Predictors Of Surgical Site Infection Research Articles

Independent Predictors for Surgical Site Infections in Patients Undergoing Complex Abdominal Wall Reconstruction

Introduction: Complex abdominal wall reconstruction (CAWR) in patients with large abdominal defects have become a common procedure. The aim of this study was to identify independent predictors of surgical site infections (SSI) in patients undergoing CAWR. Materials and Methods: This was an ambidirectional cohort study of 240 patients who underwent CAWR with biologic mesh between 2012 and 2020 at an academic tertiary/quaternary care center. Prior superficial SSI, deep SSI, organ space infections, enterocutaneous fistulae, and combined abdominal infections were defined as prior abdominal infections. Univariable and multivariable logistic regression models were performed to determine independent risk factors for SSI. Results: There were a total of 39 wound infections, with an infection rate of 16.3%. Forty percent of patients who underwent CAWR in this study had a history of prior abdominal infections. In the multivariable regression models not weighted for length of stay (LOS), prior abdominal infection (odds ratio [OR]: 2.49, p=0.013) and higher body mass index (BMI) (OR: 1.05, p=0.023) were independent predictors of SSI. In the multivariable regression model weighted for LOS, prior abdominal infection (OR: 2.2, p=0.034), higher BMI (OR: 1.05, p=0.024), and LOS (OR: 1.04, p=0.043) were independent predictors of SSI. Conclusion: The history of prior abdominal infections, higher BMI, and increased LOS are important independent predictor of SSI following CAWR.

Incidence, risk factors and outcomes of surgical site infections among patients admitted to Jimma Medical Center, South West Ethiopia: Prospective cohort study

BackgroundSurgical site infections are one of the leading health care–associated infections in developing countries. Despite improvements in surgical technique and the use of best infection prevention strategies, surgical site infections remained the major cause of hospital acquired infections. Therefore, this study aimed to determine incidence, risk factors, and outcomes of surgical site infections among patients admitted to Jimma Medical Center, South West Ethiopia. MethodsA hospital based prospective cohort study design was employed to follow adult patients admitted to general surgery ward, orthopaedic ward and gynaecologic/obstetrics ward of Jimma Medical Centre, from April 20 to August 20, 2019. All patients were followed daily before, during and after operation for 30 days to determine the incidence of surgical site infection and other outcomes. Data was entered using EpiData version 4.2 and analyzed using statistical software package for social science version 20.0. To identify the independent predictors of outcome, multiple stepwise backward cox regression analysis was done. Statistical significance was considered at p-value <0.05. ResultsOf total of 251 participants included to the study, about 126 (50.2%) of them were females. The mean ± SD age of patients was 38 ± 16.30 years. Considerable number of patients 53(21.1%) developed surgical site infections. American Society of Anaesthesiologists score ≥3 [ Adjusted Hazard Ratio (AHR) = 2.26; 95%CI = (1.03–4.93)], postoperative antibiotic prescription [AHR = 3.2; 95%CI = (1.71–6.01)], contaminated-wound [AHR = 7.9; 95%CI = (4.3–14.60)], emergency surgery [AHR = 2.8; 95% CI = (1.16–6.80)], duration of operation ≥ 2 h [AHR = 4; 95% CI = (2.17–7.50)] and comorbidity [AHR = 2.52; 95%CI = (1.28–4.94)] were independent predictors for surgical site infections. Twenty nine (11.6%) patients returned to operation room. The result of the multivariate cox regression analysis showed that SSI [AHR (95% CI) = 7(3.16–15.72)], and incision site [AHR (95% CI) = 2.5(1.14–5.42)] had statistically significant association with re-operation ConclusionThe incidence of surgical site infection was high in the study setting. There were significant numbers of contributing factors for the occurrence of surgical site infections. Although no mortality observed during the study period, significant number of patients re-operated. Large multicenter study is urgently needed to confirm the outcome of this study.

Is antibiotic prophylaxis expendable in parotid gland surgery? A retrospective analysis of surgical site infection rates.

