Impact of perioperative temperature management nursing quality on postoperative infectious complications in patients undergoing gastrointestinal surgery

Abstract

BACKGROUND

Perioperative hypothermia is a common complication of gastrointestinal surgery and is associated with an increased risk of surgical site infections and other complications. However, the relationship between comprehensive temperature management quality, including monitoring, protocol adherence, timely intervention, and postoperative infection outcomes, remains insufficiently studied, particularly in relation to surgical approach and patient comorbidities.

AIM

To investigate the impact and underlying pathophysiological mechanisms of perioperative temperature management nursing quality on postoperative infectious complications in patients undergoing gastrointestinal surgery.

METHODS

A retrospective analysis was conducted on 45 patients who underwent elective gastrointestinal surgery at our institution between 2020 and 2025: Nine patients with postoperative infections within 30 days and 36 uninfected controls. A temperature-management compliance score was constructed and conditional logistic regression analysis was used to analyze the risk of postoperative infection. The effect modification was assessed in subgroups stratified by surgical approach and diabetes status. Differences in the microcirculatory, metabolic, and inflammatory mechanism-related indicators were compared between groups.

RESULTS

The case group had significantly lower intraoperative and immediate postoperative body temperatures, intraoperative active warming use, and temperature management compliance score and a longer hypothermia duration than the control (P < 0.05). The proportion of patients who underwent preoperative warming was not significantly different (P > 0.05). Multivariate conditional logistic regression analysis revealed that “lowest intraoperative body temperature” and “temperature management compliance scores” were independent risk factors for postoperative infections (P < 0.05). Surgical approach and diabetes status significantly affected the association between the lowest intraoperative body temperature and postoperative infection (P < 0.05). The case group had significantly higher intraoperative core temperature variability, core-to-skin temperature gradient, postoperative serum interleukin-6 levels, temperature management response delay time, and intraoperative fraction of inspired oxygen exposure index (P < 0.05), whereas intraoperative lactate clearance and immediate postoperative peripheral perfusion index were significantly lower (P < 0.05).

CONCLUSION

Perioperative hypothermia and inadequate nursing quality for temperature management are independent risk factors for postoperative infections following gastrointestinal surgery. Enhanced temperature monitoring and active warming via standardized individualized temperature management protocols may reduce the risk of postoperative infection, especially in patients undergoing open surgery and those with diabetes.

Key Words: Perioperative hypothermia; Temperature management nursing quality; Gastrointestinal surgery; Postoperative infection; Retrospective study

Core Tip: This 1:4 retrospective study of 45 patients who underwent elective gastrointestinal surgery identified the lowest intraoperative body temperature and temperature management compliance scores as independent risk factors for postoperative infectious complications. This association was significantly modified by the surgical approach (open vs laparoscopic) and the diabetes status. The underlying mechanisms involve impaired microcirculation (increased core-to-skin temperature gradient and decreased peripheral perfusion index), metabolic dysregulation (reduced lactate clearance), and increased systemic inflammation (elevated interleukin-6). Standardized individualized temperature management protocols are crucial, especially in open surgery and patients with diabetes.

INTRODUCTION

Gastrointestinal surgery is one of the most common surgical procedures and includes a wide spectrum of interventions, ranging from minimally invasive appendectomies to complex resections for gastric and colorectal malignancies. Despite continued advances in surgical techniques, anesthesia, and perioperative care, postoperative infectious complications remain a major clinical challenge, leading to prolonged hospitalization, increased medical costs, delayed recovery, and increased mortality[1]. The incidence of postoperative infections after gastrointestinal surgery ranges from 5% to 15%, highlighting the need for effective preventive strategies[2].

Perioperative hypothermia, defined as a core body temperature less than 36.0 °C, is a frequent yet preventable adverse event during gastrointestinal surgery. Accumulating evidence has identified perioperative hypothermia as an independent risk factor of postoperative infectious complications[3]. Underlying mechanisms include impairment of neutrophil chemotaxis and phagocytosis, suppression of oxidative burst activity, reduction of subcutaneous tissue oxygen tension, and delayed collagen synthesis, all of which compromise the host immune defense and wound healing capacity[4].

