Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx
Background<p>Hyperglycemic crisis events (HCEs)—encompassing diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS)—constitute lethal determinants for patients with diabetic mellitus (DM) in intensive care. The triglyceride-glucose (TyG) index, an emergent insulin resistance su...
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2025
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| _version_ | 1852016991924125696 |
|---|---|
| author | Mingchen Xie (4325692) |
| author2 | Yahui Zhang (444739) Haitao Wu (2411155) Zeyu Wu (455882) Hao Han (412732) Xun Xie (15557433) Rui Zhang (13940) Jianhua Cheng (467464) Jian Xu (31545) |
| author2_role | author author author author author author author author |
| author_facet | Mingchen Xie (4325692) Yahui Zhang (444739) Haitao Wu (2411155) Zeyu Wu (455882) Hao Han (412732) Xun Xie (15557433) Rui Zhang (13940) Jianhua Cheng (467464) Jian Xu (31545) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mingchen Xie (4325692) Yahui Zhang (444739) Haitao Wu (2411155) Zeyu Wu (455882) Hao Han (412732) Xun Xie (15557433) Rui Zhang (13940) Jianhua Cheng (467464) Jian Xu (31545) |
| dc.date.none.fl_str_mv | 2025-09-04T05:32:09Z |
| dc.identifier.none.fl_str_mv | 10.3389/fnut.2025.1649553.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Table_1_Correlation_of_triglyceride-glucose_index_with_the_incidence_and_prognosis_of_hyperglycemic_crises_in_critically_ill_patients_with_diabetes_mellitus_a_machine-learning-based_multicenter_retrospective_cohort_study_docx/30049702 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Clinical and Sports Nutrition triglyceride-glucose index hyperglycemic crisis critical care machine learning mortality prediction |
| dc.title.none.fl_str_mv | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background<p>Hyperglycemic crisis events (HCEs)—encompassing diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS)—constitute lethal determinants for patients with diabetic mellitus (DM) in intensive care. The triglyceride-glucose (TyG) index, an emergent insulin resistance surrogate, lacks rigorous investigation regarding HCE occurrence trajectories and prognostic sequelae among critically ill diabetics. This study aims to evaluate the relationship between the TyG index and HCE incidence/clinical outcomes in critically ill patients with DM and to construct a risk prediction model using machine-learning algorithms.</p>Methods<p>This multi-center retrospective investigation leveraged clinical repositories from Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). Inclusion criteria encompassed critically ill subjects with diabetes possessing computable TyG indices within 24 h post-admission. The main study endpoints included death occurring during hospitalization and death within the intensive care unit. TyG index-outcome interrelationships underwent interrogation via logistic regression, restricted cubic spline (RCS), correlation, and linear analytical methodologies. Overlap weighting (OW), inverse probability treatment weighting (IPTW), and propensity score matching (PSM) mitigated confounding influences. Stratified examinations occurred per determinant factors. Five machine-learning architectures constructed mortality prognostication frameworks, with SHapley Additive exPlanations (SHAP) delineating pivotal predictors.</p>Results<p>Among 4,098 critically ill patients with DM, 328 developed HCE. Patients with HCE had significantly higher TyG levels [10.2 (9.6–11.0) vs. 9.4 (8.9–9.9)] than non-HCE patients, demonstrating TyG’s discriminative ability for HCE. Through multivariate logistic regression, TyG was pinpointed as a separate risk element for both in-hospital (OR 1.956) and ICU death (OR 2.260), linked to extended hospital stays. RCS established a direct positive correlation between increased TyG levels and death rates (nonlinear p = 0.161 and 0.457), continuing even after adjusting for PSM, OW, and IPTW. Subgroup analyses reinforced TyG’s consistent mortality correlation. Machine-learning models, particularly XGBoost, achieved higher predictive accuracy, with TyG as a key component.</p>Conclusion<p>Elevated TyG index shows a notable correlation with the occurrence of HCE and negative results in critically ill patients with DM. Advanced multivariate machine-learning models are adept at pinpointing patients at high risk, thereby facilitating prompt clinical action.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_07881952a3a8bcbc729d39c46f29d607 |
