Detection Of Surgical Site Infection Research Articles

Interleukin-6 as a critical inflammatory marker for early diagnosis of surgical site infection after spine surgery.

Early diagnosis of surgical site infections (SSIs) could prevent surgical revision. Inflammatory markers (IMs), such as procalcitonin (PCT), interleukin-6 (IL-6), and tumor necrosis factor α (TNF-α), seem more accurate in diagnosing SSI than C-reactive protein (CRP) and white blood cell (WBC) count. The aim was to compare the predictive values of CRP, WBC count, PCT, IL-6, and TNF-α in SSI detection. A total of 130 patients undergoing dorsal spondylodesis from 2019 to 2024 were enrolled in a prospective diagnostic study at a maximum care spine center. IMs were measured preoperatively and on the postoperative days (PODs) 1, 2, 3, 5, and 7. Patients with high suspicion of SSI underwent revision surgery. SSI was diagnosed when the microbiological evidence was positive. Patients were divided a posteriori into the non-infection and infection groups. IMs of 118 patients (66.9 ± 13.0years, 61.0% females) were measured. Fifteen of the 118 patients (12.7%) developed an SSI. The groups differed with respect to existing hypertension, number of instrumented segments, region of surgery, CRPPOD1,7, PCTPOD7, and IL-6POD3,5,7. Binary logistic regression for SSI detection including these parameters showed an area under the curve (AUC) of 0.88 (95% CI 0.79-0.98; P < 0.001). The main effect for SSI detection was maintained by IL-6POD7 (odds ratio = 1.13; 95% CI 1.05-1.23; P = 0.001), which itself showed an AUC of 0.86 (95% CI 0.75-0.97). Compared to CRP, WBC count, PCT, and TNF-α, IL-6 seems to be the critical IM for the early detection of an SSI. drks.de: DRKS00033773, date of registration: 29.02.2024, retrospectively registered; Postoperative Markers of Inflammation in Spine Surgery (POMIS) Trial.

Construct validation of machine learning for accurately predicting the risk of postoperative surgical site infection following spine surgery

This study aimed to evaluate the risk factors for machine learning (ML) algorithms in predicting postoperative surgical site infection (SSI) following spine surgery. This prospective cohort study included 986 patients who underwent spine surgery at Taizhou People's Hospital Affiliated to Nanjing Medical University from January 2015 to October 2022. Supervised ML algorithms included support vector machine, logistic regression, random forest, XGboost, decision tree, k-nearest neighbour, and naïve Bayes (NB), which were tested and trained to develop a predicting model. The ML model performance was evaluated from the test dataset. We gradually analysed their accuracy, sensitivity, and specificity, as well as the positive predictive value, negative predictive value, and area under the curve. The rate of SSI was 9.33%. Using a backward stepwise approach, we identified that the remarkable risk factors predicting SSI in the multi-variate Cox regression analysis were age, body mass index, smoking, cerebrospinal fluid leakage, drain duration and pre-operative albumin level. Compared with other ML algorithms, the NB model had the highest performance in seven ML models, with an average area under the curve of 0.95, sensitivity of 0.78, specificity of 0.88, and accuracy of 0.87. The NB model in the ML algorithm had excellent calibration and accurately predicted the risk of SSI compared with the existing models, and might serve as an important tool for the early detection and treatment of SSI following spinal infection.

Post-discharge occurrence of surgical site infections after hip or knee arthroplasty surgery in Poland, a population-based study

Arthroplasty is a common procedure improving functioning of patients and their quality of life. Infection is a serious complication that determines subsequent management of the prosthesis and the patient. The aim of the study was to investigate the incidence of post-discharge surgical site infections (SSI) and their risk factors. A retrospective analysis of an anonymized database from the National Health Found for 2017 of 56,068 adult patients undergoing hip replacement surgery (HPRO) and 27,457 patients undergoing knee replacement surgery (KPRO). The cumulative incidence of post-discharge SSI was 0.92% for HPRO and 0.95% for KPRO. The main risk factors for hip SSI were male gender, diseases of hematopoietic, musculoskeletal and nervous system. The risk factor for knee SSI was male gender. All comorbidities significantly increased the risk of SSI. The ICU stay and antibiotics administered at discharge in studied population increased the risk of detection of SSI after HPRO and KPRO by up to four and seven times, respectively. For both procedures rehabilitation after surgery and total endoprosthesis decreased incidence of SSIs. The lower experience of the center was related to higher SSI incidence in HPRO in primary (1.5% vs. 0.9%) and in revision surgeries (3.8% vs. 2.1%), but in KPRO, lower experience only in primary surgeries was significantly associated with SSI. The cumulative incidence of post-discharge SSI in Poland is higher than in other European countries. Special attention should be paid to patients with chronic diseases.