In recent years, perioperative temperature management has been recognized as a core component of enhanced recovery after surgery (ERAS) programs, emphasizing continuous temperature monitoring, timely warming interventions, and standardized nursing protocols throughout the preoperative, intraoperative, and postoperative phases[5]. However, in real-world clinical practice, deficiencies in temperature monitoring frequency, delayed intervention, incomplete documentation, and inconsistent protocol adherence remain common, resulting in marked variability in the quality of temperature management in nursing care. Importantly, existing studies have largely focused on the presence or absence of hypothermia, whereas the impact of temperature management on nursing quality as a process indicator of postoperative infection outcomes remains scarcely explored.

Therefore, this study aimed to evaluate the association between perioperative temperature management nursing quality and postoperative infectious complications in patients undergoing gastrointestinal surgery, as well as explore potential pathophysiological mechanisms, thereby providing evidence to optimize perioperative nursing practice and infection prevention strategies.

MATERIALS AND METHODS

General data

Adult patients who underwent elective gastrointestinal surgery in the Department of General Surgery of our hospital between January 2020 and May 2025 were retrospectively enrolled. The inclusion criteria included: (1) Age ≥ 18 years; (2) Elective open or laparoscopic gastrointestinal surgery (e.g., laparoscopic appendectomy, radical resection for rectal or gastric cancer) under general anesthesia, with an operation duration ≥ 60 minutes; and (3) Complete medical and nursing records. The exclusion criteria included: (1) Emergency surgery; (2) Pre-existing infection or fever (body temperature ≥ 38 °C) before surgery; (3) Combined with severe immunodeficiency (e.g., acquired immunodeficiency syndrome, long-term use of immunosuppressants); and (4) Intraoperative blood loss > 1000 mL or requiring re-operation. According to the “diagnostic criteria for nosocomial infections (trial)”, patients who developed infectious complications (e.g., surgical site, pulmonary, urinary tract, and intra-abdominal infection) within 30 days postoperatively were designated as the case group (n = 9). Using an individual matching design, uninfected controls (n = 36) were matched in a 1:4 ratio based on surgery location (gastric/colorectal/small intestine), age (± 5 years), and sex. Matching variables were determined based on previous literature and clinical experience to control for potential confounding biases.

Collection of clinical data

All data were independently extracted from the hospital’s electronic medical record system by two trained researchers and crosschecked to ensure accuracy.

Demographic and basic clinical data: The demographic characteristics included age and sex. The clinical characteristics included body mass index, American Society of Anesthesiologists physical status classification, presence of type 2 diabetes mellitus, and preoperative serum albumin level (g/L). Surgical variables included the surgical approach (open vs laparoscopic surgery), total operation duration (minutes), total intraoperative fluid infusion volume (mL), and total intraoperative blood loss (mL).

Perioperative temperature management-related indicators: In the preoperative phase, nursing records were reviewed to verify whether patients received active warming (e.g., warming blanket and forced-air warmer) 30-60 minutes before entering the operating room, recorded as “yes/no”. In the intraoperative phase, the following were reviewed: (1) Temperature data: Core body temperature (nasopharyngeal or bladder) recorded every 15 minutes was extracted from the anesthesia records. Two metrics were extracted, namely intraoperative lowest body temperature (°C) and duration of hypothermia (cumulative minutes with temperature < 36.0 °C); and (2) Warming intervention: Surgical nursing records were reviewed to confirm whether active warming devices (forced-air warming blanket and/or fluid/blood warmer for infusions/irrigation fluids) were used, recorded as “yes/no”. Finally, in the postoperative phase, the core body temperature immediately upon admission to the post-anesthesia care unit was extracted and recorded as the Immediate postoperative body temperature (°C). In terms of the temperature management nursing quality composite score, to quantify the nursing team’s adherence to perioperative temperature management protocols, a 5-dimension scale was constructed (each dimension scored 0-2: 0 = not performed, 1 = partially performed, 2 = fully performed) with a total score ranging from 0 to 10. The dimensions included: (1) Preoperative warming; (2) Intraoperative use of active warming devices; (3) Intraoperative temperature monitoring frequency (≥ every 15-30 minutes); (4) Completeness of temperature and intervention documentation; and (5) Timely management of intraoperative hypothermia. The scores were independently assessed by two researchers, and disagreements were arbitrated by a senior nurse.

