研究目的
To investigate the potential of non-invasive infrared thermography (IRT), performed at short times after surgery, to predict later surgical site infection (SSI) in obese women after caesarean section.
研究成果
Infrared thermography improves upon visual wound assessment for predicting surgical site infection in obese women after caesarean section. Temperature differences between wound and abdomen at early time points (days 2 and 7) are significant predictors, with correct classification rates of 70-79%. This non-invasive technology shows promise for risk stratification and rational antibiotic prescribing, potentially reducing morbidity and healthcare costs.
研究不足
The study had a small sample size (53 women), which may limit generalizability. Depth of SSI was not accurately established, and most infections were superficial. The predictive models showed moderate performance, and further validation with larger cohorts is needed. Ambient conditions were controlled but could introduce variability.
1:Experimental Design and Method Selection:
A prospective observational thermal mapping and early-stage test-accuracy investigation was conducted. Infrared thermography (IRT) was used to image the wound and abdomen, with logistic regression models applied to predict SSI based on temperature differences.
2:Sample Selection and Data Sources:
Fifty-three obese women (BMI ≥30 kg/m2) who had delivered by caesarean section were recruited. Data included thermal images, digital photographs, and clinical information from hospital and community follow-ups.
3:List of Experimental Equipment and Materials:
A FLIR T450sc thermal camera, Kestrel 3000 weather meter, black body source (P80P, Ametek-Land), platinum resistance thermometer (PRT100, ISOTECH), and digital camera for photographs.
4:Experimental Procedures and Operational Workflow:
Thermal imaging was performed on days 2, 7, 15, and 30 postpartum. Images were taken after a 15-minute camera warm-up, with ambient conditions recorded. Two regions of interest (abdomen and wound site) were analyzed. Wound assessments by clinicians were based on digital photographs.
5:Data Analysis Methods:
Data were analyzed using SPSS version 24. Logistic regression models were used to predict SSI, with Cohen's kappa for inter-rater agreement and ROC curves for predictive capability.
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