Administrative Versus Clinical Data for Coronary Artery Bypass Graft Surgery Report Cards

The impact of health maintenance organizations on improving cardiac surgery outcomes

BACKGROUND AND OBJECTIVES

California is one of a few states with mandatory reporting of mortality after coronary artery bypass graft (CABG) surgery. The Affordable Care Act restructured Medicaid, preferentially penalizing patients experiencing poverty because payments to hospitals for isolated surgical events overshadow payments to primary care clinicians. We propose outcomes are superior when hospital networks organize surgical episodes within the context of primary care inside that same network.

DESIGN AND METHODS

We listed factors impacting outcomes after CABG. CABG surgery outcome depends upon the integration of issues beginning years preoperatively and extending for decades. Therefore, we studied one health maintenance organization (HMO) from 2009 to 2020 compared to surrounding individual hospitals. We divided 58 hospitals in Northern California in 2009 according to income and population. To focus on changes introduced because of COVID-19, we compared a public database for the subset in 2009 for any relationship between poverty in a zip code and low volumes of CABG in that area to overall mortality in 2020. First, we defined low-income zip codes as those with a higher rate of poverty than the state average or with a lower per capita average income, per Census Bureau. Second, low volume was defined as a population under 165,000 because a hospital adjacent to a larger community can easily transfer care, sharing surgeons and processes. Third, we defined low volume as fewer than 180 CABG per year.

RESULTS

Our qualitative evidence synthesis reveals that informal communication and hospital HMO policies improve CABG outcomes. In our small pilot data, Chi-square analysis showed higher crude mortality rates in 1507 CABG in 17 low-income low-volume hospitals versus 8163 CABG in the other 41 Northern California hospitals (2.72% vs 1.69%, p = 0.0064). Low-income low-volume hospitals had a relative mortality risk of 1.61 (95% CI: 1.14-2.27). These hospitals had a mean mortality rate of 3.79%, readmission 11.12%, and stroke 1.84%. A patient undergoing CABG in a low-income low-volume hospital has a 61% higher chance of dying. The number needed to treat analysis shows that one life can potentially be saved for every 97 patients referred to another institution.

CONCLUSION

We describe features of an HMO that contribute to up to fourfold lower mortality rates.

A Review on the Application and Limitations of Administrative Health Care Data for the Study of Acute Kidney Injury Epidemiology and Outcomes in Children

Administrative health care databases contain valuable patient information generated by health care encounters. These "big data" repositories have been increasingly used in epidemiological health research internationally in recent years as they are easily accessible and cost-efficient and cover large populations for long periods. Despite these beneficial characteristics, it is also important to consider the limitations that administrative health research presents, such as issues related to data incompleteness and the limited sensitivity of the variables. These barriers potentially lead to unwanted biases and pose threats to the validity of the research being conducted. In this review, we discuss the effectiveness of health administrative data in understanding the epidemiology of and outcomes after acute kidney injury (AKI) among adults and children. In addition, we describe various validation studies of AKI diagnostic or procedural codes among adults and children. These studies reveal challenges of AKI research using administrative data and the lack of this type of research in children and other subpopulations. Additional pediatric-specific validation studies of administrative health data are needed to promote higher volume and increased validity of this type of research in pediatric AKI, to elucidate the large-scale epidemiology and patient and health systems impacts of AKI in children, and to devise and monitor programs to improve clinical outcomes and process of care.

Feasibility analysis of conducting observational studies with the electronic health record

Background

The secondary use of electronic health records (EHRs) promises to facilitate medical research. We reviewed general data requirements in observational studies and analyzed the feasibility of conducting observational studies with structured EHR data, in particular diagnosis and procedure codes.

Methods

After reviewing published observational studies from the University Hospital of Erlangen for general data requirements, we identified three different study populations for the feasibility analysis with eligibility criteria from three exemplary observational studies. For each study population, we evaluated the availability of relevant patient characteristics in our EHR, including outcome and exposure variables. To assess data quality, we computed distributions of relevant patient characteristics from the available structured EHR data and compared them to those of the original studies. We implemented computed phenotypes for patient characteristics where necessary. In random samples, we evaluated how well structured patient characteristics agreed with a gold standard from manually interpreted free texts. We categorized our findings using the four data quality dimensions “completeness”, “correctness”, “currency” and “granularity”.

Results

Reviewing general data requirements, we found that some investigators supplement routine data with questionnaires, interviews and follow-up examinations. We included 847 subjects in the feasibility analysis (Study 1 n = 411, Study 2 n = 423, Study 3 n = 13). All eligibility criteria from two studies were available in structured data, while one study required computed phenotypes in eligibility criteria. In one study, we found that all necessary patient characteristics were documented at least once in either structured or unstructured data. In another study, all exposure and outcome variables were available in structured data, while in the other one unstructured data had to be consulted. The comparison of patient characteristics distributions, as computed from structured data, with those from the original study yielded similar distributions as well as indications of underreporting. We observed violations in all four data quality dimensions.

Conclusions

While we found relevant patient characteristics available in structured EHR data, data quality problems may entail that it remains a case-by-case decision whether diagnosis and procedure codes are sufficient to underpin observational studies. Free-text data or subsequently supplementary study data may be important to complement a comprehensive patient history.

Comparison of cardiac surgery mortality reports using administrative and clinical data sources: a prospective cohort study

BACKGROUND

Outcomes for coronary artery bypass surgery are of broadening interest, but the impact of data type on quality reporting has not been fully examined. We compared the performance of administrative and clinical data-based risk adjustment models at a tertiary-quaternary care hospital.

METHODS

We used a prospective study design to test two risk adjustment models, one from administrative (Canadian Institute for Health Information [CIHI] Cardiac Care Quality Indicator) and one from clinical data (Society of Thoracic Surgeons), on cardiac surgical procedures performed between 2013 and 2016 (n = 1635). Our primary outcome was in-hospital mortality within 30 days of surgery. Model performance was established by comparing predicted and observed mortality, model calibration and handling of critical covariates.

