Perioperative Risk Stratification for Periprosthetic Joint Infection after Primary Total Knee Arthroplasty: A Case-Matched Cohort Study Incorporating Nutritional Index, Glycemic Status, and Intraoperative Hypothermia

Perioperative Risk Stratification for Periprosthetic Joint Infection after Primary Total Knee Arthroplasty: A Case-Matched Cohort Study Incorporating Nutritional Index, Glycemic Status, and Intraoperative Hypothermia | 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 Perioperative Risk Stratification for Periprosthetic Joint Infection after Primary Total Knee Arthroplasty: A Case-Matched Cohort Study Incorporating Nutritional Index, Glycemic Status, and Intraoperative Hypothermia Yung-Fong Tsai, Yu-Hong Ng, Shu-Yu Yeh, Yu-Hsun Sun, Huan-Tang Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9331642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Periprosthetic joint infection (PJI) remains a serious complication of primary total knee arthroplasty (TKA). This study investigated independent and combined associations of the Prognostic Nutritional Index (PNI), preoperative blood glucose, and intraoperative lowest body temperature (LBT) with PJI within one year after primary TKA. Materials and methods : This retrospective case-matched cohort study enrolled 312 patients (57 PJI cases, 255 controls) undergoing primary TKA. Infection was defined by 2018 International Consensus Meeting criteria. Multivariable logistic regression identified independent predictors. Two composite models were constructed using ROC-derived cut-offs: a two-factor preoperative model (PNI ≤ 43.4 + blood glucose ≥ 147 mg/dL) and a three-factor perioperative model additionally incorporating LBT ≤ 35.5°C. Results : A total of 4,319 adult patients who underwent TKA were identified during 2016 to 2021. Fifty-seven patients (1.3%) developed PJI. Three independent predictors were identified: higher PNI was associated with lower infection odds (OR 0.89; p = 0.027), blood glucose with increased risk (OR 1.01 per mg/dL; p = 0.039), and LBT ≤ 35.5°C with significantly increased risk (OR 2.49, 95% CI 1.29–4.83; p = 0.007). The two-factor preoperative model (PNI + glucose, AUC = 0.657) and three-factor perioperative model (adding LBT, AUC = 0.662) both showed stepwise PJI gradients across cumulative strata (ΔAUC = 0.005). Conclusions : PNI, blood glucose, and intraoperative LBT are independent modifiable predictors of PJI after primary TKA. A two-factor preoperative model (PNI + glucose) supports preoperative risk screening and optimization; a three-factor model adding LBT provides an intraoperative surveillance tool for structured perioperative normothermia protocols. Both models rely on routinely available perioperative variables and support structured infection prevention pathways. hypothermia intraoperative temperature periprosthetic joint infection prognostic nutritional index risk stratification total knee arthroplasty Figures Figure 1 Figure 2 Figure 3 Introduction Total knee arthroplasty (TKA) is one of the most frequently performed elective orthopedic procedures worldwide and consistently improves pain and functional outcomes in patients with end-stage knee osteoarthritis [ 4 , 11 ]. As populations age and surgical indications expand, the annual volume of primary TKA continues to increase [ 30 ]. With this expansion, the absolute burden of arthroplasty-related complications is expected to rise. Periprosthetic joint infection (PJI) remains among the most serious and resource-intensive adverse events after primary TKA, with incidence varying across registries and institutional cohorts depending on patient characteristics and surveillance methodology [ 20 ]. Patients frequently require additional surgical intervention, prolonged antimicrobial therapy, and extended hospitalization, and infection-related costs are several-fold higher than those of primary procedures [ 17 , 27 , 41 ]. These considerations highlight the importance of identifying modifiable perioperative risk factors that can be addressed before implantation. Numerous host and surgical variables have been associated with PJI risk [ 37 ]. However, many established predictors, such as age or baseline comorbidity burden, are not modifiable at the time of surgery. In contrast, several routinely measured perioperative physiological parameters are potentially correctable. Preoperative nutritional status represents one such domain. The Prognostic Nutritional Index (PNI), calculated from serum albumin and total lymphocyte count, integrates protein reserve with immune competence and provides a more comprehensive reflection of host physiological resilience than albumin alone [ 26 ]. Unlike classical hypoalbuminemia thresholds (< 3.5 g/dL), which identify only overtly malnourished patients, PNI captures a broader spectrum of nutritional-immunological compromise prevalent in elective arthroplasty populations. Lower PNI has been associated with increased postoperative complications, surgical site infection, and impaired wound healing after total joint arthroplasty [ 10 ]. Whether PNI identifies subclinical PJI vulnerability in patients with conventionally normal albumin has direct implications for preoperative screening and nutritional optimization [ 38 ]. Perioperative glycemic control constitutes a second actionable domain. Hyperglycemia impairs neutrophil function, reduces oxidative killing capacity, and disrupts inflammatory pathways critical for early bacterial clearance [ 12 , 39 ]. Importantly, perioperative glucose elevation frequently occurs in patients without established diabetes mellitus. Stress-mediated hyperglycemia is common in surgical populations and may represent an under-recognized contributor to infection susceptibility [ 2 , 9 ]. Emerging evidence suggests that perioperative glucose levels themselves, rather than the mere presence of diabetes, more accurately reflect biologically relevant vulnerability in arthroplasty cohorts [ 35 , 40 ]. From a practical perspective, preoperative glucose measurement is universally available and allows timely medical optimization when indicated. Intraoperative hypothermia, defined as a core temperature below 36°C, represents a third modifiable physiological exposure. Hypothermia induces peripheral vasoconstriction, reduces tissue oxygen tension, and attenuates innate immune responses, thereby potentially impairing local bacterial clearance at the implant–tissue interface [ 32 ]. Early clinical investigations demonstrated that maintenance of perioperative normothermia reduces surgical-wound infection rates [ 18 ], though more recent large non-cardiac surgical trials have reported heterogeneous findings [ 33 ]. Arthroplasty-specific evidence evaluating active warming interventions remains limited, and the implications for PJI require further clarification. As an intraoperative parameter under direct anesthetic control, lowest body temperature (LBT) is uniquely suited to function as a real-time surveillance instrument rather than a preoperative predictor, with direct implications for anesthesiologist-led warming protocols. While nutritional compromise, hyperglycemia, and hypothermia have each been examined independently, PJI is widely recognized as a multifactorial complication [ 37 ]. Patients undergoing primary TKA frequently present with multiple physiological vulnerabilities, yet the cumulative impact of potentially modifiable perioperative stressors on early PJI risk remains incompletely defined [ 26 , 31 ]. Contemporary arthroplasty research increasingly supports multivariable clinical prediction tools integrating preoperative and intraoperative parameters to improve infection risk stratification [ 15 ]. Accordingly, this single-center retrospective case-matched cohort study evaluated the independent associations of PNI, preoperative blood glucose, and intraoperative LBT with PJI within one year after primary TKA. We further assessed whether a composite scoring model based on data-driven cut-offs, PNI ≤ 43.4, preoperative blood glucose ≥ 147 mg/dL, and LBT ≤ 35.5°C, could identify a clinically meaningful, stepwise gradient in infection risk across cumulative strata, presented as both a two-factor preoperative model and a three-factor perioperative model. Materials and methods Study design and setting This was a single-center retrospective case-matched cohort study conducted at a tertiary academic medical center. Ethical approval was obtained from the Institutional Review Board of our institution (approval number withheld for blinding). The requirement for informed consent was waived by the IRB given the retrospective nature of the study and use of de-identified clinical data. All procedures were conducted in accordance with the Declaration of Helsinki. Study population We retrospectively identified all 4,319 adult patients who underwent primary TKA at our institution. Eligible patients were those with complete preoperative laboratory data, intraoperative temperature monitoring records, and a minimum postoperative follow-up of one year to capture PJI events. The study cohort comprised 312 patients, of whom 57 developed PJI (PJI group) and 255 did not (control group) ( Fig. 1 ) . To minimize potential confounding arising from variations in surgical technique and longitudinal improvements in perioperative care, patients were matched at a 1:5 ratio by the operating surgeon and the year of surgery. This approach ensures that differences in PJI outcomes are more likely attributable to patient-specific physiological factors and anesthetic management rather than inter-surgeon variability or temporal shifts in institutional infection prevention protocols. Patients were excluded if they had: (1) incomplete preoperative laboratory records; (2) a history of prior joint infection at the operative site; (3) concurrent active systemic infection at the time of surgery; (4) revision arthroplasty procedures; or (5) immunocompromising conditions not captured in the standardized dataset, such as HIV-positive status. Outcome definition The primary outcome was the occurrence of PJI within one year following the index arthroplasty procedure. PJI was defined according to the 2018 International Consensus Meeting criteria [ 25 , 34 ], incorporating clinical signs of wound inflammation, joint aspiration culture results, synovial fluid white blood cell counts, and histopathological findings when available. Diagnosis was confirmed by the treating orthopedic surgeon and verified through chart review by two independent investigators. Study variables Demographic variables collected included age, body mass index (BMI), and ASA physical status classification. Comorbidities documented at the time of surgery included diabetes mellitus, hypertension, chronic kidney disease, coronary artery disease, heart failure, chronic obstructive pulmonary disease, cerebral stroke, autoimmune disease, rheumatoid arthritis, active smoking status, and chronic corticosteroid use. Relevant medical history included prior intraarticular injection history, prior knee ligament surgery, and history of septic arthritis. Preoperative laboratory values collected within 7 days before surgery included serum albumin (g/dL), hemoglobin (g/dL), white blood cell count (WBC, ×10³/µL), C-reactive protein (mg/dL), and erythrocyte sedimentation rate (ESR, mm/h); fasting blood glucose (mg/dL) was measured the day before surgery. The Prognostic Nutritional Index (PNI) was calculated using the established formula: PNI = serum albumin (g/L) + 5 × total lymphocyte count (×10⁹/L). Intraoperative variables included the lowest recorded body temperature (°C), anesthesia type (general vs. regional), use of tranexamic acid, total intraoperative morphine dose (mg), and intraarticular drug injection type. Composite risk model construction Two composite risk models were constructed from optimal cut-off values derived by maximizing the Youden index (J = sensitivity + specificity − 1) in ROC analysis. The two-factor preoperative model (Model D) incorporated PNI ≤ 43.4 and preoperative blood glucose ≥ 147 mg/dL. The three-factor perioperative model (Model E) additionally incorporated intraoperative LBT ≤ 35.5°C. The PNI threshold was selected based on prior arthroplasty literature demonstrating an association between lower preoperative nutritional index and increased postoperative adverse outcomes [ 6 , 14 , 23 ]. The blood glucose cut-off of ≥ 147 mg/dL was derived from ROC analysis, approximating commonly referenced perioperative glycemic alert thresholds consistent with evidence linking elevated perioperative glucose to adverse surgical outcomes [ 2 , 16 ]. This threshold was selected as a pragmatic alert level within the commonly recommended inpatient glycemic target range (140–180 mg/dL), rather than as a treatment initiation threshold. The LBT cut-off of ≤ 35.5°C was derived from ROC analysis; while below the widely accepted 36°C threshold for inadvertent perioperative hypothermia, this value captures a more severe thermal exposure associated with attenuated immune defense [ 32 ]. These cut-offs were determined by ROC optimization within the present dataset and should be regarded as exploratory, pending external validation. Statistical analysis Continuous variables are presented as median with interquartile range [IQR] and compared using the Mann-Whitney U test, given non-normal data distributions confirmed by Shapiro-Wilk testing. Categorical variables are expressed as frequency and percentage, and compared using Pearson's chi-squared test or Fisher's exact test as appropriate. Multivariable binary logistic regression was performed to identify independent predictors of PJI. The regression model incorporated PNI as the primary nutritional marker, along with preoperative blood glucose, intraoperative LBT (as a binary variable, ≤ 35.5°C), BMI, age, ASA classification, diabetes mellitus, hypertension, hemoglobin, white blood cell count, C-reactive protein, and intraoperative variables including inhaled anesthetic use, intravenous fluid volume, blood transfusion, and morphine milligram equivalents. Covariates were prespecified and selected based on clinical relevance and prior literature [ 3 , 8 , 16 , 29 , 35 , 40 ]. