scholarly journals 810. Cardiac Pacemaker Implantation Surgery: Automated Prediction of Surgical Site Infection

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S499-S499
Author(s):  
Flávio Henrique Batista de Souza ◽  
Bráulio R G M Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Pacemaker Implantation ◽  
Cardiac Pacemaker ◽  
Area Under The Curve ◽  
Length Of Hospital Stay ◽  
Site Infection ◽  
Weight Decay ◽  
Hospital Infection Control ◽  
Belo Horizonte ◽  
Hidden Layer

Abstract Background A research focused on surgical site infection (SSI) was performed in patients undergoing cardiac pacemaker implantation surgery. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through pattern recognition algorithms, in this case the Multilayer Perceptron (MLP). Methods Data were collected from five hospitals in the city of Belo Horizonte (more than 3,000,000 inhabitants), between July 2016 and June 2018, on SSI by the Hospital Infection Control Committees (CCIH) of the hospitals involved in the search. All data used in the analysis during their routine SSI surveillance procedures were collected. So, three procedures were performed: a treatment of the collected database 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 - from 0 to 1) presented for each of the configurations. Results From 1394, 572 records were: 21% of deaths and 2.4% patients had SSI; from the confirmed SSI cases, approximately 64.3% had sites classified as “clean”; length of hospital stay ranged from 0 to 175 days (from 1 to 70 days); the average age is 67 years. The prediction power of SSI, the experiments achieved from 0.409 to 0.722. Conclusion Despite the considerable loss rate of more than 65% 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 reached 0.722. To optimize data collection and enable other hospitals to use the SSI prediction tool (available in www.nois.org.br ), 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

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

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S484-S485
Author(s):  
Flávio Henrique Batista de Souza ◽  
Braulio Roberto Gonçalves Marinho Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Knee Arthroplasty ◽  
Area Under The Curve ◽  
Site Infection ◽  
Weight Decay ◽  
Hospital Infection Control ◽  
Belo Horizonte ◽  
Hidden Layer ◽  
Knee Arthroplasty Surgery ◽  
Arthroplasty Surgery

Abstract Background This 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). Methods Data 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. Results From 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. Conclusion Despite 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

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

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S482-S483
Author(s):  
Flávio Henrique Batista de Souza ◽  
Braulio Roberto Gonçalves Marinho Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Bariatric Surgery ◽  
Data Collection ◽  
Surgical Site Infection ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Site Infection ◽  
Weight Decay ◽  
Hospital Infection Control ◽  
Duration Of Surgery ◽  
Belo Horizonte

Abstract Background In 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. Methods Data 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. Results From 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. Conclusion Despite 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

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

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S481-S481
Author(s):  
Flávio Henrique Batista de Souza ◽  
Braulio Roberto Gonçalves Marinho Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Data Collection ◽  
Surgical Site Infection ◽  
Area Under The Curve ◽  
Recognition Algorithm ◽  
Limb Amputation ◽  
Site Infection ◽  
Weight Decay ◽  
Hospital Infection Control ◽  
Amputation Surgery ◽  
Belo Horizonte

Abstract Background A 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. Methods Data 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. Results From 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. Conclusion Despite 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

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

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S482-S482
Author(s):  
Flávio Henrique Batista de Souza ◽  
Braulio Roberto Gonçalves Marinho Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Data Collection ◽  
Surgical Site Infection ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Analysis Tool ◽  
Site Infection ◽  
Weight Decay ◽  
Average Hospital ◽  
Hospital Infection Control ◽  
Belo Horizonte

Abstract Background A 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. Methods Through 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. Results From 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. Conclusion There 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

scholarly journals Artificial Neural Networks Applied to Prediction to Assess the Likelihood of Surgical Site Infection in Different Surgeries

2020 ◽  
Vol 41 (S1) ◽  
pp. s129-s129
Author(s):  
Flávio Souza ◽  
Braulio Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Data Collection ◽  
Surgical Site Infection ◽  
Plastic Surgery ◽  
Predictive Power ◽  
Site Infection ◽  
Weight Decay ◽  
Time Of Surgery ◽  
Hospital Infection Control ◽  
Duration Of Surgery ◽  
Belo Horizonte