RESULTS

Observed mortality was 1.96%, which was the same as that predicted by the Society of Thoracic Surgeons model (1.96%), but significantly higher than that predicted by the CIHI model (1.03%). Despite both models having similar C statistics (0.756 CIHI; 0.758 Society of Thoracic Surgeons), the CIHI model showed significant underestimation of mortality among patients at higher risk. There was significant miscalibration of risk associated with 7 covariates: New York Heart Association class IV, congestive heart failure, ejection fraction less than 20%, atrial fibrillation, acute coronary insufficiency, cardiac compromise (shock, myocardial infarction < 24 h, intra-aortic balloon pump, cardiac resuscitation or preprocedure circulatory support) and creatinine concentration of 100 mg/dL or more. Together, these factors accounted for 84% of the difference in predicted mortality between the administrative and clinical models.

INTERPRETATION

Risk prediction using administrative data underestimated risk of death, potentially inflating observed-to-predicted mortality ratios at hospitals with patients who are more ill. Caution is warranted when hospital reports of cardiac surgery outcomes are based on administrative data alone.

Sources of variability in hospital administrative data: Clinical coding of postoperative ileus

BACKGROUND

Multiple studies have questioned the validity of clinical codes in hospital administrative data. We examined variability in reporting a postoperative ileus (POI).

OBJECTIVE

We aimed to analyse sources of coding variations to understand how clinical coding professionals arrive at POI coding decisions and to verify existing knowledge that current clinical coding practices lack standardised applications of regulatory guidelines.

METHOD

Two medical records (cases 1 and 2) were provided to 15 clinical coders employed by a midsize nonprofit hospital in the northwest region of the United States. After coding these cases, the study participants completed a survey, reported on the application of guidelines, and participated in a focus group led by a health information management regulatory compliance expert.

RESULTS

Only 5 of the 15 clinical coders correctly indicated no POI complication in case 1 where the physician documentation did not establish a link between the POI as a complication of care and the surgery. In contrast, 13 of the 15 study participants correctly coded case 2, which included clear physician documentation and contained the clinical parameters for the coding of the POI as a complication of care. Clinical coder education, credentials, certifications, and experience did not relate to the coding performance. The clinical coders inconsistently prioritised coding rules and valued experience more than education.

CONCLUSION AND IMPLICATIONS

The application of International Classification of Diseases, Ninth Revision, Clinical Modification; coding conventions; Centers for Medicare and Medicaid Services coding guidelines; and American Hospital Association coding clinic advice was subject to the clinical coders' interpretation; they perceived them as conflicting guidance. Their reliance on subjective experience in dealing with this conflicting guidance may limit the accuracy of reporting outcomes of clinical performance.

Clinical and Administrative Data on the Research of Acute Coronary Syndrome in Spain. Minimum Basic Data Set Validity

INTRODUCTION AND OBJECTIVES

Health outcomes research is done from clinical registries or administrative databases. The aim of this work was to evaluate the concordance of the Minimum Basic Data Set (MBDS) with the DIOCLES (Descripción de la Cardiopatía Isquémica en el Territorio Español) registry and to analyze the implications of use of the MBDS in the study of acute coronary syndrome in Spain.

METHODS

Through indirect identifiers, DIOCLES was linked with MBDS and unique matches were selected. Some of most relevant variables for risk adjustment of in-hospital mortality due to acute myocardial infarction were considered. Kappa coefficient was used to evaluate the concordance; sensitivity, specificity and positive and negative predictive values to measure the validity of the MBDS, and the area under ROC (receiver operating characteristic) curve to calculate its discrimination. The results were compared among hospitals quintiles according to their contribution to DIOCLES. The influence of unmatched episodes on results was assessed by a sensitivity analysis, using looser linking criteria.

RESULTS

Overall, 1539 (60.85%) unique matches were achieved. The prevalence was higher in DIOCLES (acute myocardial infarction: 71.09%; Killip 3-4: 9.17%; cerebrovascular accident: 0.97%; thrombolysis: 8.64%; angioplasty: 61.92% and coronary bypass: 1.75%) than in the MBDS (P < .001). The agreement level observed was almost perfect (κ = 0.863). The MBDS showed a sensitivity of 85.10% and a specificity of 98.31%. Most results were confirmed by using sensitivity analysis (79.95% episodes matched).

CONCLUSIONS

The MBDS can be a useful tool for outcomes research of acute coronary syndrome in Spain. The contrast of DIOCLES and MBDS with medical records could verify their validity.

Hospital admission volume does not impact the in-hospital mortality of acute pancreatitis

BACKGROUND

Multiple factors influence mortality in Acute Pancreatitis (AP).

METHODS

To evaluate the association of demographic, clinical, and hospital factors with the in-hospital mortality of AP using a population-based administrative database. The Maryland HSCRC database was queried for adult (≥18 years) admissions with primary diagnosis of AP between 1/94-12/10. Organ failure (OF), interventions, hospital characteristics and referral status were evaluated.

RESULTS

There were 72,601 AP admissions across 48 hospitals in Maryland with 885 (1.2%) deaths. A total of 1657 (2.3%) were transfer patients, of whom 101 (6.1%) died. Multisystem OF was present in 1078 (1.5%), of whom 306 (28.4%) died. On univariable analysis, age, male gender, transfer status, comorbidity, OF, all interventions, and all hospital characteristics were significantly associated with mortality; however, only age, transfer status, OF, interventions, and large hospital size were significant in the adjusted analysis. Patients with commercial health insurance had significantly less mortality than those with other forms of insurance (OR 0.65, 95% CI: 0.52, 0.82, p = 0.0002).

CONCLUSION

OF is the strongest predictor of mortality in AP after adjusting for demographic, clinical, and hospital characteristics. Admission to HV or teaching hospital has no survival benefit in AP after adjusting for OF and transfer status.

Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture

BACKGROUND

The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records.

METHODS

We used PAS data from all Norwegian hospitals (2005-2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed.

RESULTS

The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes.

CONCLUSIONS

This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.

Accuracy of clinician-clinical coder information handover following acute medical admissions: implication for using administrative datasets in clinical outcomes management

BACKGROUND

We evaluated the accuracy, limitations and potential sources of improvement in the clinical utility of the administrative dataset for acute medicine admissions.

METHODS

Accuracy of clinical coding in 8888 patient discharges following an emergency medical hospital admission to a teaching hospital and a district hospital over 3 years was ascertained by a coding accuracy audit team in respect of the primary and secondary diagnoses, morbidities and financial variance.

