":"The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage","volume":"14","author":"Wang","year":"2023","journal-title":"Front. Immunol."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"4014","DOI":"10.1097\/JS9.0000000000001329","article-title":"Association of follow-up neutrophil-to-lymphocyte ratio and systemic inflammation response index with stroke-associated pneumonia and functional outcomes in cerebral hemorrhage patients: a case-controlled study","volume":"110","author":"Xu","year":"2024","journal-title":"Int. J. Surg."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.jocn.2016.02.039","article-title":"Association between pneumonia in acute stroke stage and 3-year mortality in patients with acute first-ever ischemic stroke","volume":"33","author":"Yu","year":"2016","journal-title":"J. Clin. Neurosci."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1186\/s12890-024-03160-0","article-title":"Development and validation of radiology-clinical statistical and machine learning model for stroke-associated pneumonia after first intracerebral haemorrhage","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Pulm. Med."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1177\/17474930221135983","article-title":"Increasing burden of stroke in China: a systematic review and meta-analysis of prevalence, incidence, mortality, and case fatality","volume":"18","author":"Zhao","year":"2023","journal-title":"Int. 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