Preoperative Serum Creatinine as an Independent Predictor of Intraoperative Hypothermia: Evidence from a Retrospective Cohort Study

Preoperative Serum Creatinine as an Independent Predictor of Intraoperative Hypothermia: Evidence from a Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preoperative Serum Creatinine as an Independent Predictor of Intraoperative Hypothermia: Evidence from a Retrospective Cohort Study Man Dai, Dandan Qi, Xiaoting Shi, Tianyang Jiang, Shaosheng Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8043150/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Intraoperative hypothermia (IOH) is closely associated with various intraoperative and postoperative adverse events, and identifying high-risk patients preoperatively remains a significant challenge. In recent years, the proportion of surgical patients with renal insufficiency has been increasing, and their metabolic and thermoregulatory abnormalities contribute to a higher risk of IOH. This study aimed to investigate the predictive value of preoperative serum creatinine levels for intraoperative hypothermia, providing insight for risk assessment and preventive strategies. Methods This retrospective cohort study extracted data from 4,717 adult surgical patients in the Vital DB public database. The main exposure variable was preoperative serum creatinine, and the primary outcome was IOH, defined as a core temperature below 36.0°C at any intraoperative time point. A multivariable weighted logistic regression model was applied to evaluate the association between serum creatinine (as both a continuous and quartile-categorized variable) and IOH, adjusting for age, sex, ASA classification, surgery type, and intraoperative factors. Restricted cubic spline analysis was conducted to test for nonlinearity, while an XGBoost model with SHAP values was used to assess feature importance. Results Among all patients, the incidence of IOH was 96.2%. After adjustment, preoperative serum creatinine levels were significantly associated with IOH risk (adjusted odds ratio [aOR] = 3.44, 95% CI: 1.51–8.65). Compared with the lowest quartile group (Q1), patients in the highest quartile (Q4) showed a notably higher risk of IOH (aOR = 1.77, 95% CI: 1.09–2.94). The RCS analysis demonstrated a linear dose–response relationship (P for nonlinearity = 0.972). The XGBoost model confirmed serum creatinine as a key predictor with strong predictive value. Conclusion Elevated preoperative serum creatinine is an independent risk factor for IOH, exhibiting a linear positive association. This biomarker may serve as a practical preoperative tool for identifying high-risk IOH patients and guiding personalized temperature management strategies. Intraoperative Hypothermia Preoperative Serum Creatinine Vital Db Risk Factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Intraoperative hypothermia (IOH), defined as a core temperature below 36.0°C, is a common complication during the perioperative period【1,2,3】. Despite notable advancements in temperature management, its incidence remains high—approximately 65% of surgical patients develop hypothermia within the first hour of surgery【4】. IOH is not a benign condition; it has been strongly associated with several adverse outcomes, including coagulation dysfunction【5】, increased risk of surgical site infection【6,7】, delayed drug metabolism【8,9】, and prolonged hospital stay【10】. Consequently, preventing IOH has become a crucial component of perioperative care.【11】 Effective preventive strategies, such as active prewarming, can yield maximal benefit when precisely targeted to high-risk patients, improving outcomes while optimizing healthcare resources【12,13】. However, most existing IOH prediction models depend on intraoperative variables (e.g., surgery duration, fluid infusion volume) 【14,15】, which are unavailable before surgery and therefore limit clinical applicability. Recently, growing attention has been given to preoperative laboratory indicators as potential predictors. Previous studies have demonstrated associations between IOH risk and preoperative hemoglobin or albumin levels 【16,17】; nevertheless, serum creatinine—a core indicator of renal function—has not been systematically investigated in this context. From a pathophysiological perspective, renal dysfunction indicated by elevated serum creatinine may impair thermoregulation through multiple mechanisms. Accumulated uremic toxins can directly suppress hypothalamic thermoregulatory centers【18,19】. Chronic low-grade inflammation, commonly seen in renal impairment, may reduce basal metabolic rate and heat production 【20】. Furthermore, autonomic dysfunction, prevalent among patients with kidney disease, can disrupt vasomotor responses essential for maintaining thermal stability 【21,22】. Importantly, preoperative renal impairment has been identified as an independent risk factor for various perioperative complications, including postoperative cognitive dysfunction 【23,24】, suggesting its broader involvement in perioperative physiological dysregulation. Based on this rationale, we hypothesize that preoperative serum creatinine serves as an independent predictor of intraoperative hypothermia. To test this hypothesis, we utilized the large public Vital DB database 【25】and employed multivariable logistic regression, restricted cubic spline (RCS) analysis, and the XGBoost machine learning model. This study aims to systematically explore the association between preoperative serum creatinine and IOH, providing a novel, convenient, and clinically practical biomarker for identifying high-risk patients and guiding individualized perioperative temperature management strategies. 1. Materials and Methods 1.1 Data Selection Vital DB (Vital Signs Database) is an open-access dataset established to support machine learning research on perioperative vital sign monitoring. The data were collected from patients who underwent elective or emergency non-cardiac surgeries—including general, thoracic, urologic, and gynecologic procedures—at Seoul National University Hospital, Korea, between August 2016 and June 2017. The dataset contains high-resolution, multi-parameter information from 6,388 surgical cases, encompassing 486,451 waveform and numeric data traces across 196 monitoring variables, 73 clinical parameters, and 34 time-series laboratory indicators. All data were fully anonymized and securely stored on a public cloud platform. Researchers can freely access and analyze the dataset using an application programming interface (API) and an official Python library. Overall, the Vital DB public dataset serves as a valuable resource for advancing biomedical signal research and methodological development in perioperative care. 1.2 Data Preprocessing We utilized the VitalUtils software, officially provided by the Vital DB platform, to extract intraoperative data from the Vital DB database at 1-minute intervals. This process ensured that both the start and end time points of surgery were accurately identified for each patient. Patients whose recorded core temperature dropped below 36.0°C at any time between the start and end of surgery were defined as experiencing IOH. In certain cases, data sampled at 1-minute