(Circulation. 1995;91:677-684.)
© 1995 American Heart Association, Inc.
Articles |
Multicenter Validation of a Risk Index for Mortality, Intensive Care Unit Stay, and Overall Hospital Length of Stay After Cardiac Surgery
From the Institute for Clinical Evaluative Sciences in Ontario (J.V.T., C.D.N.), North York, Ontario, Canada; Clinical Epidemiology Unit (S.B.J., C.D.N.), Sunnybrook Health Science Centre North York, Ontario, Canada; Departments of Medicine and Health Administration (C.D.N.), University of Toronto, Toronto, Ontario, Canada; and the Division of Health Policy Research and Education (J.V.T.), Harvard University, Cambridge, Mass.
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Abstract |
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 Top Abstract IntroductionMethods Results Discussion References |
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Background A multicenter population-based study was conducted to develop and validate a risk index for mortality, intensive care unit (ICU) length of stay, and postoperative length of stay after cardiac surgery.
Methods and Results Data were collected from 13 098 patients
undergoing cardiac surgery between April 1, 1991, and March 31, 1993,
at all nine adult cardiac surgery institutions in Ontario, Canada. A
six-variable risk index (age, sex, left ventricular function, type of
surgery, urgency of surgery, and repeat operation) was developed using
logistic regression analysis to predict in-hospital mortality, ICU
stay in days, and postoperative stay in days after cardiac surgery in a
derivation set of 6213 patients who had cardiac surgery during fiscal
year 1991 (April 1, 1991, to March 31, 1992). The index predicted
mortality, prolonged ICU stay (
6 days), and prolonged postoperative
length of stay (17 days) after cardiac surgery with areas under the
receiver-operating characteristic (ROC) curve of 0.75, 0.66, and 0.69,
respectively, in an independent validation set of 6885 patients who had
cardiac surgery during fiscal year 1992 (April 1, 1992, to March 31,
1993). Increasing risk scores were associated with greater mortality
rates and longer ICU and postoperative stays at all nine
institutions.
Conclusions Mortality, ICU length of stay, and postoperative length of stay after cardiac surgery can be predicted using a simple six-variable risk index. The index has potential application as a risk stratification tool for comparing patient outcomes and resource use among different hospitals and surgeons.
Key Words: surgery • mortality • risk factors
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Introduction |
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TopAbstractIntroductionMethods Results Discussion References |
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In an era of widespread concerns about variations in the quality of care and use of health care resources, methods to assess the risks of cardiac surgery are of increasing importance. Cardiac surgery is resource intensive and has a moderately high in-hospital mortality rate. Thus, it is understandable that a number of models have been developed for assessing the quality of cardiac surgical care, with a particular focus on mortality benchmarking.1 2 3 4 5 6 Although several cardiac surgery risk models exist, most of these models were not designed to predict more than one outcome measure and most apply to coronary artery bypass graft surgery (CABG) alone.1 2 3 4 5 6
If any risk model is to be easily used by clinicians, then there will likely be a tradeoff between the simplicity of a model and its statistical precision. Many of the existing models require extensive risk factor collection and are based on complex mathematical equations.1 2 3 4 5 6 Logistic regression models or bayesian equation models are statistically precise, but they require a calculator or computer for their use. Simpler additive models have been developed, but most reflect the surgical experience of a single institution, contain setting-specific risk factors, and have not necessarily validated well when used in other settings.1 4 5 7 8 Although mortality is an important outcome, other outcome measures such as intensive care unit (ICU) and postoperative (PostOp) length of stay (LOS) are also of increasing interest.9 Models are required that apply to both CABG and valve surgery patients. Accordingly, the purpose of the present study was to develop a simple risk index that could serve as a basis for interinstitutional comparisons of multiple outcomes (eg, mortality and LOS) after cardiac surgery in Ontario, Canada.
