Intraoperative accidental hypothermia as a probable cause of malignant ventricular arrhythmias in an elderly patient undergoing transurethral resection of prostate: A case report

Development and validation of a novel risk assessment model for accurate prediction of intraoperative hypothermia in adult patients undergoing different types of surgery: insights from a multicentre, retrospective cohort study

BACKGROUND

Intraoperative hypothermia is a prevalent complication that may significant clinical and economic burdens. Previous risk assessment models have demonstrated limitations in accurately predicting intraoperative hypothermia, particularly in diverse surgical populations. This study aims to develop and validate a model in adult surgical patients to improve outcomes.

METHODS

This retrospective cohort study utilized data extracted from electronic medical records and anaesthesia information management systems between June 2022 and August 2023. The analysis included information of 3,405 adult surgical patients from three independent centres in China who underwent elective surgical procedures with body temperature monitoring. Intraoperative hypothermia was defined as a core temperature below 36 °C during surgery. The Least Absolute Shrinkage and Selection Operator (LASSO) regression employed to select optimal features and multivariate logistic regression was used to identify independent predictors of intraoperative hypothermia and then built the risk assessment model. We further evaluated the discriminative ability, calibration curves, and clinical utility of the predictive model.

RESULTS

The total incidences of intraoperative hypothermia in adult surgical patients were 42.5%. The predictors in the intraoperative hypothermia model included: age, BMI, baseline HR, baseline temperature, minimally invasive surgery, smoking, previous surgery and serum creatine level. In the training cohort, the model demonstrated strong discriminatory ability, with C-index values of 0.721 (95% CI 0.697-0.744). Internal and external validation further confirmed the model's robustness and generalizability.

CONCLUSION

These findings suggest that our model may help us more accurately identify patients at risk of intraoperative hypothermia.

TRIAL REGISTRATION

China Clinical Trial Registration Center (ChiCTR2300071859), Date registered May/26/2023.

Risk factors for hypothermia after transurethral holmium laser enucleation of the prostate and development of a nomogram model

OBJECTIVES

Postoperative hypothermia is a common clinical complication with a high incidence rate, potentially adversely affecting postoperative recovery. Transurethral holmium laser enucleation of the prostate (HoLEP) is a minimally invasive procedure for benign prostatic hyperplasia (BPH). Offering advantages such as minimal bleeding, broad indications, and rapid postoperative recovery. However, research on risk factors for postoperative hypothermia following HoLEP remains limited, and predictive models for guiding clinical practice are lacking. This study aims to develop a predictive model for assessing the risk of postoperative hypothermia in HoLEP patients and to identify relevant risk factors.

METHODS

Clinical data from patients who underwent HoLEP at affiliated Hospital of Shandong Second Medical University were retrospectively collected. Patients were categorized into a hypothermia group and a non-hypothermia group based on whether postoperative hypothermia occurred. Preoperative, intraoperative, and postoperative indicators were compared between the 2 groups. Least absolute shrinkage and selection operator (LASSO) regression combined with logistic regression analysis was used to analyze clinical data. A predictive model for assessing the risk of postoperative hypothermia after HoLEP was constructed and internally validated using bootstrap resampling.

RESULTS

A total of 403 patients were included in the analysis, among whom 85 patients developed postoperative hypothermia, with an incidence rate of 21.1%. Logistic regression analysis identified operative duration (OR=1.009, 95% CI 1.003 to 1.015), underweight status (OR=9.881, 95% CI 4.038 to 24.910), and prostate weight (OR=1.021, 95% CI 1.012 to 1.030) as independent risk factors for postoperative hypothermia, and these variables were incorporated into the nomogram model. Internal validation showed strong discriminative ability of the nomogram, with an area under the receiver operating characteristic curve (AUC) of 0.755 (95% CI 0.686 to 0.820) and a C-index of 0.832 (95% CI 0.787 to 0.865). The calibration curve demonstrated good consistency between predicted and observed outcomes. Decision curve analysis showed that the nomogram provided greater clinical utility when the risk threshold for postoperative hypothermia was between 8% and 97%.

CONCLUSIONS

This study developed a nomogram model for predicting the risk of postoperative hypothermia in HoLEP patients, providing clinicians with a simple and effective predictive tool for individualized risk assessment and preoperative decision-making.

A multi-center study to predict the risk of intraoperative hypothermia in gynecological surgery patients using preoperative variables

OBJECTIVES

Hypothermia is highly common in patients undergoing gynecological surgeries under general anesthesia, so the length of hospitalization and even the risk of mortality are substantially increased. Our aim was to develop a simple and practical model to preoperatively identify gynecological surgery patients at risk of intraoperative hypothermia.

METHODS

In this retrospective study, we collected data from 802 patients who underwent gynecological surgery at three medical centers from June 2022 to August 2023. We further allocated the patients to a training group, an internal validation group, or an external validation group. The preliminary predictive factors for intraoperative hypothermia in gynecological patients were determined using the least absolute shrinkage and selection operator (LASSO) method. The final predictive factors were subsequently identified through multivariate logistic regression analysis, and a nomogram for predicting the occurrence of hypothermia was established.

RESULTS

A total of 802 patients were included, with 314 patients in the training cohort (mean age 48.5 ± 12.6 years), 130 patients in the internal validation cohort (mean age 49.9 ± 12.5 years), and 358 patients in the external validation cohort (mean age 47.6 ± 14.0 years). LASSO regression and multivariate logistic regression analyses indicated that body mass index, minimally invasive surgery, baseline heart rate, baseline body temperature, history of previous surgery, and aspartate aminotransferase level were associated with intraoperative hypothermia in gynecological surgery patients. This nomogram was constructed based on these six variables, with a C-index of 0.712 for the training cohort.

CONCLUSIONS

We established a practical predictive model that can be used to preoperatively predict the occurrence of hypothermia in gynecological surgery patients.

CLINICAL TRIAL REGISTRATION

chictr.org.cn, identifier ChiCTR2300071859.