Potential pathophysiological mechanism indicators between perioperative hypothermia and postoperative infection: To elucidate the potential pathophysiological links between perioperative hypothermia and postoperative infection and enhance the precision of temperature management quality assessment, data on the following indicators across four dimensions were collected and analyzed: Microcirculatory perfusion, inflammatory response, metabolic status, and nursing response efficiency. To this end, several measurements were taken: (1) Intraoperative core temperature variability, represented by the standard deviation of core temperatures, was recorded every 15 minutes in the anesthesia record; (2) Core-to-skin temperature gradient was calculated as the mean difference between peripheral skin temperature (e.g., forearm) and core temperature; (3) The lactate clearance rate was calculated as follows: [(Preoperative lactate - end-of-surgery lactate)/preoperative lactate]/operation duration × 60; (4) The early postoperative procalcitonin (PCT) level was measured within 2 hours after the patient was admitted to the anesthesia recovery room; (5) The peripheral perfusion index was obtained via continuous pulse oximeter monitoring; (6) Temperature intervention response delay time was defined as the interval from the first recorded core temperature < 36 °C to the initiation of active warming; and (7) The intraoperative fraction of inspired oxygen (FiO₂) exposure index was expressed as: Mean FiO₂ × operation duration (%·minutes).

Statistical analysis

Data were analyzed using SPSS software (version 26.0). Measurement data are presented as the mean ± SD, and group comparisons were made using paired t-tests. Count data are presented as n (%), and comparisons were made using the χ² test or Fisher’s exact test. Conditional logistic regression analysis was used to identify factors influencing postoperative infection, calculating the odds ratios (ORs) and their 95% confidence intervals (CIs). All tests were two-sided, and P < 0.05 was considered statistically significant.

RESULTS

Comparison of general data

No statistically significant differences were observed between the case and control groups in the baseline demographic and clinical characteristics, including age, sex, body mass index, American Society of Anesthesiologists classification, diabetes status, preoperative albumin level, surgical approach, operative duration, intraoperative fluid volume, and blood loss. Sex distribution was compared based on the proportion of men, with women constituting the remaining proportion in each group (all P > 0.05), indicating successful matching and baseline comparability between the two groups (Table 1).

Table 1 Baseline characteristics and major perioperative clinical features, mean ± SD/n (%).

Variable	Case group (n = 9)	Control group (n = 36)	t/χ2 value	P value
Age (years)	62.32 ± 8.71	61.85 ± 9.12	0.139	0.890
Sex, male	6 (66.67)	21 (58.33)		0.721
BMI (kg/m2)	23.53 ± 3.21	24.16 ± 2.91	0.570	0.572
ASA ≥ III	4 (44.44)	13 (36.11)		0.711
Complicated with diabetes	3 (33.33)	10 (27.78)		0.704
Preoperative albumin (g/L)	36.22 ± 5.11	37.85 ± 4.72	0.912	0.367
Open surgery	5 (55.56)	17 (47.22)		0.722
Operation time (minutes)	185.65 ± 42.36	178.92 ± 38.71	0.458	0.649
Intraoperative fluid volume (mL)	1520.09 ± 320.23	1480.45 ± 290.67	0.359	0.721
Intraoperative blood loss (mL)	210.62 ± 85.78	195.09 ± 78.80	0.520	0.606

BMI: Body mass index; ASA: American Society of Anesthesiologists.

Comparison of perioperative temperature management indicators

Compared with the control group, the case group showed a significantly lower intraoperative body temperature (35.22 ± 0.61 °C vs 36.15 ± 0.42 °C), longer duration of hypothermia (82.35 ± 25.61 minutes vs 28.75 ± 9.26 minutes), lower immediate postoperative body temperature (35.54 ± 0.72 °C vs 36.34 ± 0.52 °C), and a lower temperature management compliance score (5.13 ± 1.31 vs 8.47 ± 1.11; all P < 0.001). The proportion of patients receiving intraoperative active warming was also significantly lower in the case group (22.22% vs 75.00%, P = 0.006). No significant difference was observed in preoperative warming between the two groups (33.33% vs 61.11%, P > 0.05; Table 2).

Table 2 Comparison of perioperative body temperature management indicators between the two groups, mean ± SD/n (%).

