Predictive factors for early- and late-onset surgical site infections in patients undergoing elective colorectal surgery. A multicentre, prospective, cohort study

Gomila-Grange, Aina; Carratalà, Jordi; Biondo, Sebastiano; Badia, Josep M.; Fraccalvieri, Doménico; Shaw, Evelyn; Diaz-Brito, Vicens; Pagespetit, L.; Freixas, N.; Brugués, M; Mora, Laura; Sanz, C; Arroyo, Noelle S.; Iftimie, Simona; Cáceres, Enrique Limón; Gudiol, Francesc; Pujol, Miquel

doi:10.1016/j.jhin.2017.12.017

Cited by 21 publications

(14 citation statements)

References 24 publications

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“…RenderX 53 features and included not only subjective features, such as the ASA score, the NNIS risk index score, and dyspnea evaluation, but also 33 manually extracted variables from the medical history of the patient [18]. Meanwhile, our model included 47 objective features, among which age, gender, body weight, anesthesia type, emergency operation, preoperative hemoglobin, glucose, and LOS have proven to be related to the occurrences of SSI [12,38,39]. All these features were the results of routine preoperative blood tests and were automatically extracted from the EMR system using SQL (Structured Query Language) query script.…”

Section: Xsl • Fomentioning

confidence: 99%

Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Chen¹,

Lu²,

Li-jue³

et al. 2020

JMIR Med Inform

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Background Surgical site infection (SSI) is one of the most common types of health care–associated infections. It increases mortality, prolongs hospital length of stay, and raises health care costs. Many institutions developed risk assessment models for SSI to help surgeons preoperatively identify high-risk patients and guide clinical intervention. However, most of these models had low accuracies. Objective We aimed to provide a solution in the form of an Artificial intelligence–based Multimodal Risk Assessment Model for Surgical site infection (AMRAMS) for inpatients undergoing operations, using routinely collected clinical data. We internally and externally validated the discriminations of the models, which combined various machine learning and natural language processing techniques, and compared them with the National Nosocomial Infections Surveillance (NNIS) risk index. Methods We retrieved inpatient records between January 1, 2014, and June 30, 2019, from the electronic medical record (EMR) system of Rui Jin Hospital, Luwan Branch, Shanghai, China. We used data from before July 1, 2018, as the development set for internal validation and the remaining data as the test set for external validation. We included patient demographics, preoperative lab results, and free-text preoperative notes as our features. We used word-embedding techniques to encode text information, and we trained the LASSO (least absolute shrinkage and selection operator) model, random forest model, gradient boosting decision tree (GBDT) model, convolutional neural network (CNN) model, and self-attention network model using the combined data. Surgeons manually scored the NNIS risk index values. Results For internal bootstrapping validation, CNN yielded the highest mean area under the receiver operating characteristic curve (AUROC) of 0.889 (95% CI 0.886-0.892), and the paired-sample t test revealed statistically significant advantages as compared with other models (P<.001). The self-attention network yielded the second-highest mean AUROC of 0.882 (95% CI 0.878-0.886), but the AUROC was only numerically higher than the AUROC of the third-best model, GBDT with text embeddings (mean AUROC 0.881, 95% CI 0.878-0.884, P=.47). The AUROCs of LASSO, random forest, and GBDT models using text embeddings were statistically higher than the AUROCs of models not using text embeddings (P<.001). For external validation, the self-attention network yielded the highest AUROC of 0.879. CNN was the second-best model (AUROC 0.878), and GBDT with text embeddings was the third-best model (AUROC 0.872). The NNIS risk index scored by surgeons had an AUROC of 0.651. Conclusions Our AMRAMS based on EMR data and deep learning methods—CNN and self-attention network—had significant advantages in terms of accuracy compared with other conventional machine learning methods and the NNIS risk index. Moreover, the semantic embeddings of preoperative notes improved the model performance further. Our models could replace the NNIS risk index to provide personalized guidance for the preoperative intervention of SSIs. Through this case, we offered an easy-to-implement solution for building multimodal RAMs for other similar scenarios.

