Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership.

BACKGROUND: Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs. OBJECTIVES: In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM. METHODS: The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules. RESULTS: Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system's medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM. CONCLUSIONS: The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.

Discrimination and calibration of mortality risk prediction models in interventional cardiology.

OBJECTIVES: Using a local percutaneous coronary intervention (PCI) data repository, we sought to compare the performance of a number of local and well-known mortality models with respect to discrimination and calibration. BACKGROUND: Accurate risk prediction is important for a number of reasons including physician decision support, quality of care assessment, and patient education. Current evidence on the value of applying PCI risk models to individual cases drawn from a different population is controversial. METHODS: Data were collected from January 01, 2002 to September 30, 2004 on 5216 consecutive percutaneous coronary interventions at Brigham and Women's Hospital (Boston, MA). Logistic regression was used to create a local risk model for in-hospital mortality in these procedures, and a number of statistical methods were used to compare the discrimination and calibration of this new and old local risk models, as well as the Northern New England Cooperative Group, New York State (1992 and 1997), University of Michigan consortium, American College of Cardiology-National Cardiovascular Data Registry, and The Cleveland Clinic Foundation risk prediction models. Areas under the ROC (AUC) curves were used to evaluate discrimination, and the Hosmer-Lemeshow (HL) goodness-of-fit test and calibration curves assessed applicability of the models to individual cases. RESULTS: Multivariate risk factors included in the newly constructed local model were: age, prior intervention, diabetes, unstable angina, salvage versus elective procedure, cardiogenic shock, acute myocardial infarction (AMI), and left anterior descending artery intervention. The area under the ROC curve (AUC) was 0.929 (SE=0.017), and the p value for the Hosmer-Lemeshow (HL) goodness-of-fit was 0.473. This indicates good discrimination and calibration. Bootstrap re-sampling indicated AUC stability. Evaluation of the external models showed an AUC range from 0.82 to 0.90 indicating good discrimination across all models, but poor calibration (HL p value < or = 0.0001). CONCLUSIONS: Validation of AUC values across all models suggests that certain risk factors have remained important over the last decade. However, the lack of calibration suggests that small changes in patient populations and data collection methods quickly reduce the accuracy of patient level estimations over time. Possible solutions to this problem involve either recalibration of models using local data or development of new local models.

Vascular closure devices and the risk of vascular complications after percutaneous coronary intervention in patients receiving glycoprotein IIb-IIIa inhibitors.

Vascular closure devices offer advantages over traditional means of obtaining hemostasis after percutaneous coronary intervention (PCI) in terms of patient comfort and time to ambulation. We investigate whether such devices also reduce the risk of vascular complications in selected patient populations. We conducted a retrospective analysis of all patients who underwent PCI at our institution between January 1998 and December 1999. Of 3,151 consecutive patients, 3,027 were eligible to receive vascular closure devices. Of these, 1,485 received a closure device and 1,409 received glycoprotein IIb-IIIa antagonists. The overall vascular complication rate, as defined by the need for surgical repair or transfusion, or the development of arteriovenous fistula, pseudoaneurysm, or large hematoma, was 4.20%. By univariate analysis, the use of closure devices was associated with a lower vascular complication rate (3.03% vs 5.52%; p = 0.002) and a shorter length of hospital stay (2.77 vs 3.97 days, p <0.001). Multivariate analysis showed a significant reduction in vascular complications with closure devices (odds ratio 0.59, p = 0.007). For the subgroup of patients receiving glycoprotein IIb-IIIa antagonists, the use of closure devices was associated with an even more pronounced reduction in the risk of vascular complications (odds ratio 0.45, p <0.008). Thus, the use of closure devices in selected patients undergoing PCI is associated with a low rate of vascular complications and decreased length of stay. This benefit was most marked for patients receiving glycoprotein IIb-IIIa antagonists.

Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention.

The objectives of this analysis were to develop and validate simplified risk score models for predicting the risk of major in-hospital complications after percutaneous coronary intervention (PCI) in the era of widespread stenting and use of glycoprotein IIb/IIIa antagonists. We then sought to compare the performance of these simplified models with those of full logistic regression and neural network models. From January 1, 1997 to December 31, 1999, data were collected on 4,264 consecutive interventional procedures at a single center. Risk score models were derived from multiple logistic regression models using the first 2,804 cases and then validated on the final 1,460 cases. The area under the receiver operating characteristic (ROC) curve for the risk score model that predicted death was 0.86 compared with 0.85 for the multiple logistic model and 0.83 for the neural network model (validation set). For the combined end points of death, myocardial infarction, or bypass surgery, the corresponding areas under the ROC curves were 0.74, 0.78, and 0.81, respectively. Previously identified risk factors were confirmed in this analysis. The use of stents was associated with a decreased risk of in-hospital complications. Thus, risk score models can accurately predict the risk of major in-hospital complications after PCI. Their discriminatory power is comparable to those of logistic models and neural network models. Accurate bedside risk stratification may be achieved with these simple models.

Development and evaluation of models to predict death and myocardial infarction following coronary angioplasty and stenting.

Prior estimates of the risk death and myocardial infarction following percutaneous coronary intervention (PCI) may not be uniformly applicable due to recent significant changes in clinical practice. Accordingly, we studied 2,804 cases from January 1997 through February 1999, in order to develop risk models to predict death, and post-procedural myocardial infarction following PCI. Risk models were constructed using multivariate logistic regression, artificial neural networks and prognostic risk scoring systems. Composite logistic regression models and artificial neural networks performed similarly in predicting the risk of major acute complications (c-index for predicting death of 0.812 and 0.807, respectively). Risk scoring models, based on the composite logistic regression beta coefficients, performed only slightly worse (c-index death = 0.794). Risk score models appear to provide reasonable discrimination while offering the potential for simple clinical implementation in the estimation of the risk of death and myocardial infarction in interventional cardiology.

Occupational exposure among medical students and house staff at a New York City Medical Center.

BACKGROUND: The purpose of the study was to ascertain the prevalence of and reasons for underreporting of occupational exposures to patients' blood and body fluids among students and house staff. METHODS: A questionnaire surveyed 110 medical students and 275 house staff members regarding the number of reported and unreported exposures to patients' body fluids, and the reasons why the respondents did or did not report their exposures during the previous 6 months. RESULTS: Of 385 surveys returned, representing a response rate of 60%, 122 respondents (32%) experienced 330 occupational exposures during the previous 6 months. Fifty-two percent of surgical house staff, 27% of students, and 20% of medical house staff were exposed. Whereas the exposure risk to surgical house staff increased with training, the risk to medical house staff decreased with training. Only 29% of exposed respondents reported an exposure. Exposures from sources known to be positive or at high risk for human immunodeficiency virus had the highest reporting rates. The most frequent reason for not reporting an exposure was that the patient was thought not to be infectious. Forty-six percent of respondents exposed to sources of unknown human immunodeficiency virus status who did not report chose "patient thought not to be infectious" as the reason. The most common reason for reporting an exposure was "hospital policy." CONCLUSIONS: Although limited by recall bias, this study showed that a high proportion of students and house staff experience occupational exposures. The results suggest that populations at high risk for exposures are the more experienced surgical house staff and the junior medical house staff. Exposures from sources known to be positive or at high risk for human immunodeficiency virus were reported more frequently than those from unknown risk sources.