RESULTS

There was at least one change to the original coding in 4889 admissions (55%) and to the primary diagnosis of at least one finished consultant episodes of 1496 spells (16.8%). There were significant changes in the number of secondary diagnoses and the Charlson morbidity index following the audit. Charlson score increased in 8.2% and decreased in 2.3% of patients. An income variance of £816 977 (+5.0%) or £91.92 per patient was observed.

CONCLUSIONS

The importance and applications of coded healthcare big data within the NHS is increasing. The accuracy of coding is dependent on high-fidelity information transfer between clinicians and coders, which is prone to subjectivity, variability and error. We recommend greater involvement of clinicians as part of multidisciplinary teams to improve data accuracy, and urgent action to improve abstraction and clarity of assignment of strategic diagnoses like pneumonia and renal failure.

Probabilistic Linkage of Prehospital and Outcomes Data in Out-of-hospital Cardiac Arrest

OBJECTIVE

Lack of longitudinal patient outcome data is an important barrier in emergency medical services (EMS) research. We aimed to demonstrate the feasibility of linking prehospital data from the California EMS Information Systems (CEMSIS) database to outcomes data from the California Office of Statewide Health Planning and Development (OSHPD) database for patients with out-of-hospital cardiac arrest (OHCA).

METHODS

We included patients age 18 years or older who sustained nontraumatic OHCA and were included in the 2010-2011 CEMSIS databases. The CEMSIS database is a unified EMS data collection system for California. The OSHPD database is a comprehensive data collection system for patient-level inpatient and emergency department encounters in California. OHCA patients were identified in the CEMSIS database using cardiac rhythm, procedures, medications, and provider impression. Probabilistic linkage blocks were created using in-hospital death or one of the following primary or secondary diagnoses (ICD-9-CM) in the OSHPD databases: cardiac arrest (427.5), sudden death (798), ventricular tachycardia (427.1), ventricular fibrillation (427.4), and acute myocardial infarction (410.xx). Blocking variables included incident date, gender, date of birth, age, and/or destination facility. Due to the volume of cases, match thresholds were established based on clerical record review for each block individually. Match variables included incident date, destination facility, date of birth, sex, race, and ethnicity.

RESULTS

Of the 14,603 cases of OHCA we identified in CEMSIS, 91 (0.6%) duplicate records were excluded. Overall, 46% of the data used in the linkage algorithm were missing in CEMSIS. We linked 4,961/14,512 (34.2%) records. Linkage rates varied significantly by local EMS agency, ranging from 1.4 to 61.1% (OR for linkage 0.009-0.76; p < 0.0001). After excluding the local EMS agency with the outlying low linkage rate, we linked 4,934/12,596 (39.2%) records.

CONCLUSION

Probabilistic linkage of CEMSIS prehospital data with OSHPD outcomes data was severely limited by the completeness of the EMS data. States and EMS agencies should aim to overcome data limitations so that more effective linkages are possible.

Using clinical variables and drug prescription data to control for confounding in outcome comparisons between hospitals

Background

Hospital discharge records are an essential source of information when comparing health outcomes among hospitals; however, they contain limited information on acute clinical conditions. Doubts remain as to whether the addition of clinical and drug consumption information would improve the prediction of health outcomes and reduce confounding in inter-hospital comparisons. The objective of the study is to compare the performance of two multivariate risk adjustment models, with and without clinical data and drug prescription information, in terms of their capability to a) predict short-term outcome rates and b) compare hospitals’ risk-adjusted outcome rates using two risk-adjustment procedures.

Methods

Observational, retrospective study based on hospital data collected at the regional level.

Two cohorts of patients discharged in 2010 from hospitals located in the Lazio Region, Italy: acute myocardial infarction (AMI) and hip fracture (HF). Multivariate logistic regression models were implemented to predict 30-day mortality (AMI) or 48-hour surgery (HF), adjusting for demographic characteristics and comorbidities plus clinical data and drug prescription information. Risk-adjusted outcome rates were derived at the hospital level.

Results

The addition of clinical data and drug prescription information improved the capability of the models to predict the study outcomes for the two conditions investigated. The discriminatory power of the AMI model increases when the clinical data and drug prescription information are included (c-statistic increases from 0.761 to 0.797); for the HF model the increase was more slight (c-statistic increases from 0.555 to 0.574). Some differences were observed between the hospital-adjusted proportion estimated using the two different models. However, the estimated hospital outcome rates were weakly affected by the introduction of clinical data and drug prescription information.

Conclusions

The results show that the available clinical variables and drug prescription information were important complements to the hospital discharge data for characterising the acute severity of the patients. However, when these variables were used for adjustment purposes their contribution was negligible. This conclusion might not apply at other locations, in other time periods and for other health conditions if there is heterogeneity in the clinical conditions between hospitals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-014-0495-3) contains supplementary material, which is available to authorized users.

The Dutch hospital standardised mortality ratio (HSMR) method and cardiac surgery: benchmarking in a national cohort using hospital administration data versus a clinical database

Objective

To compare the accuracy of data from hospital administration databases and a national clinical cardiac surgery database and to compare the performance of the Dutch hospital standardised mortality ratio (HSMR) method and the logistic European System for Cardiac Operative Risk Evaluation, for the purpose of benchmarking of mortality across hospitals.

Methods

Information on all patients undergoing cardiac surgery between 1 January 2007 and 31 December 2010 in 10 centres was extracted from The Netherlands Association for Cardio-Thoracic Surgery database and the Hospital Discharge Registry. The number of cardiac surgery interventions was compared between both databases. The European System for Cardiac Operative Risk Evaluation and hospital standardised mortality ratio models were updated in the study population and compared using the C-statistic, calibration plots and the Brier-score.

Results

The number of cardiac surgery interventions performed could not be assessed using the administrative database as the intervention code was incorrect in 1.4–26.3%, depending on the type of intervention. In 7.3% no intervention code was registered. The updated administrative model was inferior to the updated clinical model with respect to discrimination (c-statistic of 0.77 vs 0.85, p<0.001) and calibration (Brier Score of 2.8% vs 2.6%, p<0.001, maximum score 3.0%). Two average performing hospitals according to the clinical model became outliers when benchmarking was performed using the administrative model.