Indicator	Case group (n = 9)	Control group (n = 36)	t/χ2 value	P value
Preoperative warming	3 (33.33)	22 (61.11)		0.157
Lowest intraoperative body temperature (°C)	35.22 ± 0.61	36.15 ± 0.42	5.409	< 0.001
Duration of hypothermia (minutes)	82.35 ± 25.61	28.75 ± 9.26	10.385	< 0.001
Intraoperative active warming	2 (22.22)	27 (75.00)		0.006
Immediate postoperative temperature (°C)	35.54 ± 0.72	36.34 ± 0.52	3.815	< 0.001
Temperature management compliance score	5.13 ± 1.31	8.47 ± 1.11	7.794	< 0.001

Logistic analysis of perioperative temperature management measures and postoperative infection

Univariate conditional logistic regression analysis showed that the lowest intraoperative body temperature, duration of hypothermia, use of active warming, immediate postoperative body temperature, and compliance score were significantly associated with postoperative infection (P < 0.05). Considering the collinearity among variables, the two most representative variables, lowest intraoperative body temperature and temperature management compliance score were ultimately included in the multivariate model. The results indicated that both were independent risk factors for postoperative infections (P < 0.05; Figure 1; Table 3).

Figure 1 Forest plot of logistic regression analysis comparing perioperative temperature management measures with postoperative infections.

Table 3 Logistic analysis of temperature management measures during the perioperative period and postoperative infection.

Variable	Univariate analysis			Multiplicity
	OR	95%CI	P value	OR	95%CI	P value
Minimum intraoperative body temperature (per 1 °C decrease)	4.320	1.608-11.121	0.002	3.850	1.420-10.430	0.008
Duration of hypothermia (per 30-minute increment)	2.870	1.420 to -5.811	0.003
Active heating (yes/no)	0.110	0.020-0.630	0.013
Intraoperative body temperature (per 1 °C decrease)	3.950	1.540-10.122	0.004
Compliance score (each point down)	2.050	1.210-3.480	0.008	1.820	1.050-3.160	0.032

OR: Odds ratio; CI: Confidence interval.

Subgroup analysis of postoperative infection stratified by surgical approach and diabetes status

In the open surgery subgroup, each 1 °C decrease in the lowest intraoperative body temperature and each 1-point decrease in the temperature management compliance score significantly increased the risk of postoperative infection (P < 0.05). These associations were not statistically significant in the laparoscopic surgery subgroup (P > 0.05). The surgical approach demonstrated a significant modifying effect on the association between low intraoperative body temperature and postoperative infection (P for interaction = 0.046). In the subgroup with diabetes, each 1 °C decrease in the intraoperative lowest body temperature was associated with a greater increase in infection risk than in the non-diabetic subgroup, and diabetes status had a significant modifying effect on this association (P for interaction = 0.049). There was no significant interaction between the temperature management compliance score and infection in either subgroup (P for interaction = 0.812; Table 4).

Table 4 Subgroup analysis of postoperative infections by surgical approach and diabetic status stratification.

Stratification factor	Variable	Subgroup	Case/control	OR (95%CI)	P value	P for interaction
Surgical approach	Lowest intraoperative temperature (per 1 °C decrease)	Open surgery	5/17	5.231 (2.905-9.437)	0.002	0.046
		Laparoscopic surgery	4/19	2.152 (1.441-3.222)	0.138
	Compliance score (per 1-point decrease)	Open surgery	5/17	2.311 (1.564-3.421)	0.023	0.118
		Laparoscopic surgery	4/19	1.572 (0.951-2.602)	0.121
Diabetes status	Lowest intraoperative temperature (per 1 °C decrease)	With diabetes	3/10	6.125 (3.214-11.680)	0.001	0.049
		Without diabetes	6/26	3.012 (1.822-4.983)	0.045
	Compliance score (per 1-point decrease)	With diabetes	3/10	1.927 (0.994-3.751)	0.047	0.812
		Without diabetes	6/26	1.785 (0.911-3.501)	0.058

OR: Odds ratio; CI: Confidence interval.

Comparison of mechanism indicators related to perioperative temperature management and postoperative infection

The case group had a significantly higher intraoperative core temperature variability, intraoperative core-to-skin temperature gradient, postoperative 2-hour serum interleukin-6 level, temperature management response delay time, and intraoperative FiO₂ exposure index than the control group (P < 0.05). The intraoperative lactate clearance rate and immediate postoperative peripheral perfusion index were significantly lower in the case group (P < 0.05; Table 5).

Table 5 Comparison of mechanistic indicators of temperature management during the perioperative period and postoperative infection correlation, mean ± SD.

