A Clinical Data Warehouse for Hospital Infection Control

The problems associated with hospital-acquired infection have been causing increasing concern in England in recent years. This paper reports the results of a nationwide survey of hospital infection control professionals' views concerning the organizational structures used to manage and obtain funding for control of infection. A complex picture with significant variation between hospitals emerges. Although government policy dictates that specific funding for hospital infection control is formally made available, it is not always the case that infection control professionals have adequate resources to undertake their roles. In some cases this reflects the failure of hospitals' infection control budgetary mechanisms; in others it reflects the effects of decentralizing budgets to directorate or ward level. Some use was made of informal mechanisms either to supplement or to substitute for the formal ones. But almost all infection control professionals still believed they were constrained in their ability to protect the hospital population from the risk of infectious disease. It is clear that recent government announcements that increased effort will be made to support local structures and thereby improve the control of hospital acquired infection are to be welcomed.

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

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1095 ◽

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

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Automated Risk Analysis of Surgical Site Infection in Hip Arthroplasty Surgeries

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.649 ◽

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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