intervals failed to capture one or both surgical time points. To address this issue and improve data reliability, we reprocessed these cases using a 10-second interval, which successfully retrieved complete time-point information for all patients. From the initially extracted 6,388 cases, exclusions were made for the following reasons: (1) absence of intraoperative temperature data (n = 1,347); (2) age below 18 years (n = 50); (3) ASA physical status classification of 5 or 6 (n = 104); and (4) missing preoperative serum creatinine values (n = 170). After exclusions, the final study cohort consisted of 4,717 participants, including 4,536 patients with IOH and 181 normothermic patients (Figure 1). Figure 1 1.3 Covariates For each patient, clinical characteristics, hemodynamic parameters, and laboratory indicators were collected. All variables were categorized as either categorical or continuous. Categorical variables included sex, ASA physical status classification, surgical department, surgical approach, and type of anesthesia. Continuous variables included age, ICU length of stay, height, weight, body mass index (BMI), presence of hypertension or diabetes mellitus, preoperative hemoglobin, platelet count, thrombin time, activated partial thromboplastin time (APTT), serum creatinine, serum potassium, blood glucose, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen (BUN), estimated blood loss, intraoperative urine output, red blood cell transfusion, fresh frozen plasma (FFP) transfusion, crystalloid and colloid infusion volumes, and total anesthesia duration. 1.4 Statistical Analysis All statistical analyses were conducted using R software (version 4.1.1). Continuous variables that did not follow a normal distribution were expressed as medians with interquartile ranges (IQRs), while categorical variables were summarized as counts and weighted percentages. Group comparisons were performed using the Wilcoxon rank-sum test for continuous variables and the Rao–Scott chi-square test for categorical variables. Weighted binary logistic regression models were employed to evaluate the association between preoperative serum creatinine (analyzed both as a continuous variable and by quartiles) and intraoperative hypothermia, with results reported as odds ratios (ORs) and 95% confidence intervals (CIs). Three multivariable models were constructed: Model 1 (unadjusted); Model 2 (adjusted for age, sex, ASA physical status, surgical department, surgical approach, and hypertension); and Model 3 (further adjusted for preoperative platelet count, glucose, albumin, estimated blood loss, intraoperative urine output, intraoperative crystalloid and colloid infusions, and anesthesia duration). Potential nonlinear relationships were explored using restricted cubic spline (RCS) analysis. Machine learning analysis was performed using the XGBoost algorithm, with feature importance interpreted through SHAP (Shapley Additive Explanations) values. Subgroup and interaction analyses were conducted according to sex, age, hypertension, diabetes status, and anesthesia duration. A two-sided P value < 0.05 was considered statistically significant, and selected results were corrected for false discovery rate (FDR). 2. Results 2.1 Baseline characteristics of the sample A total of 4,717 samples were included in the study, and various baseline characteristics were compared between the normothermic and hypothermic groups. Significant differences were observed between the two groups for several baseline variables, including age (P-value < 0.001), sex (P-value < 0.001), ASA classification (P-value = 0.048), department (P-value < 0.001), surgical approach (P-value < 0.001), preoperative hypertension (P-value = 0.018), preoperative platelet count (P-value = 0.019), preoperative glucose (P-value = 0.02), preoperative albumin (P-value < 0.001), preoperative blood urea nitrogen (P-value < 0.001), preoperative creatinine (P-value < 0.001), estimated blood loss (P-value = 0.002), intraoperative urine output (P-value = 0.015), intraoperative crystalloid infusion (P-value = 0.002), intraoperative colloid infusion (P-value = 0.005), and anesthesia duration (P-value = 0.003). However, no significant differences were found between the two groups regarding postoperative ICU stay duration (P-value = 0.226), height (P-value = 0.678), weight (P-value = 0.162), BMI (P-value = 0.133), type of anesthesia (P-value > 0.999), preoperative diabetes (P-value = 0.182), preoperative hemoglobin (P-value = 0.396), preoperative prothrombin time (P-value = 0.058), preoperative aPTT (P-value = 0.392), preoperative sodium (P-value = 0.257), preoperative potassium (P-value = 0.144), preoperative GOT (P-value = 0.666), preoperative GPT (P-value = 0.36), intraoperative red blood cell transfusion (P-value = 0.063), and intraoperative FFP transfusion (P-value = 0.131) (Table 1). 2.2 The relationship between intraoperative hypothermia and preoperative blood creatinine . In this study, we examined the relationship between preoperative serum creatinine levels and the risk of IOH using multivariable logistic regression analysis, as summarized in Table 2. In the unadjusted model (Model 1), preoperative serum creatinine, treated as a continuous variable, was significantly associated with IOH, with an odds ratio (OR) of 3.7 (95% CI: 1.817–8.157, P-value < 0.001). Subgroup analysis based on quartiles showed that, compared to the Q1 group (<0.66 mg/dL), the OR for the Q3 group (0.78–<0.94 mg/dL) was 1.68 (95% CI: 1.103–2.59, P-value = 0.0168), and the OR for the Q4 group (≥0.94 mg/dL) was 1.991 (95% CI: 1.28–3.159, P-value = 0.0027), with a trend test P-value of <0.001, indicating that the risk of IOH increased with higher serum creatinine levels. In Model 2, after adjusting for age, sex, ASA classification, department, surgical approach, and preoperative hypertension, the OR for preoperative serum creatinine (continuous variable) was 3.164 (95% CI: 1.48–7.5, P-value = 0.0065). In this model, subgroup analysis revealed that the Q3 group (OR = 1.629, 95% CI: 1.04–2.583, P-value = 0.035) and the Q4 group (OR = 1.771, 95% CI: 1.102–2.893, P-value = 0.02) remained significantly associated with the risk of IOH, with a trend P-value of 0.0034. In Model 3, after further adjusting for preoperative platelet count, glucose, albumin, estimated blood loss, intraoperative urine output, crystalloid infusion, colloid infusion, and anesthesia duration, the OR for preoperative serum creatinine (continuous variable) was 3.442 (95% CI: 1.51–8.653, P-value = 0.0064). Subgroup analysis in this model showed that the Q3 group (OR = 1.664, 95% CI: 1.051–2.665, P-value = 0.0315) and the Q4 group (OR = 1.774, 95% CI: 1.09–2.935, P-value = 0.0229) still exhibited a significant association with the risk of IOH, with a trend P-value of 0.0045. In conclusion, elevated preoperative serum creatinine levels were significantly associated with an increased risk of ioh, regardless of the degree of covariate adjustment. 2.3 IOH and the RCS curve of preoperative blood creatinine. The relationship between preoperative serum creatinine and IOH was examined using restricted cubic spline (RCS), adjusting for all relevant covariates. The results showed that there was no statistically significant nonlinear relationship between preoperative serum creatinine and IOH (P for nonlinearity = 0.972, Figure 2). 