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study

BackgroundPopulation based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection.MethodsThis retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision–recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated.ResultsThere were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835–0.978), PR AUC of 0.637 (95% CI 0.528–0.746), and F1 score of 0.79 (0.67–0.90).ConclusionsOur findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.

Evaluation of a client questionnaire at diagnosing surgical site infections in an active surveillance system.

To report sensitivity, specificity, predictive values and accuracy of a client questionnaire at diagnosing surgical site infections (SSIs) and describe the impact of active surveillance on SSI detection. Prospective, cohort study. Dogs and cats undergoing soft tissue or orthopedic surgery over a 12-month period at a referral hospital. Clients were emailed a questionnaire 30 days postoperatively, or 90 days where an implant was used. Three algorithms were developed to diagnose SSIs using one or both of two criteria: (1) presence of any wound healing problems; (2) wound dehiscence or antibiotic prescription, and either purulent discharge or two or more clinical signs (redness, pain, heat, swelling, discharge). Algorithmic diagnoses were compared to gold standard diagnoses made by veterinarians. Of 754 surgical procedures, 309 responses were completed with 173 corresponding gold standard diagnoses. The most accurate algorithm determined "SSI" or "No SSI" from 90.2% of responses with 95.5% (92.4-98.6) accuracy, 82.6% (77-88.3) sensitivity, 97.7% (95.5-100) specificity, 86.4% (81.2-91.5) positive predictive value, and 97% (94.5-99.6) negative predictive value. "No SSI" was diagnosed in responses not meeting criterion 1, and "SSI" in responses meeting criteria 1 and 2. "Inconclusive" responses, comprising 9.8% of responses, met criterion 1 but not 2. Overall SSI rate was 62/754 (8.2%) and 12/62 (19.4%) SSIs were detected by active surveillance only. Use of this client questionnaire accurately diagnosed SSIs; active surveillance increased SSI detection. Surveillance of SSIs should be active and can be simplified by using a client questionnaire and algorithmic diagnoses, allowing automated distribution, data collection and analysis.

Diagnostic Accuracy of Telemedicine for Detection of Surgical Site Infection: A Systematic Review and Meta-analysis

Diagnostic Accuracy of Telemedicine for Detection of Surgical Site Infection: A Systematic Review and Meta-analysis

OC-004 CAN AI HELP IN EARLY PREDICTION OF SURGICAL SITE INFECTIONS?

Abstract Background The use of Artificial Intelligence (AI) in healthcare has the potential to transform medical diagnosis, including the detection of surgical site infections (SSI). Real-time monitoring of surgical sites and early detection of infection through AI can lead to prompt treatment, improving patient outcomes and reducing healthcare costs and spread of infections in healthcare facilities. Aim This study aimed to determine the use of AI in the early detection of surgical site infections and its impact on patient recovery. Materials and methods The study analysed patients admitted to the hospital over the past year with SSIs. A novel AI device was used to image the wound and detect presence of infection through fluorescence lights that identify bacteria through bioluminescence. Targeted tissue cultures were taken, antibiotics were administered. Some wounds required targeted debridement. Daily dressings were done followed by secondary suturing. Results A total of 34 patients were included in the study, with a mean age of 52.4 years and 14 female and 20 male patients. 25 patients reported pus from the wound, 9 had serous discharge, and 19 had slough present. In 85.2% of patients, the AI device's gram stain for bacteria in the wound was similar to the tissue culture report. The mean time for wound healing was 39.52 days. Conclusion Our study suggests that AI devices can accurately identify the gram stain of bacteria in a wound, leading to prompt antibiotic treatment for patients with SSIs. This could have significant benefits in improving patient outcomes and healthcare costs.

