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Objective To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches. Methods A retrospective analysis of hospital database-derived data from patients that had undergone gastrointestinal, colorectal and hernia surgeries (identified by ICD-9-CM). The SENIC index was calculated and fitted in an LR. MLs were developed using decision-tree (DT), random forest (RF), extreme-gradient-boosting (XGBoost) and Naïve Bayes (NB). Results The prevalence of an SSI was 3.21% (404 of 12 596 surgeries; 95% confidence interval [CI] 2.91%, 3.53%). The C-statistic for the original SENIC model was 0.668 (95% CI 0.648, 0.688) with an observed/expected (O/E) ratio of 0.998 (interquartile range [IQR] 0.750, 1.047). An updated-SENIC-LR model with six predictors had a C-statistic of 0.768 (95% CI 0.745, 0.790) and O/E ratio of 0.999 (IQR 0.976, 1.004). The performance of MLs considering 14 predictors was poorer than the updated-SENIC-LR with C-statistics of 0.679, 0.675, 0.656 and 0.651 for NB, XGBoost, RF and DT, respectively. Overfitting was detected for ML approaches, particularly for DT, RF and XGBoost. Conclusion The updated-SENIC-LR model and NB may be useful for monitoring SSI risk following abdominal surgery.

Abstract Background Management of COVID-19 patients with mild and moderate symptoms could be isolated at home isolation (HI), community isolation (CI) or hospitel. However, it was still unclear which strategy was more cost-effective. Therefore, this study was conducted to evaluate this. Methods This study used data from patients who initially stayed at HI, CI, and hospitel under supervision of Ramathibodi Hospital between April and October 2021. Outcomes of interest were hospitalisation and mortality. An incremental cost-effectiveness ratios (ICER) was calculated based on hospital perspective using home isolation as the reference. Results From 7,077 patients, 4,349 2,356, and 372 were admitted at hospitel, HI, and CI, respectively. Most patients were females (57.04%) and the mean age was 40.42 (SD = 16.15). Average durations of stay were 4.47, 3.35, and 3.91 days for HI, CI, and hospitel, respectively. The average cost per day for staying in these corresponding places were 24.22, 63.69, and 65.23 US$. For hospitalisation, the ICER for hospitel was at 41.93 US$ to avoid one hospitalisation in 1,000 patients when compared to HI, while CI had more cost, but less cases avoided. The ICER for hospitel and CI were at 46.21 and 866.17 US$ to avoid one death in 1,000 patients. Conclusions HI may be cost-effective isolated strategy for preventing hospitalisation and death in developing countries with limited resources.

We aimed to report the coverage and safety of the influenza A (H1N1) 2009 monovalent vaccination (Panenza; Sanofi Pasteur, Val de Reuil Cedex, France) among health care personnel (HCP) in a university hospital setting in Thailand. The hospital set up a system to vaccinate HCP and did surveillance of the adverse effects (AEs). During a 4-week period, 6,210 (78.7%) HCP were vaccinated. There were 82 reported nonserious AEs among 32 HCP. The most common AE was fatigue/uncomfortable feeling (24%). [ABSTRACT FROM AUTHOR]

Objective: To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches., Methods: A retrospective analysis of hospital database-derived data from patients that had undergone gastrointestinal, colorectal and hernia surgeries (identified by ICD-9-CM). The SENIC index was calculated and fitted in an LR. MLs were developed using decision-tree (DT), random forest (RF), extreme-gradient-boosting (XGBoost) and Naïve Bayes (NB)., Results: The prevalence of an SSI was 3.21% (404 of 12 596 surgeries; 95% confidence interval [CI] 2.91%, 3.53%). The C-statistic for the original SENIC model was 0.668 (95% CI 0.648, 0.688) with an observed/expected (O/E) ratio of 0.998 (interquartile range [IQR] 0.750, 1.047). An updated-SENIC-LR model with six predictors had a C-statistic of 0.768 (95% CI 0.745, 0.790) and O/E ratio of 0.999 (IQR 0.976, 1.004). The performance of MLs considering 14 predictors was poorer than the updated-SENIC-LR with C-statistics of 0.679, 0.675, 0.656 and 0.651 for NB, XGBoost, RF and DT, respectively. Overfitting was detected for ML approaches, particularly for DT, RF and XGBoost., Conclusion: The updated-SENIC-LR model and NB may be useful for monitoring SSI risk following abdominal surgery., Competing Interests: Declaration of conflicting interestThe authors declare that there are no conflicts of interest.