| identifier_str_mv | 10.3389/fnut.2025.1649553.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30049702 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docxMingchen Xie (4325692)Yahui Zhang (444739)Haitao Wu (2411155)Zeyu Wu (455882)Hao Han (412732)Xun Xie (15557433)Rui Zhang (13940)Jianhua Cheng (467464)Jian Xu (31545)Clinical and Sports Nutritiontriglyceride-glucose indexhyperglycemic crisiscritical caremachine learningmortality predictionBackground<p>Hyperglycemic crisis events (HCEs)—encompassing diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS)—constitute lethal determinants for patients with diabetic mellitus (DM) in intensive care. The triglyceride-glucose (TyG) index, an emergent insulin resistance surrogate, lacks rigorous investigation regarding HCE occurrence trajectories and prognostic sequelae among critically ill diabetics. This study aims to evaluate the relationship between the TyG index and HCE incidence/clinical outcomes in critically ill patients with DM and to construct a risk prediction model using machine-learning algorithms.</p>Methods<p>This multi-center retrospective investigation leveraged clinical repositories from Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). Inclusion criteria encompassed critically ill subjects with diabetes possessing computable TyG indices within 24 h post-admission. The main study endpoints included death occurring during hospitalization and death within the intensive care unit. TyG index-outcome interrelationships underwent interrogation via logistic regression, restricted cubic spline (RCS), correlation, and linear analytical methodologies. Overlap weighting (OW), inverse probability treatment weighting (IPTW), and propensity score matching (PSM) mitigated confounding influences. Stratified examinations occurred per determinant factors. Five machine-learning architectures constructed mortality prognostication frameworks, with SHapley Additive exPlanations (SHAP) delineating pivotal predictors.</p>Results<p>Among 4,098 critically ill patients with DM, 328 developed HCE. Patients with HCE had significantly higher TyG levels [10.2 (9.6–11.0) vs. 9.4 (8.9–9.9)] than non-HCE patients, demonstrating TyG’s discriminative ability for HCE. Through multivariate logistic regression, TyG was pinpointed as a separate risk element for both in-hospital (OR 1.956) and ICU death (OR 2.260), linked to extended hospital stays. RCS established a direct positive correlation between increased TyG levels and death rates (nonlinear p = 0.161 and 0.457), continuing even after adjusting for PSM, OW, and IPTW. Subgroup analyses reinforced TyG’s consistent mortality correlation. Machine-learning models, particularly XGBoost, achieved higher predictive accuracy, with TyG as a key component.</p>Conclusion<p>Elevated TyG index shows a notable correlation with the occurrence of HCE and negative results in critically ill patients with DM. Advanced multivariate machine-learning models are adept at pinpointing patients at high risk, thereby facilitating prompt clinical action.</p>2025-09-04T05:32:09ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fnut.2025.1649553.s001https://figshare.com/articles/dataset/Table_1_Correlation_of_triglyceride-glucose_index_with_the_incidence_and_prognosis_of_hyperglycemic_crises_in_critically_ill_patients_with_diabetes_mellitus_a_machine-learning-based_multicenter_retrospective_cohort_study_docx/30049702CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300497022025-09-04T05:32:09Z |
| spellingShingle | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx Mingchen Xie (4325692) Clinical and Sports Nutrition triglyceride-glucose index hyperglycemic crisis critical care machine learning mortality prediction |
| status_str | publishedVersion |
| title | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx |
| title_full | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx |
| title_fullStr | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx |
| title_full_unstemmed | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx |
| title_short | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx |
| title_sort | Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.docx |
| topic | Clinical and Sports Nutrition triglyceride-glucose index hyperglycemic crisis critical care machine learning mortality prediction |