Background: Based on data obtained from hospitals in the city of Belo Horizonte (population ~3,000,000), we evaluated relevant factors such as death, age, duration of surgery, potential for contamination and surgical site infection, plastic surgery, and craniotomy. The possibility of predicting surgical site infection (SSI) was then analyzed using pattern recognition algorithms based on MLP (multilayer perceptron). Methods: Data were collected by the hospital infection control committees (CCIHs) in hospitals in Belo Horizonte between 2016 and 2018. The noisy records were filtered, and the occurrences were analyzed. Finally, the predictive power of SSI of 5 types MLP was evaluated experimentally: momentum, backpropagation standard, weight decay, resilient propagation, and quick propagation. The model used 3, 5, 7, and 10 neurons in the occult layer and with resamples varied the number of records for testing (65% and 75%) and for validation (35% and 25%). Comparisons were made by measuring the AUC (area under the curve (range, 0–1). Results: From 1,096 records of craniotomy, 289 were usable for analysis. Moreover, 16% died; averaged age was 56 years (range, 40–65); mean time of surgery was 186 minutes (range, 95–250 minutes); the number of hospitalizations ranged from 1 (90.6%) to 8 (0.3%). Contamination among these cases was rated as follows: 2.7% contaminated, 23.5% potentially contaminated, 72.3% clean. The SSI rate reached 4%. The prediction process in AUCs ranged from 0.7 to 0.994. In plastic surgery, from 3,693 records, 1,099 were intact, with only 1 case of SSI and no deaths. The average age for plastic surgery was 41 years (range, 16–91); the average time of surgery was 218.5 minutes (range, 19–580 minutes); the number of hospitalizations ranged from 1 (77.4%) to 6 times (0.001%). Contamination among these cases was rated as follows: 27.90% potential contamination, 1.67% contaminated, and 0.84% infected. The prediction process ranged in AUCs from 0.2 to 0.4. Conclusions: We identified a high noise index in both surgeries due to subjectivity at the time of data collection. The profiles of each surgery in the statistical analyses were different, which was reflected in the analyzed structures. The MLP for craniotomy surgery demonstrated relevant predictive power and can guide intelligent monitoring software (available in www.sacihweb.com). However, for plastic surgeries, MLPs need more SSI samples to optimize outcomes. To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed.Disclosures: NoneFunding: None

scholarly journals Automated Risk Analysis of Surgical Site Infection in Hip Arthroplasty Surgeries

2020 ◽  
Vol 41 (S1) ◽  
pp. s135-s136
Author(s):  
Flávio Souza ◽  
Braulio Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Data Collection ◽  
Infection Control ◽  
Surgical Site Infection ◽  
Hip Arthroplasty ◽  
Hospital Infection ◽  
Site Infection ◽  
Weight Decay ◽  
Hospital Infection Control ◽  
Belo Horizonte ◽  
Arthroplasty Surgery

Background: In 7 hospitals in Belo Horizonte, a city with >3,000,000 inhabitants, a survey was conducted between July 2016 and June 2018, focused on surgical site infection (SSI) in patients undergoing arthroplasty surgery procedures. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through pattern recognition algorithms, the MLPs (multilayer perceptron). Methods: Data were collected on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. All data used in the analysis during their routine SSI surveillance procedures were collected. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used 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 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 (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). The results were compared by measuring AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: Of 1,246 records, 535 were intact for analysis. We obtained the following statistics: the average surgery time was 190 minutes (range, 145–217 minutes); the average age of the patients was 67 years (range, 9–103); the prosthetic implant index was 98.13%; the SSI rate was 1.49%, and the death rate was 1.21%. Regarding the prediction power, the maximum prediction power was 0.744. Conclusions: Despite the considerable loss rate of almost 60% of the database samples due to the presence of noise, it was possible to perform relevant sampling for the profile evaluation of hospitals in Belo Horizonte. For the predictive process, some configurations have results that reached 0.744, which indicates the usefulness of the structure for automated SSI monitoring for patients undergoing hip arthroplasty surgery. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com ), a mobile application was developed.Funding: NoneDisclosures: None

