Continuous Improvement In Cardiac Surgery Program Research Articles

Predicting Mortality in High-Risk Coronary Artery Bypass: Surgeon Versus Risk Model

Risk models are useful in evaluating and comparing surgical outcomes, but surgeons may not always agree with the risk estimates derived from these models, particularly in high-risk cases. We examined the concordance between surgeons' and a risk model's predictions of operative mortality in high-risk coronary artery bypass grafting (CABG) patients, and we attempted to identify the reasons for any discrepancies. From the Veterans Affairs Continuous Improvement in Cardiac Surgery Program (CICSP), a prospective database and cardiac surgery risk model, we obtained data regarding 181 high-risk, isolated CABG cases performed at a single institution between April 1998 and April 2008. Cases were considered high risk if the surgeon estimated the patient's operative mortality risk to be ≥ 10%. We compared the mortality predictions made by surgeons and the risk model by using the signed-rank test and investigated cases in which there was a significant discrepancy (at least 2-fold) between the two predictions. The observed 30-d/in-hospital and 180-d mortality rates were 6.1% (11/181) and 11.0% (20/181), respectively. The mean operative mortality prediction made by surgeons (12.0% ± 5.3%) was higher than that made by the risk model (7.5% ± 8.5%) (P < 0.001). There was significant discrepancy between the surgeon and risk model estimates in 62% (113/181) of cases. In 53% (60/113) of these cases, the surgeon reported having considered risk factors not included in the CICSP model, including (most commonly) possible need for an additional procedure (n = 15), compromised mobility (n = 11), liver disease (n = 9), hematologic or immunologic disease (n = 6), and quality of targets (n = 5). In high-risk CABG cases, surgeon and CICSP risk estimates often disagreed markedly, partly because some disease entities of concern to surgeons are not included in the risk model. The higher mortality risk estimated by the surgeons is a better reflection of the considerable mortality risk that extends up to 180 days after surgery.

Aortic Valve Replacement: Mortality Predictions of Surgeons Versus Risk Model

We compared the abilities of surgeons and of an established risk model to predict operative mortality after aortic valve replacement (AVR), and we investigated scenarios that give rise to discrepancies between these predictions. We reviewed all AVR procedures performed at a Veterans Affairs institution between 1993 and 2008 (n = 317). The abilities of the Continuous Improvement in Cardiac Surgery Program (CICSP) risk model and of the surgeons to predict operative mortality were assessed by computing the area under the receiver operating characteristic curve (AUC). We investigated cases in which there was a significant discrepancy (2-fold or greater) between the surgeons' and the CICSP model's predictions. The predictive abilities of both the surgeons and the CICSP risk model were good-AUC values were 0.73 and 0.75, respectively (P = 0.84)-but the surgeons' mean estimate of mortality risk (8.3% +/- 8.3%) exceeded both the CICSP model's estimate (6.6% +/- 8.3%) (P < 0.0001) and the actual mortality rate (5.4%). There was significant discrepancy between the two sources of prediction in 38% (122/317) of cases. In this subset of cases, the CICSP did not adjust for factors that influenced risk stratification by the surgeon in 33% (40/122) of cases; the most common of these factors were anticipation of a more extensive procedure, severe pulmonary disease other than chronic obstructive pulmonary disease, hepatic disease, and pulmonary hypertension. Both surgeons and the CICSP model performed well in risk-stratifying AVR patients, but the surgeons tended to overestimate the risk. The CICSP model did not capture some disease entities considered relevant in estimating mortality by surgeons.

Operative Mortality after Aortic Valve Replacement: Predictions Made by Surgeons versus Risk Model

Background: Risk models are useful tools for computing risk-adjusted outcomes. However, the models are only as good as the data on which they are based and therefore have limitations. Our aim was to compare the abilities of surgeons and an established risk model to predict operative mortality after aortic valve replacement (AVR). We also investigated scenarios that give rise to discrepancies between surgeons' and the risk model's predictions. Methods: The Veterans Affairs Continuous Improvement in Cardiac Surgery Program (CICSP) is a prospective database and a well-validated cardiac surgery risk model.

Contemporary outcomes of open thoracic aortic surgery in a veteran population: do risk models exaggerate mortality?

Background We evaluated contemporary outcomes of open thoracic aortic surgery at a Veterans Affairs (VA) medical center affiliated with a major academic aortic program and examined the predictive value of 2 established cardiac risk models. Methods We retrospectively reviewed all open thoracic aortic operations performed between April 1998 and April 2008 (n = 100). Both the EuroSCORE and the VA Continuous Improvement in Cardiac Surgery Program (CICSP) scores were evaluated. Results Procedures included ascending aortic repair (n = 74, 15 with arch repair), descending thoracic repair (n = 11, 1 with arch repair), and thoracoabdominal aortic repair (n = 15). Emergency surgery was necessary in 15 cases, and 19 procedures were reoperations. The patients' logistic EuroSCORE and the CICSP scores were similar (18.7% and 18.2%, respectively), but both scores significantly exceeded the observed operative mortality rate (8.0%, P = .008). Conclusions Good outcomes can be achieved when thoracic aortic surgery is performed at an experienced VA center. The cardiac risk models we examined overpredicted operative mortality.