2.4 Machine learning analysis of IOH-related factors based on the XGBoost model. This study utilized the XGBoost model to perform machine learning analysis of factors associated with IOH. Figure 3A displays the mean absolute SHAP values for each factor. The average | SHAP value | for serum creatinine is 0.00128, which is a relatively high compared to other factors, indicating that serum creatinine made a significant contribution to the model's predictions. Figure 3B is a SHAP scatter plot that shows the relationship between each sample's SHAP value and its corresponding feature value. For serum creatinine, its SHAP values are distributed between -0.15 and 0.0, with point color indicating the feature value of serum creatinine: orange represents higher feature values, and purple represents lower values. As shown in the figure, orange points (higher feature values) for serum creatinine are more concentrated in the region with positive SHAP values. Interpreting the SHAP values, within the model framework, it suggests that higher serum creatinine feature values tend to increase the predicted risk or likelihood of IOH. This indicates a potential association between higher serum creatinine levels and an increased incidence of IOH according to the model's analysis. 2.5 The relationship between preoperative serum creatinine and baseline characteristics across subgroup . In the subgroup analysis, several variables were assessed, including sex, hypertension, diabetes, age, and anesthesia duration. The analysis showed no significant interactions between these subgroups (P for interaction > 0.05), as shown in Figure 4. This further confirms that the association between preoperative serum creatinine and IOH in patients remains consistent across subgroups. 3. Discussion This study, through a retrospective analysis of 4717 surgery patients, is the first to confirm in a large population that elevated preoperative serum creatinine levels are an independent risk factor for IOH. After sufficient multivariable adjustment, this association remained robust, and restricted cubic spline analysis revealed a linear dose-response relationship between the two, indicating that the higher the serum creatinine level are associated with an increased risk of IOH. This finding establishes renal insufficiency as an important and measurable precursor to perioperative thermoregulatory disturbances. We believe that there is a reasonable physiological and pathological mechanism behind this association. Serum creatinine, as a surrogate indicator of renal function, often reflects impaired renal excretory function and potential endocrine or metabolic dysfunction when elevated【26,27】. Uremic toxins accumulated in patients with renal insufficiency may directly affect the thermoregulatory center in the hypothalamus, altering its set point【18,19】. At the same time, the common chronic inflammatory state in these patients, characterized by elevated levels of cytokines such as IL-6,may lead to reduced resting energy expenditure, thereby decreasing endogenous heat production【28,29】. In addition, autonomic neuropathy is a common complication of renal insufficiency, which can impair the body's vasoconstriction response to cold stimuli, a key defense mechanism for maintaining core body temperature 【30,31】. The linear positive correlation observed in this study further supports the hypothesis that the more severe the renal dysfunction, the more significant the impairment of thermoregulation ability. Our research findings align with the existing literature that focusing on perioperative thermoregulatory risk factors. For example, both our study and the work of Huang et al. found that hypoalbuminemia is a risk factor for IOH 【32】, which may be associated with colloid osmotic pressure, nutrition status, and levels of inflammation. This study also found that advanced age and longer surgical duration are associated with IOH, which is consistent with established clinical understanding【33,34】. However, our findings regarding the relationship between gender and IOH (with higher risk in males) differ from some studies 【35,36,37】, This may be due to the older average age in our study population, where age-related hormonal changes may have mediated the gender differences. It is particularly noteworthy that this study shifts its focus from intraoperative and postoperative indicators to a routine and easily accessible preoperative laboratory marker—serum creatinine. This addresses the gap in the clinical practicality of existing predictive models, offering the possibility of risk stratification before anesthesia induction. We observed that the overall incidence of IOH in our study cohort was as high as 96.2%. This unusually high rate may be attributed to the completeness of vital signs records in the database (which may preferentially include surgeries with comprehensive monitoring) or to our broader definition of "core temperature <36.0°C at any time point. Although the high incidence may somewhat limit the model's ability to identify individuals at very low risk, it highlights the widespread challenges of temperature management within this population. Against this backdrop, serum creatinine still demonstrates significant predictive value and high SHAP importance, further emphasizing its robustness and potential utility as a risk stratification tool. The clinical translational value of this study lies in the fact that serum creatinine can serve as a decision-making tool for preoperative optimization and resource allocation. Based on our quartile analysis results, we recommend using a preoperative serum creatinine > 0.78 mg/dL as a practical risk stratification threshold. For such high-risk patients, more aggressive temperature protection strategies should be implemented, such as initiating forced air warming system before anesthesia induction and increasing the frequency of temperature monitoring during surgery. The limitations of this study primarily include: first, as a single-center retrospective study, there may be unmeasured confounding factors, and the generalizability of the conclusions needs further validation. Second, we were unable to obtain more precise renal function assessment indicators such as estimated glomerular filtration rate (eGFR) for comparison. Future research should focus on: conducting multi-center prospective cohorts to validate our findings; further exploring the specific impact of temperature management on the postoperative recovery quality in patients with renal insufficiency; and developing comprehensive predictive model integrating multiple indicators such as serum creatinine, albumin, and age, with the goal of achieving more precise, personalized perioperative management. 4. Conclusions Elevated preoperative serum creatinine is an independent predictor of intraoperative hypothermia. A preoperative serum creatinine level greater than 0.78 mg/dL is recommended as a risk threshold, and enhanced temperature management should be implemented for high-risk patients. Abbreviations IOH: Intraoperative hypothermia, BMI: body mass index, APTT: activated partial thromboplastin time, AST: aspartate aminotransferase, ALT: alanine aminotransferase, BUN: blood urea nitrogen, FFP: fresh frozen plasma, IQRs: interquartile ranges, ORs: odds ratios, CIs: confidence intervals, RCS: restricted cubic spline. