Accuracy of the Wound Healing Questionnaire in the diagnosis of surgical-site infection after abdominal surgery in low- and middle-income countries.

INTRODUCTION: Telemedicine is being adopted for postoperative surveillance but requires evaluation for efficacy. This study tested a telephone Wound Healing Questionnaire (WHQ) to diagnose surgical site infection (SSI) after abdominal surgery in low- and middle-income countries. METHOD: A multi-centre, international, prospective study was embedded in the FALCON trial; a factorial RCT testing measures to reduce SSI in seven low- and middle-income countries (NCT03700749). It was conducted according to a pre-registered protocol (SWAT126) and reported according to STARD guidelines. The reference test was in-person review by a trained clinician at 30 postoperative days according to US Centres for Disease Control criteria. The index test was telephone administration of an adapted WHQ at 27 to 30 postoperative days by a researcher blinded to the outcome of in-person review. The sum of item response scores generated an overall score between 0 and 29. The primary outcome was the diagnostic accuracy of the WHQ, defined as the proportion of SSI correctly identified by the telephone WHQ, and summarized using the area under the receiving operator characteristic curve (AUROC) and diagnostic test accuracy statistics. RESULTS: Patients were included from three upper-middle income (396 patients, 13 hospitals), three lower-middle income (746 patients, 19 hospitals), and one low-income country (54 patients, 4 hospitals). 90.3% (1088 of 1196) patients were successfully contacted. Those with non-midline incisions (adjusted odds ratio: 0.36, 95% c.i. 0.17 to 0.73, P=0.005) or a confirmed diagnosis of SSI on in-person assessment (odds ratio: 0.42, 95% c.i. 0.20 to 0.92, P=0.006) were harder to reach. The questionnaire correctly discriminated between most patients with and without SSI (AUROC 0.869, 95% c.i. 0.824 to 0.914), which was consistent across subgroups. A representative cut-off score of ≥4 displayed a sensitivity of 0.701 (0.610-0.792), specificity of 0.911 (0.878-0.943), positive predictive value of 0.723 (0.633-0.814) and negative predictive value of 0.901 (0.867-0.935). CONCLUSION: SSI can be diagnosed using a telephone questionnaire (obviating in-person assessment) in low resource settings.

A wound infection happens when germs enter the cut made in your body by a doctor when you are operated on. Germs are small organisms that cannot be seen by your eyes, but they can cause problems in the healing of the cut. Infection is the most common problem after surgery and can delay you getting out of hospital and back to normal life. The current way to check whether you have an infection is for a doctor or nurse to look at the cut made on your tummy and see how it is healing. For example, a doctor may check if the cut has a green liquid oozing from it or if the area of the wound is red or swollen. A month after you leave hospital, a doctor may ask you to come back for a follow-up visit. However, this will require you to travel to hospital and take a day off work or away from your family, and can be expensive and time-consuming if you travel far. We wanted to find out if talking to a doctor over the phone would work as well as you travelling to hospital to show the wound to a doctor or nurse in person. To do this, we asked over 1000 patients who had recently undergone surgery to be checked using both methods­to take a phone call from one doctor and be checked in person by a different doctor. We were able to compare the phone follow-up and in-person check to see if the doctors came to a different conclusion. We also looked at whether patients were able to receive a phone call at home and their experience of the process. For most patients, the phone call from a doctor was just as good at seeing if a patient had an infection as a face-to-face check-up by a doctor. However, the phone call was not perfect all the time, particularly for patients with very mild infections. Most patients were able to receive the phone call after a few tries and all patients were very happy with the process. As an international research team, we are now trying new ways to improve the phone call, including looking at the wound over video if possible. A phone call to check how your wound is healing can now be used as a substitute for a face-to-face check-up by a doctor. If you have any worries about your wound after the phone call you should still seek help from a doctor or nurse. We hope that the phone call will be more convenient for patients like you to avoid travelling back to hospital and taking time away from your work and family.

Quality check: concordance between two monitoring systems for postoperative organ/space-surgical site infections in rectal cancer surgery. Linkage of data from the Catalan Cancer Plan and the VINCat infection surveillance programme.

