(Circulation. 1999;99:2986-2992.)
© 1999 American Heart Association, Inc.
Clinical Investigation and Reports |
Comparing AMI Mortality Among Hospitals in Patients 65 Years of Age and Older
Evaluating Methods of Risk Adjustment
From the Sections of Cardiovascular Medicine, Department of Medicine (H.K., J.C., M.R.) and Chronic Disease Epidemiology, Department of Epidemiology and Public Health (H.K.), Yale University School of Medicine, New Haven, Conn; the Yale-New Haven Hospital Center for Outcomes Research and Evaluation, New Haven, Conn (H.K., M.R., Y.W., Y.-T.C.); Qualidigm, Middletown, Conn (H.K., M.R.); and the Health Care Financing Administration, Baltimore, Md (T.M.).
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Abstract |
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 Top Abstract IntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Background—Interest in the reporting of risk-adjusted outcomes for patients with acute myocardial infarction is growing. A useful risk-adjustment model must balance parsimony and ease of data collection with predictive ability.
Methods and Results—From our analysis of 82 359 patients
65 years of age admitted with acute myocardial infarction to 2401
hospitals, we derived a parsimonious model that predicts 30-day
mortality. The model was validated on a similar group of 78 699
patients from 2386 hospitals. Of the 73 candidate predictor
variables examined, 7 variables describing patient
characteristics on arrival were selected for inclusion in the final
model: age, cardiac arrest, anterior or lateral location of myocardial
infarction, systolic blood pressure, white blood cell count,
serum creatinine, and congestive heart failure. The area
under the receiver-operating characteristic curve for the final model
was 0.77 in the derivation cohort and 0.77 in the validation cohort.
The rankings of hospitals by performance (in deciles) with this
model were most similar to a comprehensive 27-variable model based
on medical chart review and least similar to models based on
administrative billing codes.
Conclusions—A simple 7-variable risk model performs as well as more complex models in comparing hospital outcomes for acute myocardial infarction. Although there is a continuing need to improve methods of risk adjustment, our results provide a basis for hospitals to develop a simple approach to compare outcomes.
Key Words: myocardial infarction • prognosis • mortality • risk factors • elderly
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Introduction |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Appropriate treatment of acute myocardial infarction (AMI) can substantially reduce 30-day mortality.1 However, guideline-based therapies are not uniformly applied,2 and there is much variation in mortality among hospitals. For hospitals to be accountable for the care they provide to patients with AMI, they need to be able to compare their performance. In an increasingly competitive medical environment, information about outcomes holds substantial interest for patients, healthcare providers, employers, and healthcare plans.
An impediment to the reporting of outcomes is the challenge of comparing institutions with patients who have different risk profiles. Without adjustment for these baseline differences, comparisons of crude mortality rates favor hospitals that admit the lowest-risk patients. Meaningful evaluations of hospital performance need to consider baseline differences in patient characteristics that could confound comparisons among them.
One approach to facilitate the comparison of hospitals is to use a mathematical model based on patient characteristics to predict mortality and calculate a standardized mortality ratio (SMR), the ratio of the observed mortality of a hospital divided by its predicted mortality. Hospitals can be compared more meaningfully by use of SMRs because these ratios take into account differences in baseline patient characteristics. There is, however, a paucity of information to guide the choice of risk-adjustment model to predict mortality. Many studies have identified prognostic factors for patients with AMI,3 and some studies have promoted specific predictive models.3 4 5 6 7 Complex risk-adjustment models may be preferred by some clinicians because of the breadth of information they include, but extensive data collection efforts can be costly. An ideal model would balance parsimony and ease of data collection with predictive ability.
The objective of this study was to develop a model based on a small number of easily abstracted variables that would accurately predict short-term mortality among patients with AMI. In addition, we sought to compare our model with other published models with respect to discriminant ability, calibration, and hospital ranking. To address these objectives, this study was conducted as part of the Cooperative Cardiovascular Project (CCP), a Health Care Financing Administration initiative to improve the quality of care for Medicare beneficiaries with AMI.2
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Methods |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Data Sources
The CCP
The CCP database has been described previously.2 In brief, it includes >200 000 patients hospitalized across the country with a principal discharge diagnosis of AMI (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] 410) from 1994 to 1995. Trained technicians abstracted predefined demographic, clinical, and treatment variables from copies of the hospital records and entered them directly into a computer database using interactive software. For all CCP samples, >3000 records were reabstracted, with overall variable agreement of
95%.