Background: Incisional hernia (IH) manifests in 10%-15% of abdominal surgeries and patients at elevated risk of this complication should be identified for prophylactic intervention. This study aimed to externally validate the Penn hernia risk calculator., Methods: The Ramathibodi abdominal surgery cohort was constructed by linking relevant hospital databases from 2010 to 2021. Penn hernia risk scores were calculated according to the original model which was externally validated using a seven-step approach. An updated model which included four additional predictor variables (i.e., age, immunosuppressive medication, ostomy reversal, and transfusion) added to those of the three original predictors (i.e., body mass index, chronic liver disease, and open surgery) was also evaluated. The area under the receiver operating characteristic curve (AUC) was estimated, and calibration performance was compared using the Hosmer-Lemeshow goodness-of-fit method for the observed/expected (O/E) ratio., Results: A total of 12,155 abdominal operations were assessed. The original Penn model yielded fair discrimination with an AUC (95% confidence interval (CI)) of 0.645 (0.607, 0.683). The updated model that included the additional predictor variables achieved an acceptable AUC (95% CI) of 0.733 (0.698, 0.768) with the O/E ratio of 0.968 (0.848, 1.088)., Conclusion: The updated model achieved improved discrimination and calibration performance, and should be considered for the identification of high-risk patients for further hernia prevention strategy., (Copyright © 2023. Published by Elsevier Ltd.)

What Is Known and Objective: Carbapenem-resistant Enterobacteriaceae (CRE) are virulent gram-negative bacilli and cause urgent healthcare problems worldwide. One of the main factors leading to the emergence of CRE is antimicrobial consumption. The objective of this study was to assess how closely the rate of antimicrobial consumption and the prevalences of carbapenem-resistant Escherichia coli (CR-EC) and carbapenem-resistant Klebsiella pneumoniae (CR-KP) are correlated., Methods: A retrospective study was performed at a university hospital in Thailand from January 2013 to September 2016. The prevalence of E coli and K pneumoniae was represented as percentages per species per quarter. The antimicrobial consumption rate per quarter was expressed as the defined daily dose (DDD)/1000 patient-days. Evaluation of the relationships between the rate of antimicrobial consumption and the prevalences of CR-EC and CR-KP was conducted via Pearson's or Spearman's correlation analyses., Results and Discussion: During the study period, the prevalence of CR-EC and CR-KP was less than 6%; however, significantly increasing prevalences were reported for both CR-EC (r = 0.55, P = 0.03) and CR-KP (r = 0.87, P < 0.01). There was a significant increasing trend in the consumption of meropenem (r = 0.65, P = 0.01), levofloxacin (r = 0.63, P = 0.01), ceftriaxone (r = 0.55, P = 0.03), ertapenem (r = 0.52, P = 0.05) and the carbapenem group (r = 0.64, P = 0.01). A significant correlation was observed between CR-KP prevalence and total carbapenem consumption (r = 0.55, P = 0.04). Moreover, levofloxacin consumption had a significant positive relationship with the prevalence of CR-KP (r = 0.65, P = 0.01). No positive correlation was found with the prevalence of CR-EC., What Is New and Conclusion: The rate of consumption of levofloxacin and carbapenems was the important key factor correlated with the rate of emergence of CR-KP. This is the first report demonstrating the correlation between levofloxacin consumption and CR-KP prevalence., (© 2018 John Wiley & Sons Ltd.)