BACKGROUND: The Catalan Cancer Plan (CCP) undertakes periodic audits of cancer treatment outcomes, including organ/space surgical site infections (O/S-SSI) rates, while the Catalan Healthcare-associated Infections Surveillance Programme (VINCat) carries out standardized prospective surveillance of surgical site infections (SSI) in colorectal surgery. This cohort study aimed to assess the concordance between these two monitoring systems for O/S-SSI following primary rectal cancer surgery. METHODS: The study compared O/S-SSI incidence data from CCP clinical audits versus the VINCat Programme in patients undergoing surgery for primary rectal cancer, in 2011-12 and 2015-16, in publicly funded centres in Spain. The main outcome variable was the incidence of O/S-SSI in the first 30 days after surgery. Concordance between the two registers was analysed using Cohen's kappa. Discordant cases were reviewed by an expert, and the main reasons for discrepancies evaluated. RESULTS: Pooling data from both databases generated a sample of 2867 patients. Of these, O/S-SSI was detected in 414 patients-235 were common to both registry systems, with satisfactory concordance (κ = 0.69, 95% confidence interval 0.65-0.73). The rate of discordance from the CCP (positive cases in VINCat and negative in CCP) was 2.7%, and from VINCat (positive in CCP and negative in VINCat) was 3.6%. External review confirmed O/S-SSI in 66.2% of the cases in the CCP registry and 52.9% in VINCat. CONCLUSIONS: This type of synergy shows the potential of pooling data from two different information sources with a satisfactory level of agreement as a means to improving O/S-SSI detection. CLINICALTRIALS: gov Identifier: NCT06104579. Registered 30 November 2023.

A woman in her fifties with a post-operative infection, generalised rash and organ failure. / En kvinne i 50-årene med postoperativ infeksjon, generalisert utslett og organsvikt.

Background: Toxic shock syndrome (TSS) is a rare but potentially life-threatening disease caused by superantigen-producing Gram-positive bacteria such as Staphylococcus aureus and Streptococcus pyogenes. Staphylococcal TSS received special attention from 1978 to 1981, when an epidemic was observed associated with the use of hyper-absorbent tampons. Today the disease is rare and generally not related to menstruation, but can occur postpartum or in post-surgical wounds, intrauterine devices (IUDs), burns or other soft tissue injuries, mastitis or other focal infections. The annual incidence of staphylococcal TSS is around 0.5/100 000 and around 0.4/100 000 for streptococcal TSS. The mortality in menstrual-related cases is < 5 % and up to 22 % in non-menstrual related cases. Case presentation: This article presents a case of a middle-aged woman who developed symptoms of toxic shock syndrome five days after elective breast cancer surgery, with high fever, multiorgan failure and a characteristic desquamation of the palms. Interpretation: Toxic shock syndrome is a potentially lethal, toxin-mediated disease. Symptoms develop quickly, within hours. Early recognition and appropriate surgical management, intensive care and antibiotics are therefore important to reduce mortality and sequelae.

An explainable long short-term memory network for surgical site infection identification.

BACKGROUND: Currently, surgical site infection surveillance relies on labor-intensive manual chart review. Recently suggested solutions involve machine learning to identify surgical site infections directly from the medical record. Deep learning is a form of machine learning that has historically performed better than traditional methods while being harder to interpret. We propose a deep learning model, a long short-term memory network, for the identification of surgical site infection from the medical record with an attention layer for explainability. METHODS: We retrieved structured data and clinical notes from the University of Utah Health System's electronic health care record for operative events randomly selected for manual chart review from January 2016 to June 2021. Surgical site infection occurring within 30 days of surgery was determined according to the National Surgical Quality Improvement Program definition. We trained the long short-term memory model along with traditional machine learning models for comparison. We calculated several performance metrics from a holdout test set and performed additional analyses to understand the performance of the long short-term memory, including an explainability analysis. RESULTS: Surgical site infection was present in 4.7% of the total 9,185 operative events. The area under the receiver operating characteristic curve and sensitivity of the long short-term memory was higher (area under the receiver operating characteristic curve: 0.954, sensitivity: 0.920) compared to the top traditional model (area under the receiver operating characteristic curve: 0.937, sensitivity: 0.736). The top 5 features of the long short-term memory included 2 procedure codes and 3 laboratory values. CONCLUSION: Surgical site infection surveillance is vital for the reduction of surgical site infection rates. Our explainable long short-term memory achieved a comparable area under the receiver operating characteristic curve and greater sensitivity when compared to traditional machine learning methods. With explainable deep learning, automated surgical site infection surveillance could replace burdensome manual chart review processes.