To evaluate the rate of surgical site infection (SSI) and associated risk factors after parotid gland surgery including the impact of antibiotic prophylaxis. Retrospective single-centre clinical study. Tertiary referral centre for head and neck surgery. Seven hundred and fifty four patients who underwent parotid gland surgery at the University Hospital Heidelberg, Germany, between 2007 and 2014 were enrolled in this study. Data on patient age, American Society of Anesthesiologists (ASA) classification system, smoking status, diabetes mellitus, operation time, and antibiotic prophylaxis were collected. Additionally, the National Healthcare Safety Network (NHSN) risk index was calculated. Association of these factors with SSI was evaluated in univariate analyses and a multivariate logistic regression model. Rate of SSI. Twenty four patients (3.2%) had an SSI according to the NHSN definition. In univariate analyses, only smokers (P=.048) and male patients (P=.01) had a significantly higher rate of SSI. Since the majority of smokers were men (62.3%), the effect of male gender, smoking, together with the NHSN risk index was further investigated as predictors of SSI within a logistic regression model. All three predictors showed a significant effect on SSI. Parotid gland surgery has a low rate of SSI. In our cohort, male gender, smoking and high NHSN risk index scores were significantly associated with SSI, whereas antibiotic prophylaxis had no protective effect.

Incidence and predictors of surgical site infection after distal femur fractures treated by open reduction and internal fixation: a prospective single\u2010center study

BackgroundThere remain limited data on the epidemiological characteristics and related predictors of surgical site infection (SSI) after open reduction and internal fixation (ORIF) for distal femur fractures (DFFs). We designed this single-centre prospective study to explore and forecast these clinical problems.MethodsFrom October 2014 to December 2018, 364 patients with DFFs were treated with ORIF and followed for complete data within one year. Receiver operating characteristic (ROC) analyses, univariate Chi-square analyses, and multiple logistic regression analyses were used to screen the adjusted predictors of SSI.ResultsThe incidence of SSI was 6.0 % (22/364): 2.4 % (9/364) for superficial SSIs and 3.6 % (13/364) for deep SSIs. Staphylococcus aureus (methicillin-resistant S. aureus in 2 cases) was the most common pathogenic bacteria (36.8 %,7/19). In multivariate analysis, parameters independently associated with SSI were: Open fracture (OR: 7.3, p = 0.003), drain use (OR: 4.1, p = 0.037), and incision cleanliness (OR: 3.5, p = 0.002). An albumin/globulin (A/G) level ≥ 1.35 (OR: 0.2, p = 0.042) was an adjusted protective factor for SSI.ConclusionsThe SSI after ORIF affected approximately one in 15 patients with DFFs. The open fracture, drain use, high grade of intraoperative incision cleanliness, and preoperative A/G levels lower than 1.35 were significantly related to increasing the risk of post-operative SSI after DFFs. We recommended that more attentions should be paid to these risk factors during hospitalization.Trial registrationNO 2014-015-1, October /15/2014, prospectively registered.We registered our trial prospectively in October 15, 2014 before the first participant was enrolled. This study protocol was conducted according to the Declaration of Helsinki and approved by the Institutional Review Board. The ethics committee approved the Surgical Site Infection in Orthopaedic Surgery (NO 2014-015-1). Data used in this study were obtained from the patients who underwent orthopaedic surgeries between October 2014 to December 2018.

1054 Predictors of surgical-site infection after cesarean delivery in obese women receiving evidence-based preventive measures

Obesity increases the risk of both cesarean delivery and surgical site infections. Our objective was to estimate patient and operative predictors of surgical-site infection after cesarean in a contemporary cohort of obese women who received evidence-based preventive measures. Secondary analysis of data from a multicenter randomized trial in which obese women (pre-pregnancy BMI > 30kg/m2) undergoing planned or unplanned cesarean delivery were randomized to either prophylactic negative pressure wound therapy (n=806) or standard wound dressing (n=802). Participants received standard evidence-based infection prevention measures. The primary outcome was superficial, deep or organ-space surgical-site infection according to the Centers for Disease Control and Prevention definitions. Univariable and multivariable analysis were used to estimate independent patient and surgical predictors of surgical site infection. Of the 1608 participants, 60 (3.7%) were diagnosed with surgical site infection. There was no significant difference in surgical-site infection by randomization group, and the two groups were combined for this analysis. Several patient and surgical characteristics were associated with a higher risk of surgical site infection. In multivariable analysis, tobacco use, chorioamnionitis and depth of subcutaneous tissue were associated with an increased risk of surgical site infection. Use of cefazolin prophylaxis, compared with non-cefazolin antibiotics, was associated with lower risk (Table). The multivariable model based on these variables was moderately predictive (area under the curve: 0.70, Figure). The risk of surgical-site infection in obese women after cesarean is relatively low when evidence-based preventive measures are used. Smoking cessation, closure of the subcutaneous layer and cefazolin prophylaxis may be modifiable factors to further minimize infection risk in this population.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Intraoperative subcutaneous culture as a predictor of surgical site infection in open gynecological surgery.