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

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S476-S476
Author(s):  
Flávio Henrique Batista de Souza ◽  
Braulio Roberto Gonçalves Marinho Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Data Collection ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Analysis Tool ◽  
Weight Decay ◽  
Average Hospital ◽  
Hospital Infection Control ◽  
Belo Horizonte ◽  
Hidden Layer ◽  
The City

Abstract Background Between 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. Methods Through 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. Results From 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. Conclusion There 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

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

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S476-S477
Author(s):  
Flávio Henrique Batista de Souza ◽  
Braulio Roberto Gonçalves Marinho Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Data Collection ◽  
Surgical Site Infection ◽  
Predictive Power ◽  
Artery Aneurysm ◽  
Analysis Tool ◽  
Site Infection ◽  
Weight Decay ◽  
Average Hospital ◽  
Hospital Infection Control ◽  
Aortic Artery

Abstract Background A 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. Methods Through 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. Results From 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. Conclusion There 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

scholarly journals 904. Surgical Site Infection After Bariatric Surgery: a Small Risk that Defines Life and Death of Patients

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S486-S486
Author(s):  
Lucca G Giarola ◽  
Carlos Ernesto Ferreira Starling ◽  
Braulio Roberto Gonçalves Marinho Couto ◽  
Handerson Dias Duarte de Carvalho
Keyword(s):  
Bariatric Surgery ◽  
Logistic Regression ◽  
Surgical Site Infection ◽  
Hospital Stay ◽  
Length Of Hospital Stay ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Hospital Death ◽  
Site Infection ◽  
Belo Horizonte

Abstract Background Surgical site infection (SSI) in bariatric surgery can lead to devastating outcomes such as peritonitis, sepsis, septic shock and organ space infection. The objective of our study is to answer four questions: a) What is the SSI risk after bariatric surgery? b) What are the risk factors for SSI after bariatric surgery? c) What are the main outcomes to SSI in bariatric surgery? d) What are the main bacteria responsible for SSI in bariatric surgery? Methods A retrospective cohort study assessed 8,672 patients undergoing bariatric surgery between 2014/Jan and 2018/Dec from two hospitals at Belo Horizonte, Brazil. Data were gathered by standardized methods defined by the National Healthcare Safety Network (NHSN)/CDC procedure-associated protocols for routine SSI surveillance. Outcome: SSI, hospital death and total length of hospital stay. 20 preoperative and operative variables were evaluated by univariate and multivariate analysis (logistic regression). Results 77 SSI were diagnosed (risk = 0.9% [C.I.95% = 0.7%;1.1%]). Mortality rate in patients, without infection was only 0.03% (3/8,589) while hospital death of infected patients was 4% (3/77; RR = 112; p< 0.001). Hospital length of stay in non-infected patients (days): mean = 2, std.dev.= 0.9; hospital stay in infected patients: mean = 7, std. dev. = 15.6 (p< 0.001). Two main factors associated with SSI after bariatric surgery were identified by logistic regression: duration of procedure (hours), OR = 1.4;p=0.001, and laparoscopy procedure, OR = 0.3;p=0.020. From 77 SSIs, in 28 (36%) we identified 34 etiologic agents. The majority of SSI (59%) was caused by species of Streptococcus (32%), Klebsiella (15%), and Enterobacter (12%). Conclusion SSI is rare after bariatric surgery, however, when it happens, it’s a disaster for the patient. The incidence of SSI can be reduced significantly when laparoscopy procedure is used and the surgeon is able to perform a rapid surgery. Disclosures All Authors: No reported disclosures

scholarly journals Pattern Recognition Algorithms for Predicting Surgical Site Infection in Abdominal Hysterectomy

2020 ◽  
Vol 41 (S1) ◽  
pp. s344-s345
Author(s):  
Flávio Souza ◽  
Braulio Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Data Collection ◽  
Infection Control ◽  
Surgical Site Infection ◽  
Abdominal Hysterectomy ◽  
Hospital Infection ◽  
Site Infection ◽  
Recognition Algorithms ◽  
Hospital Infection Control ◽  
Belo Horizonte

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

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