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Methods |
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TopAbstractIntroductionMethods Results Discussion References |
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Data Sources
In 1991, the Ontario Ministry of Health established the Provincial Adult Cardiac Care Network (PACCN), a provincewide computerized registry for monitoring cardiac surgery waiting lists in Ontario. All adult patients who require heart surgery in Ontario are entered into this database at the time of referral for cardiac surgery so that they can be triaged according to the urgency of their surgery.10 Clinical information was gathered and entered into the PACCN database on all patients having cardiac surgery at one of the nine adult cardiac surgery institutions in Ontario beginning in April 1991. The nine institutions include eight teaching hospitals and one nonteaching hospital, all with moderate to large surgical volumes (ie, 380 to 1823 cases per year). Missing and inconsistent data elements were completed or checked using hospital-specific registries and/or chart reviews by the network coordinators.
Clinical data from the PACCN database were linked using unique patient identifiers to information contained in the Hospital Medical Records Institute (HMRI) database, an administrative database containing outcomes (mortality, ICU LOS, PostOp LOS) and comorbidity information. Noncardiac comorbidity was determined from the HMRI database using methods described by Deyo et al.11 Complete information was available on a total of 13 098 patients, representing 97% of the patients identified in the PACCN database as having had CABG, valve surgery, or both during the study period. Other types of adult cardiac operations (eg, congenital, transplantation, and so on) were excluded from the present study.
The combined PACCN/HMRI database was divided into a derivation set consisting of the 6213 patients who had cardiac surgery in fiscal year 1991 (April 1, 1991, to March 31, 1992) and an independent validation set consisting of the 6885 patients who had cardiac surgery in fiscal year 1992 (April 1, 1992, to March 31, 1993). The derivation set was used to develop the risk index, and the validation set was used to test the model that was developed.
The quality of the data in the PACCN database was maintained through built-in logic and range checks and by random chart audits by the data entry coordinators at each site. Major fields were mandatory to ensure complete data collection. Previous audits of the HMRI database have shown the mortality data to be 100% accurate. Further assessment of the quality of data in the combined database was done by comparing common information found in both the PACCN and HMRI databases, and significant discrepancies were resolved by contacting the referring institution.
Index Derivation
A recently developed predictive index for
ICU LOS after cardiac
surgery served as a template for the development of the current risk
index.9 Variables that were studied as potential risk
factors for inclusion in the index included patient age, sex, left
ventricular function, type of surgery, urgency of surgery, repeat
operation, recent myocardial infarction, number of vessels bypassed,
left main disease, and the noncardiac comorbid diseases contained in
the Charlson comorbidity index (eg, diabetes, chronic obstructive
pulmonary disease [COPD], and so on).12
The
outcomes of interest in this study were in-hospital mortality, ICU
LOS, and PostOp LOS. A very long ICU LOS was defined as a stay 6
days, whereas a very long PostOp LOS was defined as a stay 17 days.
These correspond to the 90th percentiles for these two outcomes in the
Ontario population. These cutoff points were chosen because they are
very likely to reflect prolonged LOS secondary to the development of
morbidity rather than differences in discharge practices. ICU LOS
information was not available at one of the hospitals.
A multivariate
stepwise regression procedure was used to identify risk
factors for in-hospital mortality that were included in a logistic
regression mortality model. Risk factors were included in the model if
they fulfilled two criteria: the variable was a statistically
significant predictor of mortality at the P<.05 level, and
the variable incrementally improved the area under the
receiver-operating characteristic (ROC) curve by 0.01. The area under
the ROC curve is a commonly used measure of the predictive power of a
statistical model.13 The STATA statistical
package was used to fit all of the statistical models.14
Logistic regression models were also developed with very long ICU LOS
and very long PostOp LOS as the dependent variables. First-order
interactions were determined between each of the variables in the
mortality model.
Odds ratios were calculated from the coefficients of the variables in the three logistic regression models. The odds ratio is a measure of the odds of an outcome occurring in one risk group relative to the odds of that outcome occurring in a reference group (ie, the group at lowest risk). The goodness of fit of the three logistic regression models was assessed using the Hosmer-Lemeshow statistic.15 The risk index was created by rounding the mean of the three odds ratios for each risk factor in the different logistic models to the nearest integer. Outcome-specific risk scores were also evaluated.