A rare case of postoperative Metamycoplasma hominis surgical site infection in a patient after bilateral lung transplantation.

Following the COVID-19 infection, the sternum dislocation and wound dehiscence resulted in an infection complicating the recovery of an immunosuppressed patient after bilateral lung transplantation. Anaerobic culture (96 h) of milky cloudy wound secretion resulted in the growth of pinpoint haemolytic colonies identified as Metamycoplasma hominis (formerly Mycoplasma hominis). The search for the endogenous source of the infection found the bacterium exclusively in the patient's sputum, making a possible link to donor lung M. hominis colonization. Unfortunately, the donor samples were no longer available. The wound infection was successfully treated with 17 days of clindamycin despite the continuous PCR detection of M. hominis in the sputum after the end of the treatment.

Nomogram for predicting surgical site infections in elderly patients after open lumbar spine surgery: A retrospective study.

The aim of this study is to develop a nomogram to assess the risk of surgical site infection in elderly patients undergoing open lumbar spine surgery and explore related risk factors. We reviewed the records of 578 elderly patients who had undergone open lumbar spine surgery. The clinical parameters were subjected to lasso regression and logistic regression analyses. Subsequently, a nomogram was constructed to predict the risk of postoperative surgical site infection and validated using bootstrap resampling. A total of 578 patients were included in the analysis, of which 17 were diagnosed as postoperative surgical site infection. Following the final logistic regression analysis, obesity, hypoalbuminemia and drinking history were identified as independent risk factors and subsequently incorporated into the nomogram. The nomogram demonstrated excellent discrimination, with an area under the receiver-operating characteristic curve of 0.879 (95% CI 0.769 ~ 0.989) after internal validation. The calibration curve exhibited a high level of consistency. Decision curve analysis revealed that this nomogram had greater clinical value when the risk threshold for surgical site infection occurrence was >1% and <89%. We had developed a nomogram for predicting the risk of postoperative surgical site infection in elderly patients who had undergone open lumbar spine surgery. Validation using bootstrap resampling demonstrated excellent discrimination and calibration, indicating that the nomogram may hold potential clinical utility as a simple predictive tool for healthcare professionals.

Propensity Score-Weighted Analysis of Postoperative Infection in Patients With and Without Preoperative Urine Culture.

Importance: Although recent guidelines recommend against performance of preoperative urine culture before nongenitourinary surgery, many clinicians still order preoperative urine cultures and prescribe antibiotics for treatment of asymptomatic bacteriuria in an effort to reduce infection risk. Objective: To assess the association between preoperative urine culture testing and postoperative urinary tract infection (UTI) or surgical site infection (SSI), independent of baseline patient characteristics or type of surgery. Design, Setting, and Participants: This cohort study analyzed surgical procedures performed from January 1, 2017, to December 31, 2019, at any of 112 US Department of Veterans Affairs (VA) medical centers. The cohort comprised VA enrollees who underwent major elective noncardiac, nonurological operations. Machine learning and inverse probability of treatment weighting (IPTW) were used to balance the characteristics between those who did and did not undergo a urine culture. Data analyses were performed between January 2023 and January 2024. Exposures: Performance of urine culture within 30 days prior to surgery. Main Outcomes and Measures: The 2 main outcomes were UTI and SSI occurring within 30 days after surgery. Weighted logistic regression was used to estimate odds ratios (ORs) for postoperative infection based on treatment status. Results: A total of 250 389 VA enrollees who underwent 288 858 surgical procedures were included, with 88.9% (256 753) of surgical procedures received by males and 48.9% (141 340) received by patients 65 years or older. Baseline characteristics were well balanced among treatment groups after applying IPTW weights. Preoperative urine culture was performed for 10.5% of surgical procedures (30 384 of 288 858). The IPTW analysis found that preoperative urine culture was not associated with SSI (adjusted OR [AOR], 0.99; 95% CI, 0.90-1.10) or postoperative UTI (AOR, 1.18; 95% CI, 0.98-1.40). In analyses limited to orthopedic surgery and neurosurgery as a proxy for prosthetic implants, the adjusted risks for UTI and SSI were also not associated with preoperative urine culture performance. Conclusions and Relevance: This cohort study found no association between performance of a preoperative urine culture and lower risk of postoperative UTI or SSI. The results support the deimplementation of urine cultures and associated antibiotic treatment prior to surgery, even when using prosthetic implants.

