Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model
Abstract Background Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive mod...
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BMC
2025-01-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-05555-9 |
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| author | Yanling Zhang Kun Long Zhaojian Gong Ruping Dai Shuiting Zhang |
| author_facet | Yanling Zhang Kun Long Zhaojian Gong Ruping Dai Shuiting Zhang |
| author_sort | Yanling Zhang |
| collection | DOAJ |
| description | Abstract Background Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive model for POF following surgery of oral cancer. Methods A total of seven hundred and twenty-seven consecutive patients undergoing radical resection of oral cancer were retrospectively investigated. The analysis encompassed 34 parameters, incorporating demographic and clinical characteristics, biochemical and hematological assay results, surgical-related data, hospitalization costs and stay in hospital. Six machine learning models were compared by the area under the receiver operating characteristic curve (AUC). The best-performing models were selected for further analyze, including feature importance evaluation and nomogram analysis, identifying key POF risk factors, and establish a comprehensive prediction model. Results A total of 466 patients with surgery for oral cancer met the criteria, with an average age of (54.2 ± 11.1) years, including an POF group (n = 197) and a non-POF group (n = 269). The fever group has greater hospitalization costs, longer lengths of stay, and higher infection biochemical indicators (leucocyte ratio and erythrocyte sedimentation rate). Furthermore, Among the 6 machine learning models, logistic regression models performed best, with the higher AUC and accuracy. In univariate and multivariate logistic analysis showed that age, sex, reoperation, Charlson Comorbidity Index score (CCI), leukocyte, bleeding and blood transfusion were independent risk factors for POF of patients following surgery in oral cancer. Then seven variables were selected to establish the nomogram for predict the probability of POF by nomogram algorithm. Conclusions Postoperative fever patients following radical resection of oral cancer have greater burden. Machine learning algorithms can be effectively used to identify potential risk factors of POF, which may enhance individualized treatment plans in oral cancer patient during perioperative period. Graphical Abstract |
| format | Article |
| id | doaj-art-4d2ea7efe4c143bba3f50619e03926fe |
| institution | Kabale University |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-4d2ea7efe4c143bba3f50619e03926fe2025-02-02T12:45:08ZengBMCBMC Oral Health1472-68312025-01-0125111210.1186/s12903-025-05555-9Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive modelYanling Zhang0Kun Long1Zhaojian Gong2Ruping Dai3Shuiting Zhang4Department of Anaesthesiology, The Second Xiangya Hospital, Central South UniversityDepartment of Anaesthesiology, The Second Xiangya Hospital, Central South UniversityDepartment of Oral and Maxillofacial Surgery, The Second Xiangya Hospital, Central South UniversityDepartment of Anaesthesiology, The Second Xiangya Hospital, Central South UniversityDepartment of Anaesthesiology, The Second Xiangya Hospital, Central South UniversityAbstract Background Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive model for POF following surgery of oral cancer. Methods A total of seven hundred and twenty-seven consecutive patients undergoing radical resection of oral cancer were retrospectively investigated. The analysis encompassed 34 parameters, incorporating demographic and clinical characteristics, biochemical and hematological assay results, surgical-related data, hospitalization costs and stay in hospital. Six machine learning models were compared by the area under the receiver operating characteristic curve (AUC). The best-performing models were selected for further analyze, including feature importance evaluation and nomogram analysis, identifying key POF risk factors, and establish a comprehensive prediction model. Results A total of 466 patients with surgery for oral cancer met the criteria, with an average age of (54.2 ± 11.1) years, including an POF group (n = 197) and a non-POF group (n = 269). The fever group has greater hospitalization costs, longer lengths of stay, and higher infection biochemical indicators (leucocyte ratio and erythrocyte sedimentation rate). Furthermore, Among the 6 machine learning models, logistic regression models performed best, with the higher AUC and accuracy. In univariate and multivariate logistic analysis showed that age, sex, reoperation, Charlson Comorbidity Index score (CCI), leukocyte, bleeding and blood transfusion were independent risk factors for POF of patients following surgery in oral cancer. Then seven variables were selected to establish the nomogram for predict the probability of POF by nomogram algorithm. Conclusions Postoperative fever patients following radical resection of oral cancer have greater burden. Machine learning algorithms can be effectively used to identify potential risk factors of POF, which may enhance individualized treatment plans in oral cancer patient during perioperative period. Graphical Abstracthttps://doi.org/10.1186/s12903-025-05555-9Postoperative feverOral cancerRisk factorsMachine learningPrediction modelNomogram |
| spellingShingle | Yanling Zhang Kun Long Zhaojian Gong Ruping Dai Shuiting Zhang Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model BMC Oral Health Postoperative fever Oral cancer Risk factors Machine learning Prediction model Nomogram |
| title | Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model |
| title_full | Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model |
| title_fullStr | Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model |
| title_full_unstemmed | Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model |
| title_short | Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model |
| title_sort | postoperative fever following surgery for oral cancer incidence risk factors and the formulation of a machine learning based predictive model |
| topic | Postoperative fever Oral cancer Risk factors Machine learning Prediction model Nomogram |
| url | https://doi.org/10.1186/s12903-025-05555-9 |
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