Medicare Enrollment Database
The Medicare Enrollment Database contains accurate records
of the vital status of Medicare beneficiaries,8 but
entries from the Social Security records include unverified dates
of death recorded as the last day of the month when the exact date
from a death certificate was unavailable. We eliminated cases with
unverified days of death from the mortality analysis if
mortality could not be classified with certainty at the time of
evaluation, as described in an earlier report.7 We found
unverified days of death for 325 patients in our sample (0.2% of
such patients or 0.8% of deaths).
Study Sample
The overall study sample was restricted to patients 65 years
of age who had confirmed AMI, as previously reported,2 and
who were not received in transfer from another institution. To avoid
counting patients more than once, we included only a patient's first
confirmed AMI hospitalization in the CCP.
Derivation and Validation of Predictive Model
Candidate Predictor Variables
From our review of the medical literature and clinical
experience, we selected candidate predictor variables that
described demographic and clinical characteristics of the patients.
These variable domains (and the specific variables) included
the following: demographic characteristics (age, sex, and race),
medical history (angina, hypertension, diabetes, active ulcer disease,
bleeding disorder, internal bleeding, bypass surgery, heart failure or
pulmonary edema, chronic obstructive pulmonary disease,
cigarette smoker, stroke, AMI, angioplasty, and trauma in the past
month), functional status (mobility, urinary continence, and dementia),
clinical presentation and severity variables
(systolic and diastolic blood pressures, pulse,
respiratory rate, temperature, presence of chest pain, time since chest
pain started, hemorrhage, cardiac arrest, gallop rhythm or
S3, rales, heart failure or pulmonary
edema, cardiomegaly, height, and weight), initial laboratory results
(albumin, serum urea nitrogen, creatinine,
hematocrit, sodium, and white blood count), and first ECG (left
bundle-branch block, pacemaker rhythm, right bundle-branch block,
ST-segment elevation, transmural MI, ventricular
tachycardia, atrial fibrillation [AF]/flutter, second- or
third-degree heart block, evidence of old infarction, and location of
AMI). We did not include shock because of concerns that it would be
susceptible to intentional manipulation.
Model Development and Validation
We defined a derivation sample that randomly included half of
the hospitals in the study sample. In this derivation set, we performed
iterations of logistic regression models with 30-day mortality as the
dependent variable, gradually reducing the number of independent
predictors. We began with all 73 candidate predictors with their
associated dummy variables. When variables with missing
observations were included in or removed from
multivariate models, dummy variables indicating the
presence of missing values (yes/no) were also added or removed. We then
selected 40 variables with a significance level of
P<0.001 in the logistic regression. To identify the most
influential variables, the model was further restricted to 23
variables with a Wald 
2 value >50. At
this point, we created composite variables in which related
variables had similar ORs (eg, we combined anterior MI location
with lateral MI location). We repeated the logistic regression,
selecting 7 variables with a Wald 2 value
>300. Although this threshold is arbitrary, it allowed selection of
variables with strong clinical associations to 30-day
mortality.
Missing Data and Extreme Values
Missing observations exceeded 5% for the following candidate
predictor variables: angina, time since chest pain started,
evidence of heart failure or pulmonary edema on chest x-ray,
location of AMI, ventricular tachycardia,
height, weight, albumin, AF/flutter, heart block on ECG, left
or right bundle-branch block, and paced rhythm. Values for continuous
variables outside the following ranges were considered implausible
and set to missing: respiratory rate >80 breaths per minute,
systolic blood pressure >300 mm Hg,
diastolic blood pressure >150 mm Hg, serum urea
nitrogen >200 mg/dL, creatinine >25 mg/dL, and
albumin >20 mg/dL. We replaced values outside the following
ranges with either minimum or maximum values: systolic blood
pressure (70 to 300 mm Hg) and creatinine (0.6 to 2.5
mg/dL).
