Risk-adjustment models for clean and colorectal surgery surgical site infection for the Spanish health system

Comorbidities directly extracted from the hospital database for adjusting SSI risk in the new national semiautomated surveillance system in France: The SPICMI network

OBJECTIVE

To evaluate the performance of a comorbidity-based risk-adjustment model for surgical-site infection (SSI) reporting and benchmarking using a panel of variables extracted from the hospital discharge database (HDD), including comorbidities, compared to other models that use variables from different data sources.

METHODS

The French national surveillance program for SSI (SPICMI) has collected data from voluntary hospitals in the first 6 months of 2020 and 2021, for 16 selected surgery procedures, using a semiautomated algorithm for detection. Four risk-adjustment models were selected with logistic regression analysis, combining the different patterns of variables: National Nosocomial Infections Surveillance System (NNIS) risk-index components, individual operative data, and 6 individual comorbidities according to International Classification of Disease, Tenth Revision (ICD-10) diagnosis: obesity, diabetes, malnutrition, hypertension, cancer, or immunosuppression. Areas under the curve (AUCs) were calculated and compared.

RESULTS

Overall, 294 SSI were detected among 11,975 procedures included. All 6 comorbidities were related to SSI in the univariate analysis. The AUC of the selected model including comorbidities (0.675; 95% confidence interval [CI], 0.642-0.707), was significantly higher than the AUC of the model without comorbidities (0.641; 95% CI, 0.609-0.672; P = .016) or the AUC using the NNIS-index components (0.598; 95% CI, 0.564-0.630; P < .001). The HDD-based model AUC (0.659; 95% CI, 0.625-0.692) did not differ significantly from the selected model without comorbidities (P = .23).

CONCLUSION

Including HDD-based comorbidities as patient case-mix variables instead of NNIS risk index factors could be an effective approach for risk-adjustment of automated SSI surveillance more widely accessible to hospitals.

Prognostic models for surgical-site infection in gastrointestinal surgery: systematic review

BACKGROUND

Identification of patients at high risk of surgical-site infection may allow clinicians to target interventions and monitoring to minimize associated morbidity. The aim of this systematic review was to identify and evaluate prognostic tools for the prediction of surgical-site infection in gastrointestinal surgery.

METHODS

This systematic review sought to identify original studies describing the development and validation of prognostic models for 30-day SSI after gastrointestinal surgery (PROSPERO: CRD42022311019). MEDLINE, Embase, Global Health, and IEEE Xplore were searched from 1 January 2000 to 24 February 2022. Studies were excluded if prognostic models included postoperative parameters or were procedure specific. A narrative synthesis was performed, with sample-size sufficiency, discriminative ability (area under the receiver operating characteristic curve), and prognostic accuracy compared.

RESULTS

Of 2249 records reviewed, 23 eligible prognostic models were identified. A total of 13 (57 per cent) reported no internal validation and only 4 (17 per cent) had undergone external validation. Most identified operative contamination (57 per cent, 13 of 23) and duration (52 per cent, 12 of 23) as important predictors; however, there remained substantial heterogeneity in other predictors identified (range 2-28). All models demonstrated a high risk of bias due to the analytic approach, with overall low applicability to an undifferentiated gastrointestinal surgical population. Model discrimination was reported in most studies (83 per cent, 19 of 23); however, calibration (22 per cent, 5 of 23) and prognostic accuracy (17 per cent, 4 of 23) were infrequently assessed. Of externally validated models (of which there were four), none displayed 'good' discrimination (area under the receiver operating characteristic curve greater than or equal to 0.7).

CONCLUSION

The risk of surgical-site infection after gastrointestinal surgery is insufficiently described by existing risk-prediction tools, which are not suitable for routine use. Novel risk-stratification tools are required to target perioperative interventions and mitigate modifiable risk factors.

Beyond the operating room: do hospital characteristics have an impact on surgical site infections after colorectal surgery? A systematic review

Background

Hospital characteristics have been recognized as potential risk factors for surgical site infection for over 20 years. However, most research has focused on patient and procedural risk factors. Understanding how structural and process variables influence infection is vital to identify targets for effective interventions and to optimize healthcare services. The aim of this study was to systematically review the association between hospital characteristics and surgical site infection in colorectal surgery.

Main body

A systematic literature search was conducted using PubMed, Scopus and Web of Science databases until the 31st of May, 2021. The search strategy followed the Participants, Exposure/Intervention, Comparison, Outcomes and Study design. The primary outcome of interest was surgical site infection rate after colorectal surgery. Studies were grouped into nine risk factor typologies: hospital size, ownership affiliation, being an oncological hospital, safety-net burden, hospital volume, surgeon caseload, discharge destination and time since implementation of surveillance. The STROBE statement was used for evaluating the methodological quality.

A total of 4703 records were identified, of which 172 were reviewed and 16 were included. Studies were published between 2008 and 2021, and referred to data collected between 1996 and 2016. Surgical site infection incidence ranged from 3.2 to 27.6%. Two out of five studies evaluating hospital size adjusted the analysis to patient and procedure-related risk factors, and showed that larger hospitals were either positively associated or had no association with SSI. Public hospitals did not present significantly different infection rates than private or non-profit ones. Medical school affiliation and higher safety-net burden were associated with higher surgical site infection (crude estimates), while oncological hospitals were associated with higher incidence independently of other variables. Hospital caseload showed mixed results, while surgeon caseload and surveillance time since implementation appear to be associated with fewer infections.

Conclusions

Although there are few studies addressing hospital-level factors on surgical site infection, surgeon experience and the implementation of a surveillance system appear to be associated with better outcomes. For hospitals and services to be efficiently optimized, more studies addressing these variables are needed that take into account the confounding effect of patient case mix.