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Section: Xsl • Fomentioning

confidence: 99%

Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Chen¹,

Lu²,

Li-jue³

et al. 2020

JMIR Med Inform

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“…While the overall incidence of surgical site infections is 1 to 3%, [1][2][3] it is 10% or more after colorectal surgery. [4][5][6] The primary defense against bacterial infection is oxidative killing by neutrophils, which requires molecular tissue oxygen. 7 Increasing inspired oxygen is an easy and effective way to augment tissue oxygen.…”

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confidence: 99%

Supplemental Intraoperative Oxygen and Long-term Mortality: Subanalysis of a Multiple Crossover Cluster Trial

Jiang¹,

Kurz²,

Zhang³

et al. 2021

Anesthesiology

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Background Whether supplemental oxygen worsens long-term mortality remains unclear, with contradictory trial results. The authors therefore tested the hypothesis that supplemental oxygen (80% vs. 30%) increases the hazard for long-term mortality. Methods The authors conducted a post hoc analysis of a large multiple crossover cluster trial in which more than 5,000 colorectal surgeries on 4,088 adults were allocated to receive either 30% or 80% inspired oxygen during general anesthesia. The authors assessed the effect of 80% versus 30% target-inspired oxygen on long-term mortality and calculated Kaplan–Meier survival estimates. Analysis was restricted to patients with a home address in Ohio because the authors could obtain reliable vital status information from the Ohio Department of Health (Columbus, Ohio) for them. Results A total of 3,471 qualifying colorectal surgeries performed in 2,801 patients were analyzed, including 1,753 (51%) surgeries in 1,577 patients given 80% oxygen and 1,718 surgeries in 1,551 patients given 30% oxygen. The observed incidence of death after a median of 3 yr was 13% (234 of 1,753) in the 80% oxygen group and 14% (245 of 1,718) in the 30% oxygen group. The estimated hazard ratio for mortality was 0.94 (95% CI, 0.78 to 1.13; P = 0.493). Conclusions In this post hoc analysis of a large, controlled trial, supplemental oxygen did not increase postoperative mortality. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

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“…Risk factors relating to the patient, procedure and the environment also alter the odds of developing an SSI. Research has been done to identify risk factors for SSI with the hope that it will lead to preemptive action to reduce the incidence rate of SSI [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Risk factor identification and contribution to surgery-specific surgical site infection

Niekerk

Vos

Stein

et al. 2019

Preprint

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Background: To identify common risk factors for the future occurrence of SSI across digestive system surgical procedures, thoracic surgery and orthopaedic procedures and quantify what the marginal contribution of explaining the future occurrence of SSI is of risk factors specific to each group of surgeries. Methods: Retrospective data from serial point-prevalence studies, performed at the Erasmus MC University Medical Center, Rotterdam, The Netherlands were used, together with medical data, from 3.250 surgical procedures, during the period January 2013 to 29 June 2014. Common risk factors across three groups of surgical procedures were identified by scanning literature and univariate analysis. A multivariate forward-step logistic regression model was used to identify the marginal contribution of the risk factor specific to each group of surgical procedure using the increase in the Gini coefficient. Results: For digestive system surgical procedures, antibiotic use, temperature, smoking status, age, CRP, thrombocyte, during of surgery and surgical urgency are risk factors for SSI, where the last four were specific to the digestive system group of surgical procedures and increased the Gini coefficient by 9.5% (0.63 to 0.69). Preoperative length of stay, antibiotic use and Leukocyte are risk factors for orthopaedic surgical procedures. Temperature, age and the use of antibiotics were significant for thoracic surgical procedures. Conclusion: ASA class, body mass index (BMI), Preoperative length of stay, diabetes, antibiotic use, age, Leukocyte, temperature and smoking status as general risk factors common to digestive, orthopaedic and thoracic surgical procedures using existing literature and univariate logistic regression. Risk factors for SSI, specific to a surgical procedure group can increase the ability to explain the future occurrence of SSI by 9.5%. The models developed in this study can aid healthcare workers to improve preoperative counselling and help identify potentially modifiable risk factors.

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Predictive factors for early- and late-onset surgical site infections in patients undergoing elective colorectal surgery. A multicentre, prospective, cohort study

Cited by 21 publications

References 24 publications

Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Supplemental Intraoperative Oxygen and Long-term Mortality: Subanalysis of a Multiple Crossover Cluster Trial

Risk factor identification and contribution to surgery-specific surgical site infection

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