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Artificial Neural Networks Applied to Prediction to Assess the Likelihood of Surgical Site Infection in Different Surgeries
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Vladimir Alexei Rodrigues Rocha, Ana Luiza de Oliveira Rocha, Breno Henrique Colares Silva, Bruna Stella Vieira do Nascimento, Carolina Nunes Dutra, Luiza Pedrosa Gomes, Maria Clara Vilaça, Julia D. O. Matias, Laís L. de Araújo, Luaan S. Rossati, Layna R. Polidoro
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, p. s129
- Print publication:
- October 2020
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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: None
Funding: None
Automated Risk Analysis of Surgical Site Infection in Hip Arthroplasty Surgeries
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Ana Flavia Viana Quintão, Camila Vieira Brandão, Débora Martins Borges, Eduarda Muzzi Torres Lage, Luiza da Conceição Sabadini, Sabrina de Almeida Lopes
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s135-s136
- Print publication:
- October 2020
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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: None
Disclosures: None
A culture-sensitive semi-quantitative FFQ for use among the adult population in Nairobi, Kenya: development, validity and reproducibility
- Catarina Vila-Real, Ana Pimenta-Martins, Jack-Susan Magu, Catherine Kunyanga, Samuel Mbugua, Kati Katina, Ndegwa H Maina, Ana MP Gomes, Elisabete Pinto
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- Journal:
- Public Health Nutrition / Volume 24 / Issue 5 / April 2021
- Published online by Cambridge University Press:
- 24 July 2020, pp. 834-844
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Objective:
To develop a semi-quantitative FFQ and to evaluate its validity and reproducibility for the assessment of total dietary intake of Kenyan urban adult population, given its non-existence in Kenya.
Design:The current study adopted a cross-sectional design. A culture-sensitive semi-quantitative FFQ was developed and its validity was tested relative to three non-consecutive 24-h recalls (24hR). Reproducibility was tested by the test–retest method, with a 3-week interval. Spearman’s correlation coefficients and intra-class correlation coefficients were calculated for several macro- and micronutrients. Cross-classification into quartiles and Bland and Altman plots were analysed.
Setting:Nairobi county (Dagoreti South and Starehe constituencies).
Participants:A convenient sample was recruited in three different clusters in Nairobi.
Results:A culture-sensitive 123-food-item semi-quantitative FFQ showed higher nutrient intakes compared with the 24hR (total energy median 12543·632 v. 8501·888 kJ, P < 0·001). Energy-adjusted and deattenuated Spearman’s correlations for macronutrients ranged between 0·21 (total fat) and 0·47 (protein). The agreement in the same quartile varied from 28 % (protein) to 41 % (carbohydrates). Including adjacent quartiles, the range increased: 76 % (protein and fat) to 81 % (carbohydrates). The extreme disagreement was low. The first FFQ application resulted in higher mean values for all nutrients compared with the second FFQ (total energy median 12459·952 v. 10485·104 kJ, P < 0·001). Energy-adjusted correlations for macronutrients ranged from 0·28 (carbohydrates) to 0·61 (protein). Intra-class correlation coefficients for macronutrients were moderate, between 0·6 and 0·7.
Conclusions:The developed semi-quantitative FFQ was shown to be a valid and reproducible tool for ranking urban adult Kenyans according to their dietary intake.