Using artificial intelligence to reduce orthopedic surgical site infection surveillance workload: Algorithm design, validation, and implementation in 4 Spanish hospitals

Surgical site infection (SSI) surveillance is a labor-intensive endeavor. We present the design and validation of an algorithm for SSI detection after hip replacement surgery, and a report of its successful implementation in 4 public hospitals in Madrid, Spain. We designed a multivariable algorithm, AI-HPRO, using natural language processing (NLP) and extreme gradient boosting to screen for SSI in patients undergoing hip replacement surgery. The development and validation cohorts included data from 19,661 health care episodes from 4 hospitals in Madrid, Spain. Positive microbiological cultures, the text variable "infection", and prescription of clindamycin were strong markers of SSI. Statistical analysis of the final model indicated high sensitivity (99.18%) and specificity (91.01%) with an F1-score of 0.32, AUC of 0.989, accuracy of 91.27%, and negative predictive value of 99.98%. Implementation of the AI-HPRO algorithm reduced the surveillance time from 975person/hours to 63.5person/hours and permitted an 88.95% reduction in the total volume of clinical records to be reviewed manually. The model presents a higher negative predictive value (99.98%) than algorithms relying on NLP alone (94%) or NLP and logistic regression (97%). This is the first report of an algorithm combining NLP and extreme gradient-boosting to permit accurate, real-time orthopedic SSI surveillance.

The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model.

Surgical Site Infection (SSI) is a common healthcare-associated infection that imposes a considerable clinical and economic burden on healthcare systems. Advances in wearable sensors and digital technologies have unlocked the potential for the early detection and diagnosis of SSI, which can help reduce this healthcare burden and lower SSI-associated mortality rates. In this study, we evaluated the ability of a multi-modal bio-signal system to predict current and developing superficial incisional infection in a porcine model infected with Methicillin Susceptible Staphylococcus Aureus (MSSA) using a bagged, stacked, and balanced ensemble logistic regression machine learning model. Results demonstrated that the expression levels of individual biomarkers (i.e., peri-wound tissue oxygen saturation, temperature, and bioimpedance) differed between non-infected and infected wounds across the study period, with cross-correlation analysis indicating that a change in bio-signal expression occurred 24 to 31 hours before this change was reflected by clinical wound scoring methods employed by trained veterinarians. Moreover, the multi-modal ensemble model indicated acceptable discriminability to detect the presence of a current superficial incisional SSI (AUC = 0.77), to predict an SSI 24 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.80), and to predict an SSI 48 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.74). In sum, the results of the current study indicate that non-invasive multi-modal sensor and signal analysis systems have the potential to detect and predict superficial incisional SSIs in porcine subjects under experimental conditions.

Intrawound Vancomycin Powder Application for Preventing Surgical Site Infection Following Cranioplasty.

Surgical site infection is the most detrimental complication following cranioplasty. In other surgical fields, intrawound vancomycin powder application has been introduced to prevent surgical site infection and is widely used based on results in multiple studies. This study evaluated the effect of intrawound vancomycin powder in cranioplasty compared with the conventional method without topical antibiotics. This retrospective study included 580 patients with skull defects who underwent cranioplasty between August 1, 1998 and December 31, 2021. The conventional method was used in 475 (81.9%; conventional group) and vancomycin powder (1g) was applied on the dura mater and bone flap in 105 (18.1%; vancomycin powder group) patients. Surgical site infection was defined as infection of the incision, organ, or space that occurred after cranioplasty. Surgical site infection within one-year surveillance period was compared between the conventional and vancomycin powder groups with logistic regression analysis. Penalized likelihood estimation method was used in logistic regression to deal with zero events. All local and systemic adverse events associated with topical vancomycin application were also evaluated. Surgical site infection occurred in 31 (5.3%) patients and all were observed in the conventional group. The median time between cranioplasty and detection of surgical site infection was 13 days (range, 4 - 333). Staphylococci were the most common organisms and identified in 25 (80.6%) of 31 cases with surgical site infections. The surgical site infection rate in the vancomycin powder group (0/105 [0%]) was significantly lower than that in the conventional group (31/475 [6.5%]; crude odds ratio 0.067 [95% confidence interval 0.006 - 0.762], adjusted odds ratio 0.068 [95% confidence interval 0.006 - 0.731]; p=0.026). No adverse events associated with intrawound vancomycin powder were observed during the follow-up. Intrawound vancomycin powder effectively prevented surgical site infections following cranioplasty without local or systemic adverse events. Our results suggest that intrawound vancomycin powder is an effective and safe strategy for patients undergoing cranioplasty.

