Abstract
Background
Intraoperative hypothermia (IOH) is common among patients undergoing total joint arthroplasty (TJA) of the lower extremities. Evaluating the risk factors for IOH is critical for the success of preventive interventions. This study aims to systematically assess the prevalence and risk factors of IOH in TJA patients.
Design
Systematic review and meta-analysis.
Methods
Two independent researchers conducted a literature search across eight databases: PubMed, Web of Science, Embase, Cochrane Library, CNKI, VIP, WANFANG Data, and CBM. The quality of included studies was assessed using the Newcastle-Ottawa Scale. A random-effects model was employed to analyze the prevalence and risk factors of IOH. All analyses were performed using Stata 12.0 software.
Results
Across 21 cohort studies on TJA patients, the pooled prevalence of IOH was 35.28% (95% CI: 27.50%-43.48%). Subgroup analysis revealed an IOH prevalence of 38.66% (95% CI: 28.67%-49.16%) in THA patients and 27.23% (95% CI: 18.37%- 37.11%) in TKA patients. Significant risk factors for IOH included intraoperative blood loss, intraoperative infusion volume, operating room temperature, and surgical time. Current evidence does not support a clear association between patient age, anesthesia time, and IOH.
Conclusion
Our Meta-analysis indicates that the prevalence of IOH is high among TJA patients, despite significant heterogeneity (I² =98.7%, τ²= 0.15), the pooled analysis suggested the prevalence of IOH in TJA patients was 35.28% (95% CI: 27.50%-43.48%). However, the interpretation of these findings should be cautious due to substantial variability across studies. Additionally, assessment of intraoperative blood loss, infusion volume, operating room temperature, and surgical time should be integral to IOH risk evaluation in TJA patients. Future studies are needed to further explore the roles of age and anesthesia time as potential risk factors. Tailored IOH prevention strategies should be developed by addressing modifiable risk factors.
Trial registration
PROSPERO Registration ID: CRD42024559846
Supplementary Information
The online version contains supplementary material available at 10.1186/s12891-025-08962-9.
Keywords: Intraoperative hypothermia, THA, TKA, Prevalence, Risk factors, Meta-analysis
Introduction
Intraoperative hypothermia (IOH) is defined as a core temperature (CT) < 36.0 °C during surgery [1]. IOH is initially caused by the redistribution of heat from the core to the periphery, followed by excessive heat loss exceeding metabolic heat production [2]. IOH can lead to a variety of complications, including coagulopathy [2] and postoperative myocardial complications [3]. It also increases the risk of surgical site infections [4, 5] and intraoperative blood loss [6]. Furthermore, IOH reduces drug metabolism [7], causes thermal discomfort [8], and prolongs postoperative recovery time [9, 10]. One study has shown that a 1.9 °C decrease in core temperature doubles the prevalence of surgical site infections (SSIs) during colectomy. Moreover, patients with SSIs have an average hospital stay one week longer than those without infections. Even after excluding infected patients from the analysis, IOH still prolongs hospital stays by 20% [11]. It is evident that managing infections and extended hospitalization inevitably leads to unnecessary healthcare costs [12]. Therefore, maintaining normothermia during surgery is critical for achieving optimal surgical outcomes and ensuring patient safety and satisfaction.
Studies indicate that the prevalence of IOH among patients undergoing lower extremity TJA, varies widely, ranging from 11 to 81% [13, 14]. Compared to other surgical populations, TJA patients may have a distinct risk of IOH. For example, studies have reported that the prevalence of IOH is only 23% in cesarean Sect. [15] but as high as 72.7% in video-assisted thoracoscopic surgery [16], where prolonged operative duration and extensive exposure to a cold environment exacerbate heat loss. In contrast, minimally invasive procedures generally have a lower prevalence of IOH due to reduced tissue exposure and shorter anesthesia times. These differences underscore the need for procedure-specific risk assessments and tailored preventive strategies. IOH results from the interplay of multiple factors [17, 18]; therefore, identifying its risk factors is crucial for the early detection of high-risk patients and the development of effective preventive strategies to mitigate adverse outcomes.
In recent years, numerous studies have reported on the risk factors for IOH in TJA patients, but their conclusions are inconsistent. For instance, Zhao et al. [19] found that surgical time was not a risk factor for IOH, whereas Li et al. [20] identified it as a significant factor. Given the variability in previous findings, the risk factors for IOH in TJA patients remain controversial, and the reported prevalence of IOH also differs across studies. Therefore, a systematic evaluation of the prevalence and risk factors of IOH in TJA patients is warranted. This study aims to systematically review and synthesize existing evidence on the prevalence and risk factors of IOH in TJA patients using standardized meta-analytic techniques.
Methods
We registered the protocol in the International Prospective Register of Ongoing Systematic Reviews (PROSPERO registration ID: CRD42024559846) on July 5, 2024. We conducted this systematic review according to the updated Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) recommendation [21, 22].
Search strategy
We searched PubMed, Web of Science, Embase, Cochrane Library, China Knowledge Resource Integrated Database (CNKI), Weipu Database (VIP), Wanfang Database, and Chinese Biomedical Database (CBM) from the oldest publications available in each of the databases through August 2024. We used Medical Subject Heading (MeSH) terms in various combinations: (“hypothermia” OR “Accidental Hypothermias”) AND (“arthroplasties, replacement, knee” OR “Arthroplasty, replacement, hip” OR “total knee arthroplasty” OR “total hip arthroplasty”). Additionally, we manually screened the reference lists of included studies and relevant reviews to identify other pertinent publications. A detailed overview of the search strategy is available in the Supplementary materials (Appendix).
If the data from the included studies were not presented in a format suitable for meta-analysis, we contacted the manuscript authors via email. Due to the nature of the study, ethical committee approval was not required.
Study selection and inclusion criteria
Studies were considered eligible for inclusion in the meta-analysis if they involved lower extremity TJA patients aged 18 years or older (Population) who underwent THA or TKA and evaluated factors influencing IOH or the prevalence of IOH (Outcomes) using prospective or retrospective study designs (study design). TJA is defined as total knee arthroplasty (TKA) and total hip arthroplasty (THA). Studies that did not evaluate IOH as a complication of lower extremity TJA or involved patients under 18 years old, those with other comorbidities were excluded.