PurposeTo analyze the relationship between intraoperative cultures and the development of surgical site infection (SSI) in women undergoing laparotomy for gynecological surgery.MethodsProspective observational cohort study. Over a six-year period, women who underwent elective laparotomy at our hospital were included. Patients’ demographics, underlying co-morbidities, surgical variables, type and etiology of postoperative surgical site infections were collected. Skin and subcutaneous samples were taken just prior to skin closure and processed for microbiological analysis. Univariate and multivariate analyses (logistic regression model) were conducted to explore the association of the studied variables with SSIs.Results284 patients were included in our study, of which 20 (7%) developed surgical site infection, including 11 (55%) superficial and nine (45%) organ-space. At univariate analysis, length of surgery, colon resection, transfusion and positive intraoperative culture were associated with surgical site infection occurrence. Skin and subcutaneous cultures were positive in 25 (8.8%) and 20 (7%) patients, respectively. SSI occurred in 35% of women with positive subcutaneous culture and in 20% of those with positive skin cultures. Using multivariate analysis, the only independent factor associated with surgical site infection was a positive subcutaneous culture (OR 10.4; 95% CI 3.5–30.4; P<0.001).ConclusionIntraoperative subcutaneous cultures before skin closure may help early prediction of surgical site infection in open gynecological procedures.

897. Prediction of Surgical Site Infections in Colon Surgery in Belo Horizonte Hospitals

BackgroundIn Belo Horizonte (a 3,000,000 inhabitants city) a survey was carried out in five hospitals, between July 2016 and June 2018, about surgical site infection (SSI) in patients undergoing colon surgery procedures. The general objective is to statistically evaluate such incidences and enable an analysis of the SSI predictive power, through MLP (Multilayer Perceptron) pattern recognition algorithms.MethodsThrough the Hospital Infection Control Committees (CCIH) of the hospitals a data collection on SSI was carried out. Such data was used in the analysis during your routine SSI surveillance procedures. So, three procedures were performed: a treatment of the collected database for use of intact samples; a statistical analysis on the profile of the collected hospitals and; an assessment of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations.ResultsFrom 2126 records, 638 were complete for analysis. It was found: the average age is 55 years (from 1 to 94 years); the surgeries had an average time of approximately 197 minutes; the average hospital stay is 8 days, the death rate reached 5.625% and the SSI rate reached 6%. Regarding the predictive power, a maximum predictive power of 0.8316 was found.ConclusionThere was a loss of 70% of the database samples due to the presence of noise. However, it was possible to have a relevant sample to assess the profile of these five hospitals. The predictive process presented some configurations with results that reached 0.8316, which promises the use of the structure for the monitoring of automated SSI for patients undergoing colon surgeries. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and the other for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br.Disclosures All Authors: No reported disclosures

907. Technology and Medicine: Prediction of Surgical Site Infection in Clean Surgeries using Artificial Neural Networks

BackgroundIn Belo Horizonte, a city with 3,000,000 inhabitants, a survey was performed in six hospitals, between July 2016 and June 2018, about surgical site infection (SSI) in patients undergoing clean surgery procedures. The main objective is to statistically evaluate such incidences and enable an analysis of the SSI predictive power, through MLP (Multilayer Perceptron) pattern recognition algorithms.MethodsThrough the Hospital Infection Control Committees (CCIH) of the hospitals, a data collection on SSI was carried out through the software SACIH - Automated System for Hospital Infection Control. So, three procedures were performed: a treatment of the collected database for use of intact samples; a statistical analysis on the profile of the collected hospitals; an evaluation of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations.ResultsFrom 45,990 records, 12,811 were able for analysis. The statistical analysis results were: the average age is 49 years old (predominantly between 30 and 50); the surgeries had an average time of 134.13 minutes; the average hospital stay is 4 days (from 0 to 200 days), the death rate reached 1% and the SSI 1.49%. A maximum prediction power of 0.742 was found.ConclusionThere was a loss of 60% of the database samples due to the presence of noise. However, it was possible to have a relevant sample to assess the profile of these six hospitals. The predictive process, presented some configurations with results that reached 0.742, what promises the use of the structure for the monitoring of automated SSI for patients submitted to surgeries considered clean. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and other for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br.Disclosures All Authors: No reported disclosures