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Clinical Trial Number: Not applicable Human Ethics and Consent to Participate declarationgs: Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests" in this section. Funding no specific funding Authors' contributions Man Dai: Conceptualization, Writing – original draft, Writing – Review & Editing Dandan Qi: Methodology, Literature Search, Writing – Review & Editing Xiaoting Shi : Formal Analysis, Technical Support, Writing – Review & Editing Tianyang Jiang : Technical Support, Literature Search, Writing – Review & Editing Shaosheng Wu: Formal Analysis, Technical Support, Writing – Review & Editing Xiaoman Bai: Conceptualization, Writing – Original Draft, Literature Search, Formal Analysis, Writing – Review & Editing Acknowledgements Not applicable References Moellhoff N, Broer PN, Heide Krueger PI, Ninkovic M, Ehrl D. Impact of Intraoperative Hypothermia on Microsurgical Free Flap Reconstructions. J Reconstr Microsurg. 2021; 37(2):174-180. Doi: 10.1055/s-0040-1715880 https://pubmed.ncbi.nlm.nih.gov/32862415/ Vaughan MS, Vaughan RW, Cork RC. Postoperative hypothermia in adults: relationship of age, anesthesia, and shivering to rewarming. 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Development and evaluation of a novel predictive nomogram for assessing the risk of intraoperative hypothermia in patients undergoing thoracoscopic pulmonary tumor surgery. Heliyon. 2023;9(12): e22574. Published 2023 Dec. doi: 10.1016/j.heliyon. 2023.e22574 https://pubmed.ncbi.nlm.nih.gov/38090000/ Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table12.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 May, 2026 Reviewers agreed at journal 20 May, 2026 Reviews received at journal 18 May, 2026 Reviewers agreed at journal 16 May, 2026 Reviewers agreed at journal 16 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviews received at journal 18 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 04 Dec, 2025 Editor invited by journal 12 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 05 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8043150","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556508271,"identity":"eec13972-3483-41d8-a38f-b7cd9543f788","order_by":0,"name":"Man Dai","email":"","orcid":"","institution":"The First People's Hospital of Changde City","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Dai","suffix":""},{"id":556508272,"identity":"7a5dae12-1923-49a8-8df9-c513d15b7fda","order_by":1,"name":"Dandan Qi","email":"","orcid":"","institution":"The Second Hospital of Tianjin Medical 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1","display":"","copyAsset":false,"role":"figure","size":258909,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8043150/v1/6f7a4f5482a2ea5e166cdf50.png"},{"id":97744457,"identity":"66c4d5d5-f999-4e74-8e32-834db4721d42","added_by":"auto","created_at":"2025-12-09 00:22:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127390,"visible":true,"origin":"","legend":"\u003cp\u003eRCS curve of IOH and preoperative serum creatinine\u003cbr\u003e\n Covariates: age, sex, ASA classification, department, surgical modality, preoperative hypertension, preoperative platelet count, glucose, albumin, estimated blood loss, intraoperative urine output, crystalloid fluid, colloid fluid, and anesthesia time.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8043150/v1/3e2e8ceafb9020e3c421360e.png"},{"id":97895149,"identity":"bb17c6f7-ff10-4214-92da-7c90b8a675ed","added_by":"auto","created_at":"2025-12-10 15:33:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":273493,"visible":true,"origin":"","legend":"\u003cp\u003eThe machine learning model is illustrated through SHAP summary plots and dependence plots. (A, B) The importance matrix and SHAP summary plot show the contribution of all variables to the XGBoost model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8043150/v1/cef4b6bce32637658fe5c5e9.png"},{"id":97895672,"identity":"574dbcfd-4910-45b4-bea4-fb6e99438b90","added_by":"auto","created_at":"2025-12-10 15:34:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":289612,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between preoperative serum creatinine and IOH across subgroups of baseline characteristics.\u003c/p\u003e\n\u003cp\u003eNote: Q1: preop_cr\u0026lt;0.66, Q2: 0.66 ≤ preop_cr\u0026lt;0.78, Q3: 0.78 ≤ preop_cr\u0026lt;0.94, Q4: preop_cr≥0.94Covariates: preoperative platelet count, glucose, albumin, estimated blood loss, intraoperative urine output, crystalloid, colloid.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8043150/v1/664c0d39631bab4078e1b718.png"},{"id":97902686,"identity":"cc6d2858-2f53-4c12-aec0-7e644468e14c","added_by":"auto","created_at":"2025-12-10 15:53:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1574735,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8043150/v1/3ba8bb8d-8e10-4617-b2a0-088cf17fa7b1.pdf"},{"id":97895697,"identity":"f650da39-5759-4a56-a63e-7e61755fd39f","added_by":"auto","created_at":"2025-12-10 15:34:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1184730,"visible":true,"origin":"","legend":"","description":"","filename":"Table12.docx","url":"https://assets-eu.researchsquare.com/files/rs-8043150/v1/3a9144deaf6cb3e560a12c02.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative Serum Creatinine as an Independent Predictor of Intraoperative Hypothermia: Evidence from a Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIntraoperative hypothermia (IOH), defined as a core temperature below 36.0\u0026deg;C, is a common complication during the perioperative period【1,2,3】. Despite notable advancements in temperature management, its incidence remains high\u0026mdash;approximately 65% of surgical patients develop hypothermia within the first hour of surgery【4】. IOH is not a benign condition; it has been strongly associated with several adverse outcomes, including coagulation dysfunction【5】, increased risk of surgical site infection【6,7】, delayed drug metabolism【8,9】, and prolonged hospital stay【10】. Consequently, preventing IOH has become a crucial component of perioperative care.【11】\u003c/p\u003e\u003cp\u003eEffective preventive strategies, such as active prewarming, can yield maximal benefit when precisely targeted to high-risk patients, improving outcomes while optimizing healthcare resources【12,13】. However, most existing IOH prediction models depend on intraoperative variables (e.g., surgery duration, fluid infusion volume) 【14,15】, which are unavailable before surgery and therefore limit clinical applicability. Recently, growing attention has been given to preoperative laboratory indicators as potential predictors. Previous studies have demonstrated associations between IOH risk and preoperative hemoglobin or albumin levels 【16,17】; nevertheless, serum creatinine\u0026mdash;a core indicator of renal function\u0026mdash;has not been systematically investigated in this context.