Initial screening was conducted independently by two researchers, who reviewed titles and abstracts to remove ineligible studies. Disagreements were resolved through discussion, and if necessary, a third researcher was consulted. Studies were included if they were published in English or Chinese, focused on human subjects, and met the pre-defined inclusion criteria. Non-original studies, such as reviews (with or without meta-analyses), editorials, comments, author responses, theses, and case reports, were excluded.
For studies deemed potentially relevant, full-text reviews were conducted to verify eligibility, and final inclusion was confirmed during data extraction. Two researchers independently extracted data from the included studies using a standardized extraction form. Extracted information included: (1) Study characteristics: first author’s name, year of publication, study location; (2) Population characteristics: sample size, number and proportion of IOH cases, mean age, gender distribution; (3) Study design: retrospective vs. prospective, single-center vs. multi-center; Reported risk factors and their definitions within each study; (4) Outcome measures: odds ratios (OR) with 95% confidence intervals (CI) for each risk factor.
For risk factors reported as categorical variables, such as intraoperative blood loss and infusion volume, we extracted the OR and 95% CI directly from the original studies, as reported by the authors. Since cutoff values used to dichotomize continuous variables varied across studies (e.g., 500 mL vs. 1000 mL for blood loss), we did not standardize or redefine these thresholds, but instead preserved the study-specific definitions. This approach is consistent with recommendations in the Cochrane Handbook and other authoritative meta-analysis guidelines, which state that when original studies define risk factors using different thresholds, it is acceptable to pool reported ORs using a random-effects model, provided that heterogeneity is acknowledged and explored [23, 24].
Quality appraisal
Two researchers independently assessed the quality of the included studies using the Newcastle-Ottawa Scale (NOS) [25], which scores observational studies semi-quantitatively across three dimensions (selection, comparability, outcome) on a 9-point scale. The selection dimension (4 points) examined population representation, comparability between exposed and non-exposed groups, exposure confirmation, and no outcome at baseline. The comparability dimension (2 points) assessed confounding control (e.g., adjusting for ≥ 3 key variables for 1 point); Outcome dimensions (3 points) included outcome measurement independence, follow-up time, and completeness. The two researchers scored independently, with a third arbiter of differences. High quality studies were defined as ≥ 7 points, medium 4–6 points, and low quality ≤ 3 points were excluded.
In addition to NOS, Egger’s test and Begg’s test were also used to quantitatively evaluate the significance of publication bias, with P > 0.05 indicating no significant publication bias. A funnel plot was generated when at least 10 studies reported comparable outcomes. Sensitivity analyses were conducted to assess the impact of excluding lower-quality studies on the overall results. By implementing these rigorous methodological approaches, we aimed to ensure the reliability and validity of our meta-analysis findings.
Statistical analyses
For the meta-analysis of rate, to address the potential non-normality of raw proportions, we applied the Arcsine Square Root Transformation to stabilize variances and approximate a normal distribution for meta-analysis [26]. This transformation mitigates the influence of extreme proportions and ensures homogeneity of variances across studies.
Subgroup analyses were performed based on the surgical type, geographic region and anesthesia type. Meta-regression was conducted to explore whether the time of data collection contributed to the heterogeneity in IOH prevalence. Effect sizes (OR) and its corresponding 95% confidence interval (CI) were extracted and summarized to examine the risk factors associated with IOH occurrence.
Heterogeneity was evaluated using Cochran’s Q test (α = 0.10) and quantified via the I² statistic. A random-effects model, employing the DerSimonian and Laird (DL) estimator for between-study variance, was applied when significant heterogeneity (I² ≥50% or P ≤ 0.10) was detected. All analyses were conducted in Stata 12.0, which defaults to this estimator for random-effects meta-analyses.
Results
Study characteristics
This search strategy was implemented in August 2024. The study selection process is outlined in the PRISMA flow diagram (Fig. 1). After removing duplicates from 1,584 search results, 1,009 records remained. Titles and abstracts of these 1,009 articles were screened, and 44 studies were identified as eligible for full-text review. Ultimately, 21 studies [13, 14, 19, 20, 27–43] were deemed suitable for inclusion in the systematic review.
Fig. 1.
Flowchart of literature search
Among these 21 studies, 11 were prospective cohort studies[14,19–20 [29, 35, 37, 39–41,42,43], and 10 were retrospective cohort studies [13, 27, 28, 30–34, 36, 38]. Sixteen studies originated from Asia [14, 19, 20, 27, 32–43], 3 from North America [28, 29, 31], and the remaining 2 from Europe [13, 30]. The publication years ranged from 2016 to 2024, with a total sample size of 11,493. The characteristics of the patients and studies are summarized in Table 1.
Table 1.