Incidence and healing times of postoperative sternal wound infections: a retrospective observational single-centre study.

Objectives: Analyses of incidence and time required to heal sternal wound infections after heart surgery performed via a median sternotomy between 2020 and 2022. Results: Superficial wound infections (SWI) were five times more common (2.7%) than mediastinitis (0.5%) among 2693 patients. The median time between the operation and diagnosis of SWI was 26 (interquartile range [IQR] 15-33) days vs. 16 (IQR 9-25) days for mediastinitis (p = .12). Gram-negative bacteria caused 44% of the 85 infections. Sternal wound infection correlated to higher body mass index, female sex, smoking, diabetes mellitus, previous myocardial infarction, coronary artery bypass grafting, use of internal mammary graft, and re-entry for postoperative bleeding. Eight of 59 patients (13.6%) with sternal wound infections had bilateral mammary grafts, compared to 102 of 1191 patients (8.6%) without wound infections (p = .28). Negative pressure wound therapy was always used to treat mediastinitis and applied in 63% of patients with SWI. Two of 13 patients with mediastinitis (15%) and none of 72 patients with SWI died within 90 days after the operation. The median time until the wound healed was 1.9 (IQR 1.3-3.7) months after SWI vs. 1.7 (IQR 1.3-5.3) months after mediastinitis (p = .63). Six patients (7%) required longer than one year to treat the infection. Conclusions: Postoperative sternal wound infections usually appeared several weeks after surgery and were associated with factors as high body mass index, diabetes mellitus and coronary artery bypass. SWI were more common than mediastinitis and often required negative pressure wound therapy and similar treatment time as mediastinitis.

Recurrent sternal wound infection caused by Gordonia bronchialis after open heart surgery.

Gordonia bronchialis is an aerobic gram-positive bacilli and also weakly acid fast. It requires a long incubation time and extensive biochemical reactions for identification. Therefore, use of broad-range polymerase chain reaction (PCR) for amplification of genes such as 16S rRNA or hsp65 followed by sequencing or advanced techniques like MALDI-TOF MS is needed for identification. Here, we present a case of persistent sternal wound infection following open heart surgery, caused by G. bronchialis in a 58 years old male, identified using MALDI-TOF MS-based system. The patient improved with oral Cefpodoxime 200 mg BD for four weeks.

[Pay attention to the prevention and treatment of deep sternal wound infection after sternotomy].

Deep sternal wound infection (DSWI) is one of the potential and catastrophic complications after cardiac surgery. Despite the use of smaller incisions, routine application of prophylactic antibiotic and optimized blood glucose management, the incidence rate of DSWI still fluctuates between 1% and 5%. The early symptoms and signs of DSWI are not often obvious, making it too late for the clinicians to intervene in the process. Once left untreated, DSWI carries a very high mortality rate. Therefore, early prevention, diagnosis, and appropriate management of DSWI are crucial in preventing its progression to life-threatening outcomes. Considering the clinical severity and treatment complexity of DSWI, this article focuses on preventive experiences in each stage of DSWI, and reconstructive strategies after DSWI happens, raising attention among the medical community regarding DSWI.

Effectiveness of Closed Incision Negative Pressure Wound Therapy for Infrainguinal Bypass in the Vascular Quality Initiative.