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs). ROC curve analysis was performed to evaluate the discriminative performance of five models: three individual predictors (Models A–C: PNI, blood glucose, LBT) and two composite models (Model D: PNI + glucose; Model E: PNI + glucose + LBT). Area under the curve (AUC) values with 95% CIs were calculated, and pairwise AUC comparisons were performed using the DeLong test. Risk stratification was performed by tabulating observed PJI rates across subgroups defined by the number of concurrent risk factors present (0, 1, and 2 for the two-factor model; 0 to 3 for the three-factor model). All statistical analyses were performed using R software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria) and SPSS version 26.0 (IBM Corp., Armonk, NY, USA). A two-tailed p value < 0.05 was considered statistically significant for all analyses. Results Study population and baseline characteristics A total of 4,319 patients who underwent primary TKA were initially screened during the study period. PJI within one year occurred in 57 patients, whereas 255 patients remained infection-free, yielding an overall PJI incidence of 1.3% (Fig. 1 ; Table 1 ). Table 1 Baseline characteristics of case-matched patients following primary total knee arthroplasty, stratified by periprosthetic joint infection status (n = 312). Variable (unit) Control group (n = 255) PJI group (n = 57) p value Demographics Age (year) 70.0 (66.0–75.0) 70.0 (66.0–76.0) 0.911 Gender (female) 199 (78.0%) 40 (70.2%) 0.205 Gender (male) 56 (22.0%) 17 (29.8%) Body mass index (kg/m²) 27.2 (24.6–30.1) 27.2 (25.0–31.3) 0.275 ASA score I & II 131 (51.4%) 26 (45.6%) 0.432 ASA score III 124 (48.6%) 31 (54.4%) Comorbidities Diabetes mellitus 75 (29.4%) 21 (36.8%) 0.272 Hypertension 192 (75.3%) 46 (80.7%) 0.386 Chronic kidney disease 32 (12.5%) 8 (14.0%) 0.762 Coronary artery disease 16 (6.3%) 1 (1.8%) 0.328 Heart failure 4 (1.6%) 0 (0.0%) 1.000 Chronic obstructive pulmonary disease 4 (1.6%) 3 (5.3%) 0.117 Cerebral stroke 20 (7.8%) 5 (8.8%) 0.789 Autoimmune disease 15 (5.9%) 3 (5.3%) 1.000 Rheumatoid arthritis 10 (3.9%) 3 (5.3%) 0.712 Malignancy 34 (13.3%) 8 (14.0%) 0.888 Peripheral vascular disease 4 (1.6%) 3 (5.3%) 0.117 Smoking (recent use) 10 (3.9%) 4 (7.0%) 0.296 Steroid use 24 (9.4%) 5 (8.8%) 0.880 Medical history of knee Prior intraarticular injection 70 (27.5%) 9 (15.8%) 0.067 Prior knee ligament surgery 5 (2.0%) 3 (5.3%) 0.164 Prior septic arthritis 1 (0.4%) 0 (0.0%) 1.000 Preoperative laboratory values Albumin (g/dL) 4.50 (4.30–4.70) 4.36 (4.16–4.59) 0.001 Prognostic nutritional index 45.96 (43.84–48.23) 44.50 (42.23–47.03) 0.002 Blood glucose (mg/dL) 107.0 (95.0–128.0) 110.0 (97.5–144.0) 0.127 Hemoglobin (g/dL) 12.9 (11.9–13.7) 12.7 (11.9–13.7) 0.830 White blood cell count (×10³/µL) 6.90 (5.70–8.10) 6.80 (5.50–7.90) 0.900 C-reactive protein (mg/dL) 1.72 (0.74–3.81) 2.09 (1.04–3.90) 0.325 Erythrocyte sedimentation rate (mm/hr) 16.0 (10.0–28.0) 21.0 (10.0–37.0) 0.237 Lymphocyte (%) 28.1 (22.7–32.9) 26.1 (21.7–33.7) 0.542 Intraoperative factors LBT (°C) 35.9 (35.6–36.2) 35.7 (35.4–36.1) 0.030 LBT < 36°C 132 (51.8%) 38 (66.7%) 0.055 LBT ≤ 35.5°C 60 (23.5%) 25 (43.9%) 0.002 Intraoperative hypotension 18 (7.1%) 4 (7.0%) 1.000 General anesthesia 249 (97.6%) 51 (89.5%) 0.010 Inhaled anesthetic (mL/kg/hr) 0.21 (0.17–0.26) 0.18 (0.14–0.28) 0.116 Intravenous fluid (mL/kg/hr) 2.85 (2.17–3.44) 2.33 (1.77–3.15) 0.011 Blood transfusion 3 (1.2%) 2 (3.5%) 0.227 Surgery duration (hr) 3.0 (2.77–3.42) 3.05 (2.83–3.33) 0.499 Tranexamic acid use 241 (94.5%) 50 (87.7%) 0.079 Morphine dosage (mg) 15.0 (10.0–20.0) 15.0 (10.0–15.5) 0.240 Intraarticular injection None 120 (47.1%) 23 (40.4%) 0.326 Morphine 1 (0.4%) 0 (0.0%) Transamine 66 (25.9%) 12 (21.1%) Morphine + transamine 68 (26.7%) 22 (38.6%) Postoperative outcomes Postoperative nausea and vomiting 8 (3.1%) 2 (3.5%) 1.000 Length of stay (day) 4.0 (3.0–4.0) 4.0 (4.0–5.0) < 0.001 Data are presented as median (interquartile range) or number (percentage). Between-group comparisons were performed using the Mann-Whitney U test for continuous variables and the chi-square test or Fisher's exact test for categorical variables. Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index; UTI, urinary tract infection. Baseline demographic characteristics were similar between groups. Median age was 70.0 years (IQR 66.0–75.0) in the control group and 70.0 years (IQR 66.0–76.0) in the PJI group ( p = 0.91). Median body mass index (BMI) was 27.2 (24.6–30.1) kg/m² in the control group and 27.2 (25.0–31.3) kg/m² in the PJI group ( p = 0.275). The proportion of patients classified as ASA physical status III was 48.6% in the control group and 54.4% in the PJI group ( p = 0.432). No patient was recorded as ASA physical status IV in both groups. With respect to comorbidities, the prevalence of diabetes mellitus (29.4% vs 36.8%, p = 0.272), hypertension (75.3% vs 80.7%, p = 0.386), chronic kidney disease (12.5% vs 14.0%, p = 0.762), and coronary artery disease (6.3% vs 1.8%, p = 0.328) did not differ significantly between groups. Chronic obstructive pulmonary disease (1.6% vs 5.3%, p = 0.117), heart failure (1.6% vs 0.0%, p = 1.000), autoimmune disease (5.9% vs 5.3%, p = 1.000), rheumatoid arthritis (3.9% vs 5.3%, p = 0.712), and recent smoking (3.9% vs 7.0%, p = 0.296) were similarly distributed. Steroid use was reported in 9.4% and 8.8% of patients, respectively ( p = 0.880). Prior intraarticular injection was reported in 27.5% of patients without infection and 15.8% of those with infection ( p = 0.067). Prior knee ligament surgery (2.0% vs 5.3%, p = 0.164) and prior septic arthritis (0.4% vs 0.0%, p = 1.000) were uncommon in both groups (Table 1 ). Preoperative laboratory findings Preoperative laboratory evaluation demonstrated differences in nutritional markers between groups. Median PNI was significantly lower in the PJI group (44.50 [42.23–47.03]) than in the control group (45.96 [43.84–48.23]; p = 0.002). Serum albumin was also significantly lower in the PJI group (4.36 [4.16–4.59] g/dL) than in the control group (4.50 [4.30–4.70] g/dL; p = 0.001). Preoperative blood glucose, analyzed as a continuous measure, was numerically higher in the PJI group but did not reach significance (110.0 [97.5–144.0] vs 107.0 [95.0–128.0] mg/dL; p = 0.127). No statistically significant between-group differences were observed for hemoglobin (12.7 [11.9–13.7] vs 12.9 [11.9–13.7] g/dL; p = 0.830), white blood cell count (6.80 [5.50–7.90] vs 6.90 [5.70–8.10] ×10³/µL; p = 0.90), C-reactive protein (2.09 [1.04–3.90] vs 1.72 [0.74–3.81] mg/dL; p = 0.325), or erythrocyte sedimentation rate (21.0 [10.0–37.0] vs 16.0 [10.0–28.0] mm/h; p = 0.237) (Table 1 ). These inflammatory and hematological markers did not discriminate between groups in univariable analysis. Intraoperative variables The lowest recorded intraoperative body temperature was significantly lower in the PJI group (35.7 [35.4–36.1] °C) than in the control group (35.9 [35.6–36.2] °C; p = 0.030). The proportion of patients with LBT ≤ 35.5°C was significantly higher in the PJI group (43.9%) than in the control group (23.5%; p = 0.002). General anesthesia was used in 97.6% of patients without infection and 89.5% of those with infection ( p = 0.01). Tranexamic acid was administered in 94.5% of patients in the control group and 87.7% in the PJI group, a difference that did not reach statistical significance ( p = 0.079). Total intraoperative morphine dose was similar between groups (15.0 [10.0–20.0] vs 15.0 [10.0–15.5] mg; p = 0.240). The distribution of intraarticular drug injection types did not differ significantly between groups ( p = 0.326) (Table 1 ). Intravenous fluid administration was also lower in the PJI group (2.33 vs 2.85 mL/kg/hr; p = 0.011). Postoperative length of stay was significantly longer in the PJI group (median 4.0 [IQR 4.0–5.0] days) than in the control group (median 4.0 [IQR 3.0–4.0] days; p < 0.001) (Table 1 ). Multivariable logistic regression analysis Multivariable logistic regression was performed to incorporate PNI, preoperative blood glucose, intraoperative LBT (≤ 35.5°C), and prespecified covariates. Three variables were independently associated with PJI (Table 2 ). Higher PNI was independently associated with lower odds of PJI (OR 0.89 per unit increase, 95% CI 0.80–0.99; p = 0.027) (Table 2 ). Preoperative blood glucose was independently associated with increased infection risk (OR 1.01 per 1 mg/dL increase, 95% CI 1.001–1.02; p = 0.039). Intraoperative LBT ≤ 35.5°C was independently associated with significantly increased infection risk (OR 2.49, 95% CI 1.29–4.83; p = 0.007). No other covariate was independently associated with PJI in the adjusted model (Table 2 ). Table 2 Univariate and multivariable logistic regression analyses identifying perioperative risk factors for periprosthetic joint infection after primary total knee arthroplasty (n = 312). Variables (unit) Median (IQR) or N (%) Univariate analysis Multivariable analysis OR (95% CI) P value OR (95% CI) p value Age (year) 70.0 (66.0–75.0) 1.01 (0.97–1.04) 0.754 1.02 (0.97–1.07) 0.503 Gender (female) 239 (76.6%) 1 1 Gender (male) 73 (23.4%) 1.51 (0.80–2.86) 0.207 1.22 (0.58–2.55) 0.603 Body mass index (kg/m²) 27.2 (24.7–30.2) 1.05 (0.99–1.12) 0.116 1.05 (0.97–1.14) 0.200 ASA score I & II 157 (50.3%) 1 1 ASA score III 155 (49.7%) 1.26 (0.71–2.24) 0.432 0.79 (0.39–1.57) 0.497 Prognostic nutritional index 45.7 (43.7–47.8) 0.88 (0.80–0.96) 0.006 0.89 (0.80–0.99) 0.027 C-reactive protein (mg/dL) 1.80 (0.80–3.80) 0.98 (0.95–1.03) 0.466 0.97 (0.92–1.03) 0.297 Steroid used 29 (9.3%) 0.93 (0.34–2.54) 0.880 0.97 (0.32–2.93) 0.951 Blood glucose 107.0 (96.0-130.2) 1.01 (1.002–1.02) 0.017 1.01 (1.001–1.02) 0.039 LBT ≤ 35.5 85 (27.2%) 2.54 (1.40–4.62) 0.002 2.49 (1.29–4.83) 0.007 Inhaled anesthetic (mL/kg/hr) 0.20 (0.16–0.26) 0.20 (0.00-9.98) 0.422 2.11 (0.03-171.05) 0.739 Intravenous fluid (mL/kg/hr) 2.76 (2.10–3.40) 0.72 (0.54–0.97) 0.031 0.72 (0.52–1.01) 0.054 Blood transfusion 5 (1.6%) 3.05 (0.50-18.72) 0.227 2.47 (0.33–18.43) 0.378 Intra-operative MME (mg) 15.0 (10.0-18.2) 0.95 (0.91–0.99) 0.024 0.96 (0.91–1.01) 0.098 Diabetes mellitus 96 (30.8%) 1.40 (0.77–2.56) 0.273 0.97 (0.45–2.06) 0.929 Hypertension 238 (76.3%) 1.37 (0.67–2.81) 0.387 1.09 (0.48–2.48) 0.833 Odds ratios (ORs) with 95% confidence intervals (CIs) are reported for each variable. Multivariable ORs are adjusted for all covariates listed in the table. Statistical significance was set at p < 0.05. Abbreviations: ASA, American Society of Anesthesiologists; CI, confidence interval; LBT, lowest body temperature; MME, morphine milligram equivalent; OR, odds ratio; PNI, prognostic nutritional index. ROC analysis and composite model discrimination ROC analysis of individual predictors demonstrated that PNI provided the strongest single-variable discrimination (Model A: AUC = 0.631, 95% CI 0.547–0.715, p = 0.002), followed by LBT (Model C: AUC = 0.593, 95% CI 0.509–0.677, p = 0.031), while continuous blood glucose alone did not reach significance (Model B: AUC = 0.565, 95% CI 0.480–0.649, p = 0.134) (Fig. 2 ; Table 3 ). At the optimal Youden-index–derived cut-offs, PNI ≤ 43.4 was present in 22 PJI patients (38.6%) versus 45 controls (17.6%); preoperative blood glucose ≥ 147 mg/dL in 14 PJI patients (24.6%) versus 19 controls (7.5%); and intraoperative LBT ≤ 35.5°C in 25 PJI patients (43.9%) versus 60 controls (23.5%) (Table 4 ). The two-factor preoperative model (Model D: PNI ≤ 43.4 + blood glucose ≥ 147 mg/dL) yielded an AUC of 0.657 (95% CI 0.574–0.740, p < 0.001) and the three-factor perioperative model (Model E: PNI + glucose + LBT ≤ 35.5°C) yielded an AUC of 0.662 (95% CI 0.579–0.745, p < 0.001). DeLong test confirmed that both composite models significantly outperformed blood glucose alone ( p = 0.021 and p = 0.023, respectively), while Model D and Model E did not differ significantly (ΔAUC = 0.005, p = 0.802) (Table 5 ). Table 3 Receiver operating characteristic curve analysis of individual and composite perioperative risk models for predicting periprosthetic joint infection after primary total knee arthroplasty. Model Predictor AUC 95% CI p value Optimal Cut-off Sensitivity Specificity A PNI 0.631 0.547–0.715 0.002 ≤ 43.4 38.60% 82.40% B Glucose 0.565 0.480–0.649 0.134 ≥ 147.0 24.60% 92.50% C LBT 0.593 0.509–0.677 0.031 ≤ 35.5 43.90% 76.50% D PNI+ Glucose 0.657 0.574–0.740 < 0.001 0.204 (prob) 54.40% 77.30% E PNI+ Glucose + LBT 0.662 0.579–0.745 < 0.001 0.214 (prob) 45.60% 82.70% Optimal cut-off values were determined by maximizing the Youden index (J = sensitivity + specificity − 1). For composite models (Models D and E), the cut-off is expressed as the predicted probability derived from logistic regression. A two-tailed p value < 0.05 was considered statistically significant. Abbreviations: AUC, area under the curve; CI, confidence interval; LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index; ROC, receiver operating characteristic. Table 4 Distribution of patients meeting for each dichotomous individual perioperative risk factor, stratified by periprosthetic joint infection status. Risk factor Cut-off (yes/no) PJI group (n = 57) Control group (n = 255) p value PNI ≤ 43.4 22 (38.6%) 45 (17.6%) 0.001 Blood glucose ≥ 147 mg/dL 14 (24.6%) 19 (7.5%) < 0.001 LBT ≤ 35.5°C 25 (43.9%) 60 (23.5%) 0.002 Cut-off values were determined by maximizing the Youden index (J = sensitivity + specificity − 1). Data are presented as number of patients meeting the cut-off criterion (percentage of group total). A two-tailed p value < 0.05 was considered statistically significant. Abbreviations: LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index. Table 5 Pairwise comparisons of AUC values using the DeLong test among individual and composite models for predicting periprosthetic joint infection after primary total knee arthroplasty. Pairwise AUC comparisons among Models A–E were performed using the DeLong test. Each row shows ΔAUC, Z statistic, and p value. Comparison AUC 1 AUC 2 Delta AUC Z statistic p value Model A (PNI) vs Model B (Glucose) 0.631 0.565 0.0666 1.21 0.226 Model A (PNI) vs Model C (LBT) 0.631 0.593 0.0381 0.648 0.517 Model A (PNI) vs Model D (PNI+Glucose) 0.631 0.657 -0.0261 -1.185 0.236 Model A (PNI) vs Model E (PNI+Glucose + LBT) 0.631 0.662 -0.0311 -1.183 0.237 Model B (Glucose) vs Model C (LBT) 0.565 0.593 -0.0285 -0.436 0.663 Model B (Glucose) vs Model D (PNI+ Glucose) 0.565 0.657 -0.0928 -2.305 0.021 Model B (Glucose) vs Model E (PNI+ Glucose + LBT) 0.565 0.662 -0.0978 -2.282 0.023 Model C (LBT) vs Model D (PNI+ Glucose) 0.593 0.657 -0.0643 -1.01 0.312 Model C (LBT) vs Model E (PNI+ Glucose + LBT) 0.593 0.662 -0.0693 -1.391 0.164 Model D (PNI+ Glucose) vs Model E (PNI+ Glucose + LBT) 0.657 0.662 -0.005 -0.25 0.802 Data are presented as area under the receiver operating characteristic curve (AUC) values and pairwise comparisons using the DeLong test for correlated ROC curves. ΔAUC indicates the difference between two models. A two-tailed p value < 0.05 was considered statistically significant. Abbreviations: AUC, area under the ROC curve; LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index; TKA, total knee arthroplasty; ΔAUC, difference in AUC. Risk stratification Risk stratification using Model D revealed a stepwise PJI rate gradient across two-factor strata: 11.8% (score 0), 31.0% (score 1), and 62.5% (score 2) (χ² = 25.663, p < 0.001; Spearman r = 0.269, p < 0.001). Because PNI and blood glucose are both available from routine