Patients and studies characteristics
| Author | Publication time | Country | Type of study | Type of surgery | Number of patients | Number of IOH | Prevalence of IOH (%) | Patients age | Risk factors |
|---|---|---|---|---|---|---|---|---|---|
| Chu, K. [27] | 2016 | China | RC | THA | 68 | 30 | 44.12 | - | - |
| Frisch, N. [28] | 2017 | United States | RC | TJA | 2397 | 887 | 37.00 | - | - |
| Matos, J. [29] | 2018 | United States | PC | TJA | 102 | 72 | 70.59 | 63.5 ± 11.3 | ⑤⑦⑧⑨⑩ |
| Scholten, R. [30] | 2018 | Netherlands | RC | TJA | 2600 | 305 | 11.73 | 32–92 | - |
| Simpson, J. [31] | 2018 | United States | RC | TJA | 383 | 140 | 36.55 | - | - |
| Williams, M. [13] | 2018 | United kingdom | RC | TJA | 2055 | 240 | 11.68 | 17.8–96.6 | - |
| Pan, P. [32] | 2020 | China | RC | TJA | 616 | 83 | 13.47 | 65.9 ± 11.8 | - |
| Wang, H. [33] | 2021 | China | RC | THA | 140 | 60 | 42.86 | - | ③④⑤⑥ |
| Ukrani, R. [34] | 2021 | Pakistan | RC | TKA | 286 | 76 | 26.57 | 61.4 ± 10.4 | - |
| Song, Y. [35] | 2022 | China | PC | THA | 126 | 12 | 9.52 | - | ⑪ |
| Abugri, B. [36] | 2022 | Japan | RC | TJA | 297 | 56 | 18.86 | 63.9 ± 10.9 | - |
| Liu, X. [37] | 2023 | China | PC | THA | 295 | 113 | 38.31 | >18 | ①②⑤⑫⑬⑭ |
| Xin, A. [38] | 2023 | China | RC | THA | 86 | 37 | 43.02 | - | ③④⑤⑥ |
| Yan, Y. [39] | 2023 | China | PC | THA | 77 | 30 | 38.96 | 55.73 ± 6.81 | ③④⑥⑮ |
| Li, L. [40] | 2023 | China | PC | TJA | 404 | 148 | 36.63 | ≥ 18 | - |
| Zhao, B. [19] | 2023 | China | PC | THA | 718 | 371 | 51.67 | ≥ 60 | ①③⑯⑰⑱ |
| Qiao, T. [41] | 2024 | China | PC | THA | 120 | 39 | 32.50 | 18–80 | - |
| Xu, P. [42] | 2024 | China | PC | TKA | 108 | 45 | 41.67 | - | ①④⑤⑥ |
| Zhu, M. [43] | 2024 | China | PC | THA | 252 | 118 | 46.83 | ≥ 65 | ②③④⑤⑥ |
| Argun, G. [14] | 2024 | Turkey | PC | THA | 105 | 86 | 81.90 | 76.7 ± 8.8 | - |
| Li, T. [20] | 2024 | China | PC | TJA | 258 | 79 | 30.62 | 67.5 ± 7.6 | ③⑥⑲ |
RC Retrospective cohort study, PC Prospective cohort study, Not mentioned
①age; ②anesthesia time; ③intraoperative blood loss; ④intraoperative infusion volume; ⑤operating room temperature; ⑥surgical time; ⑦gender; ⑧procedure; ⑨anesthesia type; ⑩patient temperature entering operating room; ⑪acute pressure injury; ⑫intraoperative flush volume; ⑬whether active insulation is implemented; ⑭active Hold Duration; ⑮state of mind; ⑯preoperative hemoglobin level; ⑰postoperative hemoglobin level; ⑱postoperative systolic blood pressure; ⑲ASA score
Quality assessment
Table 2 shows the quality assessment. Each article included in this study had an NOS score above 6 points, and the mean NOS score of included studies was 7.81 with a standard deviation of 0.97, indicating that the quality of the studies was very high.
Table 2.
Quality assessment results
| Author | Publication time | Selection | Comparability | Outcome | Score | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| ① | ② | ③ | ④ | ⑤ | ⑥ | ⑦ | ⑧ | |||
| Chu, K. [27] | 2016 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Frisch, N. [28] | 2017 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Matos, J. [29] | 2018 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 7 |
| Scholten, R. [30] | 2018 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Simpson, J. [31] | 2018 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Williams, M. [13] | 2018 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 7 |
| Pan, P. [32] | 2020 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Wang, H. [33] | 2021 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Ukrani, R. [34] | 2021 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Song, Y. [35] | 2022 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Abugri, B. [36] | 2022 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Liu, X. [37] | 2023 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Xin, A. [38] | 2023 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Yan, Y. [39] | 2023 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Li, L. [40] | 2023 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Zhao, B. [19] | 2023 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 6 |
| Qiao, T. [41] | 2024 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 8 |
| Xu, P. [42] | 2024 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Zhu, M. [43] | 2024 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 7 |
| Argun, G. [14] | 2024 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
| Li, T. [20] | 2024 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 |
Meta-analysis
Meta-analysis of the prevalence of IOH
A total of 21 studies were included, including 10 studies in patients with THA, 2 studies in patients with TKA, and 9 studies in patients with TJA. Heterogeneity testing revealed I² = 98.7%, P < 0.001, τ²=0.15, indicating significant statistical heterogeneity among studies. Therefore, a random-effects model was used. Meta-analysis showed that the pooled prevalence of IOH in TJA patients was 35.28% (95% CI: 27.50-43.48%) (Fig. 2). In addition, the pooled prevalence of IOH in THA patients was 38.66% (95% CI: 28.67-49.16%) (Fig. 3); the pooled prevalence of IOH in TKA patients was 27.23% (95% CI: 18.37- 37.11%) (Fig. 4).
Fig. 2.
Meta-analysis of the prevalence of IOH
Fig. 3.
subgroup analysis of the prevalence of IOH in THA
Fig. 4.
subgroup analysis of the prevalence of IOH in TKA
Subgroup analysis
Subgroup analysis by surgical type and geographic region revealed that TJA patients in North America had the highest IOH prevalence (47.06%), followed by Asia (36.53%) and Europe (11.69%) (Fig. 2).
Subgroup analysis by anesthesia type revealed no significant difference in the prevalence of IOH between TJA patients undergoing general anesthesia (35.57%) and those receiving regional anesthesia (31.14%) (Figs. 5 and 6).
Fig. 5.
subgroup analysis of the prevalence of IOH in general anesthesia
Fig. 6.
subgroup analysis of the prevalence of IOH in regional anesthesia
THA Patients: The IOH prevalence was 38.66%, with North America showing the highest rate (45.96%), followed by Asia (41.70%) and Europe (12.74%) (Fig. 3).
TKA Patients: The IOH prevalence was 27.23%, with North America again showing the highest rate (48.56%), followed by Asia (23.58%) and Europe (10.93%) (Fig. 4).
Publication bias
Begg’s test indicated no significant publication bias (P = 0.474). However, Egger’s test (P = 0.001) and the funnel plot suggested some evidence of publication bias (Fig. 7). Therefore, there may be potential publication bias in this study.
Fig. 7.
Funnel plot of standard error by IOH rate
Sensitivity analysis
A leave-one-out sensitivity analysis was performed to assess the influence of individual studies on the overall pooled effect. The IOH prevalence ranged from 27 to 45%, with no substantial changes observed in the combined effect size. This indicates that the meta-analysis results were stable and reliable.
