Risk factors for intraoperative hypothermia in patients receiving lung transplants

Introduction

Background

With the rapid development of lung transplantation, there are almost 70,000 adult lung transplantation procedures completed worldwide (1). Lung transplantation is the most effective treatment for end-stage lung disease. However, lung transplantation is commonly complicated by intraoperative hypothermia (IOH) due to large portion exposure of the patients’ body surface, prolonged operation, extensive body cavity irrigation and blood transfusion, and implantation of a cold graft (2).

Rationale and knowledge gap

IOH, defined as a core temperature of <36 ℃, is a common and preventable complication during the perioperative period (3). Studies showed that the incidence of IOH varies between 44.3% and 83.3% (4,5). IOH is considered to be an important factor affecting perioperative morbidity and mortality (6). In particular, IOH is associated with multiple complications, including surgical site infections, blood loss and coagulopathy, perturbed drug metabolism, increased postoperative cardiovascular events, and thermal discomfort (6,7). IOH significantly prolongs the recovery time and hospitalization time of patients (6). To strengthen the standardized management of the body temperature of surgical patients, the international community has formulated a series of evidence-based guidelines for hypothermia prevention (8,9). Despite the application of several active insulation measures in clinical practice, IOH remains a persistent issue. Thus, a precise and useful estimate of the risk of IOH is required to maximize postoperative patient outcomes.

The risk prediction model estimates the possible risks of the outcome by inferring the quantitative relationship between various risk factors and the probability of occurrence of the outcome, thus informing the development of preventive strategies (10). Recent studies have investigated the association between IOH and relevant predictors in thoracic (11), laparoscopic surgery (12), joint replacement surgery (13), and other surgical fields (14). These studies identified several risk factors for IOH, such as age, anesthesia time, and baseline temperature (13-15). Additionally, risk prediction models can predict the risk of IOH in patients early and efficiently in these studies (13-15). However, lung transplantation is a complicated procedure. Lung transplantation typically has a longer surgical duration than general anesthesia surgery, such as thoracoscopic operations. The surgery required a large incision, resulting in extensive exposure of the patient’s body. Additionally, the temperature of the implanted donor lung is generally between 0–4 ℃. Moreover, extracorporeal membrane oxygenation (ECMO) support is required for critically ill patients before or during surgery, such as for patients with acute respiratory distress syndrome, acute lung injury, acute chronic heart failure. These factors may make the existing IOH prediction models unsuitable for patients undergoing lung transplantation.

Objective

Therefore, to improve IOH prevention after lung transplantation, the objective of this study was to analyze the risk factors of IOH in patients who underwent lung transplantation, and create and verify a risk prediction model for IOH in patients receiving lung transplants. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-777/rc).

Methods

Study design and population

This was a single-center retrospective study. Patients who underwent lung transplant surgery at the First Affiliated Hospital of Guangzhou Medical University between January 2019 and October 2023 were enrolled. The inclusion criteria were: (I) patients aged ≥18 years; (II) normal preoperative body temperature; (III) patients who underwent lung transplantation surgery. The exclusion criteria were: (I) no core temperature monitored or incomplete clinical data; (II) serious intraoperative complications, such as hemorrhagic shock and malignant hyperthermia; (III) a history of endocrine disease affecting body temperature, such as hypothyroidism or hyperthyroidism; (IV) lung auto-transplantation; (V) lung transplantation combined with other surgeries.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the institutional review board of The First Affiliated Hospital of Guangzhou Medical University (No. ES-2024-K020-01) and individual consent for this retrospective analysis was waived.

Core temperature measurement

The core temperature is assumed to be the temperature of the urinary bladder. Urinary bladder temperature should closely equal core temperature if the urine flow rate is within the normal range, which is a reliable method (16). After inducing anesthesia, we monitored body temperature using a bladder catheter equipped with a temperature probe, recording it through the anesthesia information management system. According to routine practice, the operating room was kept at a consistent 22–24 ℃ with a 50–60% relative humidity. Surgical drapes or bed linens were draped over each patient during the lung transplant procedure. Warm flushing fluid was administered with the infused fluid and blood products using an intraoperative infusion heater.