BACKGROUND: To analyze surgical site infections (SSIs) after infrainguinal bypass for standard dressings versus closed incision negative pressure wound therapy (ciNPWT) in the Society for Vascular Surgery's Vascular Quality Initiative (VQI). METHODS: We retrospectively analyzed SSI after infrainguinal bypass procedures in the VQI from December 2019 to December 2021 comparing ciNPWT and standard dressings. The primary outcome of any superficial or deep wound infection at 30 days was analyzed in a subset of procedures with 30-day follow-up data (cohort A, n = 1,575). Secondary outcomes including in-hospital SSI, return to the operating room (OR) for infection, and length of stay (LOS) were analyzed for all procedures (cohort B, n = 9,288). Outcomes were analyzed in propensity-matched cohorts. RESULTS: Patients who received ciNPWT (n = 1,389) were more likely to be female (34% vs. 32%, P = 0.04) with a higher rate of smoking history (90% vs. 86%, P = 0.003), diabetes (54% vs. 50%, P = 0.007), obesity (34% vs. 26%, P < 0.001), prior peripheral vascular intervention (57% vs. 51%, P < 0.001), and to prosthetic conduit (55% vs. 48%, P < 0.001) compared to patients with standard dressings (n = 7,899). After propensity matching of cohort A (n = 1,256), the 30-day SSI rate was 4% (12/341) in the ciNPWT and 6% (54/896) in the standard dressing group (P = 0.07, 95% CI 0.03-1.06). In the propensity-matched in-hospital cohort B (n = 5,435), SSI was 3% (35/1,371) in the ciNPWT group and 2% (95/4,064) in the standard dressing group (P = 0.66). There was no difference in the rate of return to the OR for infection, 1% (36/4,064) vs. 1% (19/1,371) (P = 0.13) or LOS, 9.0 vs. 9.0 days (P = 0.86) for the standard versus ciNPWT groups. CONCLUSIONS: In this analysis of the VQI registry, the use of ciNPWT after infrainguinal bypass did not result in a statistically significant decrease in 30-day SSI. We recommend that surgeons consider the use of ciNPWT as part of a bundled process of care for high risk rather than all patients, as it may reduce SSI after infrainguinal bypass.

Prediction Models for Forecasting Risk of Development of Surgical Site Infection after Lower Limb Revascularization Surgery: A Systematic Review.

BACKGROUND: Surgical site infections (SSIs) are a common and potentially preventable complication of lower limb revascularization surgery associated with increased healthcare resource utilization and patient morbidity. We conducted a systematic review to evaluate multivariable prediction models designed to forecast risk of SSI development after these procedures. METHODS: After protocol registration (CRD42022331292), we searched MEDLINE, EMBASE, CENTRAL, and Evidence-Based Medicine Reviews (inception to April 4th, 2023) for studies describing multivariable prediction models designed to forecast risk of SSI in adults after lower limb revascularization surgery. Two investigators independently screened abstracts and full-text articles, extracted data, and assessed risk of bias. A narrative synthesis was performed to summarize predictors included in the models and their calibration and discrimination, validation status, and clinical applicability. RESULTS: Among the 6,671 citations identified, we included 5 studies (n = 23,063 patients). The included studies described 5 unique multivariable prediction models generated through forward selection, backward selection, or Akaike Information Criterion-based methods. Two models were designed to predict any SSI and 3 Szyilagyi grade II (extending into subcutaneous tissue) SSI. Across the 5 models, 18 adjusted predictors (10 of which were preoperative, 3 intraoperative, and 5 postoperative) significantly predicted any SSI and 14 adjusted predictors significantly predict Szilagyi grade II SSI. Female sex, obesity, and chronic obstructive pulmonary disease significantly predicted SSI in more than one model. All models had a "good fit" according to the Hosmer-Lemeshow test (P > 0.05). Model discrimination was quantified using the area under the curve, which ranged from 0.66 to 0.75 across models. Two models were internally validated using non-exhaustive twofold cross-validation and bootstrap resampling. No model was externally validated. Three studies had a high overall risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). CONCLUSIONS: Five multivariable prediction models with moderate discrimination have been developed to forecast risk of SSI development after lower limb revascularization surgery. Given the frequency and consequences of SSI after these procedures, development and external validation of novel prediction models and comparison of these models to the existing models evaluated in this systematic review is warranted.

Predictive value of C-reactive protein for postoperative liver-specific surgical site infections.