Conclusions

In cardiac surgery, administrative data are less suitable than clinical data for the purpose of benchmarking. The use of either administrative or clinical risk-adjustment models can affect the outlier status of hospitals. Risk-adjustment models including procedure-specific clinical risk factors are recommended.

Racial disparities and patterns of ovarian cancer surgical care in California

OBJECTIVE

To investigate disparities in the frequency of ovarian cancer-related surgical procedures and access to high-volume surgical providers among women undergoing initial surgery for ovarian cancer according to race.

METHODS

The California Office of Statewide Health Planning and Development database was accessed for women undergoing a surgical procedure that included oophorectomy for a malignant ovarian neoplasm between 1/1/06 and 12/31/10. Multivariate logistic regression analyses were used to evaluate differences in the odds of selected surgical procedures and access to high-volume centers (hospitals ≥ 20 cases/year) according to racial classification.

RESULTS

A total of 7933 patients were identified: White = 5095 (64.2%), Black = 290 (3.7%), Hispanic/Latino = 1400 (17.7%), Asian/Pacific Islander = 836 (10.5%) and other = 312 (3.9%). White patients served as reference for all comparisons. All minority groups were significantly younger (Black mean age 57.7 years, Hispanic 53.2 years, Asian 54.5 years vs. 61.1 years, p < 0.01). Hispanic patients had lower odds of obtaining care at a high-volume center (adjusted OR (adj. OR) = 0.72, 95% CI = 0.64-0.82, p < 0.01) and a lower likelihood of lymphadenectomy (adj. OR = 0.80, 95% CI=0.70-0.91, p<0.01), bowel resection (adj. OR = 0.80, 95% CI = 0.71-0.91, p < 0.01), and peritoneal biopsy/omentectomy (adj. OR = 0.69, 95% CI = 0.58-0.82, p<0.01). Black racial classification was associated with a lower likelihood of lymphadenectomy (adj. OR = 0.76, 95% CI = 0.59-0.97, p = 0.03).

CONCLUSIONS

Among women undergoing initial surgery for ovarian cancer, Hispanic patients are significantly less likely to be operated on at a high-volume center, and both Black and Hispanic patients are significantly less likely to undergo important ovarian cancer-specific surgical procedures compared to White patients.

Risk of acute kidney injury associated with the use of fluoroquinolones

BACKGROUND

Case reports indicate that the use of fluoroquinolones may lead to acute kidney injury. We studied the association between the use of oral fluoroquinolones and acute kidney injury, and we examined interaction with renin-angiotensin-system blockers.

METHODS

We formed a nested cohort of men aged 40-85 enrolled in the United States IMS LifeLink Health Plan Claims Database between 2001 and 2011. We defined cases as men admitted to hospital for acute kidney injury, and controls were admitted to hospital with a different presenting diagnosis. Using risk-set sampling, we matched 10 controls to each case based on hospital admission, calendar time (within 6 wk), cohort entrance (within 6 wk) and age (within 5 yr). We used conditional logistic regression to assess the rate ratio (RR) for acute kidney injury with current, recent and past use of fluoroquinolones, adjusted by potential confounding variables. We repeated this analysis with amoxicillin and azithromycin as controls. We used a case-time-control design for our secondary analysis.

RESULTS

We identified 1292 cases and 12 651 matched controls. Current fluoroquinolone use had a 2.18-fold (95% confidence interval [CI] 1.74-2.73) higher adjusted RR of acute kidney injury compared with no use. There was no association between acute kidney injury and recent (adjusted RR 0.87, 95% CI 0.66-1.16) or past (RR 0.86, 95% CI 0.66-1.12) use. The absolute increase in acute kidney injury was 6.5 events per 10 000 person-years. We observed 1 additional case per 1529 patients given fluoroquinolones or per 3287 prescriptions dispensed. The dual use of fluoroquinolones and renin-angiotensin-system blockers had an RR of 4.46 (95% CI 2.84-6.99) for acute kidney injury. Our case-time-control analysis confirmed an increased risk of acute kidney injury with fluoroquinolone use (RR 2.16, 95% CI 1.52-3.18). The use of amoxicillin or azithromycin was not associated with acute kidney injury.

INTERPRETATION

We found a small, but significant, increased risk of acute kidney injury among men with the use of oral fluoroquinolones, as well as a significant interaction between the concomitant use of fluoroquinolones and renin-angiotensin-system blockers.

Evaluation of care and surveillance of cardiovascular disease: can we trust medico-administrative hospital data?

BACKGROUND

The evaluation of care and the surveillance of disease are important in respect to cardiovascular disease because it is prevalent and costly. In Canada, medico-administrative hospital data are readily available, continuously updated, and offer comprehensive coverage of the patient population. However, there is concern about the quality of the information.

METHODS

The reliability and predictive capability of comorbidity data contained within Québec's hospital discharge database were assessed in comparison with data collected by clinical medical record reabstraction in a sample of 1989 patients hospitalized from 2002 to 2006 in a mix of 13 hospitals. Patients either had a principal diagnosis of myocardial infarction or underwent angioplasty or bypass surgery. Twenty-one comorbidities included in the Charlson comorbidity index or known to be associated with mortality were validated via medical record reabstraction.

RESULTS

Of 14 comorbidities with > 2% prevalence, 8 had excellent agreement with medical record review (κ > 0.8) while 6 had substantial agreement (κ > 0.6). In general, positive predictive values were high, while measures of sensitivity were more variable. Univariate associations between comorbidities and 30-day and 1-year mortality were generally similar in the 2 data sources. Comorbidities retained in the final multivariate stepwise regression models from each data source were almost identical, as were the 2 models' abilities to predict mortality.

CONCLUSIONS

Hospital discharge data in Québec are, in general, reliably coded and compare favourably with clinical medical record review in their ability to predict mortality. It appears sufficiently reliable to provide useful information about clinical outcomes of cardiac care and to identify problems that warrant investigation.

Validity of the International Classification of Diseases, Tenth Revision code for acute kidney injury in elderly patients at presentation to the emergency department and at hospital admission

OBJECTIVE

To evaluate the validity of the International Classification of Diseases, Tenth Revision (ICD-10) code N17x for acute kidney injury (AKI) in elderly patients in two settings: at presentation to the emergency department and at hospital admission.

