3 results
Prediction of Surgical Risk in General Surgeries: Process Optimization Through Support Vector Machine (SVM) Algorithm
- Flávio Souza, Braulio Couto, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Gabriele Maria Braga, Jéssica Angelina Teixeira, Renata Carvalho Santos, Julia Maria Campos Martins, Karla Silvia de Sousa, Douglas Nascimento de Souza, Gustavo Barros Alves, Vladimir Alexei Rodrigues Rocha
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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. s355-s356
- Print publication:
- October 2020
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Background: In 5 hospitals in Belo Horizonte (population, 3 million) between July 2016 and June 2018, a survey was performed regarding surgical site infection (SSI). We statistically evaluated SSI incidents and optimized the power to predict SSI through pattern recognition algorithms based on support vector machines (SVMs). Methods: Data were collected on SSIs at 5 different hospitals. The hospital infection control committees (CCIHs) of the hospitals collected all data used in the analysis during their routine SSI surveillance procedures; these data were sent to the NOIS (Nosocomial Infection Study) Project. NOIS uses SACIH software (an automated hospital infection control system) to collect data from hospitals that participate voluntarily in the project. In the NOIS, 3 procedures were performed: (1) a treatment of the database collected 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 SVM with a nonlinear separation process varying in configurations including kernel function (Laplace, Radial Basis, Hyperbolic Tangent and Bessel) and the k-fold cross-validation–based resampling process (ie, the use of data varied according to the amount of folders that cross and combine the evaluated data, being k = 3, 5, 6, 7, and 10). The data were compared by measuring the area under the curve (AUC; range, 0–1) for each of the configurations. Results: From 13,383 records, 7,565 were usable, and SSI incidence was 2.0%. Most patients were aged 35–62 years; the average duration of surgery was 101 minutes, but 76% of surgeries lasted >2 hours. The mean hospital length of stay without SSI was 4 days versus 17 days for the SSI cases. The survey data showed that even with a low number of SSI cases, the prediction rate for this specific surgery was 0.74, which was 14% higher than the rate reported in the literature. Conclusions: Despite the high noise index of the database, it was possible to sample relevant data for the evaluation of general surgery patients. For the predictive process, our results were >0.50 and were 14% better than those reported in the literature. However, the database requires more SSI case samples because only 2% of positive samples unbalanced the database. To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed (available at www.sacihweb.com).
Funding: None
Disclosures: None
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
Smoking Abstinence Twelve Months after an Acute Coronary Syndrome
- Vânia Rocha, Marina P. Guerra, Marina S. Lemos, Júlia Maciel, Geoffrey C. Williams
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- Journal:
- The Spanish Journal of Psychology / Volume 20 / 2017
- Published online by Cambridge University Press:
- 20 November 2017, E63
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Studies on the cognitive working mechanism of smoking cessation in high-risk populations are few and much needed, and identifying long-term psychosocial factors to smoking cessation are relevant to improve intervention for cardiac patient groups. This longitudinal study followed patients who smoked and suffered an acute coronary syndrome from hospitalization to 12 months after clinical discharge. Questionnaires were administered to assess nicotine dependence, behavioral dependence, autonomous self-regulation, perceived competence, social support, anxiety, depressive symptoms and meaning in life at baseline, six months and twelve months after clinical discharge. The results showed that anxiety (F(2, 62) = 28.10, p < .001, ηp2 = .48) and depressive symptoms (F(2, 62) = 10.42, p < .001, ηp2 = .25) decreased over time, whereas meaning in life (F(2, 61) = 44.77, p < .001, ηp2 = .59) and social support increased (t(63) = –4.54, p < .001, 95% IC[–11.05, 4.29], η2 =.25). Smoking dependence was negatively predicted by change in perceived competence (B = –2.25, p = .011, 95% IC[.02, .60]) and positively by change in depressive symptoms (B =.37, p = .042, 95% IC[1.01, 2.05]) 12 months after clinical discharge. Nicotine dependence (t(17) = 2.76, p = .014, 95% IC[.39, 2.94], η2 =.31) and the number of cigarettes smoked per day (t(17) = 4.48, p < .001, 95% IC[5.49, 15.29], η2 =.54) decreased over time, whereas behavioral dependence increased among smokers (t(17) = –2.37, p = .030, 95% IC[–4.30, 2.54], η2 =.25). This study suggests that long term abstinence in cardiac patients may be enhanced by psychological interventions addressing perceived competence, depressive symptoms and behavioral dependence.