BACKGROUND: C-reactive protein is a useful biological tool to predict infectious complications, but its predictive value in detecting organ-specific surgical site infection after liver resection has never been studied. We aimed to evaluate the predictive value of c-reactive protein and determine the cut-off values to detect postoperative liver resection-surgical site infection. METHODS: A multicentric analysis of consecutive patients with liver resection between 2018 and 2021 was performed. The predictive value of postoperative day 1, postoperative day 3, and postoperative day 5 C-reactive protein levels was evaluated using the area under the receiver operating characteristic curve. Cut-off values were determined using the Youden index in a 500-fold bootstrap resampling of 500 patients treated at 3 centers, who comprised the development cohort and were tested in an external independent validation cohort of 166 patients at a fourth center. RESULTS: Among the 500 patients who underwent liver resection of the development cohort, liver resection-surgical site infection occurred in 66 patients (13.2%), and the median time to diagnosis was 6.0 days (interquartile range, 4.0-9.0) days. Median C-reactive protein levels were significantly higher on postoperative day 1, postoperative day 3, and postoperative day 5 in the liver resection-surgical site infection group compared with the non-surgical site infection group (50.5 vs 34.5 ng/mL, 148.0 vs 72.5 ng/mL, and 128.4 vs 35.2 ng/mL, respectively; P < .001). Postoperative day 3 and postoperative day 5 C-reactive protein-level area under the curve values were 0.76 (95% confidence interval, 0.64-0.88, P < .001) and 0.82 (95% confidence interval, 0.72-0.92, P < .001), respectively. Postoperative day 3 and postoperative day 5 optimal cut-off values of 100 mg/L and 87.0 mg/L could be used to rule out liver resection-surgical site infection, with a negative predictive value of 87.0% (interquartile range, 70.2-93.8) and 76.0% (interquartile range, 65.0-88.0), respectively, in the validation cohort. CONCLUSION: Postoperative day 3 and postoperative day 5 C-reactive protein levels may be valuable predictive tools for liver resection-surgical site infection and aid in hospital discharge decision-making in the absence of other liver-related complications.

The area ratio of Modic changes has predictive value for postoperative surgical site infection in lumbar spine surgery: a retrospective study.

BACKGROUND: Increasing evidence suggests an association between Modic changes (MC) and subclinical infection and inflammatory reactions. However, the relationship between preoperative MC and surgical site infection (SSI) has not been fully explored. This study aims to investigate the correlation between MC and SSI. METHODS: A retrospective analysis was conducted on patients (n = 646) who underwent single-level lumbar spine surgery for lower back pain in our hospital between 2018 and 2023. According to the Centers for Disease Control and Prevention (CDC) criteria, the patients were divided into an SSI group (n = 40) and a Non-SSI group (n = 606). Univariate analysis was performed to determine the statistical differences in variables between the two groups, and the variables with significant differences were included in a multivariable logistic regression analysis to identify independent risk factors for SSI. Receiver operating characteristic (ROC) curve analysis was performed on the independent risk factors. RESULTS: The SSI group and the Non-SSI group exhibited significant differences in diabetes prevalence, MC prevalence, Total endplate score (TEPS) and area ratio of MC (P < 0.05). Age, gender, American Society of Anesthesiologists(ASA)score, hypertension, coronary heart disease (CHD), chronic obstructive pulmonary disease (COPD), MC classification, and the location of MC in the endplate showed no significant differences (P > 0.05). Multivariate binary logistic regression analysis was performed on the variables with significant differences, and the results indicated a significant correlation between TEPS (P = 0.009) and the area ratio of MC changes (P = 0.001) with SSI. ROC curve analysis was performed on the TEPS and area ratio of MC changes, and the results showed that the diagnostic value of TEPS (AUC: 0.641; CI: 0.522-0.759) is lower than the area ratio of MC (AUC: 0.722; CI: 0.621-0.824), and the combined diagnosis did not significantly improve the diagnostic value (AUC: 0.747; CI: 0.653-0.842). The area ratio of MC had moderate diagnostic value for SSI (AUC: 0.722; CI: 0.621-0.824), with a cut-off value of 24.62% determined by the Youden index (sensitivity: 69.2%; specificity: 73.1%), and for every 1% increase in the area ratio of MC changes, the risk of SSI in MC patients increased by 10.3% (OR = 1.103; CI: 1.044-1.167). CONCLUSION: The area ratio MC and the TEPS are independent risk factors for SSI after lumbar spine surgery. The predictive value of the area ratio of MC is greater than TEPS, and when the two are combined, the predictive value is not significantly improved. When the rate of MC exceeds 24.62%, caution should be exercised regarding the occurrence of SSI.

