2 research outputs found
Bilateral inguinal hernioplasty in emergency surgery: Is it feasible? Comparative retrospective study using “propensity score matching”
No full textINTRODUCTION: Hernias, particularly groin hernias, are prevalent surgical pathologies worldwide, often necessitating surgery in cases of complications. This study investigates the safety and efficacy of performing bilateral inguinal hernioplasty when one side faces complications, addressing the lack of consensus in emergency groin hernia treatment. MATERIALS AND METHODS: A retrospective, single-center study spanning a duration of 10 years was conducted, including adult patients who underwent emergency surgery for inguinal hernia. Propensity score matching was employed to create similar groups for comparative analysis of unilateral versus bilateral emergency groin hernioplasty. Surgical techniques, complications, mortality, and long-term outcomes were evaluated. RESULTS: This study included 341 patients. Data obtained from the study revealed high morbidity and 90-day mortality rates, consistent with the data of existing literature. Propensity score matching yielded two comparable groups. Short-term outcomes showed no significant differences in complication rates, mortality, surgical site infection, or hospital stay between unilateral and bilateral hernioplasty groups. Bilateral surgery takes approximately 15 min of the procedure time. Long-term outcomes exhibited similar recurrence rates between groups. CONCLUSION: This study supports the practice of bilateral inguinal hernioplasty in emergency scenarios when one side faces complications. It is a safe approach without any significant increase in morbidity or hospital stay, while reducing the need for a subsequent intervention and its associated risks and costs. Further prospective research is necessary to confirm these findings
A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts
No full textBackground: Pulmonary complications are the most common cause of death after surgery. This study aimed to derive and externally validate a novel prognostic model that can be used before elective surgery to estimate the risk of postoperative pulmonary complications and to support resource allocation and prioritisation during pandemic recovery. Methods: Data from an international, prospective cohort study were used to develop a novel prognostic risk model for pulmonary complications after elective surgery in adult patients (aged ≥18 years) across all operation and disease types. The primary outcome measure was postoperative pulmonary complications at 30 days after surgery, which was a composite of pneumonia, acute respiratory distress syndrome, and unexpected mechanical ventilation. Model development with candidate predictor variables was done in the GlobalSurg-CovidSurg Week dataset (global; October, 2020). Two structured machine learning techniques were explored (XGBoost and the least absolute shrinkage and selection operator [LASSO]), and the model with the best performance (GSU-Pulmonary Score) underwent internal validation using bootstrap resampling. The discrimination and calibration of the score were externally validated in two further prospective cohorts: CovidSurg-Cancer (worldwide; February to August, 2020, during the COVID-19 pandemic) and RECON (UK and Australasia; January to October, 2019, before the COVID-19 pandemic). The model was deployed as an online web application. The GlobalSurg-CovidSurg Week and CovidSurg-Cancer studies were registered with ClinicalTrials.gov, NCT04509986 and NCT04384926. Findings: Prognostic models were developed from 13 candidate predictor variables in data from 86 231 patients (1158 hospitals in 114 countries). External validation included 30 492 patients from CovidSurg-Cancer (726 hospitals in 75 countries) and 6789 from RECON (150 hospitals in three countries). The overall rates of pulmonary complications were 2·0% in derivation data, and 3·9% (CovidSurg-Cancer) and 4·7% (RECON) in the validation datasets. Penalised regression using LASSO had similar discrimination to XGBoost (area under the receiver operating curve [AUROC] 0·786, 95% CI 0·774-0·798 vs 0·785, 0·772-0·797), was more explainable, and required fewer covariables. The final GSU-Pulmonary Score included ten predictor variables and showed good discrimination and calibration upon internal validation (AUROC 0·773, 95% CI 0·751-0·795; Brier score 0·020, calibration in the large [CITL] 0·034, slope 0·954). The model performance was acceptable on external validation in CovidSurg-Cancer (AUROC 0·746, 95% CI 0·733-0·760; Brier score 0·036, CITL 0·109, slope 1·056), but with some miscalibration in RECON data (AUROC 0·716, 95% CI 0·689-0·744; Brier score 0·045, CITL 1·040, slope 1·009). Interpretation: This novel prognostic risk score uses simple predictor variables available at the time of a decision for elective surgery that can accurately stratify patients' risk of postoperative pulmonary complications, including during SARS-CoV-2 outbreaks. It could inform surgical consent, resource allocation, and hospital-level prioritisation as elective surgery is upscaled to address global backlogs. Funding: National Institute for Health Research