Data collection

A data form was used to collect information on the demographic and risk factors for IOH. This information included: (I) demographic data: age, gender, body mass index (BMI), pre-operative diagnosis, and history of trauma; comorbidities, including diabetes and hypertension; (II) surgical and anesthetic information: American Society of Anesthesiologists (ASA) grade, lung transplantation type, anaesthesia time, operation time, intraoperative blood loss, volume of intraoperative fluid, volume of urine, intraoperative temperature, use of ECMO, use of circulating-water mattress; and volume of blood transfusion; (III) clinical outcomes: cardiac arrhythmias, the length of stay (LOS) in the intensive care unit (ICU), acute kidney injury (AKI), postoperative hemorrhage, and 30-day mortality.

The core temperature <36 ℃ was considered IOH (16). Our monitoring system automatically recorded the temperature data and we collected urinary bladder temperature data every 5 minutes during the operation. We determined the incidence of hypothermia when all records were less than 36 ℃ within 5 minutes after the start of the 5 minute-data less than 36 ℃ (17). Two investigators were trained in data collection to provide reliable results. The researchers collected demographic, relevant surgical, and anesthetic data from the hospital information system of electronic medical records using a data collection form. Data were entered and checked by two investigators.

Statistical analysis

The statistical analysis was performed using SPSS 26.0. For normally distributed quantitative data, the results were summarized using the mean ± standard deviation and an independent sample t-test was used to compare the two groups. The median and quartiles were used to describe non-normally distributed measurement data, and the rank-sum test was used to compare the two groups. Count data were presented as frequencies and percentages, and a Chi-squared test was used. Multivariate logistic regression with a forward stepwise method was used to analyze the statistically significant variables in univariate analysis, and a prediction model was established. A nomogram of IOH risk prediction was constructed using the “rms” software package in R 4.3.2. The model was verified using the Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve. Regarding the calibration evaluation of the model, the calibration curve was plotted to validate the consistency of the model. P value less than 0.05 was considered statistically significant.

Results

Patient baseline characteristics

After screening the data from 311 patients, 160 were finally included. The flow diagram is shown in Figure 1. One hundred and sixty patients undergoing lung transplantation were included and divided into hypothermia group (n=106) and non-hypothermia group (n=54) based on whether IOH occurred. The incidence of IOH was 66.25%. In IOH group, most of the patients were diagnosed as interstitial lung disease before operation, followed by chronic obstructive pulmonary disease (COPD), and lastly other diseases (46.2% vs. 34.0% vs. 19.8%; P=0.005). There were more patients without diabetes in the IOH group than in the non-hypothermia group (89.6% vs. 77.8%; P=0.04). The median [interquartile range (IQR)] duration of volume of intraoperative fluid in the IOH group was 2,412.50 (1,762.50, 3,242.50) mL, which was significantly higher than the median (IQR) duration of volume of intraoperative fluid in the normothermia group [2,150.00 (1,812.50, 2,500.00) mL; P=0.03]. The incidence of IOH in patients using ECMO was significantly lower than that in patients not using ECMO (31.1% vs. 68.9%; P<0.001). In IOH group, the patients who used circulating-water mattress were significantly lower than those who did not use warm circulating-water mattress (35.8% vs. 64.2%; P=0.002). Single-factor analysis of patient clinical data presented significant differences in pre-operative diagnosis (P=0.005), diabetes (P=0.04), erythrocyte suspension (P=0.04), volume of intraoperative fluid (P=0.03), ECMO (P<0.001), circulating-water mattress (P=0.002) (Table 1).

Figure 1 Flowchart of the study.