895. Prediction of Occurrence for Surgical Site Infection in Infected Surgeries

BackgroundA survey was carried out in five hospitals, between July 2016 and June 2018, on surgical site infection (SSI) in patients undergoing infected surgery procedures, in the city of Belo Horizonte (3,000,000 inhabitants). The general objective is to statistically evaluate such incidences and enable an analysis of the SSI predictive power, through MLP (Multilayer Perceptron) pattern recognition algorithms.MethodsThrough the Hospital Infection Control Committees (CCIH) of the hospitals, a data collection on SSI was carried out. Such data is used in the analysis during your routine SSI surveillance procedures. So, three procedures were performed: a treatment of the collected database for use of intact samples; a statistical analysis on the profile of the collected hospitals and; an assessment of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations.ResultsFrom 1770 records, 810 were intact for analysis. It was found that: the average age is 53 years old (from 0 to 98 years old); the surgeries had an average time of approximately 140 minutes; the average hospital stay is 19 days, the death rate reached 10.86% and the SSI rate was 6.04%. A maximum prediction power of 0.729 was found.ConclusionThere was a loss of 54% of the database samples due to the presence of noise. However, it was possible to have a relevant sample to assess the profile of these five hospitals. The predictive process, presented some configurations with results that reached 0.729, which promises the use of the structure for the monitoring of automated SSI for patients submitted to infected surgeries. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and another for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br.Disclosures All Authors: No reported disclosures

893. Occurrence’s Prediction of Surgical Site Infection in Limb Amputation Surgery

BackgroundA research was conducted between July 2016 and June 2018 in five hospitals in Belo Horizonte, a city of 3,000,000 inhabitants, focused on surgical site infection (SSI) in patients undergoing limb amputation surgery procedure. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through MLPs (Multilayer Perceptron), a pattern recognition algorithm.MethodsData were collected on SSI by the Hospital Infection Control Committees (CCIH) of the hospitals involved. The information was forwarded to the NOIS (Nosocomial Infection Study) Project. After data collection, three procedures were performed: a treatment of the database collected for the use of intact samples; a statistical analysis on the profile of the hospitals collected and; an assessment of the predictive power of five types of MLP (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay, and Quick Propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations.ResultsFrom 969 data, only 507 were intact for analysis. Statistically: in 12.45% there was an incidence of global infection and that in 10.67% of the cases were SSI (among which, 94.6% had to be hospitalized for more than 10 days); patients were hospitalized on average 21 days (from 0 to 141 days); the average duration is 78 minutes (maximum 360 minutes); 53 deaths (a 16.98% death rate in case of SSI). A maximum prediction power of 0.688 was found.ConclusionDespite the loss rate of almost 40% of the database samples due to the presence of noise, it was obtained a relevant sampling to evaluate the profile the hospitals. For the predictive process, although some configurations reached 0.688, which makes promising the use of the automated SSI monitoring framework for patients undergoing limb amputation surgery. To optimize data collection and enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com), two mobile application were developed: one for monitoring the patient in the hospital and the other for post-hospital discharge monitoring.Disclosures All Authors: No reported disclosures