The diagnostic test accuracy of telemedicine for detection of surgical site infection: A systematic review protocol.

Since the COVID-19 pandemic there has been a rapid uptake and utilisation of telemedicine in all aspects of healthcare. This presents a key opportunity in surgical site infection surveillance. Remote follow up methods have been used via telephone, with photographs and questionnaires for post-operative reviews with varying results. This review therefore aims to comprehensively synthesise available evidence for the diagnostic accuracy of all forms of SSI telemedicine monitoring. The protocol has been established as per both PRISMA-P (S1 Table) and the Cochrane handbook for reviews of diagnostic test accuracy. Medline, Embase, CENTRAL and CINAHL will be searched using a complete search strategy developed with librarian input, in addition to google scholar and hand searching. All study designs with patients over 18 and undergone a primarily closed surgical procedure will be eligible. Index tests will include all forms of telemedicine and a subgroup analysis performed for each of these. Comparative tests must include face to face review, and all reference standards will be included again for sub-group analyses. Search results will be screened by two investigators independently with a third providing consensus review on disagreements. Methodological quality will be assessed using the QUADAS-2 tool, first validated by two investigators as per the Cochrane handbook. Exploratory analysis will formulate summary receiver operating characteristic curves and forest plots with estimates of sensitivity and specificity of the included studies. Sources of heterogeneity will be identifying and investigated through further analysis. Potential benefits of telemedicine integration in surgical practice will reduce cost and travel time to patients in addition to avoiding wasted clinic appointments, important considerations in a peri-pandemic era. To avoid missed or further complications, there must be confidence in the ability to diagnose infection. This review will systematically determine whether telemedicine is accurate for surgical site infection diagnosis, which methods are well established and if further research is indicated.

Serum amyloid A and fibrinogen as markers for early detection of surgical site infection associated with internal fixation in the horse.

The objective of this study was to determine the diagnostic ability of serum amyloid A (SAA) and fibrinogen for early detection of surgical site infection (SSI) after equine internal fixation. Horses undergoing internal fixation for fracture, arthrodesis, or osteotomy with internal fixation for limb deformity were included in the study. SAA and fibrinogen were measured on blood samples preoperatively and on days 1, 3, 5, 7, 10, and 14 postoperatively. Statistical analysis included use of Spearman's rank correlation, logistic regression, and calculating the area under the receiver operating characteristic (ROC) curve. SAA and fibrinogen measurements were both associated with SSI, with SAA being considered an excellent marker (area under the ROC curve 0.8) and fibrinogen being considered acceptable (<0.8). As the amount of time postoperatively increased, SAA elevations indicated a higher likelihood of SSI (area under the ROC curve 0.8 compared with fibrinogen 0.7). SAA and fibrinogen were predictive markers of SSI and SAA is of greater diagnostic utility when compared with fibrinogen. Persistent elevations of SAA postoperatively are associated with the development of SSI. Serial monitoring of SAA can be used to help predict the development of SSI in horses undergoing internal fixation. This may lead to earlier suspicion, and therefore recognition and treatment of SSI.

Postoperative C-reactive protein as a predictive marker for surgical site infection after cesarean section: Retrospective analysis of 748 patients at a Japanese academic institution.