Approximately 2.7% of the sample had missing values for creatinine and white blood cell count, and 0.5% had missing values for systolic blood pressure (Appendix A). Because missing systolic blood pressure was associated with higher mortality (P<0.001), possibly representing situations in which patients were unstable, we replaced missing values with minimum values (70 mm Hg). Missing observations for creatinine and white blood cell count were replaced with median values. Observations with missing values for MI location and radiographic evidence of heart failure were set to null. Alternative methods for controlling missing values, such as including dummy variables indicating missing observations or restricting the analysis to observations without any missing values, did not substantially affect model estimates, calibration, or our conclusions.
Model Comparison
We compared the new model with the following published
AMI-specific models of 30-day mortality: the CCP-pilot
model,7 the Global Utilization of Streptokinase and Tissue
Plasminogen Activator for Occluded
Coronary Arteries Trial (GUSTO-I) model,4 the
Medicare Mortality Predictor System (MMPS) model,5 an
ICD-9 code model,6 and 2 models from the California
Hospital Outcomes Project9 (Table 1
). The CCP-pilot model and the
ICD-9 model were not modified. The GUSTO-I model included all the
demographic and clinical variables, but the type of
thrombolytic therapy was not included because this
sample was not restricted to patients who received it. Our version of
the MMPS did not include values for serum potassium in the APACHE
II10 score, which were not abstracted for the CCP. The
California risk-adjustment models included 2 ICD-9–based models: model
A (CA-A), which included risk factors most likely present only at
admission, and model B (CA-B), which included additional
characteristics believed to be present only at admission but may
have occurred during hospitalization. Our versions of CA-A and CA-B did
not include source of payment (because all CCP patients were enrolled
in Medicare) and year of admission (because CCP admissions were within
1.5 years of each other). All models were recalibrated by use of the
validation set.
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We used 4 approaches to compare models. First, in a patient-level
analysis, we assessed the discriminative ability of each model
using analysis of area under the receiver-operating
characteristic (AROC) curves.11 Second, we compared model
calibration for each of the models using the Hosmer-Lemeshow
2 statistic. Calibration is a measure of how
well a particular model fits the data across a range of patient
characteristics.12 Models with smaller
2 values are less likely to suffer from
systemic lack of fit. Third, we determined the correlation of the SMR
for each hospital calculated by the different models.
Finally, for each hospital with >50 cases, we evaluated the degree to which hospital rankings would change when different models were used. We calculated risk-adjusted 30-day mortality rates for each hospital on the basis of each of the models (Appendix B). We assigned each hospital a performance rank on the basis of the decile of risk-adjusted mortality (lowest to highest) for each model. We then determined the agreement in the ranking among the models by classifying the percentage of hospitals that were in a similar decile (defined as the same decile or 1 decile different) by each pair of models. For example, if 1 model classified the hospital in the fifth decile and the other in the sixth or fourth decile, they would be considered to agree. We also assessed the similarity of rankings by comparing each of the models with a ranking based on crude (unadjusted) mortality rates.
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Results |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Study Sample
Table 2
reports the development of
the study sample of 161 058 patients. This sample was randomly split
by hospital into a derivation set of 82 359 patients and a validation
set of 78 699 patients.
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New Model Characteristics
In a model with all 73 of the candidate predictor variables,
the AROC curve was 0.80. The variables selected for the final model
were age, cardiac arrest, anterior or lateral location of myocardial
infarction, systolic blood pressure, white blood cell count,
serum creatinine, and congestive heart failure (Table 3). This model had an AROC curve of 0.77.
In the validation cohort, the AROC was also 0.77, indicating good model
discrimination (Table 4).
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Model Comparison
There were 7 variables in our new model, 27 variables in
the CCP pilot model, 19 variables in the GUSTO-I model, 31
variables in the MMPS model, 45 variables in the ICD-9 model,
22 variables in the CA-A model for patients with no prior
admissions and 16 for patients with prior admissions, and 58
variables in the CA-B model for patients with no prior admission
and 45 for patients with prior admissions (Table 1
). The AROC
curves for the models were similar, ranging from 0.70 in the ICD-9
model to 0.78 in the CCP pilot model (Table 4). The new model
was among the 3 models with the lowest Hosmer-Lemeshow
2 values, performing similarly with the
GUSTO-I and CA-A models. The Figure
compares the SMR between the different models and the new model. The
correlation was highest among the clinically based models.