preoperative assessment, Model D functions as a preoperative screening tool enabling nutritional and glycemic optimization prior to surgery (Fig. 3 ). Model E additionally incorporated intraoperative LBT ≤ 35.5°C, a parameter unavailable before surgery but directly controllable through structured perioperative normothermia protocols, yielding a more pronounced dose-response gradient: 9.9% (score 0), 20.0% (score 1), 50.0% (score 2), and 100% (score 3) (χ² = 40.027, p < 0.001; Spearman r = 0.283, p < 0.001). The score-3 subgroup comprised only three patients and should be interpreted with caution. Model E serves a complementary intraoperative role: real-time LBT monitoring enables anesthesiologists to identify patients accruing additional thermal risk during surgery and to escalate active warming accordingly (Fig. 3 ). Discussion PJI remains one of the most consequential complications following primary TKA, contributing substantially to patient morbidity and healthcare resource utilization. Reported incidence varies across population registries and institutional cohorts, influenced by case-mix, referral patterns, diagnostic definitions, and surveillance intensity [ 20 , 29 ]. In this single-center case-matched cohort of 312 primary TKA patients with systematic one-year follow-up using International Consensus Meeting criteria, the overall PJI incidence was 1.3%. Within this context, PNI and preoperative blood glucose were independently associated with infection risk, and intraoperative LBT ≤ 35.5°C was additionally identified as an independent predictor (OR 2.49, p = 0.007). Two composite models were constructed: a two-factor preoperative model (PNI + glucose, AUC = 0.657) and a three-factor perioperative model (PNI + glucose + LBT, AUC = 0.662), each revealing a clear stepwise dose-response gradient in observed PJI rates. PNI as an integrated host-state marker and infection susceptibility PNI represents more than a simple nutritional marker; it integrates serum albumin and total lymphocyte count into a composite index reflecting protein reserve, immune competence, and overall physiological resilience. Prior arthroplasty literature has consistently linked lower preoperative nutritional and immunological status to adverse postoperative and infection-related outcomes [ 13 , 26 ]. In our adjusted analysis, each unit increment in PNI was associated with approximately 11% lower odds of PJI (OR 0.89, 95% CI 0.80–0.99, p = 0.027), underscoring the biological relevance of composite host nutritional-immunological reserve. Our ROC-derived cut-off (PNI ≤ 43.4) lies above the conventional threshold for nutritional impairment [ 13 ], suggesting that subclinical nutritional-inflammatory compromise elevates infection susceptibility before frank malnutrition is evident, extending prior observations linking preoperative nutritional deficiency to acute postoperative infection risk after total joint arthroplasty [ 13 ]. Mechanistically, reduced PNI may reflect impaired lymphocyte-mediated defense, dysregulated inflammatory resolution, and compromised wound healing at the implant interface [ 31 ]. These findings reinforce the value of systematic preoperative PNI screening and nutritional optimization as components of perioperative infection prevention pathways [ 8 , 26 , 38 ]. Perioperative hyperglycemia beyond the diabetes label Preoperative blood glucose demonstrated an independent association with PJI when analyzed continuously (OR 1.01 per 1 mg/dL increase), whereas a diagnosis of diabetes mellitus alone did not differentiate risk. This observation aligns with evidence suggesting that glycemic state, rather than diagnostic label, more accurately reflects infection-relevant biological vulnerability [ 3 , 40 ]. Hyperglycemia impairs neutrophil chemotaxis, oxidative burst activity, and microbial clearance, while promoting dysregulated inflammatory responses [ 5 , 31 ]. These mechanisms are particularly relevant in the context of stress hyperglycemia, where transient perioperative glucose elevations may occur even in patients without established diabetes [ 2 ]. Our ROC-derived threshold (≥ 147 mg/dL) aligns with established inpatient glycemic alert thresholds [ 2 ]. Together with prior surgical data emphasizing perioperative glycemic control [ 2 , 19 ], our findings support routine glucose monitoring and targeted optimization regardless of formal diabetes status. Intraoperative hypothermia as an independent and modifiable intraoperative risk factor Intraoperative LBT ≤ 35.5°C was independently associated with significantly increased odds of PJI (OR 2.49, 95% CI 1.29–4.83, p = 0.007) after multivariable adjustment, representing the largest effect estimate among the three identified predictors. This finding confirms the biological plausibility of intraoperative hypothermia as a modifiable host defense impairment, potentially facilitating bacterial survival at the implant-tissue interface [ 32 ]. Observational data from major non-cardiac surgery link intraoperative hypothermia with increased infectious complications [ 24 ], and early randomized evidence supports the protective effect of perioperative normothermia [ 18 ]. Although arthroplasty-specific data have yielded heterogeneous findings [ 1 , 43 ], the present result suggests that an LBT ≤ 35.5°C threshold may carry clinically relevant infection risk in TKA. Notably, LBT as a continuous variable was not significant on univariable analysis (OR = 0.663, 95% CI 0.395–1.111, p = 0.119), whereas dichotomization at ≤ 35.5°C yielded a significant association (OR = 2.49, p = 0.007). This pattern suggests possible threshold-dependent rather than linear risk, pending prospective validation. Importantly, unlike PNI and blood glucose, which are determined preoperatively, LBT is an intraoperative parameter under direct anesthetic control. This temporal distinction defines the complementary clinical role of our three-factor model: it functions not as a preoperative predictor, but as an intraoperative surveillance instrument that enables the perioperative team to identify patients accruing additional thermal risk during surgery and to escalate active warming strategies accordingly. In this framework, orthopedic surgeons and anesthesiologists share responsibility for infection risk optimization: surgeons may act on preoperative PNI and glycemic screening to defer or modify operative timing, while anesthesiologists implement targeted normothermia protocols intraoperatively based on real-time LBT monitoring [ 32 , 33 ]. Three additional variables demonstrated significant between-group differences but were not retained as independent predictors after multivariable adjustment. The lower rate of general anesthesia in the PJI group (89.5% vs 97.6%; p = 0.01) most likely reflects indication bias rather than a causal effect of anesthetic technique: patients selected for regional anesthesia may have had higher baseline comorbidity, independently predisposing them to infection. The association between lower intravenous fluid volume and PJI (2.33 vs 2.85 mL/kg/hr; p = 0.011; adjusted OR 0.72, p = 0.054) is similarly attributable to confounding by operative complexity and anesthetic practice rather than a direct biological mechanism. Finally, postoperative length of stay was significantly prolonged in the PJI group ( p < 0.001), consistent with the additional surgical intervention, antimicrobial therapy, and extended monitoring required for PJI management, and representing a major component of the excess healthcare burden attributable to prosthetic joint infection [ 17 , 27 ]. Sequential perioperative risk stratification: a two-model framework A central contribution of this study is the construction of a sequential two-model perioperative risk framework. The two-factor preoperative model (Model D: PNI ≤ 43.4 + blood glucose ≥ 147 mg/dL, AUC = 0.657) demonstrated a stepwise PJI gradient of 11.8%, 31.0%, and 62.5% across 0, 1, and 2 risk factor strata (Spearman r = 0.269, p < 0.001). The three-factor perioperative model (Model E: adding LBT ≤ 35.5°C, AUC = 0.662) produced a more pronounced gradient of 9.9%, 20.0%, 50.0%, and 100% across 0 to 3 strata (Spearman r = 0.283, p < 0.001), with the score-3 subgroup comprising only three patients; the observed 100% infection rate in this stratum is statistically unstable and should not be interpreted as a reliable estimate of absolute risk. Models D and E did not differ significantly (ΔAUC = 0.005, p = 0.802), supporting Model D as the more parsimonious preoperative screening tool. These three variables represent complementary perioperative domains, reduced nutritional-immunological reserve (PNI), metabolic stress (hyperglycemia), and attenuated intraoperative thermal defense (hypothermia), whose convergence may compound infection susceptibility. Contemporary orthopedic literature increasingly emphasizes multidimensional risk stratification frameworks in musculoskeletal infection, highlighting the interplay between pathogen virulence, implant environment, and host immune competence [ 21 , 31 ]. From a translational standpoint, Model D, requiring only two preoperative values available from routine blood work, may assist orthopedic surgeons in identifying high-risk patients for targeted infection prevention strategies, including nutritional optimization and perioperative glycemic control, prior to implantation. External validation studies of preoperative PJI prediction models report modest discrimination and variable calibration across cohorts [ 42 ], and systematic reviews highlight heterogeneity and limited reproducibility among existing models [ 22 ]. In this context, a simple and interpretable multidimensional framework may offer pragmatic clinical utility beyond what formal discrimination metrics convey. Model performance, interpretability, and validation needs The AUC values of 0.657 (Model D) and 0.662 (Model E) reflect modest but meaningful discriminative performance and should not be interpreted as definitive predictive accuracy. DeLong test confirmed that both composite models significantly outperformed blood glucose alone ( p = 0.021 and p = 0.023), while the negligible AUC difference between models (ΔAUC = 0.005) favors the more parsimonious two-factor preoperative model for routine clinical use. Model performance in development cohorts characteristically overestimates real-world discriminative ability [ 36 ]. Models developed within single-center or narrowly defined populations may show limited transportability due to case-mix differences and practice variation [ 28 ]. Accordingly, rigorous external validation and multicenter evaluation remain essential prior to widespread implementation [ 7 ]. Importantly, modest discrimination does not preclude clinical usefulness in structured risk stratification, particularly when models are simple, transparent, and biologically coherent. Our findings should therefore be regarded as hypothesis-generating for formal prediction purposes while simultaneously identifying clinically actionable domains, preoperative nutritional-immunological status (PNI), glycemic control, and intraoperative normothermia, that align with established perioperative infection prevention strategies [ 2 , 8 ]. Limitations and future directions This study has several limitations. Its retrospective single-center design carries potential residual confounding from unmeasured factors, including infecting organism characteristics, antibiotic timing, operative complexity, implant variables, and perioperative antimicrobial protocols [ 29 ]. The three-factor risk stratum contained only three patients, limiting precision and mandating cautious interpretation. ROC-derived cut-offs were optimized within the present dataset and require external validation before clinical implementation. Additionally, with 57 PJI events and 15 prespecified covariates, the EPV was approximately 3.8, well below the recommended threshold of 10, which may increase the risk of model overfitting and coefficient instability. Nevertheless, all covariates were prespecified based on biological plausibility and prior literature, and the observed stepwise dose-response gradients across risk strata support the robustness of the primary findings. Future work should externally validate both composite models in multicenter prospective cohorts, explore whether PNI-targeted nutritional prehabilitation or glycemic optimization reduces PJI incidence, and evaluate whether structured intraoperative warming protocols guided by LBT thresholds improve outcomes in randomized designs [ 8 ]. Abbreviations ASA — American Society of Anesthesiologists AUC — Area under the curve BMI — Body mass index CI — Confidence interval EPV — Events-per-variable ESR — Erythrocyte sedimentation rate IQR — Interquartile range LBT — Lowest body temperature MME — Morphine milligram equivalent OR — Odds ratio PJI — Periprosthetic joint infection PNI — Prognostic Nutritional Index ROC — Receiver operating characteristic TKA — Total knee arthroplasty WBC — White blood cell count Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Institutional Review Board of our institution (approval number withheld for blinding). The requirement for informed consent was waived by the nature of its retrospective design. Consent for publication Not applicable. Funding This study received no external funding. Author Contribution CRediT: Conceptualization: Author 1 , Author 7 ; Methodology: Author 2, Author 3, Author 6 ; Formal analysis and investigation: Author 2, Author 5 , Author 4 ; Writing - original draft preparation: Author 1 ; Writing - review and editing: Author 1 , Author 7 ; Project administration: Author 7 ; Resources: Author 3, Author 5 ; Supervision: Author 7 . Data Availability All data analyzed during this study are included in this published article. References Abugri BO, Matsusaki T, Ren W, Morimatsu H (2022) Intraoperative Hypothermia Is Not Associated with Surgical Site Infections after Total Hip or Knee Arthroplasty. Acta Med Okayama 76:651–660 American Diabetes Association Professional Practice C (2024) 16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2024. 