Surgical site infection (SSI) is a common but potentially serious maternal complication of cesarean section (CS). C-reactive protein (CRP) can be used in early detection of SSI. However, its predictive value for post-cesarean SSI has never been investigated. This study aims to evaluate the predictive value of CRP for the development of SSI. This was a hospital-based retrospective cohort study of 748 pregnant women who underwent CS at our university hospital between January 2017 and December 2019. CRP was measured on postoperative days 1, 3, and 6. The predictive values of CRP for SSI were evaluated using receiver operating characteristics analysis. Forty-seven (6.3%) patients developed SSI, of whom 38 (80.9%) underwent emergency CS. Serum CRP levels were significantly higher in the SSI group than in the non-SSI group from postoperative day 1 (64 vs. 81 mg/L, p = 0.001); the difference became more evident on postoperative days 3 and 6. The area under the receiver operating characteristic curve (AUC) for CRP on days 1, 3, and 6 was 0.58 (95% confidence interval [CI], 0.49 to 0.68), 0.70 (0.62 to 0.78) and 0.73 (0.65 to 0.81), respectively. The optimal cutoff value for day 3 and 6 CRP was 66.4 mg/L (sensitivity = 76.1% and specificity = 54.4%) and 22.2 mg/L (sensitivity = 76.5% and specificity = 63.2%), respectively. CRP on postoperative days 3 and 6 can be used as a predictive marker for the development of SSI after CS. Further studies to validate the predictive value in different populations is essential.

Diagnostic accuracy of telemedicine for detection of surgical site infection: a systematic review and meta-analysis

The Sars-CoV-2 pandemic catalysed integration of telemedicine worldwide. This systematic review assesses it’s accuracy for diagnosis of Surgical Site Infection (SSI). Databases were searched for telemedicine and wound infection studies. All types of studies were included, only paired designs were taken to meta-analysis. QUADAS-2 assessed methodological quality. 1400 titles and abstracts were screened, 61 full text reports were assessed for eligibility and 17 studies were included in meta-analysis, mean age was 47.1 ± 13.3 years. Summary sensitivity and specificity was 87.8% (95% CI, 68.4–96.1) and 96.8% (95% CI 93.5–98.4) respectively. The overall SSI rate was 5.6%. Photograph methods had lower sensitivity and specificity at 63.9% (95% CI 30.4–87.8) and 92.6% (95% CI, 89.9–94.5). Telemedicine is highly specific for SSI diagnosis is highly specific, giving rise to great potential for utilisation excluding SSI. Further work is needed to investigate feasibility telemedicine in the elderly population group.

Usefulness of Bacterial Culture of Drainage Fluid for Predicting Surgical Site Infection After Crohn's Disease Surgery.

AimEarly detection of surgical site infection (SSI) allows for appropriate management after Crohn's disease (CD) surgery. The aim of this study was to evaluate the usefulness of bacterial culture of postoperative drainage fluid after CD surgery.MethodsThis study included 110 patients with CD who underwent surgery with bowel resection between January 2010 and March 2020 at Osaka University Hospital. Patients with only perianal surgery or incomplete records were excluded. Risk factors for SSI were evaluated in the context of clinical findings, including bacterial culture of postoperative drainage fluid, and bacterial species related to SSI were also examined.ResultsOf 110 patients, 18 (16.4%) developed SSI. Organ/space SSI developed in six, and a positive bacterial culture of drainage fluid (D‐Posi) was found in five (83.3%). Of 104 patients without organ/space SSI, 31 (29.8%) were D‐Posi (P = .027). Similarly, 68.8% with incisional SSI were D‐Posi, whereas 26.6% without incisional SSI were D‐Posi (P = .0021). Multivariate analysis revealed that D‐Posi was an independent risk factor in both organ/space and incisional SSI. Bacterial examination showed that Pseudomonas aeruginosa and Enterococcus faecalis were significantly detected in patients with SSI.ConclusionThis study suggests the usefulness of postoperative drainage fluid bacterial culture for early diagnosis of SSI after CD surgery.

Temporal Evolution of White Blood Cell Count and Differential: Reliable and Early Detection Markers for Surgical Site Infection Following Spinal Posterior Decompression Surgery.