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The agreement in similar rankings by decile for the new model
based on risk-adjusted mortality was also highest among the clinically
based models (Table 5). Compared with a
ranking based on crude mortality rates, only 38.3% of the hospitals
were classified similarly with a ranking based on the new model, 40.6%
for the CCP-pilot model, 38.3% for the GUSTO-I model, 45.2% for the
MMPS model, 36.0% for the ICD-9 code model, 37.5% for the CA-A model,
and 39.5% for the CA-B model.
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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In this study, we developed and validated a simple 7-variable risk model for 30-day mortality after admission for AMI. The model is based on data from medical records obtained from acute-care hospitals throughout the United States. The variables are easy to collect and available at admission. This parsimonious model yields a predictive performance for 30-day mortality that is comparable to other published clinically based risk indexes that are based on larger numbers of variables. Moreover, the assessment of hospital performance with this model is similar to those obtained by other published models based on clinical information.
The clinically based models produced rankings of hospitals that were more similar to each other than to the administrative code–based models. Among the models tested, the new model had the worst agreement in the ranking of hospitals with the 3 models derived exclusively from administrative data. These models were based on billing codes and may have included information from events that occurred during hospitalization. As a result, the secondary codes may have represented either comorbidities or complications. Previous work has shown that these codes may not commonly agree with documentation in the medical charts.13 The advantage of the code-based models is that administrative data are readily available on all patients without further data collection required.
The study of risk-adjustment models presents an important challenge.14 There is no gold standard to compare model performance. We sought to compare published models for their ability to classify hospitals and to determine the SMR. Nevertheless, the fact that the models produce similar results does not ensure that they accurately indicate similar levels of performance. Differences in the characteristics of patients admitted to the various hospitals indicate the need for risk adjustment, as demonstrated by the difference in agreement of the risk-adjustment models with crude mortality rates. However, critics of risk adjustment may be concerned that even the best models cannot explain most of the variation in patient outcome.
None of the models in this study demonstrated perfect agreement in the ranking of hospitals by similar decile. The best agreement achieved between models was 80.3%. Iezzoni and others15 have expressed concern that the use of different risk-adjustment models can result in different rankings of hospitals. The failing of these models may result from random variation (a particular problem with a small number of cases), imprecision in the measurement of the variables, unmeasured differences among patients, and other uncertainties pertaining to the human condition. Our model is also based on baseline patient characteristics and does not consider treatments that may modify outcome. Although these models provide the best current approach to comparing performance, there is a continuing need to develop better methods of comparison.
When developing a risk-adjustment model, we must consider how variable selection would impact data quality. Because hospitals have an incentive to overstate illness severity, a risk-adjustment model would ideally select clinical measures that could not easily be manipulated. Most of the variables in our new model were not subject to clinical interpretation. Congestive heart failure may have been subject to some variability in clinical interpretation but included radiographic evidence for heart failure in its definition.
This study has several limitations. First, we focused on 30-day mortality. Although short-term mortality is only a single domain with which to evaluate hospitals in terms of performance, it is an outcome that is important to patients and can be measured reliably. Our focus on this outcome is not meant to diminish the importance of other domains that include functioning, satisfaction, and cost. Future studies may address the best approach to evaluating performance across hospitals in these other domains.
Second, the study population included patients who were 65 years of
age, and the generalizability of the results to younger populations was
not explored. However, most patients with AMI are in this older age
group. In addition, given the competing risks of older patients, these
disease-specific models would be expected to perform less well in a
group of older compared with younger patients.
In conclusion, we demonstrate that a simple 7-variable risk model can perform as well as more complex models in comparing hospital mortality rates for AMI. These results can provide a basis for hospitals to develop a simple approach to comparing outcomes for this important diagnosis.
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Acknowledgments |
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Dr Krumholz is a Paul Beeson Faculty Scholar. Jersey Chen is a Merck/American Federation for Aging Research Scholar in Geriatric Pharmacology and an American Heart Association Student Scholar in Cardiovascular Disease and Stroke.
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Footnotes |
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Reprint requests to Harlan M. Krumholz, MD, Yale University School of Medicine, 333 Cedar St, PO Box 208025, New Haven, CT 06520-8025.