Meta-regression
In this study, the time variable in the meta-regression was defined as the year data collected rather than the year of publication. For studies in which data collection spanned multiple years, the midpoint between the start and end years was used as the representative time point. For example, if a study collected data from 2021 to 2024, the midpoint year would be 2022.5, which was then used in the regression analysis. As shown in Fig. 8, there was no significant association between the IOH prevalence and the median year of data collection (P = 0.098), suggesting no statistical significance.
Fig. 8.
Meta-regression of IOH by the period of data collection
Meta-analysis of the risk factors of IOH. (Table 3)
Table 3.
Results of meta-analysis of the risk factors of IOH
| Risk factors | Number of included studies | Heterogeneity test result | Effect model | Meta-analysis results | |||
|---|---|---|---|---|---|---|---|
| p-value | I2 value (%) | OR | 95%CI | p-value | |||
| Age | 3 | < 0.001 | 89.5 | REM | 2.79 | 0.89–8.80 | 0.079 |
| Anesthesia time | 2 | < 0.001 | 97.5 | REM | 5.39 | 0.21-141.31 | 0.312 |
| Intraoperative blood loss | 6 | < 0.001 | 90.0 | REM | 1.04 | 1.00-1.08 | 0.031 |
| Intraoperative infusion volume | 5 | < 0.001 | 83.1 | REM | 2.42 | 1.12–5.24 | 0.025 |
| Operating room temperature | 6 | < 0.001 | 92.0 | REM | 3.02 | 1.36–6.68 | 0.006 |
| Surgical time | 6 | < 0.001 | 88.2 | REM | 1.20 | 1.04–1.38 | 0.013 |
P values are presented as exact values unless <0.001, following contemporary statistical reporting guidelines; REM is random effects model
Age
Three studies involving a total of 1,121 TJA patients were included. The meta-analysis results showed that age was not a significant risk factor for IOH (OR = 2.79, 95% CI: 0.89–8.80, I² = 89.5%, τ²=0.89). This indicates considerable heterogeneity between the studies. Please refer to Fig. 9a for further details.
Fig. 9.
Meta-analysis of the risk factors of IOH
Anesthesia time
Two studies involving a total of 547 TJA patients were included. The meta-analysis results showed that anesthesia time was not a significant risk factor for IOH (OR = 5.39, 95% CI: 0.21-141.31, I² = 97.5%, τ²=5.42). This indicates high heterogeneity between the studies. Please refer to Fig. 9b for further details.
Intraoperative blood loss
Six studies involving a total of 1531 TJA patients were included. The meta-analysis results showed that intraoperative blood loss was a significant risk factor for IOH (OR = 1.04, 95% CI: 1.00-1.08, I² = 90.0%, τ²=0.01). Current evidence suggests that patients with TJA with intraoperative blood loss have a 1.04-fold increased risk of IOH. Please refer to Fig. 9c for further details.
Intraoperative infusion volume
Five studies involving a total of 663 TJA patients were included. The meta-analysis results showed that intraoperative infusion volume was a significant risk factor for IOH (OR = 2.42, 95% CI: 1.12–5.24, I² = 83.1%, τ²=0.59). Current evidence suggests that patients with TJA with intraoperative infusion volume have a 2.42-fold increased risk of IOH. Please refer to Fig. 9d for further details.
Operating room temperature
Six studies involving a total of 983 TJA patients were included. The meta-analysis results showed that operating room temperature was a significant risk factor for IOH (OR = 3.02, 95% CI: 1.36–6.68, I² = 92.0%, τ²=0.84). Current evidence suggests that patients with TJA with operating room temperature have a 3.02-fold increased risk of IOH. Please refer to Fig. 9e for further details.
Surgical time
Six studies involving a total of 921 TJA patients were included. The meta-analysis results showed that surgical time was a significant risk factor for IOH (OR = 1.20, 95% CI: 1.04–1.38, I² = 88.2%, τ²=0.01). Current evidence suggests that patients with TJA with surgical time have a 1.20-fold increased risk of IOH. Please refer to Fig. 9f for further details.
Sensitivity analyses
The four risk factors associated with IOH in TJA patients were subjected to sensitivity analyses using random-effects and fixed-effects models, and the results showed that the ORs and 95% CIs were relatively close to each other, indicating good stability. See Table 4.
Table 4.
Sensitivity analysis results
| Risk factors | REM | FEM |
|---|---|---|
| OR (95% CI) | OR (95% CI) | |
| Intraoperative blood loss | 1.04 (1.00-1.08) | 1.01 (1.01–1.01) |
| Intraoperative infusion volume | 2.42 (1.12–5.24) | 1.01 (1.00-1.01) |
| Operating room temperature | 3.02 (1.36–6.68) | 1.27 (1.14–1.40) |
| Surgical time | 1.20 (1.04–1.38) | 1.04 (1.02–1.06) |
REM is random effects model; FEM is fixed effects model
Due to the fact that the number of studies analyzing each risk factor was fewer than 10, we did not conduct further analysis (such as meta-regression) on publication bias.
Discussion
This study systematically reviewed published evidence on the prevalence and risk factors of IOH in TJA patients and reported pooled estimates from a meta-analysis. The overall IOH prevalence was 35.28% (95% CI: 27.50-43.48%), notably lower than the 60% reported in Penny’s review of orthopedic surgeries [44]. This difference may stem from the standardized procedures in THA and TKA, which generally involve shorter and more predictable operative durations [45], reducing exposure to low ambient temperatures and, consequently, the risk of IOH. Additionally, inter-patient variability in anesthesia duration may further explain this discrepancy [46].
Subgroup analysis revealed a higher IOH prevalence in THA patients (38.66%) compared to those undergoing TKA (27.23%), consistent with Williams et al. [13] but contrasting with Leijtens et al. [47]. Several factors may explain this discrepancy. First, THA typically involves larger incisions and greater exposure of deep tissues—particularly in minimally invasive approaches—leading to increased heat loss. Second, THA procedures often take longer, especially in complex cases such as hip dysplasia or joint ankylosis, thereby heightening the risk of intraoperative heat dissipation. Additionally, patient positioning and exposed surface area further contribute to IOH risk. THA generally requires lateral or supine positioning with full hip exposure, increasing the surface area for heat loss, whereas TKA involves more localized exposure, primarily around the knee joint [48]– [49].