Introduction For early detection of surgical site infection (SSI) following spinal decompression surgery, we compared temporal changes in the values of laboratory markers that are not affected by operative parameters. Methods The study included 302 patients, which were divided into an SSI group (patients who developed deep SSI) and a non-SSI group for analysis. We reviewed data on C-reactive protein level, total white blood cell (WBC) count, and WBC differential percentage and count before spinal decompression, on postoperative day 1, and on postoperative day 4. We identified laboratory markers that are not affected by operative parameters (operating time, intraoperative blood loss, and number of operative segments). Laboratory markers with a significant difference observed between the peak or nadir value and the value in the subsequent survey day were considered as an indicator of SSI. We examined the utility of each indicator by calculating sensitivity and specificity. Furthermore, we investigated the utility of the combination of all five indicators (wherein the recognition of one marker was considered positive). Results Temporal changes in five laboratory markers were considered indicators of SSI. The changes from postoperative day 1 to postoperative day 4 were as follows: (1) increased WBC count (42% sensitivity, 88% specificity), (2) increased neutrophil percentage (25% sensitivity, 96% specificity), (3) increased neutrophil count (25% sensitivity, 94% specificity), (4) decreased lymphocyte percentage (25% sensitivity, 95% specificity), and (5) decreased lymphocyte count (25% sensitivity, 85% specificity). The combination of these five markers showed a 50% sensitivity, 81% specificity, and 0.65 AUC. Conclusions Five markers were found to be reliable indicators of SSI following spinal decompression surgery because they were not affected by operative parameters. The combination of all five indicators had moderate sensitivity and high specificity. Therefore, this may be reliable and useful for the early detection of SSI.

A reliable diagnostic method of surgical site infection after posterior lumbar surgery based on serial C-reactive protein

Study design: A observational study. Purpose: To evaluate the detection of surgical site infection (SSI) after posterior lumbar surgery by the laboratory parameters. Materials and methods: The study included 189 patients undergoing posterior lumbar fusion or decompression between January 2018 and December 2019. Patients with chronic renal disease, rheumatoid arthritis, hepatic disease were excluded. Patients with tumor, trauma, cervical and thoracic lesions were also excluded. Leukocyte counts, segmented neutrophils, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) were evaluated preoperatively and on postoperative days 1 (POD1), 3 (POD3), 7 (POD7), 14 (POD14), and 30 (POD30). Surgical site infection was defined as (1) purulent discharge at incision site, (2) organisms isolated aseptically from the incision site, and (3) evidence of infection based on MRI. Increasing CRP (ICRP) and decreasing CRP (DCRP) groups were classified according to the value of CRP on POD7. The change rate in CRP was calculated using the formula: (Late value−Previous value/Previous value)×100. Receiver operating characteristic curves were used to validate laboratory data involving SSI. Results: The mean age was 64.5±12.5 years. Male to female ratio was 91–98. The incidence of SSI was 8.8%. Leukocyte count, segmented neutrophil, and ESR were not specific. ICRP group were 43 patients. SSI was found in 14% of ICRP group and 18.6% was associated with secondary infection. Even 7.7% of the infection in DCRP group was identified as SSI. Based on the change rate of CRP, −50% of decreasing rate of CRP in ICRP group between POD7 and POD14 showed the validity that sensitivity was 90.9% and specificity was 68.1%. In DCRP group, −43.3% of decreasing rate of CRP in DCRP group between POD7 and POD14 showed the validity that sensitivity and specificity were 83.3% and 82.4%, respectively. Only 43% of all patients showed normalization of CRP can rule out the SSI. Conclusion: The serial change rate of CRP is a reliable method for detecting SSI more than the criteria of normal value. Nevertheless, close monitoring of patient’s signs and symptoms is important to detect early SSI.

