Reliability and validity of multicentre surveillance of surgical site infections after colorectal surgery

Automated surveillance for surgical site infections (SSI) in hospitals and surveillance networks-expert perspectives for implementation

BACKGROUND

This work aims at providing practical recommendations for implementing automated surveillance (AS) of surgical site infections (SSI) in hospitals and surveillance networks. It also provides an overview of the steps, choices, and obstacles that need to be taken into consideration when implementing such surveillance. Hands-on experience with existing automated surveillance systems of SSI (AS SSI systems) in Denmark, France, the Netherlands and Spain is described regarding trend monitoring, benchmarking, quality control, and research for surveillance purposes.

METHODS

Between April and October 2023, specific aspects/options of various surveillance purposes for AS SSI were identified during regular meetings of the SSI working group in the PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network. Expert discussions provided the basis for this perspective article.

RESULTS

Decisions for implementation of AS SSI systems highly depend on the purpose of the surveillance. AS SSI systems presented here differ according to study population, setting, central or local implementation; the level of automation, design, and the data sources used. However, similarities were found for the rationales of automation, design principles and obstacles that were identified. There was consensus among all the experts that shortcomings in interoperability of databases, limited time, a want of commitment on the part of stakeholders, and a shortage of resources for information technology (IT) specialists represent the main obstacles for implementing AS SSI. To overcome obstacles, various solutions were reported, including training in the development of AS systems and the interpretation of AS SSI results, early consultation of end-users, and regular exchanges between management levels, IT departments, infection prevention and control (IPC) teams, and clinicians.

CONCLUSION

Clarity on the intended application (e.g. purpose of surveillance) and information on the availability of electronic and structured data are crucial first steps necessary for guiding decisions on the design of AS systems. Adequate resources for IT specialists and regular communication between management, IT departments, IPC teams, and clinicians were identified as essential for successful implementation. This perspective article may be helpful for a wider implementation of more homogeneous AS SSI systems in Europe.

Reinforced tension-line suture after laparotomy: early results of the Rein4CeTo1 randomized clinical trial

BACKGROUND

The aim was to investigate whether adding a reinforced tension-line (RTL) suture to a standard 4 : 1 small-bite closure would reduce the incidence of incisional hernia after colorectal cancer surgery.

METHODS

Patients aged at least 18 years, who were scheduled for elective colorectal cancer surgery through a midline incision at two Swedish hospitals (2017-2021), were randomized in a 1 : 1 ratio to either fascial closure with RTL and 4 : 1 small-bite closure with polypropylene sutures (RTL group) or 4 : 1 small-bite closure with polydioxanone suture alone (PDS group). The primary outcome was CT-detected incisional hernia 1 year after surgery. CT interpreters were blinded regarding treatment group.

RESULTS

In all, 160 patients were randomized, 80 in each group. The study closed early to recruitment and follow-up. Some 134 patients were analysed at 1 year: 63 in the RTL group and 71 in the PDS group. Nineteen patients were found to have an incisional hernia: 4 (6%) in the RTL group and 15 (21%) in the PDS group (OR 3.95, 95% c.i. 1.24 to 12.60; P = 0.014). No unintended effects were found in either group.

CONCLUSION

Adding an RTL suture at fascial closure decreased the incidence of incisional hernia in patients undergoing surgery for colorectal cancer. Trial registration: NCT03390764 (https://clinicaltrials.gov).

The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery

BACKGROUND

In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR).

METHODS

Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated.

RESULTS

From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm.

CONCLUSIONS

The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.

Semiautomated surveillance of deep surgical site infections after colorectal surgeries: A multicenter external validation of two surveillance algorithms

Objective:

Automated surveillance methods increasingly replace or support conventional (manual) surveillance; the latter is labor intensive and vulnerable to subjective interpretation. We sought to validate 2 previously developed semiautomated surveillance algorithms to identify deep surgical site infections (SSIs) in patients undergoing colorectal surgeries in Dutch hospitals.

Design:

Multicenter retrospective cohort study.

Methods:

From 4 hospitals, we selected colorectal surgery patients between 2018 and 2019 based on procedure codes, and we extracted routine care data from electronic health records. Per hospital, a classification model and a regression model were applied independently to classify patients into low- or high probability of having developed deep SSI. High-probability patients need manual SSI confirmation; low-probability records are classified as no deep SSI. Sensitivity, positive predictive value (PPV), and workload reduction were calculated compared to conventional surveillance.

Results:

In total, 672 colorectal surgery patients were included, of whom 28 (4.1%) developed deep SSI. Both surveillance models achieved good performance. After adaptation to clinical practice, the classification model had 100% sensitivity and PPV ranged from 11.1% to 45.8% between hospitals. The regression model had 100% sensitivity and 9.0%–14.9% PPV. With both models, <25% of records needed review to confirm SSI. The regression model requires more complex data management skills, partly due to incomplete data.

Conclusions:

In this independent external validation, both surveillance models performed well. The classification model is preferred above the regression model because of source-data availability and less complex data-management requirements. The next step is implementation in infection prevention practices and workflow processes.