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Can the Same Criteria be Used for Both Joints?: Proceedings of International Consensus on Orthopedic Infections. J Arthroplasty 34:S325-S327 Shohat N, Restrepo C, Allierezaie A, Tarabichi M, Goel R, Parvizi J (2018) Increased Postoperative Glucose Variability Is Associated with Adverse Outcomes Following Total Joint Arthroplasty. J Bone Joint Surg Am 100:1110–1117 Siontis GC, Tzoulaki I, Castaldi PJ, Ioannidis JP (2015) External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol 68:25–34 Szymski D, Walter N, Straub J, Wu Y, Melsheimer O, Grimberg A, et al. (2024) Low implantation volume, comorbidities, male sex and implantation of constrained TKA identified as risk factors for septic revision in knee arthroplasty: A register-based study from the German Arthroplasty Registry. Knee Surg Sports Traumatol Arthrosc 32:1743–1752 Torchia MT, Khan IA, Christensen DD, Moschetti WE, Fillingham YA (2023) Universal Screening for Malnutrition Prior to Total Knee Arthroplasty Is Cost-Effective: A Markov Analysis. J Arthroplasty 38:443–449 Turina M, Fry DE, Polk HC, Jr. (2005) Acute hyperglycemia and the innate immune system: clinical, cellular, and molecular aspects. Crit Care Med 33:1624–1633 Wier J, Liu KC, Richardson MK, Gettleman BS, Kistler NM, Heckmann ND, et al. (2024) Higher Blood Glucose Levels on the Day of Surgery Are Associated with an Increased Risk of Periprosthetic Joint Infection After Total Hip Arthroplasty. J Bone Joint Surg Am 106:276–287 Wildeman P, Rolfson O, Soderquist B, Wretenberg P, Lindgren V (2021) What Are the Long-term Outcomes of Mortality, Quality of Life, and Hip Function after Prosthetic Joint Infection of the Hip? A 10-year Follow-up from Sweden. Clin Orthop Relat Res 479:2203–2213 Yoon SJ, Jutte PC, Soriano A, Sousa R, Zijlstra WP, Wouthuyzen-Bakker M (2024) Predicting periprosthetic joint infection: external validation of preoperative prediction models. J Bone Jt Infect 9:231–239 Zhou YD, Zhang WY, Xie GH, Ye H, Chu LH, Guo YQ, et al. (2024) Inadvertent perioperative hypothermia and surgical site infections after liver resection. Hepatobiliary Pancreat Dis Int 23:579–585 Additional Declarations No competing interests reported. Supplementary Files GraphicAbstract.tiff Cite Share Download PDF Status: Posted Version 1 posted 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-9331642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622699222,"identity":"1c91cb28-8a43-4dd9-8086-cb11395b4f2e","order_by":0,"name":"Yung-Fong Tsai","email":"","orcid":"","institution":"Chang Gung Memorial Hospital, Linkou Branch","correspondingAuthor":false,"prefix":"","firstName":"Yung-Fong","middleName":"","lastName":"Tsai","suffix":""},{"id":622699223,"identity":"f7f02c0c-4c79-4387-975f-47599d752866","order_by":1,"name":"Yu-Hong Ng","email":"","orcid":"","institution":"Kaohsiung Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu-Hong","middleName":"","lastName":"Ng","suffix":""},{"id":622699224,"identity":"59a9b025-1ebb-48aa-8be7-0ca0c028843e","order_by":2,"name":"Shu-Yu Yeh","email":"","orcid":"","institution":"Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Shu-Yu","middleName":"","lastName":"Yeh","suffix":""},{"id":622699225,"identity":"3b2a2921-41b4-416e-a0f5-0491f75401b7","order_by":3,"name":"Yu-Hsun Sun","email":"","orcid":"","institution":"Kaohsiung Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu-Hsun","middleName":"","lastName":"Sun","suffix":""},{"id":622699230,"identity":"f356c83f-1994-437c-b256-2580644d3240","order_by":4,"name":"Huan-Tang Lin","email":"","orcid":"","institution":"Chang Gung Memorial Hospital, Linkou Branch","correspondingAuthor":false,"prefix":"","firstName":"Huan-Tang","middleName":"","lastName":"Lin","suffix":""},{"id":622699231,"identity":"63ebc84b-8a77-4850-a919-ea568ab9e2a8","order_by":5,"name":"Yu-Fang Liu","email":"","orcid":"","institution":"China Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu-Fang","middleName":"","lastName":"Liu","suffix":""},{"id":622699232,"identity":"62935c20-6995-45a8-b18c-07834505ca3f","order_by":6,"name":"Shao-Chun Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCSBmbACxmA+AuDKkaGFLAHF5SNHCYwAmCeqQn9388MHPHYfzDG7kfH51o8aCh4H98NEN+LQY3DlmbNh75nCxwY3cbdY5x4AO40lLu4FXi0SCmTRj2+HEbUAtxjlsQC0SPGZ4tcjPSP8G1ZLzzDjnHxFaGG7kwGzJYX6c20aEFqCviw1729IT9595Zsac2yfBw0bIL0CHbXzws806cWZ78uPPOd/q5PjZDx/D7zAkwCYBJolVDgLMH0hRPQpGwSgYBSMHAACyhktVTBS/1QAAAABJRU5ErkJggg==","orcid":"","institution":"Chang Gung University","correspondingAuthor":true,"prefix":"","firstName":"Shao-Chun","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-04-06 08:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9331642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9331642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107485426,"identity":"61dbac0c-2db7-48aa-b365-00e4a3343dfd","added_by":"auto","created_at":"2026-04-22 02:34:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11465602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of study design and patient selection for a case-matched cohort study of periprosthetic joint infection following primary total knee arthroplasty.\u003c/strong\u003e A total of 4,319 patients who underwent primary total knee arthroplasty (TKA) were initially screened. Fifty-seven patients who developed periprosthetic joint infection (PJI) within one year postoperatively were identified as cases. Controls were selected at an intended 1:5 ratio matched by operating surgeon and year of surgery, yielding 285 candidate controls. After exclusion of 30 controls due to abnormal preoperative inflammatory markers (elevated CRP, ESR, or leukocytosis; n = 6), active infection (urinary tract infection; n = 6), or incomplete medical records (n = 24), a final matched cohort of 312 patients (57 cases and 255 controls) was retained for analysis. Abbreviations: CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; PJI, periprosthetic joint infection; TKA, total knee arthroplasty; UTI, urinary tract infection.\u003c/p\u003e","description":"","filename":"RevizedFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9331642/v1/a900b739bcdfd2712646c707.png"},{"id":107485575,"identity":"a02288ab-8023-4c23-ab9b-51137cbf0eca","added_by":"auto","created_at":"2026-04-22 02:35:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3524949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curves for individual and composite perioperative risk models predicting periprosthetic joint infection after primary total knee arthroplasty.\u003c/strong\u003e Receiver operating characteristic (ROC) curves are shown for five predictive models: three individual risk factors, prognostic nutritional index (Model A), preoperative blood glucose (Model B), and intraoperative lowest body temperature (Model C), and two composite models comprising PNI and blood glucose (Model D) and all three variables combined (Model E). Optimal cut-off values were determined by maximizing the Youden index. For composite models, the cut-off is expressed as the predicted probability from logistic regression. The diagonal dashed line represents chance-level discrimination (AUC = 0.50). Abbreviations: AUC, area under the curve; LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index; ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"RevizedFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9331642/v1/60feae50aba627a30e70ac88.png"},{"id":107484263,"identity":"37b32eba-b30a-4276-ad9b-8638285d140e","added_by":"auto","created_at":"2026-04-22 02:31:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3305644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk stratification for periprosthetic joint infection using preoperative and perioperative composite scoring models. \u003c/strong\u003e(A) Two-factor preoperative model (PNI ≤ 43.4 + blood glucose ≥ 147 mg/dL; cut-offs by Youden index). Stacked bar charts show PJI cases and matched controls by number of preoperative risk factors present (0, 1, or 2). Observed PJI rates increased progressively: 11.8%, 31.0%, and 62.5% (Spearman \u003cem\u003er\u003c/em\u003e = 0.269, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001); AUC = 0.657 (95% CI 0.574–0.740, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001); χ² = 25.663, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. (B) Three-factor perioperative model (PNI ≤ 43.4 + blood glucose ≥ 147 mg/dL + LBT ≤ 35.5°C; cut-offs by Youden index). Stacked bar charts show distribution by number of perioperative risk factors present (0, 1, 2, or 3). Observed PJI rates: 9.9%, 20.0%, 50.0%, and 100%† (Spearman \u003cem\u003er\u003c/em\u003e= 0.283, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001); AUC = 0.662 (95% CI 0.579–0.745, p \u0026lt; 0.001); χ² = 40.027, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e†Small subgroup (n = 3); the 100% infection rate is statistically unstable and should be interpreted with caution. AUC, area under the curve; LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index.\u003c/p\u003e","description":"","filename":"RevizedFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9331642/v1/5016ae12c19b10cade9e4d41.png"},{"id":107488061,"identity":"1aa6a8e3-d79e-4e3c-836e-f2da01a429ab","added_by":"auto","created_at":"2026-04-22 02:43:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19167476,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9331642/v1/054b94a9-54ef-4b51-9d14-d2e73b7047bc.pdf"},{"id":107484107,"identity":"a8e6005d-5d6f-4310-8391-8c5ccd70ad61","added_by":"auto","created_at":"2026-04-22 02:30:47","extension":"tiff","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6050668,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicAbstract.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9331642/v1/a5da5a97c8c11055a1d97f12.tiff"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perioperative Risk Stratification for Periprosthetic Joint Infection after Primary Total Knee Arthroplasty: A Case-Matched Cohort Study Incorporating Nutritional Index, Glycemic Status, and Intraoperative Hypothermia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTotal knee arthroplasty (TKA) is one of the most frequently performed elective orthopedic procedures worldwide and consistently improves pain and functional outcomes in patients with end-stage knee osteoarthritis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As populations age and surgical indications expand, the annual volume of primary TKA continues to increase [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. With this expansion, the absolute burden of arthroplasty-related complications is expected to rise. Periprosthetic joint infection (PJI) remains among the most serious and resource-intensive adverse events after primary TKA, with incidence varying across registries and institutional cohorts depending on patient characteristics and surveillance methodology [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Patients frequently require additional surgical intervention, prolonged antimicrobial therapy, and extended hospitalization, and infection-related costs are several-fold higher than those of primary procedures [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These considerations highlight the importance of identifying modifiable perioperative risk factors that can be addressed before implantation.\u003c/p\u003e \u003cp\u003eNumerous host and surgical variables have been associated with PJI risk [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, many established predictors, such as age or baseline comorbidity burden, are not modifiable at the time of surgery. In contrast, several routinely measured perioperative physiological parameters are potentially correctable. Preoperative nutritional status represents one such domain. The Prognostic Nutritional Index (PNI), calculated from serum albumin and total lymphocyte count, integrates protein reserve with immune competence and provides a more comprehensive reflection of host physiological resilience than albumin alone [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Unlike classical hypoalbuminemia thresholds (\u0026lt;\u0026thinsp;3.5 g/dL), which identify only overtly malnourished patients, PNI captures a broader spectrum of nutritional-immunological compromise prevalent in elective arthroplasty populations. Lower PNI has been associated with increased postoperative complications, surgical site infection, and impaired wound healing after total joint arthroplasty [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Whether PNI identifies subclinical PJI vulnerability in patients with conventionally normal albumin has direct implications for preoperative screening and nutritional optimization [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePerioperative glycemic control constitutes a second actionable domain. Hyperglycemia impairs neutrophil function, reduces oxidative killing capacity, and disrupts inflammatory pathways critical for early bacterial clearance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Importantly, perioperative glucose elevation frequently occurs in patients without established diabetes mellitus. Stress-mediated hyperglycemia is common in surgical populations and may represent an under-recognized contributor to infection susceptibility [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Emerging evidence suggests that perioperative glucose levels themselves, rather than the mere presence of diabetes, more accurately reflect biologically relevant vulnerability in arthroplasty cohorts [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. From a practical perspective, preoperative glucose measurement is universally available and allows timely medical optimization when indicated.\u003c/p\u003e \u003cp\u003eIntraoperative hypothermia, defined as a core temperature below 36\u0026deg;C, represents a third modifiable physiological exposure. Hypothermia induces peripheral vasoconstriction, reduces tissue oxygen tension, and attenuates innate immune responses, thereby potentially impairing local bacterial clearance at the implant\u0026ndash;tissue interface [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Early clinical investigations demonstrated that maintenance of perioperative normothermia reduces surgical-wound infection rates [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], though more recent large non-cardiac surgical trials have reported heterogeneous findings [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Arthroplasty-specific evidence evaluating active warming interventions remains limited, and the implications for PJI require further clarification. As an intraoperative parameter under direct anesthetic control, lowest body temperature (LBT) is uniquely suited to function as a real-time surveillance instrument rather than a preoperative predictor, with direct implications for anesthesiologist-led warming protocols.