The analyses on which this article is based were performed under contract No. 500–96-P549, entitled "Utilization and Quality Control Peer Review Organization for the State of Connecticut," sponsored by the Health Care Financing Administration, Department of Health and Human Services. The contents of this article do not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. The authors assume full responsibility for the accuracy and completeness of the ideas presented. This article is a direct result of the Health Care Quality Improvement Program initiated by the Health Care Financing Administration, which has encouraged identification of quality improvement projects derived from analysis of patterns of care and therefore required no special funding on the part of this contractor. Ideas and contributions to the author concerning experience in engaging with issues presented are welcomed.
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Appendix A |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Age (years): Date of birth from Medicare UB-92 claims form.
Cardiac arrest (yes/no): Ventricular fibrillation, ventricular tachycardia, or some other cardiac disturbance within 6 hours before arrival to the hospital that required cardiopulmonary resuscitation, defibrillation, or chemical cardioversion.
Location of MI (anterior/septal, lateral, posterior, inferior, subendocardial, other): As determined from ECG.
Systolic blood pressure (mm Hg): First value documented within 48 hours of admission.
White blood cell count (thousands): First value documented within 24 hours after admission. If none, closest value within 24 hours before admission.
Creatinine (mg/dL): First value documented within 24 hours after admission. If none, closest value within 24 hours before admission. Divide metric (SI) values recorded in µmol/L by 88.4 to convert to mg/dL.16
Congestive heart failure (yes/no): Congestive heart failure and/or pulmonary edema present at time of arrival on the basis of clinical or radiographic evidence.
See Table 6 for additional
data.
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Appendix B |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Calculation of risk-adjusted mortality rate (or indirectly standardized rate) represents an estimate of the mortality rate that would have been observed for a particular hospital if its patients were similar to those of the overall population with respect to the risk-adjustment variables of interest. The following steps should be used to calculate risk-adjusted mortality:
1. Collect data on observed 30-day outcomes for each patient and the 7 independent variables in Appendix A.
2. Estimate the predicted risk of mortality for each patient (P)
from a logistic regression model for 30-day outcomes on the 7
independent variables using the following equation:
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3. Calculate the SMR for a particular hospital by dividing its observed mortality rate by its expected mortality rate. An SMR <1 indicates that a hospital was observed to have a lower mortality rate than predicted by the risk-adjustment model; an SMR >1 indicates that a hospital was observed to have a higher mortality rate than predicted by the risk-adjustment model.
4. A risk-adjusted mortality rate for a particular hospital can be calculated by multiplying the SMR for a hospital by the overall population mortality rate.
Received December 17, 1998; revision received March 25, 1999; accepted March 26, 1999.
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References |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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82 359 patients 65 years of age who were admitted with acute
myocardial infarction to 2401 hospitals and derived a model predicting
30-day mortality. It included 7 variables: age, cardiac arrest,
anterior or lateral location of myocardial infarction, systolic
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models. The 7-variable risk model performed as well as more complex
models in comparing hospital outcomes.
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E. M. Antman, D. T. Anbe, P. W. Armstrong, E. R. Bates, L. A. Green, M. Hand, J. S. Hochman, H. M. Krumholz, F. G. Kushner, G. A. Lamas, et al. ACC/AHA Guidelines for the Management of Patients With ST-Elevation Myocardial Infarction--Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Revise the 1999 Guidelines for the Management of Patients With Acute Myocardial Infarction) Circulation, August 3, 2004; 110(5): 588 - 636. [Full Text] [PDF]