Low TGF-β1 in Wound Exudate Predicts Surgical Site Infection After Axillary Lymph Node Dissection

Surgical site infection (SSI) after axillary lymph node dissection (ALND) for breast cancer increases morbidity and delays the onset of adjuvant treatment. Only a few studies have investigated the feasibility of wound exudate analysis in SSI prediction. This study assessed changes in cytokine levels in postsurgical wound exudate after ALND and examined their predictive value for the early diagnosis of SSI. An observational prospective pilot study was conducted in 47 patients with breast cancer undergoing ALND. Wound exudate samples were collected on the first and sixth postoperative days (POD). Interleukin (IL)-1α, IL-1β, IL-4, IL-10, IL-13, tumor necrosis factor alpha (TNF-α), transforming growth factor beta1 (TGF-β1) and vascular endothelial growth factor (VEGF) C and D levels were measured by immunoassay. Patients were followed to detect SSI. SSI was diagnosed in 8/47 (17.0%) patients. Four SSI patients were hospitalized and treated with intravenous antibiotics. The concentration of TGF-β1 in wound exudate was significantly lower on POD#1 in the SSI group compared to the no SSI group (p=0.008). The receiving operator characteristics (ROC) curve for TGF-β1 showed an area under curve of 0.773 (p=0.0149) indicating good diagnostic potential. On POD#6, the concentration of TGF-β1 remained significantly lower (p=0.043) and the concentrations of IL-10 (p=0.000) and IL-1β (0.004) significantly higher in the SSI group compared to the no SSI group. To our knowledge, this is the first study suggesting a predictive role of wound exudate TGF-β1 levels for SSI. Our results suggest that the risk for SSI can be detected already on POD#1 and that the assessment of TGF-β1 levels in the wound exudate after ALND can provide a usefull method for the early detection of SSI. The key findings of this pilot study warrant verification in a larger patient population.

Drain Tip Culture: Would It Help Us Predict and Prevent Surgical Site Infection After Spinal Surgery?

Background: Systematic cultures of drain tips or drainage fluids are commonly used by surgical teams for the early detection of Surgical Site Infection (SSI), even in the absence of clinical suspicion of infection. However, their prognostic values are controversial. Method: This was a prospective study of patients undergoing spine surgery at our institute during the study period. Patients already diagnosed with spine infection were excluded from the study. At the time of drain removal, the drain tip was cut and sent to microbiology laboratory for bacterial culture. All patients were treated with antimicrobial prophylaxis based on evidence-based guidelines and were monitored for at least 6 months after surgery for the development of Surgical Site Infection (SSI). SSI was defined according to Centers for Disease Control and Prevention criteria. Results: The study comprised of 183 patients including 85 males and 98 females. The rate of Surgical Site Infection in our study was 2.73 % (5 patients). Drain Tip Culture (DTC) was positive in 4 patients (2.18 %). Association of DTC with SSI was found to have high Specificity (98.31 %) and Negative Predictive Value (97.76 %) but low Sensitivity (20 %) and Positive Predictive Value (25 %). Conclusion: Culture of drain tip after spine surgery does not conclusively predict the presence or absence of surgical site infection. However, statistical significance was observed between drain tip culture and surgical site infection with high specificity, high negative predictive value, low sensitivity and low positive predictive value. Keywords: Drain tip culture; Spinal Surgery; Surgical site infection.

Applying Machine Learning Across Sites: External Validation of a Surgical Site Infection Detection Algorithm

Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. To understand the generalizability of a machine learning algorithm between sites, automated surgical site infection (SSI) detection algorithms developed at one center were tested at another distinct center. NSQIP patients had electronic health record (EHR) data extracted at one center (University of Minnesota Medical Center, Site A) over a 4-year period for model development and internal validation, and at a second center (University of California San Francisco, Site B) over a subsequent 2-year period for external validation. Models for automated NSQIP SSI detection of superficial, organ space, and total SSI within 30 days postoperatively were validated using area under the curve (AUC) scores and corresponding 95% confidence intervals. For the 8,883 patients (Site A) and 1,473 patients (Site B), AUC scores were not statistically different for any outcome including superficial (external 0.804, internal [0.784, 0.874] AUC); organ/space (external 0.905, internal [0.867, 0.941] AUC); and total (external 0.855, internal [0.854, 0.908] AUC) SSI. False negative rates decreased with increasing case review volume and would be amenable to a strategy in which cases with low predicted probabilities of SSI could be excluded from chart review. Our findings demonstrated that SSI detection machine learning algorithms developed at 1 site were generalizable to another institution. SSI detection models are practically applicable to accelerate and focus chart review.