\u003c/p\u003e\u003cp\u003eFrom a pathophysiological perspective, renal dysfunction indicated by elevated serum creatinine may impair thermoregulation through multiple mechanisms. Accumulated uremic toxins can directly suppress hypothalamic thermoregulatory centers【18,19】. Chronic low-grade inflammation, commonly seen in renal impairment, may reduce basal metabolic rate and heat production 【20】. Furthermore, autonomic dysfunction, prevalent among patients with kidney disease, can disrupt vasomotor responses essential for maintaining thermal stability 【21,22】. Importantly, preoperative renal impairment has been identified as an independent risk factor for various perioperative complications, including postoperative cognitive dysfunction 【23,24】, suggesting its broader involvement in perioperative physiological dysregulation.\u003c/p\u003e\u003cp\u003eBased on this rationale, we hypothesize that preoperative serum creatinine serves as an independent predictor of intraoperative hypothermia. To test this hypothesis, we utilized the large public Vital DB database 【25】and employed multivariable logistic regression, restricted cubic spline (RCS) analysis, and the XGBoost machine learning model. This study aims to systematically explore the association between preoperative serum creatinine and IOH, providing a novel, convenient, and clinically practical biomarker for identifying high-risk patients and guiding individualized perioperative temperature management strategies.\u003c/p\u003e"},{"header":"1. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Data Selection\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Vital DB (Vital Signs Database) is an open-access dataset established to support machine learning research on perioperative vital sign monitoring. The data were collected from patients who underwent elective or emergency non-cardiac surgeries\u0026mdash;including general, thoracic, urologic, and gynecologic procedures\u0026mdash;at Seoul National University Hospital, Korea, between August 2016 and June 2017. The dataset contains high-resolution, multi-parameter information from 6,388 surgical cases, encompassing 486,451 waveform and numeric data traces across 196 monitoring variables, 73 clinical parameters, and 34 time-series laboratory indicators. All data were fully anonymized and securely stored on a public cloud platform. Researchers can freely access and analyze the dataset using an application programming interface (API) and an official Python library. Overall, the Vital DB public dataset serves as a valuable resource for advancing biomedical signal research and methodological development in perioperative care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Data Preprocessing\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;We utilized the VitalUtils software, officially provided by the Vital DB platform, to extract intraoperative data from the Vital DB database at 1-minute intervals. This process ensured that both the start and end time points of surgery were accurately identified for each patient. Patients whose recorded core temperature dropped below 36.0\u0026deg;C at any time between the start and end of surgery were defined as experiencing IOH.\u003c/p\u003e\n\u003cp\u003eIn certain cases, data sampled at 1-minute intervals failed to capture one or both surgical time points. To address this issue and improve data reliability, we reprocessed these cases using a 10-second interval, which successfully retrieved complete time-point information for all patients.\u003c/p\u003e\n\u003cp\u003eFrom the initially extracted 6,388 cases, exclusions were made for the following reasons: (1) absence of intraoperative temperature data (n = 1,347); (2) age below 18 years (n = 50); (3) ASA physical status classification of 5 or 6 (n = 104); and (4) missing preoperative serum creatinine values (n = 170). After exclusions, the final study cohort consisted of 4,717 participants, including 4,536 patients with IOH \u0026nbsp;and 181 normothermic patients (Figure 1).\u003c/p\u003e\n\u003cp\u003eFigure 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Covariates\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;For each patient, clinical characteristics, hemodynamic parameters, and laboratory indicators were collected. All variables were categorized as either categorical or continuous.\u003c/p\u003e\n\u003cp\u003eCategorical variables included sex, ASA physical status classification, surgical department, surgical approach, and type of anesthesia. Continuous variables included age, ICU length of stay, height, weight, body mass index (BMI), presence of hypertension or diabetes mellitus, preoperative hemoglobin, platelet count, thrombin time, activated partial thromboplastin time (APTT), serum creatinine, serum potassium, blood glucose, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen (BUN), estimated blood loss, intraoperative urine output, red blood cell transfusion, fresh frozen plasma (FFP) transfusion, crystalloid and colloid infusion volumes, and total anesthesia duration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Statistical Analysis\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All statistical analyses were conducted using R software (version 4.1.1). Continuous variables that did not follow a normal distribution were expressed as medians with interquartile ranges (IQRs), while categorical variables were summarized as counts and weighted percentages. Group comparisons were performed using the Wilcoxon rank-sum test for continuous variables and the Rao\u0026ndash;Scott chi-square test for categorical variables.\u003c/p\u003e\n\u003cp\u003eWeighted binary logistic regression models were employed to evaluate the association between preoperative serum creatinine (analyzed both as a continuous variable and by quartiles) and intraoperative hypothermia, with results reported as odds ratios (ORs) and 95% confidence intervals (CIs). Three multivariable models were constructed: Model 1 (unadjusted); Model 2 (adjusted for age, sex, ASA physical status, surgical department, surgical approach, and hypertension); and Model 3 (further adjusted for preoperative platelet count, glucose, albumin, estimated blood loss, intraoperative urine output, intraoperative crystalloid and colloid infusions, and anesthesia duration).\u003c/p\u003e\n\u003cp\u003ePotential nonlinear relationships were explored using restricted cubic spline (RCS) analysis. Machine learning analysis was performed using the XGBoost algorithm, with feature importance interpreted through SHAP (Shapley Additive Explanations) values. Subgroup and interaction analyses were conducted according to sex, age, hypertension, diabetes status, and anesthesia duration. A two-sided P value \u0026lt; 0.05 was considered statistically significant, and selected results were corrected for false discovery rate (FDR).