A clinical prediction model for predicting the surgical site infection after an open reduction and internal fixation procedure considering the NHSN/SIR risk model: a multicenter case–control study - Taherpour, Niloufar; Mehrabi, Yadollah; Seifi, Arash; Hashemi Nazari, Seyed Saeed (Frontiers in Surgery, 2023)
Introduction: Surgical Site Infection (SSI) is one of the most common surgical-related problems in the world, especially in developing countries. SSI is responsible for mortality, long hospitalizatio n period, and a high economic burden .Method: This hospital-based case-control study, was conducted in six educational hospitals in Tehran Iran.Totally, 244 patients at the age of 18-85 years who had ORIF surgery were selected. Of those, 122 selected patients who developed SSIs were compared to 122 non-infected controls. At the second stage, all patients (n =350) who underwent ORIF surgery in a hospital were selected for estimation of Standardized Infection Ratio (SIR). Logistic regression model was used for predicting the most important factors associated with the occurrence of SSIs. Finally, the performance of ORIF prediction model was evaluated using discrimination and calibration indices. Data were analyzed using R.3.6.2 and STATA.14 software.Results: Klebsiella (14.75%) was the most frequently detected bacterium in SSIs following ORIF surgery. Results revealed that the most important factors associated with SSI following ORIF procedure were, elder age, elective surgery, prolonged operation time, ASA score ≥ 2, class 3 and 4 wound and preoperative blood glucose levels of > 200 mg/dL; while, preoperative higher haemoglobin (g/dl) was a protective factor. Evidences for interaction effect between age and gender, BMI and gender, age and elective surgery were also observed. After assessing the internal validity of the model, the overall performance of the models was good with an area under the curve of 95%.The SIR of SSI for ORIF surgery in the selected hospital was 0.66 among 18-85 years old patients.New risk prediction models can help to detect high-risk patients and monitor the infection rate in hospitals based on infection prevention and control programs. Physicians using prediction models can recognize high risk patients with these factors before ORIF procedure.