Subgroup analysis revealed that IOH prevalence was highest in North America, followed by Asia, and lowest in Europe. This variation may reflect regional differences in surgical time, ambient operating room temperature, anesthesia techniques, and perioperative thermal management strategies [50]. Additionally, discrepancies in monitoring methods may contribute to the reported variation. However, few comparative studies have directly examined IOH prevalence across geographic regions, and the underlying causes of these disparities remain unclear. Further research is needed to elucidate the influence of regional factors on IOH prevalence.
Meta-regression analysis indicated no significant association between IOH prevalence and the median year of data collection, suggesting a lack of temporal decline in IOH among TJA patients. However, this non-significant finding must be interpreted cautiously due to limitations in the time variable. The use of median year may obscure short-term fluctuations and assume linearity where nonlinear patterns may exist. Additionally, temporal trends may be confounded by unmeasured factors, such as evolving surgical techniques or warming protocols. Future studies should consider more granular time stratification to better capture potential temporal effects.
Our findings suggest that neither patient age nor anesthesia time is significantly associated with IOH. Although individual studies have identified these variables as potential risk factors, our pooled meta-analysis results revealed no statistically significant associations. This discrepancy may be attributable to substantial heterogeneity among studies, as indicated by large variations in OR values, or to the limited number of included studies, which may have diluted or obscured consistent effect estimates. Therefore, these findings should be interpreted with caution.
Our meta-analysis revealed significant associations between fluid-related variables—intraoperative blood loss and infusion volume—and the prevalence of IOH. These findings align with prior research [51], which indicates that substantial blood loss may decrease circulating volume and impair thermoregulatory capacity, while the administration of large volumes of unwarmed fluids may intensify heat loss, possibly through vasoconstriction-mediated pathways [52]– [53]. Furthermore, excessive fluid infusion can exert a dilutional effect, diminishing oxygen-carrying capacity and coagulation efficiency, thereby compromising tissue oxygenation and hemodynamic stability and accelerating the onset of IOH [54]. Nonetheless, because our analysis did not account for the temperature of infused fluids or the application of warming interventions, these physiological mechanisms remain speculative and warrant validation in future prospective studies.
Translating these findings into clinical practice requires that any extrapolated intervention strategies be supported by external evidence. For instance, the 2023 Chinese Expert Consensus on Perioperative Hypothermia recommends active warming strategies—such as forced-air warming devices—as Class A evidence for maintaining a core temperature of ≥ 36 °C [55]. Similarly, one study reported that fluids exceeding 1,000 mL and refrigerated blood products should be preheated to 37 °C using intravenous infusion heating devices before administration [56]. Although these interventions were not explicitly evaluated in the included studies, their implementation may complement the identified risk factors to optimize IOH prevention.
Among surgery-related risk factors, surgical time and operating room temperature were identified as significant contributors to IOH. Extended surgical time exacerbates the physiological stress and tissue injury associated with anesthesia and surgical trauma, potentially impairing thermoregulatory mechanisms [52]. Additionally, longer surgeries require higher doses of anesthetics and prolonged exposure, both of which contribute to vasodilation and a rapid decline in core temperature, thereby increasing the risk of IOH [52]– [53]. These findings underscore the importance of collaboration between orthopedic surgeons and anesthesiologists to comprehensively assess patient functional status preoperatively, develop precise surgical plans, refine technical skills, streamline intraoperative procedures, and facilitate effective team coordination to optimize surgical time without compromising clinical outcomes. Furthermore, maintaining appropriate ambient temperatures and implementing effective perioperative warming strategies are critical to minimizing the occurrence of IOH.
During surgery, patients are already prone to discomfort such as shivering due to psychological tension, and a low operating room temperature exacerbates this by causing them to inhale cold, dry air, further contributing to body temperature reduction. Operating room temperatures below 21 °C [57] can affect the temperature of fluids administered during surgery. Additionally, during TJA, patients often have significant skin exposure in the surgical environment, and the temperature gradient between the exposed skin and the cool operating room environment leads to convective heat loss, resulting in a continuous drop in body temperature [58]– [59]. Rapid air circulation in the operating room also enhances heat dissipation from the body surface, significantly increasing the risk of hypothermia and ultimately triggering IOH.
As long as the heat generated by metabolism equals the heat lost to the environment, the average body temperature remains stable. However, during surgery, environmental heat loss can be substantial, and general anesthesia may reduce metabolic heat production by approximately 30% [52]. Therefore, maintaining a relatively higher operating room temperature is essential, and the use of warming blankets or coverings can effectively reduce skin heat loss, preventing the onset of cold stress and minimizing the risk of IOH during surgery.
Overall, orthopedic surgeons play a crucial decision-making role in optimizing the operating room temperature and choosing active heating equipment to reduce the risk of IOH. As the surgical leaders, they should do their best to avoid the occurrence of the above risk factors during the operation.
The principal strength of this study lies in its rigorous methodological design, which strictly adhered to PRISMA guidelines and incorporated a comprehensive search across eight English- and Chinese-language databases, enabling the first systematic review and meta-analysis of IOH epidemiology in TJA patients. Nevertheless, several limitations should be acknowledged.
First, the relatively small number of included studies precluded meta-regression analyses for many specific risk factors. Second, for certain variables—such as patient age and anesthesia duration—the limited number of available studies and considerable variability in effect sizes introduced substantial heterogeneity, potentially affecting the reliability of pooled estimates. Third, the predominance of Chinese studies raises concerns about publication and regional biases, which may limit the external validity of the results. Fourth, the meta-analysis of IOH prevalence showed substantial heterogeneity (I² = 98.7%), despite subgroup analysis and meta-regression analysis, the source of heterogeneity still could not be fully explained. While high heterogeneity is frequently observed in prevalence meta-analyses due to genuine differences in populations, surgical practices, and methodological designs [60], its presence limits the precision and generalizability of the pooled prevalence estimate. Additionally, subgroup analysis by surgical approach could not be performed due to insufficient data, highlighting the need for standardized reporting in future research.