Improving Quality of Care in Cardiac Surgery: Evaluating Risk Factors, Processes of Care, Structures of Care, and Outcomes

The 1985 release of hospital report cards by the Health Care Financing Administration awakened the public's awareness of variations in outcomes following patient treatment. In 1972, the Department of Veterans Affairs initiated an oversight process for all VA-based cardiac surgery programs. In response to Public Law 99-166, the Continuous Improvement in Cardiac Surgery Program (CICSP) national database was developed in 1987. This CICSP effort reported variations in outcomes across VA cardiac programs. In 1997, the CICSP expanded (CICSP-X) to identify the interrelationships of risk factors with processes and structures of care, as well as clinical outcomes. Based on VA findings to date, these quality improvement endeavors appear to have positively affected short-term and longer-term cardiac surgical outcomes. To advance a new patient-focused paradigm for continuous improvement in cardiac surgical care quality for all US citizens, an integrated data-driven reporting approach with broad-based participation should be implemented to optimally improve patient care.

Mortality After Cardiac Bypass Surgery

Risk-adjusted outcome rates frequently are used to make inferences about hospital quality of care. We calculated risk-adjusted mortality rates in veterans undergoing isolated coronary artery bypass surgery (CABS) from administrative data and from chart-based clinical data and compared the assessment of hospital high and low outlier status for mortality that results from these 2 data sources. We studied veterans who underwent CABS in 43 VA hospitals between October 1, 1993, and March 30, 1996 (n=15,288). To evaluate administrative data, we entered 6 groups of International Classification of Diseases (ICD)-9-CM codes for comorbid diagnoses from the VA Patient Treatment File (PTF) into a logistic regression model predicting postoperative mortality. We also evaluated counts of comorbid ICD-9-CM codes within each group, along with 3 common principal diagnoses, weekend admission or surgery, major procedures associated with CABS, and demographic variables. Data from the VA Continuous Improvement in Cardiac Surgery Program (CICSP) were used to create a separate clinical model predicting postoperative mortality. For each hospital, an observed-to-expected (O/E) ratio of mortality was calculated from (1) the PTF model and (2) the CICSP model. We defined outlier status as an O/E ratio outside of 1.0 (based on the hospital's 90% confidence interval). To improve the statistical and predictive power of the PTF model, selected clinical variables from CICSP were added to it and outlier status reassessed. Significant predictors of postoperative mortality in the PTF model included 1 group of comorbid ICD-9-CM codes, intraortic balloon pump insertion before CABS, angioplasty on the day of or before CABS, weekend surgery, and a principal diagnosis of other forms of ischemic heart disease. The model's c-index was 0.698. As expected, the CICSP model's predictive power was significantly greater than that of the administrative model (c=0.761). The addition of just 2 CICSP variables to the PTF model improved its predictive power (c=0.741). This model identified 5 of 6 high mortality outliers identified by the CICSP model. Additional CICSP variables were statistically significant predictors but did not improve the assessment of high outlier status. Models using administrative data to predict postoperative mortality can be improved with the addition of a very small number of clinical variables. Limited clinical improvements of administrative data may make it suitable for use in quality improvement efforts.

Predicting acute renal failure after coronary bypass surgery: Cross-validation of two risk-stratification algorithms

Acute renal failure (ARF) requiring dialysis after coronary artery bypass grafting (CABG) occurs in 1 to 5% of patients and is independently associated with postoperative mortality, even after case-mix adjustment. A risk-stratification algorithm that could reliably identify patients at increased risk of ARF could help improve outcomes. To assess the validity and generalizability of a previously published preoperative renal risk-stratification algorithm, we analyzed data from the Quality Measurement and Management Initiative (QMMI)1 patient cohort. The QMMI includes all adult patients (N = 9498) who underwent CABG at 1 of 12 academic tertiary care hospitals from August 1993 to October 1995. ARF requiring dialysis was the outcome of interest. Cross-validation of a recursive partitioning algorithm developed from the VA Continuous Improvement in Cardiac Surgery Program (CICSP) was performed on the QMMI. An additive severity score derived from logistic regression was also cross-validated on the QMMI. The CICSP recursive partitioning algorithm discriminated well (ARF vs. no ARF) in QMMI patients, even though the QMMI cohort was more diverse. Rates of ARF were similar among risk subgroups in the CICSP tree, as was the overall ranking of subgroups by risk. Using logistic regression, independent predictors of ARF in the QMMI cohort were similar to those found in the CICSP. The CICSP additive severity score performed well in the QMMI cohort, successfully stratifying patients into low-, medium-, high-, and very high-risk groups. The CICSP preoperative renal-risk algorithms are valid and generalizable across diverse populations.