DESIGN

A population-based retrospective validation study.

SETTING

Southwestern Ontario, Canada, from 2003 to 2010.

PARTICIPANTS

Elderly patients with serum creatinine measurements at presentation to the emergency department (n=36 049) or hospital admission (n=38 566). The baseline serum creatinine measurement was a median of 102 and 39 days prior to presentation to the emergency department and hospital admission, respectively.

MAIN OUTCOME MEASURES

Sensitivity, specificity and positive and negative predictive values of ICD-10 diagnostic coding algorithms for AKI using a reference standard based on changes in serum creatinine from the baseline value. Median changes in serum creatinine of patients who were code positive and code negative for AKI.

RESULTS

The sensitivity of the best-performing coding algorithm for AKI (defined as a ≥2-fold increase in serum creatinine concentration) was 37.4% (95% CI 32.1% to 43.1%) at presentation to the emergency department and 61.6% (95% CI 57.5% to 65.5%) at hospital admission. The specificity was greater than 95% in both settings. In patients who were code positive for AKI, the median (IQR) increase in serum creatinine from the baseline was 133 (62 to 288) µmol/l at presentation to the emergency department and 98 (43 to 200) µmol/l at hospital admission. In those who were code negative, the increase in serum creatinine was 2 (-8 to 14) and 6 (-4 to 20) µmol/l, respectively.

CONCLUSIONS

The presence or absence of ICD-10 code N17× differentiates two groups of patients with distinct changes in serum creatinine at the time of a hospital encounter. However, the code underestimates the true incidence of AKI due to a limited sensitivity.

A population-based comparison of the incidence of adverse outcomes after simultaneous-bilateral and staged-bilateral total knee arthroplasty

BACKGROUND

It is unclear whether simultaneous-bilateral total knee arthroplasty is as safe as staged-bilateral arthroplasty is. We are aware of no randomized trials comparing the safety of these surgical strategies. The purpose of this study was to retrospectively compare these two strategies, with use of an intention-to-treat approach for the staged-bilateral arthroplasty cohort.

METHODS

We used linked hospital discharge data to compare the safety of simultaneous-bilateral and staged-bilateral knee arthroplasty procedures performed in California between 1997 and 2007. Estimates were generated to take into account patients who had planned to undergo staged-bilateral arthroplasty but never underwent the second procedure because of death, a major complication, or elective withdrawal. Hierarchical logistic regression modeling was used to adjust the comparisons for patient and hospital characteristics. The principal outcomes of interest were death, a major complication involving the cardiovascular system, and a periprosthetic knee infection or mechanical malfunction requiring revision surgery.

RESULTS

Records were available for 11,445 simultaneous-bilateral arthroplasty procedures and 23,715 staged-bilateral procedures. On the basis of an intermediate estimate of the number of complications that occurred after the first procedure in a staged-bilateral arthroplasty, patients who underwent simultaneous-bilateral arthroplasty had a significantly higher adjusted odds ratio (OR) of myocardial infarction (OR = 1.6, 95% confidence interval [CI] = 1.2 to 2.2) and of pulmonary embolism (OR = 1.4, 95% CI = 1.1 to 1.8), similar odds of death (OR = 1.3, 95% CI = 0.9 to 1.9) and of ischemic stroke (OR = 1.0, 95% CI = 0.6 to 1.6), and significantly lower odds of major joint infection (OR = 0.6, 95% CI = 0.5 to 0.7) and of major mechanical malfunction (OR = 0.7, 95% CI = 0.6 to 0.9) compared with patients who planned to undergo staged-bilateral arthroplasty. The unadjusted thirty-day incidence of death or a coronary event was 3.2 events per thousand patients higher after simultaneous-bilateral arthroplasty than after staged-bilateral arthroplasty, but the one-year incidence of major joint infection or major mechanical malfunction was 10.5 events per thousand lower after simultaneous-bilateral arthroplasty.

CONCLUSIONS

Simultaneous-bilateral total knee arthroplasty was associated with a clinically important reduction in the incidence of periprosthetic joint infection and malfunction within one year after arthroplasty, but it was associated with a moderately higher risk of an adverse cardiovascular outcome within thirty days. If patients who are at higher risk for cardiovascular complications can be identified, simultaneous-bilateral knee arthroplasty may be the preferred surgical strategy for the remaining lower-risk patients.

A comparison of comorbidities obtained from hospital administrative data and medical charts in older patients with pneumonia

Background

The use of comorbidities in risk adjustment for health outcomes research is frequently necessary to explain some of the observed variations. Medical charts reviews to obtain information on comorbidities is laborious. Increasingly, electronic health care databases have provided an alternative for health services researchers to obtain comorbidity information. However, the rates obtained from databases may be either over- or under-reported. This study aims to (a) quantify the agreement between administrative data and medical charts review across a set of comorbidities; and (b) examine the factors associated with under- or over-reporting of comorbidities by administrative data.

Methods

This is a retrospective cross-sectional study of patients aged 55 years and above, hospitalized for pneumonia at 3 acute care hospitals. Information on comorbidities were obtained from an electronic administrative database and compared with information from medical charts review. Logistic regression was performed to identify factors that were associated with under- or over-reporting of comorbidities by administrative data.

Results

The prevalence of almost all comorbidities obtained from administrative data was lower than that obtained from medical charts review. Agreement between comorbidities obtained from medical charts and administrative data ranged from poor to very strong (kappa 0.01 to 0.78). Factors associated with over-reporting of comorbidities were increased length of hospital stay, disease severity, and death in hospital. In contrast, those associated with under-reporting were number of comorbidities, age, and hospital admission in the previous 90 days.

Conclusions

The validity of using secondary diagnoses from administrative data as an alternative to medical charts for identification of comorbidities varies with the specific condition in question, and is influenced by factors such as age, number of comorbidities, hospital admission in the previous 90 days, severity of illness, length of hospitalization, and whether inhospital death occurred. These factors need to be taken into account when relying on administrative data for comorbidity information.

Validation of a case definition to define chronic dialysis using outpatient administrative data

BACKGROUND

Administrative health care databases offer an efficient and accessible, though as-yet unvalidated, approach to studying outcomes of patients with chronic kidney disease and end-stage renal disease (ESRD). The objective of this study is to determine the validity of outpatient physician billing derived algorithms for defining chronic dialysis compared to a reference standard ESRD registry.

