Estimated Costs Associated with Surgical Site Infections in Patients Undergoing Cholecystectomy

Risk factors for surgical site infections following hepatobiliary surgery: An umbrella review and meta-analyses

BACKGROUND

In the hepatobiliary (HPB) surgical cohort, surgical site infections (SSI) can extend hospital stays, result in higher morbidity, and poor patient outcomes. This umbrella review and meta-analysis aimed to synthesise the evidence for the association between clinical and patient risk factors and SSI in patients following HPB surgery.

METHODS

We searched MEDLINE, CINAHL, EMBASE and Scopus from January 2000 to April 2023 to identify systematic reviews and meta-analyses where patient and/or clinical factors of SSIs following HPB surgery were reported. The summary effect size, its 95 % CI and the 95 % PI were calculated for each meta-analysis using random-effects models. 30-day cumulative SSI incidence was presented as the pooled estimate with 95 % CIs. Between-study heterogeneity was explored using the I2 statistic.

RESULTS

Nine systematic reviews and meta-analyses were included. Our findings suggest open surgical approach, type of pancreas procedure, preoperative biliary drainage, older age, male sex and high BMI (>25mg/k2) as statistically significant factors for increasing a patient's risk of SSI following HPB surgery. The cumulative incidence of SSI in the HPB cohort of 43,296 was 11 % (95 % CI 6%-20 %), with substantial variation between the reviews.

CONCLUSION

We identified several patient and clinical factors, however only one was graded as a high level of evidence.

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods

BACKGROUND

Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare.

METHODS

The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility.

RESULTS

For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort).

CONCLUSION

We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

Unexpected Benefits of Coronavirus Disease 2019: Impact of Coronavirus Disease 2019 Pandemic on Surgical Site Infection: A Systematic Review and Meta-Analysis

Background: We aimed to summarize and synthesize the current evidence regarding the indirect impact of the coronavirus disease 2019 (COVID-19) pandemic and its associated measures on the surgical site infection (SSI) rate compared with the pre-pandemic period. Methods: A computerized search was conducted on MEDLINE via PubMed, Web of Science, and Scopus using the relevant keywords. Two-stage screening and data extraction were done. The National Institutes of Health (NIH) tools were used for the quality assessment. The Review Manager 5.4.1 program was used for the analysis. Results: Sixteen articles (n = 157,426 patients) were included. The COVID-19 pandemic and lockdown were associated with reduced risk of SSIs after surgery (odds ratio [OR], 0.65; 95% confidence interval [CI], 0.56-0.75; p < 0.00001) and (OR, 0.49; 95% CI, 0.29-0.84; p = 0.009), respectively. There was no significant reduction in the SSIs rate after applying the extended use of masks (OR, 0.73; 95% CI, 0.30-1.73; p = 0.47). A reduction in the superficial SSI rate during the COVID-19 pandemic compared with the pre-COVID-19 pandemic period was observed (OR, 0.58; 95% CI, 0.45-0.75; p < 0.0001). Conclusions: The current evidence suggests that the COVID-19 pandemic may have some unexpected benefits, including improved infection control protocols, which resulted in reduced SSI rates, especially superficial SSIs. In contrast to extended mask use, the lockdown was associated with reduced rates of SSIs.

Sustaining Improvements of Surgical Site Infections by Six Sigma DMAIC Approach

SSIs (surgical site infections) are associated with increased rates of morbidity and mortality. The traditional quality improvement strategies focusing on individual performance did not achieve sustainable improvement. This study aimed to implement the Six Sigma DMAIC method to reduce SSIs and to sustain improvements in surgical quality. The surgical procedures, clinical data, and surgical site infections were collected among 42,233 hospitalized surgical patients from 1 January 2019 to 31 December 2020. Following strengthening leadership and empowering a multidisciplinary SSI prevention team, DMAIC (Define, Measure, Analyze, Improve, and Control) was used as the performance improvement model. An evidence-based prevention bundle for reduction of SSI was adopted as performance measures. Environmental monitoring and antimicrobial stewardship programs were strengthened to prevent the transmission of multi-drug resistant microorganisms. Process change was integrated into a clinical pathway information system. Improvement cycles by corrective actions for the risk events of SSIs were implemented to ensure sustaining improvements. We have reached the targets of the prevention bundle elements in the post-intervention period in 2020. The carbapenem resistance rates of Enterobacteriaceae and P. aeruginosa were lower than 10%. A significant 22.2% decline in SSI rates has been achieved, from 0.9% for the pre-intervention period in 2019 to 0.7% for the post-intervention period in 2020 (p = 0.004). Application of the Six Sigma DMAIC approach could significantly reduce the SSI rates. It also could help hospital administrators and quality management personnel to create a culture of patient safety.