Fifth, for certain risk factors such as intraoperative blood loss and infusion volume, cutoff values varied across studies (e.g., 500 mL vs. 1000 mL), and we extracted adjusted ORs and 95% CIs as reported, without reclassifying thresholds. While this approach is methodologically appropriate in the absence of individual patient data, it may contribute to heterogeneity and limit comparability across studies.
Finally, although the included studies originated from various regions, the small number from each country limited our ability to conduct meta-regression or subgroup analyses exploring the influence of socio-cultural or economic factors. Therefore, future large-scale, multicenter studies with standardized reporting are needed to address these limitations and improve generalizability.
Conclusion
This systematic review found that the prevalence of IOH in TJA patients was 35.28%, with rates of 38.66% in THA and 27.23% in TKA. However, from 2016 to 2024, there has been no downward trend in the prevalence of IOH. Given the high heterogeneity observed, these results should be interpreted with caution. Future studies are needed to explore potential sources of heterogeneity. The findings highlight that evaluating intraoperative blood loss, infusion volume, operating room temperature, and surgical time are critical factors in assessing the risk of IOH. Additionally, further investigation is required to clarify the roles of patient age and anesthesia time. Future strategies for preventing IOH in TJA patients should focus on addressing these modifiable risk factors.
Supplementary Information
Acknowledgements
We are deeply grateful for the support and assistance we received during the preparation of this systematic review.
Not applicable.
Clinical trial number
Not applicable.
Authors' contributions
Fan determines the topic, Li and Wang develop the search query and retrieve the literature, Wu and Huang screen the literature, Fan and Li extract the literature data and analyze the data, and Fan and Liu write the paper. All authors reviewed the manuscript.
Funding
None.
Data availability
All data generated or analysed during this study and the material contained are included in this published article.
Declarations
Ethics approval and consent to participate
The review committee of the School of Nursing, Chengdu University of Chinese Medicine approved this study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Sessler DI, Pei L, Li K, et al. Aggressive intraoperative warming versus routine thermal management during non-cardiac surgery (PROTECT): a multicentre, parallel group, superiority trial. Lancet. 2022;399(10337):1799–808. 10.1016/S0140-6736(22)00560-8. [DOI] [PubMed] [Google Scholar]
- 2.Rajagopalan S, Mascha E, Na J, Sessler DI. The effects of mild perioperative hypothermia on blood loss and transfusion requirement. Anesthesiology. 2008;108(1):71–7. 10.1097/01.anes.0000296719.73450.52. [DOI] [PubMed] [Google Scholar]
- 3.Frank SM, Fleisher LA, Breslow MJ, et al. Perioperative maintenance of normothermia reduces the incidence of morbid cardiac events. A randomized clinical trial. JAMA. 1997;277(14):1127–34. [PubMed] [Google Scholar]
- 4.Kurz A, Sessler DI, Lenhardt R. Perioperative normothermia to reduce the incidence of surgical-wound infection and shorten hospitalization. Study of wound infection and temperature group. N Engl J Med. 1996;334(19):1209–15. 10.1056/NEJM199605093341901. [DOI] [PubMed] [Google Scholar]
- 5.Melling AC, Ali B, Scott EM, Leaper DJ. Effects of preoperative warming on the incidence of wound infection after clean surgery: a randomised controlled trial [published correction appears in lancet 2002;359(9309):896]. Lancet. 2001;358(9285):876–80. 10.1016/S0140-6736(01)06071-8. [DOI] [PubMed] [Google Scholar]
- 6.Wong PF, Kumar S, Bohra A, Whetter D, Leaper DJ. Randomized clinical trial of perioperative systemic warming in major elective abdominal surgery. Br J Surg. 2007;94(4):421–6. 10.1002/bjs.5631. [DOI] [PubMed] [Google Scholar]
- 7.Leslie K, Sessler DI, Bjorksten AR, Moayeri A. Mild hypothermia alters Propofol pharmacokinetics and increases the duration of action of atracurium. Anesth Analg. 1995;80(5):1007–14. 10.1097/00000539-199505000-00027. [DOI] [PubMed] [Google Scholar]
- 8.Kurz A, Sessler DI, Narzt E, et al. Postoperative hemodynamic and thermoregulatory consequences of intraoperative core hypothermia. J Clin Anesth. 1995;7(5):359–66. 10.1016/0952-8180(95)00028-g. [DOI] [PubMed] [Google Scholar]
- 9.Lenhardt R, Marker E, Goll V, et al. Mild intraoperative hypothermia prolongs postanesthetic recovery. Anesthesiology. 1997;87(6):1318–23. 10.1097/00000542-199712000-00009. [DOI] [PubMed] [Google Scholar]
- 10.Bock M, Müller J, Bach A, Böhrer H, Martin E, Motsch J. Effects of preinduction and intraoperative warming during major laparotomy. Br J Anaesth. 1998;80(2):159–63. 10.1093/bja/80.2.159. [DOI] [PubMed] [Google Scholar]
- 11.Reynolds L, Beckmann J, Kurz A. Perioperative complications of hypothermia. Best Pract Res Clin Anaesthesiol. 2008;22(4):645–57. 10.1016/j.bpa.2008.07.005. [DOI] [PubMed] [Google Scholar]
- 12.Al-Mulhim FA, Baragbah MA, Sadat-Ali M, Alomran AS, Azam MQ. Prevalence of surgical site infection in orthopedic surgery: a 5-year analysis. Int Surg. 2014 May-Jun;99(3):264–8. 10.9738/INTSURG-D-13-00251.1. [DOI] [PMC free article] [PubMed]