METHODS

A cohort of incident dialysis patients (Jan. 1-Dec. 31, 2008) and prevalent chronic dialysis patients (Jan 1, 2008) was selected from a geographically inclusive ESRD registry and administrative database. Four administrative data definitions were considered: at least 1 outpatient claim, at least 2 outpatient claims, at least 2 outpatient claims at least 90 days apart, and continuous outpatient claims at least 90 days apart with no gap in claims greater than 21 days. Measures of agreement of the four administrative data definitions were compared to a reference standard (ESRD registry). Basic patient characteristics are compared between all 5 patient groups.

RESULTS

1,118,097 individuals formed the overall population and 2,227 chronic dialysis patients were included in the ESRD registry. The three definitions requiring at least 2 outpatient claims resulted in kappa statistics between 0.60-0.80 indicating "substantial" agreement. "At least 1 outpatient claim" resulted in "excellent" agreement with a kappa statistic of 0.81.

CONCLUSIONS

Of the four definitions, the simplest (at least 1 outpatient claim) performed comparatively to other definitions. The limitations of this work are the billing codes used are developed in Canada, however, other countries use similar billing practices and thus the codes could easily be mapped to other systems. Our reference standard ESRD registry may not capture all dialysis patients resulting in some misclassification. The registry is linked to on-going care so this is likely to be minimal. The definition utilized will vary with the research objective.

Ranking hospitals on surgical mortality: the importance of reliability adjustment

OBJECTIVE

We examined the implications of reliability adjustment on hospital mortality with surgery.

DATA SOURCE

We used national Medicare data (2003-2006) for three surgical procedures: coronary artery bypass grafting (CABG), abdominal aortic aneurysm (AAA) repair, and pancreatic resection.

STUDY DESIGN

We conducted an observational study to evaluate the impact of reliability adjustment on hospital mortality rankings. Using hierarchical modeling, we adjusted hospital mortality for reliability using empirical Bayes techniques. We assessed the implication of this adjustment on the apparent variation across hospitals and the ability of historical hospital mortality rates (2003-2004) to forecast future mortality (2005-2006).

PRINCIPAL FINDINGS

The net effect of reliability adjustment was to greatly diminish apparent variation for all three operations. Reliability adjustment was also particularly important for identifying hospitals with the lowest future mortality. Without reliability adjustment, hospitals in the "best" quintile (2003-2004) with pancreatic resection had a mortality of 7.6 percent in 2005-2006; with reliability adjustment, the "best" hospital quintile had a mortality of 2.7 percent in 2005-2006. For AAA repair, reliability adjustment also improved the ability to identify hospitals with lower future mortality. For CABG, the benefits of reliability adjustment were limited to the lowest volume hospitals.

CONCLUSION

Reliability adjustment results in more stable estimates of mortality that better forecast future performance. This statistical technique is crucial for helping patients select the best hospitals for specific procedures, particularly uncommon ones, and should be used for public reporting of hospital mortality.

Quality improvement in surgery: the American College of Surgeons National Surgical Quality Improvement Program approach

The history and development of the NSQIP, from its inception in the Veterans Administration Health System to its implementation within the private sector sponsored by the ACS, documents the growth of a program that has substantially improved the quality of surgical care and has had a considerable influence on the culture of quality improvement in the profession. The success of the ACS NSQIP is the result of providing hospitals with rigorous, clinical data, networking opportunities, and resources to improve their risk-adjusted outcomes. In this manner, the ACS NSQIP challenges its hospitals and health care providers to continually improve the care they provide. In addition to reducing the complications and mortality experienced by patients after surgical procedures, hospitals that participate in the ACS NSQIP have seen the financial rewards of their quality improvement efforts. Continued growth of the ACS NSQIP will facilitate achievement of the primary goal surrounding the current health care reform debate: efficient, high-quality care.

Acute myocardial infarction and coronary artery bypass grafting outcomes in specialty and general hospitals: analysis of state inpatient data

OBJECTIVE

Compare characteristics and outcomes of patients hospitalized in specialty cardiac and general hospitals for acute myocardial infarction (AMI) and coronary artery bypass grafting (CABG).

DATA

2000-2005 all-payor administrative data from Arizona, California, Texas, and Wisconsin.

STUDY DESIGN

We identified patients admitted to specialty and competing general hospitals with AMI or CABG and compared patient demographics, comorbidity, and risk-standardized mortality in specialty and general hospitals.

PRINCIPAL FINDINGS

Specialty hospitals admitted a lower proportion of women and blacks and treated patients with less comorbid illness than general hospitals. Unadjusted in-hospital AMI mortality for Medicare enrollees in specialty and general hospitals was 6.1 and 10.1 percent (p<.0001) and for non-Medicare enrollees was 2.8 and 4.0 percent (p<.04). Unadjusted in-hospital CABG mortality for Medicare enrollees in specialty and general hospitals was 3.2 and 4.7 percent (p<.01) and for non-Medicare enrollees was 1.1 and 1.8 percent (p=.02). After adjusting for patient characteristics and hospital volume, risk-standardized in-hospital mortality for all AMI patients was 2.7 percent for specialty hospitals and 4.1 percent for general hospitals (p<.001) and for CABG was 1.5 percent for specialty hospitals and 2.0 percent for general hospitals (p=.07).

CONCLUSIONS

In-hospital mortality in specialty hospitals was lower than in general hospitals for AMI but similar for CABG. Our results suggest that specialty hospitals may offer significantly better outcomes for AMI but not CABG.

Data enhancement for co-morbidity measurement among patients referred for sleep diagnostic testing: an observational study

Background

Observational outcome studies of patients with obstructive sleep apnea (OSA) require adjustment for co-morbidity to produce valid results. The aim of this study was to evaluate whether the combination of administrative data and self-reported data provided a more complete estimate of co-morbidity among patients referred for sleep diagnostic testing.

Methods

A retrospective observational study of 2149 patients referred for sleep diagnostic testing in Calgary, Canada. Self-reported co-morbidity was obtained with a questionnaire; administrative data and validated algorithms (when available) were also used to define the presence of these co-morbid conditions within a two-year period prior to sleep testing.