\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Baseline characteristics of the sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 4,717 samples were included in the study, and various baseline characteristics were compared between the normothermic and hypothermic groups. Significant differences were observed between the two groups for several baseline variables, including age (P-value \u0026lt; 0.001), sex (P-value \u0026lt; 0.001), ASA classification (P-value = 0.048), department (P-value \u0026lt; 0.001), surgical approach (P-value \u0026lt; 0.001), preoperative hypertension (P-value = 0.018), preoperative platelet count (P-value = 0.019), preoperative glucose (P-value = 0.02), preoperative albumin (P-value \u0026lt; 0.001), preoperative blood urea nitrogen (P-value \u0026lt; 0.001), preoperative creatinine (P-value \u0026lt; 0.001), estimated blood loss (P-value = 0.002), intraoperative urine output (P-value = 0.015), intraoperative crystalloid infusion (P-value = 0.002), intraoperative colloid infusion (P-value = 0.005), and anesthesia duration (P-value = 0.003). However, no significant differences were found between the two groups regarding postoperative ICU stay duration (P-value = 0.226), height (P-value = 0.678), weight (P-value = 0.162), BMI (P-value = 0.133), type of anesthesia (P-value \u0026gt; 0.999), preoperative diabetes (P-value = 0.182), preoperative hemoglobin (P-value = 0.396), preoperative prothrombin time (P-value = 0.058), preoperative aPTT (P-value = 0.392), preoperative sodium (P-value = 0.257), preoperative potassium (P-value = 0.144), preoperative GOT (P-value = 0.666), preoperative GPT (P-value = 0.36), intraoperative red blood cell transfusion (P-value = 0.063), and intraoperative FFP transfusion (P-value = 0.131) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 The relationship between intraoperative hypothermia and preoperative blood creatinine\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we examined the relationship between preoperative serum creatinine levels and the risk of IOH using multivariable logistic regression analysis, as summarized in Table 2. In the unadjusted model (Model 1), preoperative serum creatinine, treated as a continuous variable, was significantly associated with IOH, with an odds ratio (OR) of 3.7 (95% CI: 1.817\u0026ndash;8.157, P-value \u0026lt; 0.001). Subgroup analysis based on quartiles showed that, compared to the Q1 group (\u0026lt;0.66 mg/dL), the OR for the Q3 group (0.78\u0026ndash;\u0026lt;0.94 mg/dL) was 1.68 (95% CI: 1.103\u0026ndash;2.59, P-value = 0.0168), and the OR for the Q4 group (\u0026ge;0.94 mg/dL) was 1.991 (95% CI: 1.28\u0026ndash;3.159, P-value = 0.0027), with a trend test P-value of \u0026lt;0.001, indicating that the risk of IOH increased with higher serum creatinine levels. In Model 2, after adjusting for age, sex, ASA classification, department, surgical approach, and preoperative hypertension, the OR for preoperative serum creatinine (continuous variable) was 3.164 (95% CI: 1.48\u0026ndash;7.5, P-value = 0.0065). In this model, subgroup analysis revealed that the Q3 group (OR = 1.629, 95% CI: 1.04\u0026ndash;2.583, P-value = 0.035) and the Q4 group (OR = 1.771, 95% CI: 1.102\u0026ndash;2.893, P-value = 0.02) remained significantly associated with the risk of IOH, with a trend P-value of 0.0034. In Model 3, after further adjusting for preoperative platelet count, glucose, albumin, estimated blood loss, intraoperative urine output, crystalloid infusion, colloid infusion, and anesthesia duration, the OR for preoperative serum creatinine (continuous variable) was 3.442 (95% CI: 1.51\u0026ndash;8.653, P-value = 0.0064). Subgroup analysis in this model showed that the Q3 group (OR = 1.664, 95% CI: 1.051\u0026ndash;2.665, P-value = 0.0315) and the Q4 group (OR = 1.774, 95% CI: 1.09\u0026ndash;2.935, P-value = 0.0229) still exhibited a significant association with the risk of IOH, with a trend P-value of 0.0045. In conclusion, elevated preoperative serum creatinine levels were significantly associated with an increased risk of ioh, regardless of the degree of covariate adjustment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 IOH and the RCS curve of preoperative blood creatinine.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between preoperative serum creatinine and IOH was examined using restricted cubic spline (RCS), adjusting for all relevant covariates. The results showed that there was no statistically significant nonlinear relationship between preoperative serum creatinine and IOH (P for nonlinearity = 0.972, Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Machine learning analysis of IOH-related factors based on the XGBoost model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized the XGBoost model to perform machine learning analysis of factors associated with IOH.\u0026nbsp;Figure 3A displays the mean absolute SHAP values for each factor. The average | SHAP value | for serum creatinine is 0.00128, which is a relatively high compared to other factors, indicating that serum creatinine made a significant contribution to the model\u0026apos;s predictions.\u003c/p\u003e\n\u003cp\u003eFigure 3B is a SHAP scatter plot that shows the relationship between each sample\u0026apos;s SHAP value and its corresponding feature value. For serum creatinine, its SHAP values are distributed between -0.15 and 0.0, with point color indicating the feature value of serum creatinine: orange represents higher feature values, and purple represents lower values. As shown in the figure, orange points (higher feature values) for serum creatinine are more concentrated in the region with positive SHAP values.\u003c/p\u003e\n\u003cp\u003eInterpreting the SHAP values, within the model framework, it suggests that higher serum creatinine feature values tend to increase the predicted risk or likelihood of IOH. This indicates a potential association between higher serum creatinine levels and an increased incidence of IOH according to the model\u0026apos;s analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 The relationship between preoperative serum creatinine and baseline characteristics across subgroup\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIn the subgroup analysis, several variables were assessed, including sex, hypertension, diabetes, age, and anesthesia duration. The analysis showed no significant interactions between these subgroups (P for interaction \u0026gt; 0.05), as shown in Figure 4. This further confirms that the association between preoperative serum creatinine and IOH in patients remains consistent across subgroups.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis study, through a retrospective analysis of 4717 surgery patients, is the first to confirm in a large population that elevated preoperative serum creatinine levels are an independent risk factor for IOH. After sufficient multivariable adjustment, this association remained robust, and restricted cubic spline analysis revealed a linear dose-response relationship between the two, indicating that the higher the serum creatinine level are associated with an increased risk of IOH. This finding establishes renal insufficiency as an important and measurable precursor to perioperative thermoregulatory disturbances.