901. Risk Prediction for Surgical Site Infection in Patients Subject to Knee Arthroplasty Surgery

BackgroundThis research represents an experiment on surgical site infection (SSI) in patients undergoing knee arthroplasty surgery procedures in hospitals in Belo Horizonte, between July 2016 and June 2018. The objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI of pattern recognition algorithms, in this case the Multilayer Perceptron (MLP).MethodsData were collected on SSI in five hospitals. The Hospital Infection Control Committees (CCIH) of the hospitals involved collected all data used in the analysis during their routine SSI surveillance procedures and sent the information to the Nosocomial Infection Study Project (NOIS). Three procedures were performed: a treatment of the database collected for use of intact samples; a statistical analysis on the profile of the hospitals collected and; an assessment of the predictive power of five types of MLP (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay, and Quick Propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% and 75% for testing, 35% and 25% for validation). They were compared by measuring AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations.ResultsFrom the 1438 data collected, 390 records were usable and it was verified: the average age of the patients who underwent this surgical procedure was 70 (ranging from 29 to 92), average surgery time was 171 minutes (between 50 and 480), 47% presented a hospital contamination, 1% SSI and no deaths. During the MLP experiments, due to the low number of SSI cases, the prediction rate for this specific surgery was 0.5.ConclusionDespite the large noise index of the database, it was possible to have a relevant sampling to evaluate the profile of hospitals in Belo Horizonte. However, for the predictive process, despite some results equal to 0.5, the database demands more samples of SSI cases, as only 1% of positive samples generated an unbalance of the database. To optimize data collection and enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com), two mobile application were developed: one for monitoring the patient in the hospital and the other for monitoring after hospital discharge.Disclosures All Authors: No reported disclosures

896. Prediction of Surgical Site Infection Risk in Patients Undergoing Bariatric Surgery

BackgroundIn the hospitals of Belo Horizonte (a city with more than 3,000,000 inhabitants), a survey was conducted between July 2016 and June 2018, focused on surgical site infection (SSI) in patients undergoing bariatric surgery procedures. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through MLPs (Multilayer Perceptron), a pattern recognition algorithm.MethodsData were collected on SSI by the Hospital Infection Control Committees (CCIH) of the hospitals involved in the research. After data collection, three procedures were performed: a treatment of the database collected for the use of intact samples; a statistical analysis on the profile of the hospitals collected and; an assessment of the predictive power of five types of MLP (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay, and Quick Propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations.ResultsFrom 3473 initial data, only 2491 were intact for analysis. Statistically, it was found that: the average age of the patients was 39 years (ranging from 16 to 65); the average duration of surgery was 138 minutes; and 0.8% of patients had SSI. Regarding the predictive power of SSI, the experiments have a minimum value of 0.350 and a maximum of 0.756.ConclusionDespite the loss rate of almost 30% of the database samples due to the presence of noise, it was possible to have a relevant sampling for the profile evaluation of Belo Horizonte hospitals. Moreover, for the predictive process, although some configurations have results that reached 0.755, which makes promising the use of the structure for automated SSI monitoring for patients undergoing bariatric surgery. To optimize data collection and enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com), two mobile application were developed: one for monitoring the patient in the hospital and the other for monitoring after hospital discharge.Disclosures All Authors: No reported disclosures

880. Artificial Neural Networks to Predict Surgical Site Infection in Aorta Artery Aneurysm Correction

BackgroundA survey was conducted in three hospitals, between July 2016 and June 2018, about surgical site infection (SSI) in patients undergoing surgeries to correct aortic artery aneurysms in the city of Belo Horizonte, with more than 3,000,000 of inhabitants. The general objective is to statistically evaluate such incidences and enable an analysis of the predictive power of SSI, through MLP (Multilayer Perceptron) pattern recognition algorithms.MethodsThrough the Hospital Infection Control Committees (CCIH) of the hospitals involved in the research, data collection on SSI was carried out. Such data is used in the analysis during your routine SSI surveillance procedures. Thus, three procedures were performed: a treatment of the database collected for use of intact samples; a statistical analysis on the profile of the collected hospitals and; an assessment of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) for each of the configurations.ResultsFrom 600 records, 575 were complete for analysis. It was found that: the average age is 68 years (from 24 to 98 years); the average hospital stay is 9 days (with a maximum of 127 days), the death rate reached 6.43% and the SSI rate 2.78%. A maximum prediction power of 0.75 was found.ConclusionThere was a loss of 4% of the database samples due to the presence of noise. It was possible to evaluate the profile of the three hospitals. The predictive process presented configurations with results that reached 0.75, which promises the use of the structure for the monitoring of automated SSI for patients undergoing surgery to correct aortic artery aneurysms. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and another for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br.Disclosures All Authors: No reported disclosures