Results

Patient self-report of co-morbid conditions had varying levels of agreement with those derived from administrative data, ranging from substantial agreement for diabetes (κ = 0.79) to poor agreement for cardiac arrhythmia (κ = 0.14). The enhanced measure of co-morbidity using either self-report or administrative data had face validity, and provided clinically meaningful trends in the prevalence of co-morbidity among this population.

Conclusion

An enhanced measure of co-morbidity using self-report and administrative data can provide a more complete measure of the co-morbidity among patients with OSA when agreement between the two sources is poor. This methodology will aid in the adjustment of these coexisting conditions in observational studies in this area.

Databases for assessing the outcomes of the treatment of patients with congenital and paediatric cardiac disease--a comparison of administrative and clinical data

The introduction of the reporting of medical and surgical outcomes to the public and the potential implementation of initiatives involving pay-for-performance have invigorated debates about the relative benefits of administrative and clinical databases for comparing rates of mortality at the level of the hospital and surgeon. While general agreement exists that public performance report cards must use the highest quality data available, debate continues regarding whether administrative or clinical data should be utilized for this purpose. Clinical databases may contain information more relevant to risk-adjustment, but the currently available clinical databases are voluntary and suffer from validity concerns. Administrative data, however, suffer from inaccuracies of coding and a lack of potentially informative covariates. Particularly problematic to congenital heart surgery is the non-uniform application of coding algorithms to define complex reconstructive procedures for which there is no unique code assignment. The purposes of this manuscript are; therefore, to discuss the relative advantages and limitations of both clinical and administrative data, and to provide a brief introduction to currently available databases germane to the study of congenital cardiac disease.

Impact of date stamping on patient safety measurement in patients undergoing CABG: experience with the AHRQ Patient Safety Indicators

Background

The Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) provide information on hospital risk-adjusted rates for potentially preventable adverse events. Although designed to work with routine administrative data, it is unknown whether the PSIs can accurately distinguish between complications and pre-existing conditions. The objective of this study is to examine whether the AHRQ PSIs accurately measure hospital complication rates, using the data with present-on-admission (POA) codes to distinguish between complications and pre-existing conditions

Methods

Retrospective cohort study of patients undergoing isolated CABG surgery in California conducted using the 1998–2000 California State Inpatient Database. We calculated the positive predictive value of selected AHRQ PSIs using information from the POA as the gold standard, and the intra-class correlation coefficient to assess the level of agreement between the hospital risk-adjusted PSI rates with and without the information contained in the POA modifier.

Results

The false positive error rate, defined as one minus the positive predictive value, was greater than or equal to 20% for four of the eight PSIs examined: decubitus ulcer, failure-to-rescue, postoperative physiologic and metabolic derangement, and postoperative pulmonary embolism or deep venous thrombosis. Pairwise comparison of the hospital risk-adjusted PSI rates, with and without POA information, demonstrated almost perfect agreement for five of the eight PSI's. For decubitus ulcer, failure-to-rescue, and postoperative pulmonary embolism or DVT, the intraclass-correlation coefficient ranged between 0.63 to 0.79.

Conclusion

For some of the AHRQ Patient Safety Indicators, there are significant differences in the risk-adjusted rates of adverse events depending on whether the POA indicator is used to distinguish between pre-existing conditions and complications. The use of the POA indicator will increase the accuracy of the AHRQ PSIs as measures of adverse outcomes.

Comparison of mortality risk adjustment using a clinical data algorithm (American College of Surgeons National Surgical Quality Improvement Program) and an administrative data algorithm (Solucient) at the case level within a single institution

BACKGROUND

There is great interest in efficiently evaluating health care quality, but there is controversy over the use of administrative versus clinical data methods. We sought to compare actual mortality with risk-adjusted expected mortality in a sample population calculated by two different methods; one based on preexisting administrative records and one based on chart reviews.

STUDY DESIGN

We examined a sample of patients (n = 1,234) undergoing surgical procedures at an academic teaching hospital during 1 year. The first risk-adjustment method was that used by the National Surgical Quality Improvement Program, which is based on dedicated medical record review. The second method was that used by Solucient, LLC, which is based on preexisting administrative records.

RESULTS

The ratio of observed to expected mortality for this population set was higher using the National Surgical Quality Improvement Program algorithm (1.1; 95% CI, 0.8 to 1.5) than using the Solucient algorithm (0.9; 95% CI, 0.6 to 1.2) but neither estimate was notably different from 1.0. Similarly, when observed to expected mortality ratios were calculated separately for each quartile of mortality, there were no marked differences within quartiles, although minor differences with potential importance were noted. Fit was comparable by age categories, gender, and American Society of Anesthesiologists' categories. A number of actual deaths had higher predicted mortality scores using the Solucient algorithm.

CONCLUSIONS

Risk-adjusted mortality estimates were comparable using administrative or clinical data. Minor performance differences might still have implications. Because of the potential lower cost of using administrative data, this type of algorithm can be an efficient alternative and should continue to be investigated.

Estimating the potential impact of regionalizing health care delivery based on volume standards versus risk-adjusted mortality rate

OBJECTIVE

To examine whether basing regionalization on risk-adjusted mortality would lead to better population outcomes than basing regionalization on procedure volume.

DATA SOURCE

We used secondary data from the California State Inpatient Database obtained from the Healthcare Costs and Utilization Project.

STUDY DESIGN

A population-based retrospective cohort study of 243 thousand patients who underwent either abdominal aortic aneurysm surgery, coronary artery bypass surgery or coronary angioplasty between 1998 and 2000 in California. Four regionalization strategies were compared: (i) selective referral to high-quality hospitals; (ii) selective referral to high-volume hospitals; (iii) selective avoidance of low-quality hospitals; (iv) selective avoidance of low-volume hospitals.

PRINCIPAL FINDINGS

Selective referral to high volume centers would be only moderately effective (2-20% relative reduction in mortality) and extremely disruptive (70-99% reduction in the number of hospitals treating these conditions). Selective referral to high quality centers was estimated to result in dramatic reduction in mortality (50%) but would also be highly disruptive with greater than 80% of the patients re-directed to high quality centers. Selective avoidance of low volume hospitals would not improve mortality, whereas selective avoidance of low quality hospitals was estimated to result in a small improvement in overall mortality (2-6%) while causing relatively minor disruptions in patient referral patterns.