\u003c/p\u003e\n\u003cp\u003eWe believe that there is a reasonable physiological and pathological mechanism behind this association. Serum creatinine, as a surrogate indicator of renal function, often reflects impaired renal excretory function and potential endocrine or metabolic dysfunction when elevated【26,27】. Uremic toxins accumulated in patients with renal insufficiency may directly affect the thermoregulatory center in the hypothalamus, altering its set point【18,19】. At the same time, the common chronic inflammatory state in these patients, characterized by elevated levels of cytokines such as IL-6,may lead to reduced resting energy expenditure, thereby decreasing endogenous heat production【28,29】. In addition, autonomic neuropathy is a common complication of renal insufficiency, which can impair the body\u0026apos;s vasoconstriction response to cold stimuli, a key defense mechanism for maintaining core body temperature\u0026nbsp;【30,31】. The linear positive correlation observed in this study further supports the hypothesis that the more severe the renal\u0026nbsp;dysfunction, the more significant the impairment of thermoregulation ability.\u003c/p\u003e\n\u003cp\u003eOur research findings align with the existing literature that focusing on perioperative thermoregulatory risk factors. For example, both our study and the work of Huang et al. found that hypoalbuminemia is a risk factor for IOH\u0026nbsp;【32】, which may be associated with colloid osmotic pressure, nutrition status, and levels of inflammation. This study also found that advanced age and longer surgical duration are associated with IOH, which is consistent with established clinical understanding【33,34】. However, our findings regarding the relationship between gender and IOH (with higher risk in males) differ from some studies\u0026nbsp;【35,36,37】, This may be due to the older average age in our study population, where age-related hormonal changes may have\u0026nbsp;mediated the gender differences.\u003c/p\u003e\n\u003cp\u003eIt is particularly noteworthy that this study shifts its focus from intraoperative and postoperative indicators to a routine and easily accessible preoperative laboratory marker\u0026mdash;serum creatinine. This addresses the gap in the clinical practicality of existing predictive models, offering the possibility of risk stratification before anesthesia induction.\u003c/p\u003e\n\u003cp\u003eWe\u0026nbsp;observed that the overall incidence of IOH in our study cohort was as high as 96.2%. This unusually high rate may be attributed to the completeness of vital signs records in the database (which may preferentially include surgeries with comprehensive monitoring) or to our broader definition of \u0026quot;core temperature \u0026lt;36.0\u0026deg;C at any time point. Although the high incidence may somewhat limit the model\u0026apos;s ability to identify individuals at very low risk, it highlights the widespread challenges of temperature management within this population. Against this backdrop, serum creatinine still demonstrates significant predictive value and high SHAP importance, further emphasizing its robustness and potential utility as a risk stratification tool.\u003c/p\u003e\n\u003cp\u003eThe clinical translational value of this study lies in the fact that serum creatinine can serve as a decision-making tool for preoperative optimization and resource allocation. Based on our quartile analysis results, we recommend using a preoperative serum creatinine \u0026gt; 0.78 mg/dL as a practical risk stratification threshold. For such high-risk patients, more aggressive temperature protection strategies should be\u0026nbsp;implemented, such as initiating forced air warming system before anesthesia induction and increasing the frequency of temperature monitoring during surgery.\u003c/p\u003e\n\u003cp\u003eThe limitations of this study primarily include: first, as a single-center retrospective study, there may be unmeasured confounding factors, and the generalizability of the conclusions needs further validation. Second, we were unable to obtain more precise renal function assessment indicators such as estimated glomerular filtration rate (eGFR) for comparison. Future research should focus on: conducting multi-center prospective cohorts to validate our findings; further exploring the specific impact of temperature management on the postoperative recovery quality in patients with renal insufficiency; and developing comprehensive predictive model integrating multiple indicators such as serum creatinine, albumin, and age, with the goal of achieving more precise, personalized perioperative management.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eElevated preoperative serum creatinine is an independent predictor of intraoperative hypothermia. A preoperative serum creatinine level greater than 0.78 mg/dL is recommended as a risk threshold, and enhanced temperature management should be implemented for high-risk patients.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIOH: Intraoperative hypothermia, BMI: body mass index, APTT: activated partial thromboplastin time, AST: aspartate aminotransferase, ALT: alanine aminotransferase, BUN: blood urea nitrogen, FFP: fresh frozen plasma, IQRs: interquartile ranges, ORs: odds ratios, CIs: confidence intervals, RCS: restricted cubic spline.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarationgs:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u0026quot; in this section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eno specific funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMan Dai:\u003c/strong\u003e Conceptualization, Writing \u0026ndash; original draft, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDandan Qi:\u003c/strong\u003e Methodology, Literature Search,\u0026nbsp;Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaoting Shi\u003c/strong\u003e: Formal Analysis, Technical Support, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTianyang Jiang\u003c/strong\u003e: Technical Support, Literature Search, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShaosheng Wu:\u003c/strong\u003e Formal Analysis, Technical Support, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaoman Bai:\u003c/strong\u003e Conceptualization, Writing \u0026ndash; Original Draft, Literature Search, Formal Analysis,\u0026nbsp;Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMoellhoff N, Broer PN, Heide Krueger PI, Ninkovic M, Ehrl D. 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Clin Chim Acta. 438:350-7. doi: 10.1016/j.cca.2014.08.039 https://pubmed.ncbi.nlm.nih.gov/25195004/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Abi N, Xu X, Yang Z, Ma T, Dong J. Association of Serum Adipokines and Resting Energy Expenditure in Patients with Chronic Kidney Disease. Front Nutr. 9:828341. Published 2022 None. doi:10.3389/fnut.2022.828341 https://pubmed.ncbi.nlm.nih.gov/35369060/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Al-Rawi KF, Ali HH, Guma MA, et al. Relationship Between IL-2, IL-17 Concentrations, and Serum Creatinine Levels in Men with Chronic Kidney Diseases. Rep Biochem Mol Biol. 2022;10(4):664-674. doi:10.52547/rbmb.10.4.664 https://pubmed.ncbi.nlm.nih.gov/35291613/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Cole RT, Masoumi A, Triposkiadis F, et al. Renal dysfunction in heart failure. Med Clin North Am. 2012;96(5):955-74. doi: 10.1016/j.mcna.2012.07.005 https://pubmed.ncbi.nlm.nih.gov/22980058/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Heidbreder E, Schaffer Hans K, Heidland A. Autonomic neuropathy in chronic renal insufficiency. Comparative analysis of diabetic and nondiabetic patients. Nephron. 