\u003c/p\u003e \u003cp\u003eWhile nutritional compromise, hyperglycemia, and hypothermia have each been examined independently, PJI is widely recognized as a multifactorial complication [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Patients undergoing primary TKA frequently present with multiple physiological vulnerabilities, yet the cumulative impact of potentially modifiable perioperative stressors on early PJI risk remains incompletely defined [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Contemporary arthroplasty research increasingly supports multivariable clinical prediction tools integrating preoperative and intraoperative parameters to improve infection risk stratification [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Accordingly, this single-center retrospective case-matched cohort study evaluated the independent associations of PNI, preoperative blood glucose, and intraoperative LBT with PJI within one year after primary TKA. We further assessed whether a composite scoring model based on data-driven cut-offs, PNI\u0026thinsp;\u0026le;\u0026thinsp;43.4, preoperative blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;147 mg/dL, and LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C, could identify a clinically meaningful, stepwise gradient in infection risk across cumulative strata, presented as both a two-factor preoperative model and a three-factor perioperative model.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis was a single-center retrospective case-matched cohort study conducted at a tertiary academic medical center. Ethical approval was obtained from the Institutional Review Board of our institution (approval number withheld for blinding). The requirement for informed consent was waived by the IRB given the retrospective nature of the study and use of de-identified clinical data. All procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eWe retrospectively identified all 4,319 adult patients who underwent primary TKA at our institution. Eligible patients were those with complete preoperative laboratory data, intraoperative temperature monitoring records, and a minimum postoperative follow-up of one year to capture PJI events. The study cohort comprised 312 patients, of whom 57 developed PJI (PJI group) and 255 did not (control group) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. To minimize potential confounding arising from variations in surgical technique and longitudinal improvements in perioperative care, patients were matched at a 1:5 ratio by the operating surgeon and the year of surgery. This approach ensures that differences in PJI outcomes are more likely attributable to patient-specific physiological factors and anesthetic management rather than inter-surgeon variability or temporal shifts in institutional infection prevention protocols.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePatients were excluded if they had: (1) incomplete preoperative laboratory records; (2) a history of prior joint infection at the operative site; (3) concurrent active systemic infection at the time of surgery; (4) revision arthroplasty procedures; or (5) immunocompromising conditions not captured in the standardized dataset, such as HIV-positive status.\u003c/p\u003e\n\u003ch3\u003eOutcome definition\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the occurrence of PJI within one year following the index arthroplasty procedure. PJI was defined according to the 2018 International Consensus Meeting criteria [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], incorporating clinical signs of wound inflammation, joint aspiration culture results, synovial fluid white blood cell counts, and histopathological findings when available. Diagnosis was confirmed by the treating orthopedic surgeon and verified through chart review by two independent investigators.\u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eDemographic variables collected included age, body mass index (BMI), and ASA physical status classification. Comorbidities documented at the time of surgery included diabetes mellitus, hypertension, chronic kidney disease, coronary artery disease, heart failure, chronic obstructive pulmonary disease, cerebral stroke, autoimmune disease, rheumatoid arthritis, active smoking status, and chronic corticosteroid use. Relevant medical history included prior intraarticular injection history, prior knee ligament surgery, and history of septic arthritis.\u003c/p\u003e \u003cp\u003ePreoperative laboratory values collected within 7 days before surgery included serum albumin (g/dL), hemoglobin (g/dL), white blood cell count (WBC, \u0026times;10\u0026sup3;/\u0026micro;L), C-reactive protein (mg/dL), and erythrocyte sedimentation rate (ESR, mm/h); fasting blood glucose (mg/dL) was measured the day before surgery. The Prognostic Nutritional Index (PNI) was calculated using the established formula:\u003c/p\u003e \u003cp\u003ePNI\u0026thinsp;=\u0026thinsp;serum albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; total lymphocyte count (\u0026times;10⁹/L).\u003c/p\u003e \u003cp\u003eIntraoperative variables included the lowest recorded body temperature (\u0026deg;C), anesthesia type (general vs. regional), use of tranexamic acid, total intraoperative morphine dose (mg), and intraarticular drug injection type.\u003c/p\u003e\n\u003ch3\u003eComposite risk model construction\u003c/h3\u003e\n\u003cp\u003eTwo composite risk models were constructed from optimal cut-off values derived by maximizing the Youden index (J\u0026thinsp;=\u0026thinsp;sensitivity\u0026thinsp;+\u0026thinsp;specificity\u0026thinsp;\u0026minus;\u0026thinsp;1) in ROC analysis. The two-factor preoperative model (Model D) incorporated PNI\u0026thinsp;\u0026le;\u0026thinsp;43.4 and preoperative blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;147 mg/dL. The three-factor perioperative model (Model E) additionally incorporated intraoperative LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C. The PNI threshold was selected based on prior arthroplasty literature demonstrating an association between lower preoperative nutritional index and increased postoperative adverse outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The blood glucose cut-off of \u0026ge;\u0026thinsp;147 mg/dL was derived from ROC analysis, approximating commonly referenced perioperative glycemic alert thresholds consistent with evidence linking elevated perioperative glucose to adverse surgical outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This threshold was selected as a pragmatic alert level within the commonly recommended inpatient glycemic target range (140\u0026ndash;180 mg/dL), rather than as a treatment initiation threshold. The LBT cut-off of \u0026le;\u0026thinsp;35.5\u0026deg;C was derived from ROC analysis; while below the widely accepted 36\u0026deg;C threshold for inadvertent perioperative hypothermia, this value captures a more severe thermal exposure associated with attenuated immune defense [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These cut-offs were determined by ROC optimization within the present dataset and should be regarded as exploratory, pending external validation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as median with interquartile range [IQR] and compared using the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, given non-normal data distributions confirmed by Shapiro-Wilk testing. Categorical variables are expressed as frequency and percentage, and compared using Pearson's chi-squared test or Fisher's exact test as appropriate.\u003c/p\u003e \u003cp\u003eMultivariable binary logistic regression was performed to identify independent predictors of PJI. The regression model incorporated PNI as the primary nutritional marker, along with preoperative blood glucose, intraoperative LBT (as a binary variable, \u0026le; 35.5\u0026deg;C), BMI, age, ASA classification, diabetes mellitus, hypertension, hemoglobin, white blood cell count, C-reactive protein, and intraoperative variables including inhaled anesthetic use, intravenous fluid volume, blood transfusion, and morphine milligram equivalents. Covariates were prespecified and selected based on clinical relevance and prior literature [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs).\u003c/p\u003e \u003cp\u003eROC curve analysis was performed to evaluate the discriminative performance of five models: three individual predictors (Models A\u0026ndash;C: PNI, blood glucose, LBT) and two composite models (Model D: PNI\u0026thinsp;+\u0026thinsp;glucose; Model E: PNI\u0026thinsp;+\u0026thinsp;glucose\u0026thinsp;+\u0026thinsp;LBT). Area under the curve (AUC) values with 95% CIs were calculated, and pairwise AUC comparisons were performed using the DeLong test. Risk stratification was performed by tabulating observed PJI rates across subgroups defined by the number of concurrent risk factors present (0, 1, and 2 for the two-factor model; 0 to 3 for the three-factor model).\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria) and SPSS version 26.0 (IBM Corp., Armonk, NY, USA). A two-tailed \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 4,319 patients who underwent primary TKA were initially screened during the study period. PJI within one year occurred in 57 patients, whereas 255 patients remained infection-free, yielding an overall PJI incidence of 1.3% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of case-matched patients following primary total knee arthroplasty, stratified by periprosthetic joint infection status (n\u0026thinsp;=\u0026thinsp;312).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable (unit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;255)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePJI group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.0 (66.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.0 (66.0\u0026ndash;76.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199 (78.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (70.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.2 (24.6\u0026ndash;30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.2 (25.0\u0026ndash;31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA score I \u0026amp; II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA score III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192 (75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (80.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutoimmune disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (recent use)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroid use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical history of knee\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior intraarticular injection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior knee ligament surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior septic arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreoperative laboratory values\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.50 (4.30\u0026ndash;4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.36 (4.16\u0026ndash;4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrognostic nutritional index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.96 (43.84\u0026ndash;48.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.50 (42.23\u0026ndash;47.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107.0 (95.0\u0026ndash;128.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110.0 (97.5\u0026ndash;144.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.9 (11.9\u0026ndash;13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7 (11.9\u0026ndash;13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.90 (5.70\u0026ndash;8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.80 (5.50\u0026ndash;7.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72 (0.74\u0026ndash;3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.09 (1.04\u0026ndash;3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErythrocyte sedimentation rate (mm/hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.0 (10.0\u0026ndash;28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0 (10.0\u0026ndash;37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.1 (22.7\u0026ndash;32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.1 (21.7\u0026ndash;33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntraoperative factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBT (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.9 (35.6\u0026ndash;36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.7 (35.4\u0026ndash;36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBT\u0026thinsp;\u0026lt;\u0026thinsp;36\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative hypotension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral anesthesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249 (97.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (89.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhaled anesthetic (mL/kg/hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21 (0.17\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18 (0.14\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravenous fluid (mL/kg/hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.85 (2.17\u0026ndash;3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.33 (1.77\u0026ndash;3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery duration (hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0 (2.77\u0026ndash;3.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.05 (2.83\u0026ndash;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranexamic acid use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e241 (94.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (87.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorphine dosage (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0 (10.0\u0026ndash;20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0 (10.0\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntraarticular injection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorphine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorphine\u0026thinsp;+\u0026thinsp;transamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePostoperative outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative nausea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0 (4.0\u0026ndash;5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as median (interquartile range) or number (percentage). Between-group comparisons were performed using the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test for continuous variables and the chi-square test or Fisher's exact test for categorical variables. Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index; UTI, urinary tract infection.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBaseline demographic characteristics were similar between groups. Median age was 70.0 years (IQR 66.0\u0026ndash;75.0) in the control group and 70.0 years (IQR 66.0\u0026ndash;76.0) in the PJI group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91). Median body mass index (BMI) was 27.2 (24.6\u0026ndash;30.1) kg/m\u0026sup2; in the control group and 27.2 (25.0\u0026ndash;31.3) kg/m\u0026sup2; in the PJI group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.275). The proportion of patients classified as ASA physical status III was 48.6% in the control group and 54.4% in the PJI group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.432). No patient was recorded as ASA physical status IV in both groups. With respect to comorbidities, the prevalence of diabetes mellitus (29.4% vs 36.