879. Artificial Neural Networks to Predict Infection in the Surgical Site in Patients over 70 Years Old

BackgroundBetween July 2016 and June 2018, a survey was carried out in five hospitals on surgical site infection (SSI) in patients over 70 years old, who underwent surgery procedures, in the city of Belo Horizonte, a city with more of 3,000,000 inhabitants. The general objective is to statistically evaluate such incidences and enable an analysis of the predictive power of SSI, through MLP (Multilayer Perceptron) pattern recognition algorithms.MethodsThrough the Hospital Infection Control Committees (CCIH) of the hospitals involved in the research, data collection on SSI was carried out. Such data is used in the analysis during your routine SSI surveillance procedures. Thus, three procedures were performed: a treatment of the database collected for use of intact samples; a statistical analysis on the profile of the collected hospitals and; an evaluation of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) for each of the configurations.ResultsFrom 11277 records, 3350 were complete for analysis. It was found that: the average age is 79 years (from 74 to 84 years); the average surgery time is 123 minutes; the average hospital stay is 58 days (with a maximum of 114 days), the death rate reached 7.1% and that of SSI 2.59%. A maximum prediction power of 0.642 was found.ConclusionThere was a loss of almost 70% of the database samples due to the presence of noise, however it was possible to evaluate the hospitals profile. The predictive process, presented configurations with results that reached 0.642, what promises the use of the structure for the monitoring of automated SSI for patients over 70 years submitted to surgeries. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and another for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br.Disclosures All Authors: No reported disclosures

Abdominal Wall Thickness is a Predictor for Surgical Site Infections in Patients Undergoing Colorectal Operations.

Surgical site infections (SSIs) are an established complication following colorectal operations, with rates up to 30% reported in the literature. Obesity is a known risk factor for SSI; however, body mass index (BMI), body fat percentage, waist-hip ratio, or abdominal circumference are imperfect measures. The purpose of our study was to determine whether abdominal wall thickness (AWT) is predictive of SSI. We queried our American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database for patients (age ≥18years) undergoing a colectomy at the University of Kentucky (UK) from January 1, 2013 to December 31, 2018. The exclusion criteria included patients with open abdomens or the lack of preoperative computed tomography (CT) within 3months of their operation. AWT was measured at the level of the anterior superior iliac spine (ASIS) on abdominal CT. SSI was defined by superficial SSI, deep SSI, and wound dehiscence. Of 1261 patients enrolled, 52.2% were female, with an average age of 57.4years. More patients had laparoscopic operations (51%), and the median length of stay was 7days. Our study demonstrated an SSI rate of 9.4% and a 30-day readmission rate of 11%. The overall mean AWT was 2.6cm (range .1-13.1), and patients with the highest AWT quintile were more likely to develop an SSI than the lowest quintile (12% vs. 5%). After controlling for risk factors and confounders, the odds of an SSI were 3.6 times higher for patients with the highest AWT than patients with the lowest AWT. Among colorectal surgery patients, AWT is an independent risk factor predictive for SSI.

&lt;p&gt;Evaluation of Surgical Antimicrobial Prophylaxis and Incidence of Surgical Site Infection at Borumeda Hospital, Northeast Ethiopia: Retrospective Cross-Sectional Study&lt;/p&gt;