1985;41(1):50-6.doi:10.1159/000183546 https://pubmed.ncbi.nlm.nih.gov/4033842/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Huang X, Ren M, Pan J, et al. Albumin: a novel biomarker for predicting intraoperative hypothermia in HSCR. Ann Med. 2025;57(1):2540019. doi:10.1080/07853890.2025.2540019 https://pubmed.ncbi.nlm.nih.gov/40736083/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Sari S, Aksoy SM, But A. The incidence of inadvertent perioperative hypothermia in patients undergoing general anesthesia and an examination of risk factors. Int J Clin Pract. 2021;75(6): e14103. doi:10.1111/ijcp.14103 https://pubmed.ncbi.nlm.nih.gov/33616248/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;G\u0026uuml;ven B,\u0026nbsp;İbrahimoğlu \u0026Ouml;, Kuşİ. Inadvertent Perioperative Hypothermia in Ambulatory Surgery Patients: Incidence, Risk Factors, and Prevention Initiatives. J Perianesth Nurs. 2023;38(5):792-798. doi: 10.1016/j.jopan.2023.02.002 https://pubmed.ncbi.nlm.nih.gov/37269277/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Sagiroglu G, Ozturk GA, Baysal A, Turan FN. Inadvertent Perioperative Hypothermia and Important Risk Factors during Major Abdominal Surgeries. J Coll Physicians Surg Pak. 2020;30(2):123-128. doi:10.29271/jcpsp.2020.02.123 https://pubmed.ncbi.nlm.nih.gov/32036816/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Tu YH, Zhang D. Risk Factors for Hypothermia during Laparoscopic or Open Surgery of Colorectal Cancer under General Anesthesia. Ann Ital Chir. 2025;96(1):108-115.doi:10.62713/aic.3548https://pubmed.ncbi.nlm.nih.gov/39815844/\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Zhang B, Pan AF. Development and evaluation of a novel predictive nomogram for assessing the risk of intraoperative hypothermia in patients undergoing thoracoscopic pulmonary tumor surgery. Heliyon. 2023;9(12): e22574. Published 2023 Dec. doi: 10.1016/j.heliyon. 2023.e22574 https://pubmed.ncbi.nlm.nih.gov/38090000/\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intraoperative Hypothermia, Preoperative Serum Creatinine, Vital Db, Risk Factor","lastPublishedDoi":"10.21203/rs.3.rs-8043150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8043150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIntraoperative hypothermia (IOH) is closely associated with various intraoperative and postoperative adverse events, and identifying high-risk patients preoperatively remains a significant challenge. In recent years, the proportion of surgical patients with renal insufficiency has been increasing, and their metabolic and thermoregulatory abnormalities contribute to a higher risk of IOH. This study aimed to investigate the predictive value of preoperative serum creatinine levels for intraoperative hypothermia, providing insight for risk assessment and preventive strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study extracted data from 4,717 adult surgical patients in the Vital DB public database. The main exposure variable was preoperative serum creatinine, and the primary outcome was IOH, defined as a core temperature below 36.0\u0026deg;C at any intraoperative time point. A multivariable weighted logistic regression model was applied to evaluate the association between serum creatinine (as both a continuous and quartile-categorized variable) and IOH, adjusting for age, sex, ASA classification, surgery type, and intraoperative factors. Restricted cubic spline analysis was conducted to test for nonlinearity, while an XGBoost model with SHAP values was used to assess feature importance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong all patients, the incidence of IOH was 96.2%. After adjustment, preoperative serum creatinine levels were significantly associated with IOH risk (adjusted odds ratio [aOR]\u0026thinsp;=\u0026thinsp;3.44, 95% CI: 1.51\u0026ndash;8.65). Compared with the lowest quartile group (Q1), patients in the highest quartile (Q4) showed a notably higher risk of IOH (aOR\u0026thinsp;=\u0026thinsp;1.77, 95% CI: 1.09\u0026ndash;2.94). The RCS analysis demonstrated a linear dose\u0026ndash;response relationship (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.972). The XGBoost model confirmed serum creatinine as a key predictor with strong predictive value.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eElevated preoperative serum creatinine is an independent risk factor for IOH, exhibiting a linear positive association. This biomarker may serve as a practical preoperative tool for identifying high-risk IOH patients and guiding personalized temperature management strategies.\u003c/p\u003e","manuscriptTitle":"Preoperative Serum Creatinine as an Independent Predictor of Intraoperative Hypothermia: Evidence from a Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 00:22:03","doi":"10.21203/rs.3.rs-8043150/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-20T06:07:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271840487491664661135960197247550657180","date":"2026-05-20T05:44:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-18T14:14:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105083117528988504530429944370233114131","date":"2026-05-16T22:26:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90458761976876770475655557697828664932","date":"2026-05-16T05:33:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172045809751789603885408217209519198222","date":"2026-05-14T23:57:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T14:55:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100883383364052771357271776121947903638","date":"2025-12-09T14:43:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-04T13:23:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-12T05:30:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T14:16:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-11T14:13:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2025-11-06T03:19:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"426b592a-acf8-4b68-a0a3-55b35aa7d33f","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-20T06:07:43+00:00","index":97,"fulltext":""},{"type":"reviewerAgreed","content":"271840487491664661135960197247550657180","date":"2026-05-20T05:44:52+00:00","index":96,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-18T14:14:00+00:00","index":94,"fulltext":""},{"type":"reviewerAgreed","content":"105083117528988504530429944370233114131","date":"2026-05-16T22:26:51+00:00","index":92,"fulltext":""},{"type":"reviewerAgreed","content":"90458761976876770475655557697828664932","date":"2026-05-16T05:33:04+00:00","index":91,"fulltext":""},{"type":"reviewerAgreed","content":"172045809751789603885408217209519198222","date":"2026-05-14T23:57:08+00:00","index":90,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T00:22:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 00:22:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8043150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8043150","identity":"rs-8043150","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}