Risk factors and clinical characteristics of surgical site infections in athletes undergoing Achilles tendon repair surgery.

Achilles tendon ruptures are common in athletes, requiring surgical intervention. However, the risk of surgical site infections (SSIs) post-surgery poses significant challenges. This study aims to analyse the risk factors and microbial aetiology associated with SSIs in athletes undergoing Achilles tendon repair. A comprehensive retrospective analysis was conducted from May 2021 to July 2023. The study included 25 patients with SSIs (case group) and 50 patients without SSIs (control group) post Achilles tendon repair surgery. Inclusion criteria encompassed patients with medically confirmed Achilles tendon ruptures who underwent surgical repair. Exclusion criteria included prior tendon pathologies and significant chronic illnesses. Diagnostic criteria for SSIs involved symptoms like elevated body temperature and localized tenderness, along with laboratory confirmations such as positive microbiological cultures. The study utilized VITEK® 2 for bacterial identification and involved statistical analyses like univariate and multivariate logistic regression. The study identified Staphylococcus aureus as the primary pathogen in SSIs. Significant risk factors included lack of prophylactic antibiotic use, presence of diabetes, open wounds and prolonged surgery duration. Univariate analysis revealed stark contrasts in these factors between infected and non-infected groups, while multivariate analysis underscored their importance in SSI development. S. aureus emerged as the predominant pathogen in SSIs post Achilles tendon repair. Critical risk factors such as absence of prophylactic antibiotics, diabetes, open wounds and extended surgery duration play a vital role in SSIs. Addressing these factors is essential for better postoperative outcomes in Achilles tendon repair surgeries.

Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy. Our approach has surpassed the performance of logistic regression, random forest, Naive Bayes classifier, gradient boosting decision tree, ANN, and lasso regression, with sensitivity at 0.798, specificity at 0.779, precision at 0.789, NPV at 0.798, and accuracy at 0.788 and has greatly improved the classification performance of the inferior class.

Establishing semi-automated infection surveillance in obstetrics and gynaecology.

BACKGROUND: Surveillance is an acknowledged method to decrease nosocomial infections, such as surgical site infections (SSIs). Electronic healthcare records create the opportunity for automated surveillance. While approaches for different types of surgeries and indicators already exist, there are very few for obstetrics and gynaecology. AIM: To analyse the sensitivity and workload reduction of semi-automated surveillance in obstetrics and gynaecology. METHODS: In this retrospective, single-centre study at a 1438-bed tertiary care hospital in Germany, semi-automated SSI surveillance using the indicators 'antibiotic prescription', 'microbiological data' and 'administrative data' (diagnosis codes, readmission, post-hospitalization care) was compared with manual analysis and categorization of all patient files. Breast surgeries (BSs) conducted in 2018 and caesarean sections (CSs) that met the inclusion criteria between May 2013 and December 2019 were included. Indicators were analysed for sensitivity, number of analysed procedures needed to identify one case, and potential workload reduction in detecting SSIs in comparison with the control group. FINDINGS: The reference standard showed nine SSIs in 416 BSs (2.2%). Sensitivities for the indicators 'antibiotic prescription', 'diagnosis code', 'microbiological sample taken', and the combination 'diagnosis code or microbiological sample' were 100%, 88.9%, 66.7% and 100%, respectively. The reference standard showed 54 SSIs in 3438 CSs (1.6%). Sensitivities for the indicators 'collection of microbiological samples', 'diagnosis codes', 'readmission/post-hospitalization care', and the combination of all indicators were 38.9%, 27.8%, 85.2% and 94.4%, respectively. CONCLUSIONS: Semi-automated surveillance systems may reduce workload by maintaining high sensitivity depending on the type of surgery, local circumstances and thorough digitalization.

