Will individual participant data (IPD) be shared publicly (including data dictionaries)?  

Response - NO

The purpose of Clean Cut is to:

(1) help facilitate improved adherence to critical perioperative infection prevention practices

and lower surgical site infection rates; postoperative morbidity and mortality.

(2) create an environment conducive to successful implementation of the WHO Surgical Safety

Checklist;

(3) foster a culture of teamwork, individual value, and accountability that facilitates quality

improvement more broadly.

Intervention: Clean Cut has been developed through a collaboration informed and led by

LMIC healthcare staff. Further modifications to this intervention have been added to this by the India Hub of the NIHR Health Research Unit On Global Surgery following our work especially in the Cheetah Trial. It involves a sequential intervention to address problems in adhering to seven critical standards of perioperative infection prevention and control:

1) Appropriate skin preparation of both the surgeon’s hands and the surgical site with proper

antiseptic agents

2) Maintenance of the sterile field by ensuring the integrity and sterility of surgical gowns,

drapes, and gloves.

3) Appropriate antibiotic selection and administration, when indicated, within one hour prior

to surgical incision to optimise prophylaxis

4) Proper confirmation of instrument sterility by the operating team

5) Complete gauze/swab counts every case at appropriate times (preoperatively, before organ

space and/or cavity closure, and postoperatively)

6) Change of Gloves and Instruments by the Surgical Team at the time of Fascial Closure in

Contaminated and Dirty Abdominal Operations.

7) Use of the Surgical Safety Checklist to promote teamwork, communication, and a culture

of safety in the operating room

Statistical Analysis Plan: Key variables including compliance with perioperative standards and

patient outcomes will be measured using standardized tools and assessments. Data will be

collected using electronic data capture systems and analyzed using appropriate statistical

methods, including regression analysis to evaluate the intervention’s impact on primary and

secondary outcomes. Data will be summarized using frequency distribution and descriptive

analysis. The normality of the data will be assessed through the statistical test and graphical

method. The p-value of 0.05 will be considered as statistically significant. Summary statistics

will be presented for all outcome measures, with the relevant adjusted effect measures, 95%

confidence intervals and p-values from two-sided tests. The adjusted model will include hospital

as fixed effects and centre as a random effect to account for the clustered nature of the sample.

Quantitative data will be analyzed using statistical software such as R or SPSS, while qualitative

data will be analyzed thematically.