CONCLUSION

Efforts to use volume standards as the basis for evidence-based hospital referrals should be re-evaluated by all stake-holders before promoting further efforts to regionalize health care delivery using volume cutoffs.

Co-morbidity is a strong predictor of early death and multi-organ system failure among patients with acute pancreatitis

A small but significant percentage of patients with acute pancreatitis die within 2 weeks of hospitalization, usually with multiorgan system failure. To determine the effect of chronic medical comorbidities on early death, we conducted a retrospective analysis of all patients who were hospitalized in California with first-time pancreatitis between 1992 and 2002. Among 84,713 patients, 1514 (1.8%) died within 2 weeks. In a risk-adjusted multivariate model, the strongest predictors of early death were age 65 to 75 years (OR = 2.6, 95% CI: 2.2-3.1 versus <55 years), age over 75 years (OR = 5.2, 95% CI: 4.4-6.1), and the presence of either two chronic comorbid conditions (OR = 3.5, CI: 2.7-4.6) or three or more comorbidities (OR = 7.4, 95% CI: 5.7-9.5). Among the 14,280 patients younger than 55 years who had no chronic comorbid conditions, only 14 (0.1%) died in the first 14 days compared to 701 (5.9%) of 24,852 patients 64 years or older who had three or more comorbidities (RR = 29, 95% CI: 17-50). Comorbid conditions associated with early death included recent cancer, heart failure, renal disease, and liver disease. We conclude that advancing age and the number of chronic comorbid conditions are very strong predictors of early death among patients with acute pancreatitis.

Case Volume and Mortality in Pediatric Cardiac Surgery Patients in California, 1998–2003

Background— Previous reports have found an inverse relationship between pediatric cardiac surgery case volume and in-hospital mortality. This association has been noted recently to be decreasing for coronary artery bypass grafting, possibly because of improved training programs, quality improvement activities, or other innovations to improve outcomes. It is unknown whether the volume-mortality association among pediatric cardiac surgery patients is decreasing similarly.

Methods and Results— We used data from the state of California’s patient discharge data set from the years 1998–2003 to replicate 4 previous research studies of pediatric cardiac surgery volume and mortality. The total number of pediatric surgeries varied from 12 801 to 13 971 depending on the selection criteria applied. Using this larger and more contemporary data set, we found a weaker and less consistent volume-mortality relationship than had been reported previously. We also developed a new model, which incorporated elements of the old models, and found a statistically significant relationship with higher volume and lower mortality (odds ratio=0.86 per 100-patient increase in annual volume; 95% CI, 0.81 to 0.92). Post hoc analyses show that this relationship was related to the performance of the single largest-volume hospital.

Conclusions— With the use of data from California, the volume-mortality relationship among pediatric cardiac surgery patients has changed since previous research, such that the old models no longer describe a clear or consistent association. With the use of a continuous definition of volume and an updated model, an association is observed but is dependent on highly leveraged covariate patterns found in the largest-volume hospital.

Assessing and using comorbidity measures in elderly veterans with lower extremity amputations

BACKGROUND

Understanding comorbidity prevalence and the effects of comorbidities in older veterans with lower extremity amputations may aid in assessing patient outcomes, resource use, and facility-level quality of care.

OBJECTIVES

To determine the degree to which adding outpatient to inpatient administrative data sources yields higher comorbidity prevalence estimates and improved explanatory power of models predicting 1-year mortality and to compare the Charlson/Deyo and Elixhauser comorbidity measures.

METHODS

A retrospective cohort study applying frequencies, cross-tabulations, and logistic regression models was conducted, including data from 2,375 veterans with lower extremity amputations. Comorbidity prevalence according to the Charlson/Deyo and Elixhauser measures, 1-year mortality rates, and standardized mortality ratios (SMRs) were analyzed.

RESULTS

Comorbidity prevalence estimates increased sharply for both the Charlson/Deyo and Elixhauser measures with the addition of data from multiple settings. The Elixhauser compared to the Charlson/Deyo generally yielded higher estimates but did not improve explanatory power for mortality. Modeling expected versus actual deaths produced varying SMRs across geographic regions but was not dependent on which measure or data sources were used.

CONCLUSIONS

Merging outpatient with inpatient data may reduce the under coding of comorbidities but does not enhance mortality prediction. Compared to the Charlson/Deyo, the Elixhauser has a more complete coding scheme for comorbid conditions, such as diabetes mellitus and peripheral vascular disease, important to addressing lower extremity amputation etiology.

Comparison of Clinical and Administrative Data Sources for Hospital Coronary Artery Bypass Graft Surgery Report Cards

Background— Regardless of statistical methodology, public performance report cards must use the highest-quality validated data, preferably from a prospectively maintained clinical database. Using logistic regression and hierarchical models, we compared hospital cardiac surgery profiling results based on clinical data with those derived from contemporaneous administrative data.

Methods and Results— Fiscal year 2003 isolated coronary artery bypass grafting surgery results based on an audited and validated Massachusetts clinical registry were compared with those derived from a contemporaneous state administrative database, the latter using the inclusion/exclusion criteria and risk model of the Agency for Healthcare Research and Quality. There was a 27.4% disparity in isolated coronary artery bypass grafting surgery volume (4440 clinical, 5657 administrative), a 0.83% difference in observed in-hospital mortality (2.05% versus 2.88%), corresponding differences in risk-adjusted mortality calculated by various statistical methodologies, and 1 hospital classified as an outlier only with the administrative data–based approach. The discrepancies in volumes and risk-adjusted mortality were most notable for higher-volume programs that presumably perform a higher proportion of combined procedures that were misclassified as isolated coronary artery bypass grafting surgery in the administrative cohort. Subsequent analyses of a patient cohort common to both databases revealed the smoothing effect of hierarchical models, a 9% relative difference in mortality (2.21% versus 2.03%) resulting from nonstandardized mortality end points, and 1 hospital classified as an outlier using logistic regression but not using hierarchical regression.

Conclusions— Cardiac surgery report cards using administrative data are problematic compared with those derived from audited and validated clinical data, primarily because of case misclassification and nonstandardized end points.