- 13.Williams M, El-Houdiri Y. Inadvertent hypothermia in hip and knee total joint arthroplasty. J Orthop. 2018;15(1):151–8. 10.1016/j.jor.2018.01.035. Published 2018 Jan 20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Argun G, Has Selmi N, Sahin H. Effects of intraoperative body temperature, blood pressure, cerebral tissue oxygenation, and anesthesia type on postoperative cognitive functions in geriatric arthroplasty surgery for hip fracture. Jt Dis Relat Surg. 2024;35(3):662–73. 10.52312/jdrs.2024.1782. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Desgranges FP, Bapteste L, Riffard C, Pop M, Cogniat B, Gagey AC, Boucher P, Bonnard C, Paturel B, Mullet C, Chassard D, Bouvet L. Predictive factors of maternal hypothermia during Cesarean delivery: a prospective cohort study. Can J Anaesth. 2017;64(9):919–27. 10.1007/s12630-017-0912-2. English. [DOI] [PubMed] [Google Scholar]
- 16.Li Y, Liang H, Feng Y. Prevalence and multivariable factors associated with inadvertent intraoperative hypothermia in video-assisted thoracoscopic surgery: a single-center retrospective study. BMC Anesthesiol. 2020;20(1):25. 10.1186/s12871-020-0953-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Stobbe AY, Mertens MJ, Nolte PA, van Stralen KJ. A warm air blanket is superior to a heated mattress in preventing perioperative hypothermia in orthopedic arthroplasties, a Time-Series analyses. J Arthroplasty. 2024;39(2):326–e3312. 10.1016/j.arth.2023.08.039. [DOI] [PubMed] [Google Scholar]
- 18.Özsoy H, Dolgun E. The effect of active warming before total knee arthroplasty on intraoperative body temperature and comfort: A randomized control trial. J Perianesth Nurs Published Online September. 2024;30. 10.1016/j.jopan.2024.06.116. [DOI] [PubMed]
- 19.Zhao B, Zhu Z, Qi W, et al. Construction and validation of a risk prediction model for intraoperative hypothermia in elderly patients undergoing total hip arthroplasty. Aging Clin Exp Res. 2023;35(10):2127–36. 10.1007/s40520-023-02500-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Li T, Chen L, Shi Y, Mao L, Liu Q. Construction of a risk prediction model for intraoperative hypothermia in patients undergoing lower extremity joint replacement. J Perianesth Nurs Published Online June. 2024;13. 10.1016/j.jopan.2024.03.001. [DOI] [PubMed]
- 21.Moher D, Shamseer L, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1. 10.1186/2046-4053-4-1. Published 2015 Jan 1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. 10.1136/bmj.n71. Published 2021 Mar 29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Li T, Higgins JPT, Deeks JJ. Chapter 5: Collecting data [last updated October 2019]. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.5. Cochrane, 2024. Available from cochrane.org/handbook.
- 24.Lipsey MW, Wilson DB. Practical Meta-Analysis. Thousand Oaks, CA: Sage; 2001. [Google Scholar]
- 25.Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5. 10.1007/s10654-010-9491-z. [DOI] [PubMed] [Google Scholar]
- 26.Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T. Meta-analysis of prevalence. J Epidemiol Community Health. 2013;67(11):974–8. 10.1136/jech-2013-203104. [DOI] [PubMed] [Google Scholar]
- 27.Chu K, Pan Y, Shi H. Effect of hypothermia in orthopedic surgery on cognition in patients undergoing hip arthroplasty under general anesthesia. J Clin Res. 2016;33(12):2461–2. 10.3969/j.issn.1671-7171.2016.12.060. [Google Scholar]
- 28.Frisch NB, Pepper AM, Rooney E, Silverton C. Intraoperative hypothermia in total hip and knee arthroplasty. Orthopedics. 2017;40(1):56–63. 10.3928/01477447-20161017-04. [DOI] [PubMed] [Google Scholar]
- 29.Matos JR, McSwain JR, Wolf BJ, Doty JW, Wilson SH. Examination of intra-operative core temperature in joint arthroplasty: a single-institution prospective observational study. Int Orthop. 2018;42(11):2513–9. 10.1007/s00264-018-3967-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Scholten R, Leijtens B, Kremers K, Snoeck M, Koëter S. The incidence of mild hypothermia after total knee or hip arthroplasty: A study of 2600 patients. J Orthop. 2018;15(2):408–11. 10.1016/j.jor.2018.03.014. Published 2018 Mar 17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Simpson JB, Thomas VS, Ismaily SK, Muradov PI, Noble PC, Incavo SJ. Hypothermia in total joint arthroplasty: A Wake-Up call. J Arthroplasty. 2018;33(4):1012–8. 10.1016/j.arth.2017.10.057. [DOI] [PubMed] [Google Scholar]
- 32.Pan P, Song K, Yao Y, Jiang T, Jiang Q. The impact of intraoperative hypothermia on blood loss and allogenic blood transfusion in total knee and hip arthroplasty: A retrospective study. Biomed Res Int. 2020;2020:1096743. 10.1155/2020/1096743. Published 2020 May 3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Wang H. Factors influencing intraoperative hypothermia in elderly hip replacement patients. Mod Diagnosis Treat. 2021;32(11):1801–2. [Google Scholar]
- 34.Ukrani RD, Arif A, Sadruddin A, Hasan O, Noordin S. Intraoperative hypothermia in patients undergoing Total knee arthroplasty: a cross-sectional study from a developing country. BMC Musculoskelet Disord. 2021;22(1):504. Published 2021 May 31. 10.1186/s12891-021-04390-7 [DOI] [PMC free article] [PubMed]
- 35.Song Y, Cao L, Ye Z, Chang L. Research on the influencing factors of acute intraoperative pressure injury in patients undergoing total hip arthroplasty and predictive preventive interventions. Chongqing Med. 2022;51(13):2194–8. 10.3969/j.issn.1671-8348.2022.13.007. [Google Scholar]
- 36.Abugri BO, Matsusaki T, Ren W, Morimatsu H. Intraoperative hypothermia is not associated with surgical site infections after total hip or knee arthroplasty. Acta Med Okayama. 2022;76(6):651–60. 10.18926/AMO/64115. [DOI] [PubMed] [Google Scholar]