Patient outcomes

Compared to non-hypothermia group, patients with IOH had more likely to experience cardiac arrhythmias (17.0% vs. 5.6%, P=0.04). However, our result showed that there was no significant difference between hypothermia and non-hypothermia in ICU LOS [11.00 (6.00, 26.00) vs. 12.00 (8.00, 31.25) days], AKI (8.5% vs. 1.9%), postoperative hemorrhage (4.7% vs. 1.9%), and 30-day mortality (93.4% vs. 88.9%) (Table 2).

Prediction model of IOH during lung transplantation

Six statistically significant variables were identified as potential predictors, but only three remained in the final model. Multivariate logistic regression showed that volume of intraoperative fluid [odds ratio (OR) =1.001, 95% confidence interval (CI): 1.000649 to 1.002, P<0.001] was associated with increased risk of developing IOH during lung transplantation, while ECMO (OR =0.091, 95% CI: 0.036 to 0.229, P<0.001) and circulating-water mattress (OR =0.389, 95% CI: 0.178 to 0.852, P=0.02) were protective factors against IOH (Table 3). The model was constructed as follows: logit (P) =−0.636 + 0.001x_{volume of intraoperative fluid} − 2.400x_ECMO − 0.944x_{circulating-water mattress}.

Predictive ability of the hypothermia after lung transplantation prediction model

The nomogram prediction model used volume of intraoperative fluid, ECMO, and circulating-water mattress as predictors (Figure 2). The area under the ROC curve was 0.820 (Figure 3). The prediction model had a P value of 0.18 in the Hosmer-Lemeshow test, indicating good calibration performance. The nomogram calibration curve showed that the actual curve was basically in line with the ideal curve (Figure 4).

Figure 2 Nomogram of high-risk factors for hypothermia during lung transplantation.

Figure 3 Receiver operating characteristic curve of the nomogram. AUC, area under the curve; CI, confidence interval.

Figure 4 Calibration curve of the nomogram.

Discussion

The growing interests of IOH in clinical practice motivated us to explore its risk factors. Hitherto, studies on risk factors for IOH in lung transplantation have not been reported. This study found that the overall incidence rate of IOH among patients who received lung transplants was 66.25%. The incidence of IOH is considerably high in lung transplantation patients due to the implantation of an ice-cooled graft, the lengthy and intricate nature of the procedure, and the exposure of a significant portion of the body. Volume of intraoperative fluid was associated with increased risk of developing IOH during lung transplantation, while ECMO and circulating-water mattress were protective factors against IOH.

ECMO is an effective circulating aid; it drains the patient’s blood into the body via the cardiopulmonary bypass route and then sends it back into the patient after artificial pulmonary oxygenation (18). In lung transplantation, ECMO is responsible for gas exchange and part of the blood circulation function. In this study, receiving ECMO support reduced the risk of hypothermia, mainly because of the active warming of circulating blood through the heat exchanger in the ECMO circuit (2). Similar results were observed in a retrospective analysis, which showed that patients undergoing off-pump lung transplant were at high risk of IOH despite multimodal preventive therapy (2). Dong et al. (2) also found that the decrease in body temperature among patients on ECMO support was smaller than that in patients undergoing off-pump. Moreover, ECMO has several pathophysiological advantages compared with other rewarming methods (19). It provides adequate and immediate circulatory support and rewarms the heart before the rest of the body. Prekker et al. (20) reported that the use of ECMO was associated with faster rewarming than the traditional rewarming methods. Hence, using ECMO in lung transplantation can effectively reduce the incidence of hypothermia.

