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

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

Background: In 5 hospitals located in Belo Horizonte city (>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 >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

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 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

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

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S487-S487
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):  
Statistical Analysis ◽  
Data Collection ◽  
Infection Control ◽  
Surgical Site Infection ◽  
Predictive Power ◽  
Hospital Infection ◽  
Automated System ◽  
Site Infection ◽  
Average Hospital ◽  
Hospital Infection Control

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

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 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

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