Indicator	Case group (n = 9)	Control group (n = 36)	t value	P value
Intraoperative core temperature variability (°C)	0.48 ± 0.12	0.21 ± 0.07	8.873	< 0.001
Intraoperative core-to-skin temperature gradient (°C)	4.35 ± 1.02	2.67 ± 0.84	5.144	< 0.001
Intraoperative lactate clearance rate (%/hour)	18.32 ± 3.51	32.73 ± 4.11	9.654	< 0.001
Serum PCT (ng/mL) 2 hours postoperative	2.65 ± 0.76	0.82 ± 0.21	12.969	< 0.001
Immediate postoperative peripheral perfusion index	0.82 ± 0.31	1.45 ± 0.47	3.802	< 0.001
Temperature management response delay time (minutes)	24.65 ± 3.81	17.33 ± 2.24	7.541	< 0.001
Intraoperative FiO2 exposure index (%·minutes)	4820.20 ± 1050.13	4100.04 ± 890.23	2.096	0.042

PCT: Procalcitonin; FiO₂: Fraction of inspired oxygen.

DISCUSSION

Utilizing a real-world clinical database and a 1:4 matched case-control design, this study systematically evaluated the impact of perioperative temperature management nursing quality on infectious complications after gastrointestinal surgery. The results demonstrated that each 1 °C decrease in lowest intraoperative body temperature and each 1-point decrease in the temperature management compliance score were independent risk factors for postoperative infection. This association showed significant effect modification in patients undergoing open surgery and those with diabetes. Furthermore, by integrating mechanism indicators, such as core temperature variability, core-to-skin temperature gradient, lactate clearance rate, PCT potency, peripheral perfusion index, and temperature intervention response delay time, a pathophysiological pathway model of perioperative hypothermia linking perioperative hypothermia with microcirculatory dysfunction, metabolic disturbance, immunosuppression, and inflammatory imbalance was constructed. This study provides mechanistic evidence clarifying the biological basis of temperature management for perioperative infection prevention and control.

Perioperative hypothermia (< 36 °C) has been identified as an intervenable risk factor for postoperative complications[6-8]. The results of this study showed that each 1 °C decreases in lowest intraoperative body temperature increased the risk of postoperative infection by 2.85 times (OR = 3.85; 95%CI: 1.420-10.430). Mechanistic analysis suggested that hypothermia directly suppresses innate immunity, significantly impairing neutrophil chemotaxis, migration, and oxidative burst activity, thereby reducing pathogen recognition and clearance efficiency[9,10]. Moreover, hypothermia induces peripheral vasoconstriction, reduces tissue perfusion, and decreases subcutaneous tissue oxygen tension (PtO₂), which is the rate-limiting factor in collagen synthesis and wound healing[11]. Additionally, the hypoperfusion state prolongs lactate clearance time, further exacerbating local acidosis and hypoxia. In this study, the intraoperative lactate clearance rate was significantly lower in the case group compared to the control group (18.32 ± 3.51 %/hour vs 32.73 ± 4.11 %/hour), and the immediate postoperative peripheral perfusion index was also concurrently reduced (0.82 ± 0.31 vs 1.45 ± 0.47). This cycle of “hypoxia-metabolic disorder-delayed repair” creates a favorable microenvironment for bacterial colonization and invasion, ultimately manifesting as an exponential increase in the infection risk[12-14].

The results of this study showed that the temperature management response delay time was significantly longer in the case group compared to the control group (24.65 ± 3.81 minutes vs 17.33 ± 2.24 minutes). This delay increases the “time window” of hypothermia exposure for patients and results in the lack of intervention during the critical early window for microcirculatory dysfunction[15]. From a systems perspective, response delay essentially represents a breakdown in the “recognition, decision, and action” chain, revealing the structural defect of “disruption between monitoring and action” in clinical practice[16]. Further analysis of the temperature management compliance score revealed it was significantly lower in the case group (5.13 ± 1.31 vs 8.47 ± 1.11), suggesting that inadequate temperature management is not merely a technical error but a reflection of nursing system quality. Logistic regression showed that each 1-point decrease in the compliance score increased the infection risk by 1.82 times (OR = 1.82, 95%CI: 1.05-3.16), strengthening from an evidence-based perspective the core value of structured, standardized temperature management processes in infection prevention and control.