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D. A. Morrow, E. M. Antman, S. A. Murphy, S. F. Assmann, R. P. Giugliano, C. P. Cannon, C. Michael Gibson, C. H. McCabe, H. V. Barron, F. Van de Werf, et al. The Risk Score Profile: a novel approach to characterising the risk of populations enrolled in clinical studies Eur. Heart J., July 1, 2004; 25(13): 1139 - 1145. [Abstract] [Full Text] [PDF]
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K. A. Eagle, M. J. Lim, O. H. Dabbous, K. S. Pieper, R. J. Goldberg, F. Van de Werf, S. G. Goodman, C. B. Granger, P. G. Steg, J. M. Gore, et al. A Validated Prediction Model for All Forms of Acute Coronary Syndrome: Estimating the Risk of 6-Month Postdischarge Death in an International Registry JAMA, June 9, 2004; 291(22): 2727 - 2733. [Abstract] [Full Text] [PDF]
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D. T. Ko, M. Mamdani, and D. A. Alter Lipid-Lowering Therapy With Statins in High-Risk Elderly Patients: The Treatment-Risk Paradox JAMA, April 21, 2004; 291(15): 1864 - 1870. [Abstract] [Full Text] [PDF]
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D. S. Lee, P. C. Austin, J. L. Rouleau, P. P. Liu, D. Naimark, and J. V. Tu Predicting Mortality Among Patients Hospitalized for Heart Failure: Derivation and Validation of a Clinical Model JAMA, November 19, 2003; 290(19): 2581 - 2587. [Abstract] [Full Text] [PDF]
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C. B. Granger, R. J. Goldberg, O. Dabbous, K. S. Pieper, K. A. Eagle, C. P. Cannon, F. Van de Werf, A. Avezum, S. G. Goodman, M. D. Flather, et al. Predictors of Hospital Mortality in the Global Registry of Acute Coronary Events Arch Intern Med, October 27, 2003; 163(19): 2345 - 2353. [Abstract] [Full Text] [PDF]
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J. M. Foody, F. D. Ferdinand, D. Galusha, S. S. Rathore, F. A. Masoudi, E. P. Havranek, D. Nilasena, M. J. Radford, and H. M. Krumholz Patterns of Secondary Prevention in Older Patients Undergoing Coronary Artery Bypass Grafting During Hospitalization for Acute Myocardial Infarction Circulation, September 9, 2003; 108(90101): II-24 - 28. [Abstract] [Full Text] [PDF]
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J. A. Spertus, M. J. Radford, N. R. Every, E. F. Ellerbeck, E. D. Peterson, and H. M. Krumholz Challenges and opportunities in quantifying the quality of care for acute myocardial infarction: Summary from the acute myocardial infarction working group of the American heart association/American college of cardiology first scientific forum on quality of care and outcomes research in cardiovascular disease and stroke J. Am. Coll. Cardiol., May 7, 2003; 41(9): 1653 - 1663. [Full Text] [PDF]
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C Mueller, F-J Neumann, A P Perruchoud, and H J Buettner White blood cell count and long term mortality after non-ST elevation acute coronary syndrome treated with very early revascularisation Heart, April 1, 2003; 89(4): 389 - 392. [Abstract] [Full Text] [PDF]
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J. A. Spertus, M. J. Radford, N. R. Every, E. F. Ellerbeck, E. D. Peterson, and H. M. Krumholz Challenges and Opportunities in Quantifying the Quality of Care for Acute Myocardial Infarction: Summary From the Acute Myocardial Infarction Working Group of the American Heart Association/American College of Cardiology First Scientific Forum on Quality of Care and Outcomes Research in Cardiovascular Disease and Stroke Circulation, April 1, 2003; 107(12): 1681 - 1691. [Full Text] [PDF]
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R. V. Freeman, R. H. Mehta, W. Al Badr, J. V. Cooper, E. Kline-Rogers, and K. A. Eagle Influence of concurrent renal dysfunction on outcomes of patients with acute coronary syndromes and implications of the use of glycoprotein IIb/IIIa inhibitors J. Am. Coll. Cardiol., March 5, 2003; 41(5): 718 - 724. [Abstract] [Full Text] [PDF]
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S. S. Rathore, K. P. Weinfurt, C. P. Gross, and H. M. Krumholz Validity of a Simple ST-Elevation Acute Myocardial Infarction Risk Index: Are Randomized Trial Prognostic Estimates Generalizable to Elderly Patients? Circulation, February 18, 2003; 107(6): 811 - 816. [Abstract] [Full Text] [PDF]
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J Harrop, R Donnelly, A Rowbottom, M Holt, and A R Scott Improvements in total mortality and lipid levels after acute myocardial infarction in an English health district (1995-1999) Heart, May 1, 2002; 87(5): 428 - 431. [Abstract] [Full Text] [PDF]