IOH was affected by the volume of fluid, which is consistent with previous studies (21,22). Extensive intraoperative infusion of fluid has become a recognized risk factor for IOH. The mean body temperature may reduce by 0.25 ℃ for each liter of fluid infused at an ambient temperature (23). Preheating the liquid helps avoid hypothermia, as demonstrated by the findings of Sari et al. (22) which showed that the percentage of patients with hypothermia who had an unheated fluid infusion larger than 1,000 mL was substantially higher than that of patients without hypothermia. In this study, despite heating the infusion liquid, the temperature difference between the liquid and room during infusion contributed to heat loss. Additionally, due to the prohibition of drinking and eating before the operation and the long operation time, patients require significant fluid replacement during lung transplantation to maintain their cardiac blood volume and blood pressure. This infused fluid will absorb more heat from the body to reach normal body temperature. In the actual clinical work, medical staff should pay more attention to the use of infusion heating equipment and choose the appropriate heating temperature to reduce the incidence of IOH.

The use of active heating devices is widely advocated and practiced to prevent hypothermia. The circulating-water mattress conducts and radiates heat to the body by circulating warm water at a constant temperature, directly warming the patient’s skin and peripheral tissues. Kim et al. (24) confirmed that the circulating-water mattress can effectively maintain the core temperature and prevent IOH. In the present study, the lack of circulating-water mattress was significantly associated with IOH. These results suggest that the circulating-water mattress has a good thermal insulation effect. Therefore, medical staff should strengthen the use of circulating-water mattress to reduce the incidence of IOH.

We investigated the incidence of complications in individuals with IOH both during and after surgery. IOH causes a number of symptoms associated with cardiac arrhythmias, including the contraction of peripheral blood vessels, an increase in peripheral resistance, an increase in myocardial oxygen consumption, and an increase in the workload of the heart (25). IOH has been linked to blood loss and coagulopathy, transfusion needs, protracted recuperation, kidney damage, and longer hospital stays, according to earlier studies (6,26). Nevertheless, the small sample size and retrospective nature of this study may have hampered the findings, which showed that IOH had no effect on these clinical outcomes. Thus, to further examine the impact of IOH on lung transplant patients, researchers can conduct large-sample, multi-center, prospective studies in the future.

In this study, we selected the high-risk factors of IOH during lung transplantation based on multivariate regression analysis and successfully constructed a predictive model. Meanwhile, the nomogram appeared to have good discrimination and predictive ability based on the ROC and calibration curves. Compared with the risk prediction models of hypothermia during thoracoscopic pulmonary tumor surgery (11), the nomogram prediction model constructed in this study included more lung transplantation surgery-related variables, such as lung transplantation type and ECMO, improving the accuracy of the risk prediction model. Therefore, this prediction model can effectively predict the risk of IOH in lung transplantation patients, providing a rapid and effective means to evaluate the prevention of IOH and the development of targeted interventions.

There were some limitations in this study when compared to similar studies. First, it was a retrospective study conducted in a single center, which can introduce bias. Second, risk factors that may influence IOH were not fully included, such as preoperative physiological indicators, which were not collected. Third, due to the limited number of warm blankets and the clinical practice of carrying out multiple lung transplantation operations at the same time, so we did not use circulating-water mattress for all patients. Finally, some inaccuracies were allowed due to a small sample size and a lack of external validation for the nomogram. In the future, multicenter and large sample prospective studies should be conducted to improve and validate the prediction model.

Conclusions

Volume of intraoperative fluid was associated with increased risk of developing IOH, while ECMO and circulating-water mattress were protective factors against IOH during lung transplantation. IOH can increase the risk of developing cardiac arrhythmias in patients. In this study, the predictive model has good predictive value, providing a reference for the clinical work to prevent IOH during lung transplantation.

Acknowledgments

The authors thank Yuyu Duan, Wen Dang, Yingfen Li, and Zihao Liu for their valuable comments and help.

Funding: This work was supported by grants from the Nursing Research Project of Chinese Medical Association Publishing House (No. CMAPH-NRG2022010), Guangzhou Health Science and Technology General Guidance Project (No. 20221A010047), and the research project of Guangdong Nurse Association (No. gdshsxh2023ms52).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-777/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-777/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-777/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-777/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the institutional review board of The First Affiliated Hospital of Guangzhou Medical University (No. ES-2024-K020-01) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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