Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx
Objective<p>To develop a risk predictive model for inadvertent hypothermia (IH) in intensive care unit (ICU) patients and to validate the accuracy of the model.</p>Methods<p>The data was collected at the ICU of a tertiary hospital in Zunyi from November 2022 to June 2023 for model...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852017549400604672 |
|---|---|
| author | Xueting Wang (1785895) |
| author2 | Yuxuan Chen (1511149) Lan Hua (2059246) Dongmei Wang (282313) Xia Zhang (41398) Lianhong Wang (1496806) |
| author2_role | author author author author author |
| author_facet | Xueting Wang (1785895) Yuxuan Chen (1511149) Lan Hua (2059246) Dongmei Wang (282313) Xia Zhang (41398) Lianhong Wang (1496806) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xueting Wang (1785895) Yuxuan Chen (1511149) Lan Hua (2059246) Dongmei Wang (282313) Xia Zhang (41398) Lianhong Wang (1496806) |
| dc.date.none.fl_str_mv | 2025-08-18T05:16:11Z |
| dc.identifier.none.fl_str_mv | 10.3389/fmed.2025.1596030.s003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Data_Sheet_3_Development_and_validation_of_a_LASSO-based_predictive_model_for_inadvertent_hypothermia_in_ICU_patients_xlsx/29928719 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Foetal Development and Medicine intensive care unit critical patient hypothermia LASSO predictive model |
| dc.title.none.fl_str_mv | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Objective<p>To develop a risk predictive model for inadvertent hypothermia (IH) in intensive care unit (ICU) patients and to validate the accuracy of the model.</p>Methods<p>The data was collected at the ICU of a tertiary hospital in Zunyi from November 2022 to June 2023 for model construction and internal validation. Data collected at the ICU of another tertiary hospital in Zunyi from July 2023 to December 2023 was used for external validation. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen for strongly correlated predictors and build a predictive model, which was presented in the form of a nomogram and perform internal and external validation.</p>Results<p>This study included a total of 720 participants, the incidence of IH in ICU patients was 18.19%. Six predictor variables were ultimately screened to construct the model: risk of IH in ICU patients = 1/(1 + exp−(−3.631 + 0.984 × catecholamines − 3.200 × antipyretic analgesics + 1.611 × RRT + 1.291 × invasive mechanical ventilation + 1.160 × GCS + 0.096 × lactate)). The results of the prediction model evaluation showed an AUC of 0.852 (95%CI: 0.805, 0.898) and internal validation yielded a C-statistic of 0.851. The Hosmer-Lemeshow test showed that x<sup>2</sup> = 7.438, p = 0.282 and the calibration curve showed that the actual prediction was close to the ideal prediction. The results of the DCA showed that the model is able to provide effective evidence to support clinical decision making. External validation showed an AUC of 0.846 (95%CI: 0.779, 0.913). The Hosmer-Lemeshow test showed x<sup>2</sup> = 13.041, p = 0.071 and the calibration curve was close to the ideal prediction situation.</p>Conclusion<p>The IH predictive model for ICU patients constructed in this study passed both internal and external validation, and has good differentiation, calibration, clinical utility, and generalizability, which can help healthcare professionals to effectively identify high-risk groups for IH in the ICU.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_2cbc36e4e2a55e7734b986e63d0c0144 |
| identifier_str_mv | 10.3389/fmed.2025.1596030.s003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29928719 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsxXueting Wang (1785895)Yuxuan Chen (1511149)Lan Hua (2059246)Dongmei Wang (282313)Xia Zhang (41398)Lianhong Wang (1496806)Foetal Development and Medicineintensive care unitcritical patienthypothermiaLASSOpredictive modelObjective<p>To develop a risk predictive model for inadvertent hypothermia (IH) in intensive care unit (ICU) patients and to validate the accuracy of the model.</p>Methods<p>The data was collected at the ICU of a tertiary hospital in Zunyi from November 2022 to June 2023 for model construction and internal validation. Data collected at the ICU of another tertiary hospital in Zunyi from July 2023 to December 2023 was used for external validation. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen for strongly correlated predictors and build a predictive model, which was presented in the form of a nomogram and perform internal and external validation.</p>Results<p>This study included a total of 720 participants, the incidence of IH in ICU patients was 18.19%. Six predictor variables were ultimately screened to construct the model: risk of IH in ICU patients = 1/(1 + exp−(−3.631 + 0.984 × catecholamines − 3.200 × antipyretic analgesics + 1.611 × RRT + 1.291 × invasive mechanical ventilation + 1.160 × GCS + 0.096 × lactate)). The results of the prediction model evaluation showed an AUC of 0.852 (95%CI: 0.805, 0.898) and internal validation yielded a C-statistic of 0.851. The Hosmer-Lemeshow test showed that x<sup>2</sup> = 7.438, p = 0.282 and the calibration curve showed that the actual prediction was close to the ideal prediction. The results of the DCA showed that the model is able to provide effective evidence to support clinical decision making. External validation showed an AUC of 0.846 (95%CI: 0.779, 0.913). The Hosmer-Lemeshow test showed x<sup>2</sup> = 13.041, p = 0.071 and the calibration curve was close to the ideal prediction situation.</p>Conclusion<p>The IH predictive model for ICU patients constructed in this study passed both internal and external validation, and has good differentiation, calibration, clinical utility, and generalizability, which can help healthcare professionals to effectively identify high-risk groups for IH in the ICU.</p>2025-08-18T05:16:11ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fmed.2025.1596030.s003https://figshare.com/articles/dataset/Data_Sheet_3_Development_and_validation_of_a_LASSO-based_predictive_model_for_inadvertent_hypothermia_in_ICU_patients_xlsx/29928719CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299287192025-08-18T05:16:11Z |
| spellingShingle | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx Xueting Wang (1785895) Foetal Development and Medicine intensive care unit critical patient hypothermia LASSO predictive model |
| status_str | publishedVersion |
| title | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx |
| title_full | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx |
| title_fullStr | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx |
| title_full_unstemmed | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx |
| title_short | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx |
| title_sort | Data Sheet 3_Development and validation of a LASSO-based predictive model for inadvertent hypothermia in ICU patients.xlsx |
| topic | Foetal Development and Medicine intensive care unit critical patient hypothermia LASSO predictive model |