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.272), hypertension (75.3% vs 80.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.386), chronic kidney disease (12.5% vs 14.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.762), and coronary artery disease (6.3% vs 1.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.328) did not differ significantly between groups. Chronic obstructive pulmonary disease (1.6% vs 5.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.117), heart failure (1.6% vs 0.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000), autoimmune disease (5.9% vs 5.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000), rheumatoid arthritis (3.9% vs 5.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.712), and recent smoking (3.9% vs 7.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.296) were similarly distributed. Steroid use was reported in 9.4% and 8.8% of patients, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.880). Prior intraarticular injection was reported in 27.5% of patients without infection and 15.8% of those with infection (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.067). Prior knee ligament surgery (2.0% vs 5.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.164) and prior septic arthritis (0.4% vs 0.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000) were uncommon in both groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePreoperative laboratory findings\u003c/h2\u003e \u003cp\u003ePreoperative laboratory evaluation demonstrated differences in nutritional markers between groups. Median PNI was significantly lower in the PJI group (44.50 [42.23\u0026ndash;47.03]) than in the control group (45.96 [43.84\u0026ndash;48.23]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Serum albumin was also significantly lower in the PJI group (4.36 [4.16\u0026ndash;4.59] g/dL) than in the control group (4.50 [4.30\u0026ndash;4.70] g/dL; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Preoperative blood glucose, analyzed as a continuous measure, was numerically higher in the PJI group but did not reach significance (110.0 [97.5\u0026ndash;144.0] vs 107.0 [95.0\u0026ndash;128.0] mg/dL; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.127). No statistically significant between-group differences were observed for hemoglobin (12.7 [11.9\u0026ndash;13.7] vs 12.9 [11.9\u0026ndash;13.7] g/dL; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.830), white blood cell count (6.80 [5.50\u0026ndash;7.90] vs 6.90 [5.70\u0026ndash;8.10] \u0026times;10\u0026sup3;/\u0026micro;L; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90), C-reactive protein (2.09 [1.04\u0026ndash;3.90] vs 1.72 [0.74\u0026ndash;3.81] mg/dL; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.325), or erythrocyte sedimentation rate (21.0 [10.0\u0026ndash;37.0] vs 16.0 [10.0\u0026ndash;28.0] mm/h; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.237) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These inflammatory and hematological markers did not discriminate between groups in univariable analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIntraoperative variables\u003c/h2\u003e \u003cp\u003eThe lowest recorded intraoperative body temperature was significantly lower in the PJI group (35.7 [35.4\u0026ndash;36.1] \u0026deg;C) than in the control group (35.9 [35.6\u0026ndash;36.2] \u0026deg;C; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030). The proportion of patients with LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C was significantly higher in the PJI group (43.9%) than in the control group (23.5%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). General anesthesia was used in 97.6% of patients without infection and 89.5% of those with infection (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Tranexamic acid was administered in 94.5% of patients in the control group and 87.7% in the PJI group, a difference that did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.079). Total intraoperative morphine dose was similar between groups (15.0 [10.0\u0026ndash;20.0] vs 15.0 [10.0\u0026ndash;15.5] mg; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.240). The distribution of intraarticular drug injection types did not differ significantly between groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.326) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Intravenous fluid administration was also lower in the PJI group (2.33 vs 2.85 mL/kg/hr; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). Postoperative length of stay was significantly longer in the PJI group (median 4.0 [IQR 4.0\u0026ndash;5.0] days) than in the control group (median 4.0 [IQR 3.0\u0026ndash;4.0] days; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable logistic regression analysis\u003c/h2\u003e \u003cp\u003eMultivariable logistic regression was performed to incorporate PNI, preoperative blood glucose, intraoperative LBT (\u0026le;\u0026thinsp;35.5\u0026deg;C), and prespecified covariates. Three variables were independently associated with PJI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Higher PNI was independently associated with lower odds of PJI (OR 0.89 per unit increase, 95% CI 0.80\u0026ndash;0.99; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Preoperative blood glucose was independently associated with increased infection risk (OR 1.01 per 1 mg/dL increase, 95% CI 1.001\u0026ndash;1.02; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039). Intraoperative LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C was independently associated with significantly increased infection risk (OR 2.49, 95% CI 1.29\u0026ndash;4.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). No other covariate was independently associated with PJI in the adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariable logistic regression analyses identifying perioperative risk factors for periprosthetic joint infection after primary total knee arthroplasty (n\u0026thinsp;=\u0026thinsp;312).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables (unit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) or N (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.0 (66.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.97\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239 (76.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51 (0.80\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22 (0.58\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.2 (24.7\u0026ndash;30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.99\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 (0.97\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASA score I \u0026amp; II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157 (50.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASA score III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155 (49.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (0.71\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79 (0.39\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrognostic nutritional index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.7 (43.7\u0026ndash;47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.80\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.80\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC-reactive protein (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.80 (0.80\u0026ndash;3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.95\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.92\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSteroid used\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.34\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.32\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood glucose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107.0 (96.0-130.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (1.002\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (1.001\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54 (1.40\u0026ndash;4.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.49 (1.29\u0026ndash;4.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInhaled anesthetic (mL/kg/hr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20 (0.16\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20 (0.00-9.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11 (0.03-171.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntravenous fluid (mL/kg/hr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.76 (2.10\u0026ndash;3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.54\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 (0.52\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood transfusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.05 (0.50-18.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.47 (0.33\u0026ndash;18.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntra-operative MME (mg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0 (10.0-18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.91\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (0.91\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (0.77\u0026ndash;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.45\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e238 (76.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (0.67\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.48\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eOdds ratios (ORs) with 95% confidence intervals (CIs) are reported for each variable. Multivariable ORs are adjusted for all covariates listed in the table. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Abbreviations: ASA, American Society of Anesthesiologists; CI, confidence interval; LBT, lowest body temperature; MME, morphine milligram equivalent; OR, odds ratio; PNI, prognostic nutritional index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eROC analysis and composite model discrimination\u003c/h2\u003e \u003cp\u003eROC analysis of individual predictors demonstrated that PNI provided the strongest single-variable discrimination (Model A: AUC\u0026thinsp;=\u0026thinsp;0.631, 95% CI 0.547\u0026ndash;0.715, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), followed by LBT (Model C: AUC\u0026thinsp;=\u0026thinsp;0.593, 95% CI 0.509\u0026ndash;0.677, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), while continuous blood glucose alone did not reach significance (Model B: AUC\u0026thinsp;=\u0026thinsp;0.565, 95% CI 0.480\u0026ndash;0.649, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.134) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At the optimal Youden-index\u0026ndash;derived cut-offs, PNI\u0026thinsp;\u0026le;\u0026thinsp;43.4 was present in 22 PJI patients (38.6%) versus 45 controls (17.6%); preoperative blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;147 mg/dL in 14 PJI patients (24.6%) versus 19 controls (7.5%); and intraoperative LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C in 25 PJI patients (43.9%) versus 60 controls (23.5%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The two-factor preoperative model (Model D: PNI\u0026thinsp;\u0026le;\u0026thinsp;43.4\u0026thinsp;+\u0026thinsp;blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;147 mg/dL) yielded an AUC of 0.657 (95% CI 0.574\u0026ndash;0.740, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the three-factor perioperative model (Model E: PNI\u0026thinsp;+\u0026thinsp;glucose\u0026thinsp;+\u0026thinsp;LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C) yielded an AUC of 0.662 (95% CI 0.579\u0026ndash;0.745, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). DeLong test confirmed that both composite models significantly outperformed blood glucose alone (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, respectively), while Model D and Model E did not differ significantly (ΔAUC\u0026thinsp;=\u0026thinsp;0.005, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.802) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReceiver operating characteristic curve analysis of individual and composite perioperative risk models for predicting periprosthetic joint infection after primary total knee arthroplasty.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOptimal Cut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.547\u0026ndash;0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGlucose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.480\u0026ndash;0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;147.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e92.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLBT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.509\u0026ndash;0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePNI+ Glucose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.574\u0026ndash;0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.204 (prob)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePNI+ Glucose\u0026thinsp;+\u0026thinsp;LBT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.579\u0026ndash;0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.214 (prob)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eOptimal cut-off values were determined by maximizing the Youden index (J\u0026thinsp;=\u0026thinsp;sensitivity\u0026thinsp;+\u0026thinsp;specificity\u0026thinsp;\u0026minus;\u0026thinsp;1). For composite models (Models D and E), the cut-off is expressed as the predicted probability derived from logistic regression. A two-tailed \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Abbreviations: AUC, area under the curve; CI, confidence interval; LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index; ROC, receiver operating characteristic.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of patients meeting for each dichotomous individual perioperative risk factor, stratified by periprosthetic joint infection status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off (yes/no)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePJI group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;255)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood glucose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;147 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;35.5\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCut-off values were determined by maximizing the Youden index (J\u0026thinsp;=\u0026thinsp;sensitivity\u0026thinsp;+\u0026thinsp;specificity\u0026thinsp;\u0026minus;\u0026thinsp;1). Data are presented as number of patients meeting the cut-off criterion (percentage of group total). A two-tailed \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Abbreviations: LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePairwise comparisons of AUC values using the DeLong test among individual and composite models for predicting periprosthetic joint infection after primary total knee arthroplasty.