BackgroundSurgical site infections are global healthcare problems. Although surgical site infections are preventable, they still cause significant morbidity, high death rates, and financial stress on national budgets and individual patients. Inappropriate uses of surgical antimicrobial prophylaxis are increasing and worsening patients’ quality of life. This study determined the incidence and risk factors of surgical site infections.MethodsInstitution-based retrospective cross-sectional study was conducted using a structured data abstraction format on patients who were attending at the surgical ward of Borumeda hospital from April 1, 2017, to March 31, 2019. The data were collected during July 15–30, 2019. A systematic random sampling technique was employed to select 227 surgical cases. Multivariate logistic regression was computed using the statistical package for social sciences version 23.ResultsThe incidence of surgical site infections was 46.7%. Prophylaxis was administered to 188 (82.8%) surgical cases. Prophylaxis was recommended for 151 (66.5%). Out of these, only 143 (94.7%) received prophylaxis. One hundred seventy-four (78.4%) of the procedures had appropriate indication. The compliance of surgical antimicrobial prophylaxis use was 13.7%. The predictors of surgical site infections were receiving prophylaxis more than 24 h after surgery (AOR=3.53, 95% CI: 1.22–10.17), clean-contaminated wounds (AOR=4.54, 95% CI: 1.33–15.53), surgical procedure of thyroidectomy (AOR=5.2, 95% CI: 0.9–21.4), appendectomy (AOR = 29, 95% CI: 6.2–141.7), cholecystectomy (AOR = 21, 95% CI: 3.5 −126.7), hernia (AOR= 8.8, 95% CI: 1.2–62.2), skin and deep tissue (AOR = 125, 95% CI: 7.8–196.7), and orthopedic (AOR=57, 95% CI: 1.6–209.5).ConclusionThere was high inconsistency between surgical antimicrobial prophylaxis practice and international surgical site infections prevention guideline. Wrong selection of antimicrobial agents was the most noncompliant to the guidelines. The incidence of surgical antimicrobial prophylaxis was high and requires due attention. The duration of postoperative prophylaxis should be kept to less than 24 h.

Incidence and predictors of surgical site infection following cesarean section in North-west Ethiopia: a prospective cohort study

BackgroundFollowing delivery by caesarean section, surgical site infection is the most common infectious complication. Despite a large number of caesarean sections performed at Debre Markos Referral Hospital, there was no study documenting the incidence of surgical site infection after caesarean section. Therefore, this study aimed to estimate the incidence of surgical site infection following caesarean section at Debre-Markos Referral Hospital in Amhara region, North-west Ethiopia.MethodsA prospective cohort study was conducted among 520 pregnant women who had a caesarean section between March 28, 2019 and August 31, 2019. Preoperative, intraoperative, and postoperative data were collected using a standardized questionnaire. Data was entered using EpiData™ Entry Version 4.1 software and analyzed using R Version 3.6.1 software. A descriptive analysis was conducted using tables, interquartile ranges and median. The time to development of surgical site infection was estimated using Kaplan-Meier method. The Cox regression model for bivariable and multivariable analyses was done. Adjusted Hazard Ratio (AHR) with 95% Confidence Interval (CI) was reported to show the strength of association.ResultThe mean age of the study cohort was 27.4 ± 4.8 years. The overall cumulative incidence of surgical site infection was 25.4% with an incidence of 11.7 (95% CI:9.8,13.9) per 1000 person/days. Not able to read and write (AHR = 1.30,95% CI:1.19,2.11), no antenatal care (AHR = 2.16, 95%CI:1.05,4.53), previous history of CS (AHR = 1.21, 95% CI:1.11,2.31), HIV positive (AHR = 1.39, 95% CI:1.21,2.57), emergency procedure (AHR = 1.13, 95% CI:1.11,2.43), vertical type of incision (AHR = 2.60, 95% CI:1.05,6.44), rupture of membrane (AHR = 1.50, 95% CI:1.31,1.64), multiple vaginal examination (AHR = 1.88, 95% CI: 1.71, 3.20) were significant predictors of surgical site infection in this study.ConclusionThis study concluded that the incidence of surgical site infection following caesarean section was relatively high compared to previous studies. Not able to read and write, have no ante natal care, previous history of caesarean section, HIV, emergency surgery, vertical type of incision, rupture of membranes before caesarean section, and multiple vaginal examinations were significant predictors of surgical site infection in this study. Therefore, intervention programs should focus on and address the identified factors to minimize and prevent the infection rate after caesarean section.