A comprehensive study on the risk factors and pathogen analysis of postoperative wound infections following caesarean section procedures.

Postoperative wound infections (PWIs), a subtype of surgical site infections, are a significant concern for patients undergoing caesarean sections (C-sections). Understanding risk factors and pathogen profiles can greatly assist in early diagnosis and effective treatment. This study aimed to identify risk factors and analyse the pathogenic landscape contributing to PWIs in C-sections. A nested case-control study was carried out, utilising stringent criteria for case selection and control matching. Diagnostic criteria for surgical site infections included both clinical and microbiological parameters. Risk variables examined included patient age, Body Mass Index, duration of surgery and several other clinical indicators. Microbiological analysis was performed using the BD Phoenix-100 Automated Bacterial Identification System. Statistical analyses were conducted using SPSS version 26.0, and risk factors were evaluated through both univariate and multivariate analyses. A total of 50 patients, aged between 20 and 45 years (mean age 26.3 ± 5.6), developed PWIs following C-sections. The study revealed a temporal distribution and various clinical indicators of PWIs, including elevated white blood cell count and C-reactive protein levels. Gram-negative bacteria were found to be more prevalent at 57.4%. Notable pathogens included Pseudomonas aeruginosa and Acinetobacter baumannii. Antimicrobial resistance patterns were also identified, highlighting the need for a targeted antibiotic approach. Increased infection risks were linked to lack of prophylactic antibiotics, absence of preoperative povidone-iodine antisepsis, operations over an hour, anaemia, amniotic fluid contamination, diabetes, GTI, premature rupture of membranes and white blood cells counts above 10 × 109 /L. The study provides critical insights into the risk factors and microbial agents contributing to PWIs following C-sections. Our findings emphasise the importance of early diagnosis through clinical and laboratory parameters, as well as the need for constant surveillance and reassessment of antibiotic stewardship programs.

Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review.

Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like 'machine learning', 'surgical' and 'wound'. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn-grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.

A predictive nomogram for surgical site infection in patients who received clean orthopedic surgery: a retrospective study.

BACKGROUND: Surgical site infection (SSI) is a common and serious complication of elective clean orthopedic surgery that can lead to severe adverse outcomes. However, the prognostic efficacy of the current staging systems remains uncertain for patients undergoing elective aseptic orthopedic procedures. This study aimed to identify high-risk factors independently associated with SSI and develop a nomogram prediction model to accurately predict the occurrence of SSI. METHODS: A total of 20,960 patients underwent elective clean orthopedic surgery in our hospital between January 2020 and December 2021, of whom 39 developed SSI; we selected all 39 patients with a postoperative diagnosis of SSI and 305 patients who did not develop postoperative SSI for the final analysis. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were conducted in the training cohort to screen for independent risk factors of SSI, and a nomogram prediction model was developed. The predictive performance of the nomogram was compared with that of the National Nosocomial Infections Surveillance (NNIS) system. Decision curve analysis (DCA) was used to assess the clinical decision-making value of the nomogram. RESULTS: The SSI incidence was 0.186%. Univariate and multivariate logistic regression analysis identified the American Society of Anesthesiology (ASA) class (odds ratio [OR] 1.564 [95% confidence interval (CI) 1.029-5.99, P = 0.046]), operative time (OR 1.003 [95% CI 1.006-1.019, P < 0.001]), and D-dimer level (OR 1.055 [95% CI 1.022-1.29, P = 0.046]) as risk factors for postoperative SSI. We constructed a nomogram prediction model based on these independent risk factors. In the training and validation cohorts, our predictive model had concordance indices (C-indices) of 0.777 (95% CI 0.672-0.882) and 0.732 (95% CI 0.603-0.861), respectively, both of which were superior to the C-indices of the NNIS system (0.668 and 0.543, respectively). Calibration curves and DCA confirmed that our nomogram model had good consistency and clinical predictive value, respectively. CONCLUSIONS: Operative time, ASA class, and D-dimer levels are important clinical predictive indicators of postoperative SSI in patients undergoing elective clean orthopedic surgery. The nomogram predictive model based on the three clinical features demonstrated strong predictive performance, calibration capabilities, and clinical decision-making abilities for SSI.