Raval MV, Cohen ME, Ingraham AM, Dimick JB, Osborne NH, Hamilton BH, Ko CY, Hall BL. Improving American College of Surgeons National Surgical Quality Improvement Program risk adjustment: Incorporation of a novel procedure risk score

Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL 60611-3211, USA.
Journal of the American College of Surgeons (Impact Factor: 5.12). 12/2010; 211(6):715-23. DOI: 10.1016/j.jamcollsurg.2010.07.021
Source: PubMed


Risk-adjusted evaluation is a key component of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). The purpose of this study was to improve standard ACS NSQIP risk adjustment using a novel procedure risk score.
Current Procedural Terminology codes (CPTs) represented in ACS NSQIP data were assigned to 136 procedure groups. Log odds predicted risk from preliminary logistic regression modeling generated a continuous risk score for each procedure group, used in subsequent modeling. Appropriate subsets of 271,368 patients in the 2008 ACS NSQIP were evaluated using logistic models for overall 30-day morbidity, 30-day mortality, and surgical site infection (SSI). Models were compared when including either work Relative Value Unit (RVU), RVU and the standard ACS NSQIP CPT range variable (CPT range), or RVU and the newly constructed CPT risk score (CPT risk), plus routine ACS NSQIP predictors.
When comparing the CPT risk models with the CPT range models for morbidity in the overall general and vascular surgery dataset, CPT risk models provided better discrimination through higher c statistics at earlier steps (0.81 by step 3 vs 0.81 by step 46), more information through lower Akaike's information criterion (127,139 vs 130,019), and improved calibration through a smaller Hosmer-Lemeshow chi-square statistic (48.76 vs 116.79). Improved model characteristics of CPT risk over CPT range were most apparent for broader patient populations and outcomes. The CPT risk and standard CPT range models were moderately consistent in identification of outliers as well as assignment of hospitals to quality deciles (weighted kappa ≥ 0.870).
Information from focused, clinically meaningful CPT procedure groups improves the risk estimation of ACS NSQIP models.

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    • "Our model was similar to, but distinct from, other studies examining SSI risk in patients. The CPT3 Score in our model used an approach similar to that by Raval et al [17], who used a more clinically robust method to cluster CPT codes into procedural groups. Multiple previous studies have found significant associations between the risk of SSI and factors in our model [including wound class, body mass index, surgical location and urgency, ASA class, the performance of more than one procedure, metastatic cancer, the presence of steroids, and surgical duration] [18]. "
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    ABSTRACT: 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. 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. 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. SSIRS can be calculated using patient and surgery information to estimate individual risk of SSI for a broad range of surgery types.
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