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H. M. Krumholz, S. S. Rathore, J. Chen, Y. Wang, and M. J. Radford Evaluation of a Consumer-Oriented Internet Health Care Report Card: The Risk of Quality Ratings Based on Mortality Data JAMA, March 13, 2002; 287(10): 1277 - 1287. [Abstract] [Full Text] [PDF]
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H. V. Barron, S. D. Harr, M. J. Radford, Y. Wang, and H. M. Krumholz The association between white blood cell count and acute myocardial infarction mortality in patients >=65 years of age: findings from the cooperative cardiovascular project J. Am. Coll. Cardiol., November 15, 2001; 38(6): 1654 - 1661. [Abstract] [Full Text] [PDF]
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J. Butler, R. Ness, and T. Speroff Improving Care for Elderly Patients With Peptic Ulcer Disease: Should the Focus Be on Drugs or Bugs? JAMA, October 24, 2001; 286(16): 2023 - 2024. [Full Text] [PDF]
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D. A. Morrow, E. M. Antman, L. Parsons, J. A. de Lemos, C. P. Cannon, R. P. Giugliano, C. H. McCabe, H. V. Barron, and E. Braunwald Application of the TIMI Risk Score for ST-Elevation MI in the National Registry of Myocardial Infarction 3 JAMA, September 19, 2001; 286(11): 1356 - 1359. [Abstract] [Full Text] [PDF]
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R. H. Mehta, S. S. Rathore, M. J. Radford, Y. Wang, Y. Wang, and H. M. Krumholz Acute myocardial infarction in the elderly: differences by age J. Am. Coll. Cardiol., September 1, 2001; 38(3): 736 - 741. [Abstract] [Full Text] [PDF]
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M F Dorsch, R A Lawrance, R J Sapsford, J Oldham, D C Greenwood, B M Jackson, C Morrell, S G Ball, M B Robinson, and A S Hall A simple benchmark for evaluating quality of care of patients following acute myocardial infarction Heart, August 1, 2001; 86(2): 150 - 154. [Abstract] [Full Text] [PDF]
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 J. Chen, S. S. Rathore, M. J. Radford, Y. Wang, and H. M. Krumholz Racial Differences in the Use of Cardiac Catheterization after Acute Myocardial Infarction N. Engl. J. Med., May 10, 2001; 344(19): 1443 - 1449. [Abstract] [Full Text] [PDF]
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J. V. Tu, P. C. Austin, R. Walld, L. Roos, J. Agras, and K. M. McDonald Development and validation of the ontario acute myocardial infarction mortality prediction rules J. Am. Coll. Cardiol., March 15, 2001; 37(4): 992 - 997. [Abstract] [Full Text] [PDF]
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J. G. Jollis and P. S. Romano Volume-Outcome Relationship in Acute Myocardial Infarction: The Balloon and the Needle JAMA, December 27, 2000; 284(24): 3169 - 3171. [Full Text] [PDF]
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D. A. Morrow, E. M. Antman, A. Charlesworth, R. Cairns, S. A. Murphy, J. A. de Lemos, R. P. Giugliano, C. H. McCabe, and E. Braunwald TIMI Risk Score for ST-Elevation Myocardial Infarction: A Convenient, Bedside, Clinical Score for Risk Assessment at Presentation : An Intravenous nPA for Treatment of Infarcting Myocardium Early II Trial Substudy Circulation, October 24, 2000; 102(17): 2031 - 2037. [Abstract] [Full Text] [PDF]
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J. J. Allison, C. I. Kiefe, N. W. Weissman, S. D. Person, M. Rousculp, J. G. Canto, S. Bae, O. D. Williams, R. Farmer, and R. M. Centor Relationship of Hospital Teaching Status With Quality of Care and Mortality for Medicare Patients With Acute MI JAMA, September 13, 2000; 284(10): 1256 - 1262. [Abstract] [Full Text] [PDF]
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S. C. Gan, S. K. Beaver, P. M. Houck, R. F. MacLehose, H. W. Lawson, and L. Chan Treatment of Acute Myocardial Infarction and 30-Day Mortality among Women and Men N. Engl. J. Med., July 6, 2000; 343(1): 8 - 15. [Abstract] [Full Text] [PDF]
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 Measuring and Improving Quality of Care : A Report From the American Heart Association/American College of Cardiology First Scientific Forum on Assessment of Healthcare Quality in Cardiovascular Disease and Stroke Stroke, April 1, 2000; 31(4): 1002 - 1012. [Full Text] [PDF]
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Measuring and Improving Quality of Care : A Report From the American Heart Association/American College of Cardiology First Scientific Forum on Assessment of Healthcare Quality in Cardiovascular Disease and Stroke Circulation, March 28, 2000; 101(12): 1483 - 1493. [Full Text] [PDF]
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