\u003c/b\u003e Pairwise AUC comparisons among Models A\u0026ndash;E were performed using the DeLong test. Each row shows ΔAUC, Z statistic, and \u003cem\u003ep\u003c/em\u003e value.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDelta AUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel A (PNI) vs Model B (Glucose)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel A (PNI) vs Model C (LBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel A (PNI) vs Model D (PNI+Glucose)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel A (PNI) vs Model E (PNI+Glucose\u0026thinsp;+\u0026thinsp;LBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel B (Glucose) vs Model C (LBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel B (Glucose) vs Model D (PNI+ Glucose)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel B (Glucose) vs Model E (PNI+ Glucose\u0026thinsp;+\u0026thinsp;LBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel C (LBT) vs Model D (PNI+ Glucose)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel C (LBT) vs Model E (PNI+ Glucose\u0026thinsp;+\u0026thinsp;LBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel D (PNI+ Glucose) vs Model E (PNI+ Glucose\u0026thinsp;+\u0026thinsp;LBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are presented as area under the receiver operating characteristic curve (AUC) values and pairwise comparisons using the DeLong test for correlated ROC curves. ΔAUC indicates the difference between two models. A two-tailed \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Abbreviations: AUC, area under the ROC curve; LBT, lowest body temperature; PJI, periprosthetic joint infection; PNI, prognostic nutritional index; TKA, total knee arthroplasty; ΔAUC, difference in AUC.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRisk stratification\u003c/h2\u003e \u003cp\u003eRisk stratification using Model D revealed a stepwise PJI rate gradient across two-factor strata: 11.8% (score 0), 31.0% (score 1), and 62.5% (score 2) (χ\u0026sup2; = 25.663, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Spearman \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.269, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Because PNI and blood glucose are both available from routine preoperative assessment, Model D functions as a preoperative screening tool enabling nutritional and glycemic optimization prior to surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Model E additionally incorporated intraoperative LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C, a parameter unavailable before surgery but directly controllable through structured perioperative normothermia protocols, yielding a more pronounced dose-response gradient: 9.9% (score 0), 20.0% (score 1), 50.0% (score 2), and 100% (score 3) (χ\u0026sup2; = 40.027, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Spearman \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.283, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The score-3 subgroup comprised only three patients and should be interpreted with caution. Model E serves a complementary intraoperative role: real-time LBT monitoring enables anesthesiologists to identify patients accruing additional thermal risk during surgery and to escalate active warming accordingly (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePJI remains one of the most consequential complications following primary TKA, contributing substantially to patient morbidity and healthcare resource utilization. Reported incidence varies across population registries and institutional cohorts, influenced by case-mix, referral patterns, diagnostic definitions, and surveillance intensity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this single-center case-matched cohort of 312 primary TKA patients with systematic one-year follow-up using International Consensus Meeting criteria, the overall PJI incidence was 1.3%. Within this context, PNI and preoperative blood glucose were independently associated with infection risk, and intraoperative LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C was additionally identified as an independent predictor (OR 2.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Two composite models were constructed: a two-factor preoperative model (PNI\u0026thinsp;+\u0026thinsp;glucose, AUC\u0026thinsp;=\u0026thinsp;0.657) and a three-factor perioperative model (PNI\u0026thinsp;+\u0026thinsp;glucose\u0026thinsp;+\u0026thinsp;LBT, AUC\u0026thinsp;=\u0026thinsp;0.662), each revealing a clear stepwise dose-response gradient in observed PJI rates.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePNI as an integrated host-state marker and infection susceptibility\u003c/h2\u003e \u003cp\u003ePNI represents more than a simple nutritional marker; it integrates serum albumin and total lymphocyte count into a composite index reflecting protein reserve, immune competence, and overall physiological resilience. Prior arthroplasty literature has consistently linked lower preoperative nutritional and immunological status to adverse postoperative and infection-related outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our adjusted analysis, each unit increment in PNI was associated with approximately 11% lower odds of PJI (OR 0.89, 95% CI 0.80\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), underscoring the biological relevance of composite host nutritional-immunological reserve. Our ROC-derived cut-off (PNI\u0026thinsp;\u0026le;\u0026thinsp;43.4) lies above the conventional threshold for nutritional impairment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], suggesting that subclinical nutritional-inflammatory compromise elevates infection susceptibility before frank malnutrition is evident, extending prior observations linking preoperative nutritional deficiency to acute postoperative infection risk after total joint arthroplasty [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Mechanistically, reduced PNI may reflect impaired lymphocyte-mediated defense, dysregulated inflammatory resolution, and compromised wound healing at the implant interface [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings reinforce the value of systematic preoperative PNI screening and nutritional optimization as components of perioperative infection prevention pathways [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePerioperative hyperglycemia beyond the diabetes label\u003c/h2\u003e \u003cp\u003ePreoperative blood glucose demonstrated an independent association with PJI when analyzed continuously (OR 1.01 per 1 mg/dL increase), whereas a diagnosis of diabetes mellitus alone did not differentiate risk. This observation aligns with evidence suggesting that glycemic state, rather than diagnostic label, more accurately reflects infection-relevant biological vulnerability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Hyperglycemia impairs neutrophil chemotaxis, oxidative burst activity, and microbial clearance, while promoting dysregulated inflammatory responses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These mechanisms are particularly relevant in the context of stress hyperglycemia, where transient perioperative glucose elevations may occur even in patients without established diabetes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Our ROC-derived threshold (\u0026ge;\u0026thinsp;147 mg/dL) aligns with established inpatient glycemic alert thresholds [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Together with prior surgical data emphasizing perioperative glycemic control [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], our findings support routine glucose monitoring and targeted optimization regardless of formal diabetes status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIntraoperative hypothermia as an independent and modifiable intraoperative risk factor\u003c/h2\u003e \u003cp\u003eIntraoperative LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C was independently associated with significantly increased odds of PJI (OR 2.49, 95% CI 1.29\u0026ndash;4.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) after multivariable adjustment, representing the largest effect estimate among the three identified predictors. This finding confirms the biological plausibility of intraoperative hypothermia as a modifiable host defense impairment, potentially facilitating bacterial survival at the implant-tissue interface [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Observational data from major non-cardiac surgery link intraoperative hypothermia with increased infectious complications [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and early randomized evidence supports the protective effect of perioperative normothermia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although arthroplasty-specific data have yielded heterogeneous findings [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], the present result suggests that an LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C threshold may carry clinically relevant infection risk in TKA. Notably, LBT as a continuous variable was not significant on univariable analysis (OR\u0026thinsp;=\u0026thinsp;0.663, 95% CI 0.395\u0026ndash;1.111, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.119), whereas dichotomization at \u0026le;\u0026thinsp;35.5\u0026deg;C yielded a significant association (OR\u0026thinsp;=\u0026thinsp;2.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). This pattern suggests possible threshold-dependent rather than linear risk, pending prospective validation. Importantly, unlike PNI and blood glucose, which are determined preoperatively, LBT is an intraoperative parameter under direct anesthetic control. This temporal distinction defines the complementary clinical role of our three-factor model: it functions not as a preoperative predictor, but as an intraoperative surveillance instrument that enables the perioperative team to identify patients accruing additional thermal risk during surgery and to escalate active warming strategies accordingly. In this framework, orthopedic surgeons and anesthesiologists share responsibility for infection risk optimization: surgeons may act on preoperative PNI and glycemic screening to defer or modify operative timing, while anesthesiologists implement targeted normothermia protocols intraoperatively based on real-time LBT monitoring [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThree additional variables demonstrated significant between-group differences but were not retained as independent predictors after multivariable adjustment. The lower rate of general anesthesia in the PJI group (89.5% vs 97.6%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) most likely reflects indication bias rather than a causal effect of anesthetic technique: patients selected for regional anesthesia may have had higher baseline comorbidity, independently predisposing them to infection. The association between lower intravenous fluid volume and PJI (2.33 vs 2.85 mL/kg/hr; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; adjusted OR 0.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054) is similarly attributable to confounding by operative complexity and anesthetic practice rather than a direct biological mechanism. Finally, postoperative length of stay was significantly prolonged in the PJI group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with the additional surgical intervention, antimicrobial therapy, and extended monitoring required for PJI management, and representing a major component of the excess healthcare burden attributable to prosthetic joint infection [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSequential perioperative risk stratification: a two-model framework\u003c/h2\u003e \u003cp\u003eA central contribution of this study is the construction of a sequential two-model perioperative risk framework. The two-factor preoperative model (Model D: PNI\u0026thinsp;\u0026le;\u0026thinsp;43.4\u0026thinsp;+\u0026thinsp;blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;147 mg/dL, AUC\u0026thinsp;=\u0026thinsp;0.657) demonstrated a stepwise PJI gradient of 11.8%, 31.0%, and 62.5% across 0, 1, and 2 risk factor strata (Spearman \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.269, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The three-factor perioperative model (Model E: adding LBT\u0026thinsp;\u0026le;\u0026thinsp;35.5\u0026deg;C, AUC\u0026thinsp;=\u0026thinsp;0.662) produced a more pronounced gradient of 9.9%, 20.0%, 50.0%, and 100% across 0 to 3 strata (Spearman \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.283, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the score-3 subgroup comprising only three patients; the observed 100% infection rate in this stratum is statistically unstable and should not be interpreted as a reliable estimate of absolute risk. Models D and E did not differ significantly (ΔAUC\u0026thinsp;=\u0026thinsp;0.005, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.802), supporting Model D as the more parsimonious preoperative screening tool. These three variables represent complementary perioperative domains, reduced nutritional-immunological reserve (PNI), metabolic stress (hyperglycemia), and attenuated intraoperative thermal defense (hypothermia), whose convergence may compound infection susceptibility. Contemporary orthopedic literature increasingly emphasizes multidimensional risk stratification frameworks in musculoskeletal infection, highlighting the interplay between pathogen virulence, implant environment, and host immune competence [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. From a translational standpoint, Model D, requiring only two preoperative values available from routine blood work, may assist orthopedic surgeons in identifying high-risk patients for targeted infection prevention strategies, including nutritional optimization and perioperative glycemic control, prior to implantation. External validation studies of preoperative PJI prediction models report modest discrimination and variable calibration across cohorts [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and systematic reviews highlight heterogeneity and limited reproducibility among existing models [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this context, a simple and interpretable multidimensional framework may offer pragmatic clinical utility beyond what formal discrimination metrics