The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections.
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ABSTRACT: Background
Surgical site infections (SSI) are an important cause of peri-surgical morbidity with risks that vary extensively between patients and surgeries. Quantifying SSI risk would help identify candidates most likely to benefit from interventions to decrease the risk of SSI.Methods
We randomly divided all surgeries recorded in the National Surgical Quality Improvement Program from 2010 into a derivation and validation population. We used multivariate logistic regression to determine the independent association of patient and surgical covariates with the risk of any SSI (including superficial, deep, and organ space SSI) within 30 days of surgery. To capture factors particular to specific surgeries, we developed a surgical risk score specific to all surgeries having a common first 3 numbers of their CPT code.Results
Derivation (n = 181 894) and validation (n = 181 146) patients were similar for all demographics, past medical history, and surgical factors. Overall SSI risk was 3.9%. The SSI Risk Score (SSIRS) found that risk increased with patient factors (smoking, increased body mass index), certain comorbidities (peripheral vascular disease, metastatic cancer, chronic steroid use, recent sepsis), and operative characteristics (surgical urgency; increased ASA class; longer operation duration; infected wounds; general anaesthesia; performance of more than one procedure; and CPT score). In the validation population, the SSIRS had good discrimination (c-statistic 0.800, 95% CI 0.795-0.805) and calibration.Conclusion
SSIRS can be calculated using patient and surgery information to estimate individual risk of SSI for a broad range of surgery types.
SUBMITTER: van Walraven C
PROVIDER: S-EPMC3694979 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveWe aimed to measure the association between 2013 guideline concordant prophylactic antibiotic use prior to surgery and infection with Clostridium difficile.DesignWe conducted a retrospective case-control study by selecting patients who underwent a surgical procedure between January 1, 2012 and December 31, 2013.SettingLarge urban community hospital.PatientsCases and controls were patients age 18+ years who underwent an eligible surgery (i.e., colorectal, neurosurgery, vascular/cardiac/thoracic, hysterectomy, abdominal/pelvic and orthopedic surgical procedures) within six months prior to infection diagnosis. Cases were diagnosed with C. difficile infection while controls were not.MethodsThe primary exposure was receiving (vs. not receiving) the recommended prophylactic antibiotic regimen, based on type and duration. Potential confounders included age, sex, length of hospital stay, comorbidities, type of surgery, and prior antibiotic use. Crude and adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression.ResultsWe enrolled 68 cases and 220 controls. The adjusted OR among surgical patients between developing C. difficile infection and not receiving the recommended prophylactic antibiotic regimen (usually receiving antimicrobial prophylaxis for more than 24 hours) was 6.7 (95% CI: 2.9-15.5). Independent risk factors for developing C. difficile infection included having severe comorbidities, receiving antibiotics within the previous 6 months, and undergoing orthopedic surgery.ConclusionsAdherence to the recommended prophylactic antibiotics among surgical patients likely reduces the probability of being case of C. difficile. Antibiotic stewardship should be a priority in strategies to decrease the morbidity, mortality, and costs associated with C. difficile infection.
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Project description:BackgroundSurgical site complications (SSCs) contribute to increased healthcare costs. Predictive analytics can aid in identifying high-risk patients and implementing optimization strategies. This study aimed to develop and validate a risk-assessment score for SSC-associated readmissions (SSC-ARs) in patients undergoing open spine surgery.MethodsThe Premier Healthcare Database (PHD) of adult patients (n=157,664; 3,182 SSC-ARs) between January 2019 and September 2020 was used for retrospective data analysis to create an SSC risk score using mixed effects logistic regression modeling. Full and reduced models were developed using patient-, facility-, or procedure-related predictors. The full model used 37 predictors and the reduced used 19.ResultsThe reduced model exhibited fair discriminatory capability (C-statistic =74.12%) and demonstrated better model fit [Pearson chi-square/degrees of freedom (DF) =0.93] compared to the full model (C-statistic =74.56%; Pearson chi-square/DF =0.92). The risk scoring system, based on the reduced model, comprised the following factors: female (1 point), blood disorder [2], congestive heart failure [2], dementia [3], chronic pulmonary disease [2], rheumatic disease [3], hypertension [2], obesity [2], severe comorbidity [2], nicotine dependence [1], liver disease [2], paraplegia and hemiplegia [3], peripheral vascular disease [2], renal disease [2], cancer [1], diabetes [2], revision surgery [2], operative hours ≥5 [4], emergency/urgent surgery [2]. A final risk score (sum of the points for each surgery; range, 0-40) was validated using a 1,000-surgery random hold-out sample (C-statistic =85.16%).ConclusionsThe resulting SSC-AR risk score, composed of readily obtainable clinical information, could serve as a robust predictive tool for unplanned readmissions related to wound complications in the preoperative setting of open spine surgery.
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