Nomogram prediction of surgical site infection of HIV-infected patients following orthopedic surgery: a retrospective study

BackgroundSurgical site infection (SSI) is a devastating complication of orthopedic surgery, related with increased morbidity and mortality. This study was performed with the aim to compare the SSI rate in human immunodeficiency virus HIV-positive patients, to identify other risk factors for SSI and to establish a nomogram model to predict the risk of SSI.MethodsA total of 101 HIV-positive individuals following orthopedic surgery patients admitted to Beijing Ditan Hospital. Their characteristics were gathered. The univariate and multiple logistic regression analysis were performed to explore the risk factors of SSI. And the Nomogram prediction model was constructed and verified.ResultsThe independent predictive factors of SSI included CD4 (Odds ratio [OR], 0.041; P = 0.040), erythrocyte sedimentation rate (ESR) (OR, 89.773; P = 0.030), and procalcitonin (PCT) (OR, 220.746; P = 0.006). The scoring nomogram model was as follows: Logit (SSI) = − 2.63589–0.00314*CD4 < 430.75 = 1) + 0.04695*(ESR < 17.46 = 1) + 2.93694*(PCT < 0.22 = 1). The area under the Receiver Operating Characteristic (ROC) curve was 0.946. The cutoff score was − 2.1026 with a sensitivity of 93.33% and a specificity of 84.88%.ConclusionsCD4, ESR, PCT might affect the occurrence of SSI after orthopedic surgery. The nomogram model constructed in this study is helpful for predicting the probability of SSI.

Pattern Recognition Algorithms for Predicting Surgical Site Infection in Abdominal Hysterectomy

Background: This research represents an experiment based in surgical site infection (SSI) to patients undergoing abdominal hysterectomy surgery procedures in hospitals in Belo Horizonte, (population, 3 million). We statistically evaluated such incidences and studied the SSI prediction power of pattern recognition algorithms, the artificial neural networks based in multilayer perceptron (MLP). Methods: Between July 2016 and June 2018, data on SSI were collected by the hospital infection control committees (CCIH) of the 3 hospitals involved in the research. They collected all data used in the analysis during their routine SSI surveillance procedures. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used SACIH (ie, automated hospital infection control system software) to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed for SSI prediction: (1) a treatment of the database collected for the use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (ie, backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation). MLPs were tested with 3, 5, 7, and 10 hidden-layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring area under the curve (AUC; range, 0–1) presented for each of the configurations. Results: From 1,166 records collected, only 665 records were enabled for analysis. Regarding statistical data: the average duration of surgery was 100 minutes (range, 31–180); patients were aged 41–49 years; the SSI rate was low (only 10 cases); the average length of stay was 2 days; and there were no deaths among the cases. Moreover, 29% of the operative sites were contaminated and 57% were potentially contaminated, revealing a high rate of potential contamination in the operative sites. The prediction process achieved 0.995. Conclusions: Despite the noise in the database, it was possible to obtain a relevant sampling to evaluate the profile of hospitals in Belo Horizonte. In addition, for the predictive process, although some settings achieved AUC results of 0.5, others achieved and AUC of 0.995, indicating the promise of the automated SSI monitoring framework for abdominal hysterectomy surgery (available in www.sacihweb.com). To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed.Funding: NoneDisclosures: None

Automated Prediction of Surgical Site Infection Coronary Artery Bypass (CABG) Grafting Surgery

Background: In 5 hospitals located in Belo Horizonte city (&gt;3,000,000 inhabitants) a focused survey on surgical site infection (SSI) was performed in patients undergoing CABG surgery. We statistically evaluated such incidences to enable study of the prediction power of SSI through pattern recognition algorithms, in this case the multilayer perceptron (MLP) artificial neural networks. Methods: Data were collected between July 2016 and June 2018 on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. We collected all data used in the analysis during their routine SSI surveillance procedures. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which uses the SACIH (Automated Hospital Infection Control System) software to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed: (1) a treatment of the collected database for use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (ie, backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing and 35% or 25% for validation). They were compared by measuring the AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: From 666 initial data, only 278 were able for analysis. We obtained the following statistics: 9.35% manifested SSIs; length of stay varied from 1 to 119 days, with ~40% staying between 10 and 19 days; 15.1% of the patients died. Regarding the prediction power of SSI, the experiments have a maximum value of 0.713. Conclusions: Despite the considerable loss rate of &gt;50% of the database samples due to the presence of noise, it was possible to have a relevant sampling to evaluate the profile of hospitals in Belo Horizonte. In addition, for the predictive process, although some configurations had results equal to 0.5, others reached 0.713, which indicates that the automated SSI monitoring framework for patients undergoing coronary artery bypass grafting surgery is promising. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available at www.sacihweb.com), a mobile application was developed.Funding: NoneDisclosures: None