- 37.Liu X, Li W, Han Q, Liu D, Yu A. Construction and validation of a risk prediction model for intraoperative hypothermia in patients undergoing total hip arthroplasty. Chin Gen Pract Nurs. 2023;21(10):1388–92. 10.12104/j.issn.1674-4748.2023.10.024. [Google Scholar]
- 38.Xin A. Risk factors and interventions for intraoperative hypothermia in patients with total hip arthroplasty. Jilin Med J. 2023;44(02):496–9. [Google Scholar]
- 39.Yan Y. Risk factors associated with intraoperative hypothermia in hip arthroplasty patients. Chin J Med Device. 2023;36(14):147–9. 10.3969/j.issn.1002-2376.2023.14.047. [Google Scholar]
- 40.Li L, Lu Y, Yang LL, Xu W, Yu JK. Construction and validation of postoperative hypothermia prediction model for patients undergoing joint replacement surgery. J Clin Nurs. 2023;32(13–14):3831–9. 10.1111/jocn.16503. [DOI] [PubMed] [Google Scholar]
- 41.Qiao T, Wu X, Dong P, Lu L. Effect of postoperative hypothermia on hemorheology and immune function in elderly patients undergoing total hip arthroplasty. Chin Evidence-based Nurs. 2024;10(16):2952–6. 10.12102/j.issn.2095-8668.2024.16.021. [Google Scholar]
- 42.Xu P. Analysis of the influencing factors of hypothermia in patients undergoing knee resurfacing during surgery. Med Innov China. 2024;21(9):159–62. 10.3969/j.issn.1674-4985.2024.09.036. [Google Scholar]
- 43.Zhu M, Zhang H, Yang M. Construction of intraoperative hypothermia risk prediction model for elderly hip replacement patients. J Jinan University(Natural Sci Med Edition). 2024;45(2):160–7. 10.11778/j.jdxb.20230185. [Google Scholar]
- 44.Heidke P, Duff J, Keogh S, Munday J. Barriers and facilitators to evidence-based perioperative hypothermia management for orthopaedic patients: A systematic review. J Clin Nurs. 2024;33(9):3329–54. 10.1111/jocn.17338. [DOI] [PubMed] [Google Scholar]
- 45.Edwards J, Clement ND, Afzal I, Jones S, Sochart DH. Cancellation and short postponement of primary hip or knee arthroplasty does not influence postoperative joint specific function or health related quality of life. Eur J Orthop Surg Traumatol. 2024;34(8):3973–9. 10.1007/s00590-024-04088-1. [DOI] [PubMed] [Google Scholar]
- 46.Yi J, Xiang Z, Deng X, et al. Incidence of inadvertent intraoperative hypothermia and its risk factors in patients undergoing general anesthesia in beijing: A prospective regional survey. PLoS ONE. 2015;10(9):e0136136. 10.1371/journal.pone.0136136. Published 2015 Sep 11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Leijtens B, Koëter M, Kremers K, Koëter S. High incidence of postoperative hypothermia in total knee and total hip arthroplasty: a prospective observational study. J Arthroplasty. 2013;28(6):895–8. 10.1016/j.arth.2012.10.006. [DOI] [PubMed] [Google Scholar]
- 48.Choi YS, Kim TW, Chang MJ, Kang SB, Chang CB. Enhanced recovery after surgery for major orthopedic surgery: a narrative review. Knee Surg Relat Res. 2022;34(1):8. 10.1186/s43019-022-00137-3. Published 2022 Feb 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Joint replacement. (primary): hip, knee and shoulder. London: National Institute for Health and Care Excellence (NICE); June 4, 2020. [PubMed]
- 50.Ward MM. Regional variation in surgical procedure rates: going beyond description. JAMA Surg. 2022;157(2):91–2. 10.1001/jamasurg.2021.4892. [DOI] [PubMed] [Google Scholar]
- 51.Chen IW, Wang WT, Hung KC. Association between intraoperative hypothermia and postoperative delirium: a preliminary meta-analysis. Syst Rev. 2024;13(1):248. 10.1186/s13643-024-02669-z. Published 2024 Sep 30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Sessler DI. Perioperative thermoregulation and heat balance. Lancet. 2016;387(10038):2655–64. 10.1016/S0140-6736(15)00981-2. [DOI] [PubMed] [Google Scholar]
- 53.Sun Z, Honar H, Sessler DI, et al. Intraoperative core temperature patterns, transfusion requirement, and hospital duration in patients warmed with forced air. Anesthesiology. 2015;122(2):276–85. 10.1097/ALN.0000000000000551. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Pit MJ, Tegelaar RJ, Venema PL. Isothermic irrigation during transurethral resection of the prostate: effects on peri-operative hypothermia, blood loss, resection time and patient satisfaction. Br J Urol. 1996;78(1):99–103. 10.1046/j.1464-410x.1996.04819.x. [DOI] [PubMed] [Google Scholar]
- 55.Gu X, Yi J, Pei L. Expert consensus on prevention and treatment of perioperative hypothermia. J Clin Anesthesiology 2023,39(07):764–71. 10.12089/jca.2023.07.018
- 56.Roxby D, Sobieraj-Teague M, von Wielligh J, Sinha R, Kuss B, Smith AL, McEwen M. Warming blood prior to transfusion using latent heat. Emerg Med Australas. 2020;32(4):604–10. 10.1111/1742-6723.13471. [DOI] [PubMed] [Google Scholar]
- 57.Hypothermia. Prevention and management in adults having surgery. London: National Institute for Health and Care Excellence (NICE); December 2016. [PubMed]
- 58.Matsukawa T, Sessler DI, Sessler AM, et al. Heat flow and distribution during induction of general anesthesia. Anesthesiology. 1995;82(3):662–73. 10.1097/00000542-199503000-00008. [DOI] [PubMed] [Google Scholar]
- 59.Frank SM, Raja SN, Bulcao CF, Goldstein DS. Relative contribution of core and cutaneous temperatures to thermal comfort and autonomic responses in humans. J Appl Physiol (1985). 1999;86(5):1588–93. 10.1152/jappl.1999.86.5.1588. [DOI] [PubMed] [Google Scholar]
- 60.Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. 2011;342:d549. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data generated or analysed during this study and the material contained are included in this published article.