scholarly journals Factors hindering the implementation of surgical site infection control guidelines in the operating rooms of low-income countries: a mixed-method study

2018 ◽  
Vol 37 (10) ◽  
pp. 1923-1929 ◽  
Author(s):  
Muhammad Nasir Ayub Khan ◽  
Daniëlle M. L. Verstegen ◽  
Abu Bakar Hafeez Bhatti ◽  
Diana H. J. M Dolmans ◽  
Walther Nicolaas Anton van Mook
Keyword(s):  
Infection Control ◽  
Surgical Site Infection ◽  
Low Income ◽  
Mixed Method ◽  
Operating Rooms ◽  
Low Income Countries ◽  
Mixed Method Study ◽  
Site Infection ◽  
Infection Control Guidelines

scholarly journals Global incidence of surgical site infection after appendectomy: a systematic review and meta-analysis

BMJ Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. e034266 ◽  
Author(s):  
Celestin Danwang ◽  
Jean Joel Bigna ◽  
Joel Noutakdie Tochie ◽  
Aimé Mbonda ◽  
Clarence Mvalo Mbanga ◽  
...  
Keyword(s):  
Systematic Review ◽  
Surgical Site Infection ◽  
Low Income ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Open Appendectomy ◽  
Risk Of Bias ◽  
Low Income Countries ◽  
Site Infection

BackgroundAlthough surgical site infection (SSI) is one of the most studied healthcare-associated infections, the global burden of SSI after appendectomy remains unknown.ObjectiveWe estimated the incidence of SSI after appendectomy at global and regional levels.DesignSystematic review and meta-analysis.ParticipantsAppendectomy patients.Data sourcesEMBASE, PubMed and Web of Science were searched, with no language restrictions, to identify observational studies and clinical trials published between 1 January 2000 and 30 December 2018 and reporting on the incidence of SSI after appendectomy. A random-effect model meta-analysis served to obtain the pooled incidence of SSI after appendectomy.ResultsIn total, 226 studies (729 434 participants from 49 countries) were included in the meta-analysis. With regard to methodological quality, 59 (26.1%) studies had low risk of bias, 147 (65.0%) had moderate risk of bias and 20 (8.8%) had high risk of bias. We found an overall incidence of SSI of 7.0 per 100 appendectomies (95% prediction interval: 1.0–17.6), varying from 0 to 37.4 per 100 appendectomies. A subgroup analysis to identify sources of heterogeneity showed that the incidence varied from 5.8 in Europe to 12.6 per 100 appendectomies in Africa (p<0.0001). The incidence of SSI after appendectomy increased when the level of income decreased, from 6.2 in high-income countries to 11.1 per 100 appendectomies in low-income countries (p=0.015). Open appendectomy (11.0 per 100 surgical procedures) was found to have a higher incidence of SSI compared with laparoscopy (4.6 per 100 appendectomies) (p=0.0002).ConclusionThis study suggests a high burden of SSI after appendectomy in some regions (especially Africa) and in low-income countries. Strategies are needed to implement and disseminate the WHO guidelines to decrease the burden of SSI after appendectomy in these regions.Prospero registration numberCRD42017075257.

USE OF AN ELECTRONIC DATA WAREHOUSE TO ENHANCE CARDIAC SURGICAL SITE SURVEILLANCE AT A LARGE CANADIAN CENTRE

2008 ◽  
Vol 31 (4) ◽  
pp. 21 ◽  
Author(s):  
G W Rose ◽  
V R Roth ◽  
K N Suh ◽  
M Taljaard ◽  
C Van Walraven ◽  
...  
Keyword(s):  
Infection Control ◽  
Surgical Site Infection ◽  
Data Warehouse ◽  
Stepwise Logistic Regression ◽  
Site Infection ◽  
Electronic Data ◽  
Trigger Mechanism ◽  
Trigger Tool ◽  
Infection Surveillance ◽  
Trigger Mechanisms

Background/Purpose: Surgical site infection surveillance to determineincidence is a key infection control activity. Case detection is labour-intensive, therefore most infection control programs use manual or simple electronic mechanisms to “trigger” chart review. However, such “trigger” mechanisms are also labour-intensive, and often of poor specificity. Our objective is to develop a complex trigger mechanism using data from an electronic data warehouse, to improve specificity of surveillance of surgical site infection compared to current trigger mechanisms. Methods: We will derive an electronic trigger tool for cardiac surgical site infection surveillance using a nested case-control design, among a cohort of all patients undergoing coronary artery bypass grafting, cardiac valve repairor replacement, or heart transplant at the University of Ottawa Heart Institute, from July 1 2004 to June 30 2007. We will perform a systematic literature review to identify potential trigger factors to include in the model, then construct the trigger tool by backwards stepwise logistic regression. The best-fit model will be used to calculate the probability of surgical site infection. We will select the threshold probability to use in surveillance by visual inspection of receiver-operator-characteristic curves. The accuracy of this electronic trigger mechanism will be compared to pre-existing manual and simple electronic mechanisms using relative true positive ratios and relative false positive ratios. Results/Conclusions: We have selected 200 cases of surgical site infection and 541 controls from among 3744 procedures performed during the study period. As of the date ofthis abstract we are still undertaking the systematic review.

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 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

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