Further analysis of the pathophysiological mechanisms by which hypothermia promotes infection revealed that the intraoperative core temperature fluctuation amplitude was significantly larger in the case group (0.48 ± 0.12 °C vs 0.21 ± 0.07 °C), indicating thermoregulatory imbalance[17,18]. Concurrently, the core-to-skin temperature gradient increased from 2.67 ± 0.84 °C to 4.35 ± 1.02 °C, reflecting intense peripheral vasoconstriction and an increased core-to-peripheral heat gradient, which is a typical sign of inadequate tissue perfusion[19,20]. In this context, the body increases oxygen consumption to maintain core functions. If hypothermia persists, anaerobic metabolism increases, leading to lactate accumulation. The decreased lactate clearance rate further supports mitochondrial dysfunction and energy metabolism disturbance[21,22]. Crucially, postoperative PCT levels in the case group were significantly elevated at 2 hours (2.65 ± 0.76 ng/mL vs 0.82 ± 0.21 ng/mL), indicating that systemic inflammatory responses to bacterial infection were markedly activated under hypothermic conditions. As a highly specific biomarker for bacterial infection, elevated PCT indicates an increased pathogen load and signals an overactivated immune system. Persistently high PCT expression is frequently associated with endothelial injury, coagulation dysfunction, and increased risk of multiple organ dysfunction, resulting in a cycle of “infection-inflammation-coagulation-microcirculatory impairment”. This cycle ultimately compromises the tissue barrier integrity and accelerates the spread of infection[23].

The subgroup analysis indicated that clinical heterogeneity had a significant modifying effect on the association between hypothermia and infection. In the open surgery subgroup, each 1 °C decrease in intraoperative lowest body temperature increased the postoperative infection risk by 5.23 times, far exceeding the corresponding increase in the laparoscopic surgery subgroup, and a significant interaction existed between surgical approach and body temperature. This difference may be attributed to the larger exposed body cavity area, longer duration of heat loss, and stronger traumatic stress in open surgery, which more easily disrupts the thermoregulatory center and amplifies the immunosuppressive effects of hypothermia. Similarly, patients with diabetes exhibited a hypersensitivity to hypothermia: Each 1 °C decrease in the lowest body temperature drastically increased their infection risk by 6.13 times. Patients with diabetes are in a state of chronic low-grade inflammation, impaired neutrophil chemotaxis, phagocytosis, and microangiopathy. Hypothermia can further aggravate vascular endothelial injury and tissue hypoxia, creating a metabolic-immune-microcirculatory triple hit that synergistically amplifies infection susceptibility[24]. These results suggest that high-risk populations urgently require individualized temperature management to reduce infection incidence, including intensified preoperative warming, combined intraoperative use of forced-air warming blankets and fluid warming devices, continuous postoperative core temperature monitoring, and stricter intervention thresholds (e.g., initiating rewarming when core temperature < 36.2 °C).

This study has several limitations. First, this was a single-center retrospective study with a relatively small sample size, which may have limited the statistical power and generalizability of the findings. However, a 1:4 retrospective study design was employed to control major confounding factors, and conditional logistic regression was applied to enhance analytical robustness. Given the relatively low incidence of postoperative infections in elective gastrointestinal surgery, this study should be regarded as exploratory and hypothesis generating. Larger multicenter prospective studies are warranted to validate these findings.

From a clinical perspective, these findings highlight the importance of incorporating perioperative temperature management into routine quality assessment indicators within ERAS pathways. Establishing standardized protocol-driven temperature monitoring and intervention workflows supported by electronic medical record-based early warning systems may reduce response delays and improve patient outcomes. Future studies should focus on multicenter validation, as well as an in-depth exploration of the effects of temperature management on intestinal barrier function, immune cell phenotypes, and postoperative microbiota translocation to further elucidate its role in infection prevention.

CONCLUSION

In summary, this study verified the protective role of perioperative temperature management in reducing the incidence of postoperative infections and systematically constructed a complete pathological pathway linking hypothermia with microcirculatory dysfunction, metabolic disturbance, immunosuppression, inflammatory imbalance, and subsequent infection occurrence through mechanistic indicators, thereby clarifying the key regulatory role of body temperature in the occurrence of perioperative infections. These findings highlight the importance of incorporating temperature management as a mandatory quality assessment indicator within ERAS pathways and support the building of an intelligent early warning platform based on the electronic medical record system to enable early identification and rapid response to interventions, thereby shortening intervention delay time. Simultaneously, individualized and intensified temperature intervention strategies should be developed for high-risk populations (e.g., those undergoing open surgery and patients with diabetes). Future research could further elucidate the effects of temperature management on gut microbiota translocation, intestinal mucosal barrier function, and postoperative immune cell phenotype remodeling, delving deeper into its multidimensional mechanisms of action in perioperative infection prevention and control.