convey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eModel performance, interpretability, and validation needs\u003c/h2\u003e \u003cp\u003eThe AUC values of 0.657 (Model D) and 0.662 (Model E) reflect modest but meaningful discriminative performance and should not be interpreted as definitive predictive accuracy. DeLong test confirmed that both composite models significantly outperformed blood glucose alone (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), while the negligible AUC difference between models (ΔAUC\u0026thinsp;=\u0026thinsp;0.005) favors the more parsimonious two-factor preoperative model for routine clinical use. Model performance in development cohorts characteristically overestimates real-world discriminative ability [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Models developed within single-center or narrowly defined populations may show limited transportability due to case-mix differences and practice variation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Accordingly, rigorous external validation and multicenter evaluation remain essential prior to widespread implementation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Importantly, modest discrimination does not preclude clinical usefulness in structured risk stratification, particularly when models are simple, transparent, and biologically coherent. Our findings should therefore be regarded as hypothesis-generating for formal prediction purposes while simultaneously identifying clinically actionable domains, preoperative nutritional-immunological status (PNI), glycemic control, and intraoperative normothermia, that align with established perioperative infection prevention strategies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eThis study has several limitations. Its retrospective single-center design carries potential residual confounding from unmeasured factors, including infecting organism characteristics, antibiotic timing, operative complexity, implant variables, and perioperative antimicrobial protocols [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The three-factor risk stratum contained only three patients, limiting precision and mandating cautious interpretation. ROC-derived cut-offs were optimized within the present dataset and require external validation before clinical implementation. Additionally, with 57 PJI events and 15 prespecified covariates, the EPV was approximately 3.8, well below the recommended threshold of 10, which may increase the risk of model overfitting and coefficient instability. Nevertheless, all covariates were prespecified based on biological plausibility and prior literature, and the observed stepwise dose-response gradients across risk strata support the robustness of the primary findings. Future work should externally validate both composite models in multicenter prospective cohorts, explore whether PNI-targeted nutritional prehabilitation or glycemic optimization reduces PJI incidence, and evaluate whether structured intraoperative warming protocols guided by LBT thresholds improve outcomes in randomized designs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASA \u0026mdash; American Society of Anesthesiologists\u003cbr\u003e\u0026nbsp;AUC \u0026mdash; Area under the curve\u003cbr\u003e\u0026nbsp;BMI \u0026mdash; Body mass index\u003cbr\u003e\u0026nbsp;CI \u0026mdash; Confidence interval\u003cbr\u003e\u0026nbsp;EPV \u0026mdash; Events-per-variable\u003cbr\u003e\u0026nbsp;ESR \u0026mdash; Erythrocyte sedimentation rate\u003cbr\u003e\u0026nbsp;IQR \u0026mdash; Interquartile range\u003cbr\u003e\u0026nbsp;LBT \u0026mdash; Lowest body temperature\u003cbr\u003e\u0026nbsp;MME \u0026mdash; Morphine milligram equivalent\u003cbr\u003e\u0026nbsp;OR \u0026mdash; Odds ratio\u003cbr\u003e\u0026nbsp;PJI \u0026mdash; Periprosthetic joint infection\u003cbr\u003e\u0026nbsp;PNI \u0026mdash; Prognostic Nutritional Index\u003cbr\u003e\u0026nbsp;ROC \u0026mdash; Receiver operating characteristic\u003cbr\u003e\u0026nbsp;TKA \u0026mdash; Total knee arthroplasty\u003cbr\u003e\u0026nbsp;WBC \u0026mdash; White blood cell count\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e Ethical approval for this study was obtained from the Institutional Review Board of our institution (approval number withheld for blinding). The requirement for informed consent was waived by the nature of its retrospective design.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCRediT: Conceptualization: Author 1 , Author 7 ; Methodology: Author 2, Author 3, Author 6 ; Formal analysis and investigation: Author 2, Author 5 , Author 4 ; Writing - original draft preparation: Author 1 ; Writing - review and editing: Author 1 , Author 7 ; Project administration: Author 7 ; Resources: Author 3, Author 5 ; Supervision: Author 7 .\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data analyzed during this study are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbugri BO, Matsusaki T, Ren W, Morimatsu H (2022) Intraoperative Hypothermia Is Not Associated with Surgical Site Infections after Total Hip or Knee Arthroplasty. 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J Arthroplasty 37:367\u0026ndash;372 e361\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoriarty TF, Hickok NJ, Saeed K, Schaer TP, Chen AF, Schwarz EM (2024) The 2023 Orthopaedic Research Society International Consensus Meeting on musculoskeletal infection. J Orthop Res 42:497\u0026ndash;499\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaufal E, Shadbolt C, Wouthuyzen-Bakker M, Rele S, Sahebjada S, Thuraisingam S, et al. (2025) Clinical prediction models to guide treatment of periprosthetic joint infections: a systematic review and meta-analysis. J Hosp Infect 162:53\u0026ndash;61\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson CL, Elkassabany NM, Kamath AF, Liu J (2015) Low Albumin Levels, More Than Morbid Obesity, Are Associated With Complications After TKA. Clin Orthop Relat Res 473:3163\u0026ndash;3172\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePang QY, Yang YJ, Feng YM, Sun SF, Liu HL (2024) Relationship between intraoperative hypothermia and hyperthermia with postoperative pulmonary infection and surgical site infection in major non-cardiac surgery. Front Med (Lausanne) 11:1408342\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParvizi J, Tan TL, Goswami K, Higuera C, Della Valle C, Chen AF, et al. (2018) The 2018 Definition of Periprosthetic Hip and Knee Infection: An Evidence-Based and Validated Criteria. J Arthroplasty 33:1309\u0026ndash;1314 e1302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips JLH, Ennis HE, Jennings JM, Dennis DA (2023) Screening and Management of Malnutrition in Total Joint Arthroplasty. J Am Acad Orthop Surg 31:319\u0026ndash;325\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePremkumar A, Kolin DA, Farley KX, Wilson JM, McLawhorn AS, Cross MB, et al. (2021) Projected Economic Burden of Periprosthetic Joint Infection of the Hip and Knee in the United States. J Arthroplasty 36:1484\u0026ndash;1489 e1483\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, et al. (2016) External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 353:i3140\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez-Merchan EC, Delgado-Martinez AD (2022) Risk Factors for Periprosthetic Joint Infection after Primary Total Knee Arthroplasty. J Clin Med 11:6128\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartz AM, Farley KX, Guild GN, Bradbury TL, Jr. (2020) Projections and Epidemiology of Revision Hip and Knee Arthroplasty in the United States to 2030. J Arthroplasty 35:S79-S85\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwarz EM, Archer NK, Atkins GJ, Bentley KLM, Botros M, Cassat JE, et al. (2024) The 2023 Orthopaedic Research Society's International Consensus Meeting on musculoskeletal infection: Summary from the host immunity section. J Orthop Res 42:518\u0026ndash;530\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSessler DI (2016) Perioperative thermoregulation and heat balance. Lancet 387:2655\u0026ndash;2664\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSessler DI, Pei L, Li K, Cui S, Chan MTV, Huang Y, et al. (2022) Aggressive intraoperative warming versus routine thermal management during non-cardiac surgery (PROTECT): a multicentre, parallel group, superiority trial. Lancet 399:1799\u0026ndash;1808\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShohat N, Bauer T, Buttaro M, Budhiparama N, Cashman J, Della Valle CJ, et al. (2019) Hip and Knee Section, What is the Definition of a Periprosthetic Joint Infection (PJI) of the Knee and the Hip? Can the Same Criteria be Used for Both Joints?: Proceedings of International Consensus on Orthopedic Infections. J Arthroplasty 34:S325-S327\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShohat N, Restrepo C, Allierezaie A, Tarabichi M, Goel R, Parvizi J (2018) Increased Postoperative Glucose Variability Is Associated with Adverse Outcomes Following Total Joint Arthroplasty. J Bone Joint Surg Am 100:1110\u0026ndash;1117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiontis GC, Tzoulaki I, Castaldi PJ, Ioannidis JP (2015) External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol 68:25\u0026ndash;34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzymski D, Walter N, Straub J, Wu Y, Melsheimer O, Grimberg A, et al. (2024) Low implantation volume, comorbidities, male sex and implantation of constrained TKA identified as risk factors for septic revision in knee arthroplasty: A register-based study from the German Arthroplasty Registry. Knee Surg Sports Traumatol Arthrosc 32:1743\u0026ndash;1752\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorchia MT, Khan IA, Christensen DD, Moschetti WE, Fillingham YA (2023) Universal Screening for Malnutrition Prior to Total Knee Arthroplasty Is Cost-Effective: A Markov Analysis. J Arthroplasty 38:443\u0026ndash;449\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurina M, Fry DE, Polk HC, Jr. (2005) Acute hyperglycemia and the innate immune system: clinical, cellular, and molecular aspects. Crit Care Med 33:1624\u0026ndash;1633\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWier J, Liu KC, Richardson MK, Gettleman BS, Kistler NM, Heckmann ND, et al. (2024) Higher Blood Glucose Levels on the Day of Surgery Are Associated with an Increased Risk of Periprosthetic Joint Infection After Total Hip Arthroplasty. J Bone Joint Surg Am 106:276\u0026ndash;287\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWildeman P, Rolfson O, Soderquist B, Wretenberg P, Lindgren V (2021) What Are the Long-term Outcomes of Mortality, Quality of Life, and Hip Function after Prosthetic Joint Infection of the Hip? A 10-year Follow-up from Sweden. Clin Orthop Relat Res 479:2203\u0026ndash;2213\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon SJ, Jutte PC, Soriano A, Sousa R, Zijlstra WP, Wouthuyzen-Bakker M (2024) Predicting periprosthetic joint infection: external validation of preoperative prediction models. J Bone Jt Infect 9:231\u0026ndash;239\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou YD, Zhang WY, Xie GH, Ye H, Chu LH, Guo YQ, et al. (2024) Inadvertent perioperative hypothermia and surgical site infections after liver resection. Hepatobiliary Pancreat Dis Int 23:579\u0026ndash;585\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hypothermia, intraoperative temperature, periprosthetic joint infection, prognostic nutritional index, risk stratification, total knee arthroplasty","lastPublishedDoi":"10.21203/rs.3.rs-9331642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9331642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePeriprosthetic joint infection (PJI) remains a serious complication of primary total knee arthroplasty (TKA). This study investigated independent and combined associations of the Prognostic Nutritional Index (PNI), preoperative blood glucose, and intraoperative lowest body temperature (LBT) with PJI within one year after primary TKA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods\u003c/strong\u003e: This retrospective case-matched cohort study enrolled 312 patients (57 PJI cases, 255 controls) undergoing primary TKA. Infection was defined by 2018 International Consensus Meeting criteria. Multivariable logistic regression identified independent predictors. Two composite models were constructed using ROC-derived cut-offs: a two-factor preoperative model (PNI ≤ 43.4 + blood glucose ≥ 147 mg/dL) and a three-factor perioperative model additionally incorporating LBT ≤ 35.5°C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 4,319 adult patients who underwent TKA were identified during 2016 to 2021. Fifty-seven patients (1.3%) developed PJI. Three independent predictors were identified: higher PNI was associated with lower infection odds (OR 0.89; \u003cem\u003ep\u003c/em\u003e = 0.027), blood glucose with increased risk (OR 1.01 per mg/dL; \u003cem\u003ep\u003c/em\u003e = 0.039), and LBT ≤ 35.5°C with significantly increased risk (OR 2.49, 95% CI 1.29–4.83; \u003cem\u003ep\u003c/em\u003e = 0.007). The two-factor preoperative model (PNI + glucose, AUC = 0.657) and three-factor perioperative model (adding LBT, AUC = 0.662) both showed stepwise PJI gradients across cumulative strata (ΔAUC = 0.005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: PNI, blood glucose, and intraoperative LBT are independent modifiable predictors of PJI after primary TKA. A two-factor preoperative model (PNI + glucose) supports preoperative risk screening and optimization; a three-factor model adding LBT provides an intraoperative surveillance tool for structured perioperative normothermia protocols. Both models rely on routinely available perioperative variables and support structured infection prevention pathways.\u003c/p\u003e","manuscriptTitle":"Perioperative Risk Stratification for Periprosthetic Joint Infection after Primary Total Knee Arthroplasty: A Case-Matched Cohort Study Incorporating Nutritional Index, Glycemic Status, and Intraoperative Hypothermia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 08:10:12","doi":"10.21203/rs.3.rs-9331642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95371a63-3195-4e87-b096-41e239a77be5","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-07T14:39:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T13:47:46+00:00","index":20,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T07:04:44+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"151078260911317031002750720202550401417","date":"2026-04-30T04:18:54+00:00","index":18,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T14:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 08:10:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9331642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9331642","identity":"rs-9331642","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}