Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study
AimTo analyze the correlation between preoperative time-in-range (TIR) levels and postoperative infection in patients with type 2 diabetes mellitus (T2DM) and to evaluate the value of the TIR as a predictor of postoperative infection in patients with T2DM.MethodsA total of 656 patients with T2DM dur...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1539039/full |
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| author | Ying Wu Rui Xv Qinyun Chen Ranran Zhang Min Li Chen Shao Guoxi Jin Guoxi Jin Xiaolei Hu Xiaolei Hu |
| author_facet | Ying Wu Rui Xv Qinyun Chen Ranran Zhang Min Li Chen Shao Guoxi Jin Guoxi Jin Xiaolei Hu Xiaolei Hu |
| author_sort | Ying Wu |
| collection | DOAJ |
| description | AimTo analyze the correlation between preoperative time-in-range (TIR) levels and postoperative infection in patients with type 2 diabetes mellitus (T2DM) and to evaluate the value of the TIR as a predictor of postoperative infection in patients with T2DM.MethodsA total of 656 patients with T2DM during the perioperative period were divided into a TIR standard group (TIR≥70%) and a TIR nonstandard group (TIR<70%) according to the TIR value. Modified Poisson regression was used to analyze postoperative risk factors in patients with T2DM. All patients were subsequently divided into a training set and a validation set at a ratio of 7:3. LASSO regression and the Boruta algorithm were used to screen out the predictive factors related to postoperative infection in T2DM patients in the training set. The discrimination and calibration of the model were evaluated by the area under the receiver operating characteristic curve (ROC) and calibration curve, and the clinical net benefit of the model was evaluated and verified through the decision analysis (DCA) curve. Finally, a forest plot was used for relevant subgroup analysis.ResultsModified Poisson regression analysis revealed that the TIR was a risk factor for postoperative infection in T2DM patients, and when the TIR was <70%, the risk of postoperative infection increased by 52.2% (P <0.05). LASSO regression and Boruta algorithm screening variables revealed that the TIR, lymphocytes, neutrophils, total serum cholesterol, superoxide dismutase and type of incision were predictive factors for postoperative infection in patients with T2DM (P<0.05). The calibration curve confirmed that the model predictions were consistent with reality, and the decision curve confirmed that the model had better clinical benefits. Finally, the results of the subgroup analysis revealed that in each subgroup, the risk of postoperative infection was greater when the TIR was <70% than when the TIR was ≥70%, and there was no interaction between subgroups.ConclusionThe TIR is related to postoperative infection and can be used as a new indicator to predict the risk of postoperative infection in patients with type 2 diabetes mellitus. |
| format | Article |
| id | doaj-art-ebca2d14346a4b39bcfe2e0341d244d1 |
| institution | OA Journals |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Endocrinology |
| spelling | doaj-art-ebca2d14346a4b39bcfe2e0341d244d12025-08-20T02:26:30ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-04-011610.3389/fendo.2025.15390391539039Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort studyYing Wu0Rui Xv1Qinyun Chen2Ranran Zhang3Min Li4Chen Shao5Guoxi Jin6Guoxi Jin7Xiaolei Hu8Xiaolei Hu9The Department of Endocrinology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe Department of Endocrinology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe Department of Endocrinology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe Department of Endocrinology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe Department of Endocrinology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe Department of Endocrinology, The Second Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe Department of Endocrinology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe National Metabolic Management Center, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe Department of Endocrinology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaThe National Metabolic Management Center, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaAimTo analyze the correlation between preoperative time-in-range (TIR) levels and postoperative infection in patients with type 2 diabetes mellitus (T2DM) and to evaluate the value of the TIR as a predictor of postoperative infection in patients with T2DM.MethodsA total of 656 patients with T2DM during the perioperative period were divided into a TIR standard group (TIR≥70%) and a TIR nonstandard group (TIR<70%) according to the TIR value. Modified Poisson regression was used to analyze postoperative risk factors in patients with T2DM. All patients were subsequently divided into a training set and a validation set at a ratio of 7:3. LASSO regression and the Boruta algorithm were used to screen out the predictive factors related to postoperative infection in T2DM patients in the training set. The discrimination and calibration of the model were evaluated by the area under the receiver operating characteristic curve (ROC) and calibration curve, and the clinical net benefit of the model was evaluated and verified through the decision analysis (DCA) curve. Finally, a forest plot was used for relevant subgroup analysis.ResultsModified Poisson regression analysis revealed that the TIR was a risk factor for postoperative infection in T2DM patients, and when the TIR was <70%, the risk of postoperative infection increased by 52.2% (P <0.05). LASSO regression and Boruta algorithm screening variables revealed that the TIR, lymphocytes, neutrophils, total serum cholesterol, superoxide dismutase and type of incision were predictive factors for postoperative infection in patients with T2DM (P<0.05). The calibration curve confirmed that the model predictions were consistent with reality, and the decision curve confirmed that the model had better clinical benefits. Finally, the results of the subgroup analysis revealed that in each subgroup, the risk of postoperative infection was greater when the TIR was <70% than when the TIR was ≥70%, and there was no interaction between subgroups.ConclusionThe TIR is related to postoperative infection and can be used as a new indicator to predict the risk of postoperative infection in patients with type 2 diabetes mellitus.https://www.frontiersin.org/articles/10.3389/fendo.2025.1539039/fulltype 2 diabetestime in rangepostoperative infectionrisk factorsclinical prediction model |
| spellingShingle | Ying Wu Rui Xv Qinyun Chen Ranran Zhang Min Li Chen Shao Guoxi Jin Guoxi Jin Xiaolei Hu Xiaolei Hu Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study Frontiers in Endocrinology type 2 diabetes time in range postoperative infection risk factors clinical prediction model |
| title | Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study |
| title_full | Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study |
| title_fullStr | Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study |
| title_full_unstemmed | Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study |
| title_short | Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study |
| title_sort | assessing the predictive value of time in range level for the risk of postoperative infection in patients with type 2 diabetes a cohort study |
| topic | type 2 diabetes time in range postoperative infection risk factors clinical prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1539039/full |
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