An unknown left ventricular function was considered equal to a grade 1 left ventricle, an approach recommended by Pierpont et al16 and adopted by others in their cardiac surgery risk studies.3 Left ventricular function was missing primarily among isolated valve patients, where it was not a mandatory variable. Sensitivity analyses were done to determine whether treating unknown left ventricular function as a grade 2, 3, or 4 left ventricular equivalent improved the model, but it did not.
The risk factors in the
risk index that was developed were age, which
was categorized into one of three groups (<65, 65 to 74, and 75
years); sex (female); grade of left ventricular function based on
ejection fraction as assessed by echocardiography or angiography (grade
1, >50%; grade 2, 35% to 50%; grade 3, 20% to 34%; and grade 4,
<20%); type of surgery (CABG, single valve, or complex [multivalve
or CABG plus valve]); urgency of surgery (emergency, urgent, or
elective); and repeat operation (previous CABG). A consensus panel of
cardiovascular practitioners in Ontario developed urgency definitions
and an urgency rating score for cardiac surgery patients based on the
severity and stability of their angina symptoms, coronary anatomy from
angiographic studies, and the results of noninvasive tests for ischemic
risk.10 Emergency surgery is defined as any surgery that
is required within 24 hours at the time of referral (eg,
postcatheterization "crash," unstable angina with hemodynamic
instability); urgent surgery is surgery required within the same
hospital admission (eg, unstable angina stabilized with maximal medical
therapy); and elective surgery applies for all other procedures.
Index Validation
With the resulting six-variable risk index,
risk scores (range,
0 to 16) were assigned to each patient in both the 1991 derivation and
1992 validation sets based on their clinical characteristics. The
in-hospital mortality rate, mean ICU LOS, and mean PostOp LOS for each
risk score level were determined. Patients with risk scores 8 were
combined into one category because of the small numbers. To assess the
performance of the index at the institutional level, risk scores were
combined into three categories with similar outcomes—0 to 3, 4 to 7,
and 8—and the mortality rate, mean ICU LOS, and mean PostOp LOS were
determined at each of the nine adult cardiac surgery institutions in
Ontario.
The overall predictive ability of the risk index and the three logistic models was assessed by calculating the area under the ROC curve in both the derivation and validation sets using a nonparametric method.13 The ability of the risk index to predict mortality as measured by the area under the ROC curve was compared with that for very long ICU LOS and very long PostOp LOS using methods described by Hanley and McNeil.17 The ability of the risk index to predict outcomes in the validation set was further assessed by comparing observed outcomes in both the 1991 derivation and 1992 validation sets at each risk score level. The 95% confidence intervals (CIs) were calculated for the observed mortality rate, mean ICU LOS, and mean PostOp LOS in the 1991 derivation set. The 1992 validation set outcomes were then compared to determine whether the observed results lay within the 95% CIs of the outcomes predicted from the 1991 reference derivation set.
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Results |
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TopAbstractIntroductionMethods Results Discussion References |
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Table 1
is a summary of the in-hospital mortality
rate, mean ICU LOS, and mean PostOp LOS by each of the risk factors
contained in the risk index in the 1991 derivation set. The overall
Ontario mortality rate for cardiac surgery was 3.69% in fiscal 1991
(3.01% for isolated CABG only), with a mean ICU LOS of 3.21 days and a
mean PostOp LOS of 10.60 days. In fiscal 1992, the overall mortality
rate for cardiac surgery was 3.54% (2.88% for isolated CABG only),
with a mean ICU LOS of 3.01 days and a mean PostOp LOS of 10.21 days.
These mortality results are comparable to the best outcomes reported in
other population-based registries and large institutional
databases.2 3 6
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Logistic Regression Models
Table 2 shows the
logistic regression models with
the three dependent variables of mortality, very long ICU LOS, and very
long PostOp LOS. A significant first-order interaction
(P<.05) was observed between age and left ventricular
function and between left ventricular function and urgency in the
mortality model. Because these interaction terms complicate the
interpretation of the logistic regression model and do not
significantly affect the relative magnitude of the odds ratios
associated with the main effects, they were excluded from the final
model. It was interesting to note that for three of the six risk
factors, the relative magnitude of the odds ratios for each of the
three outcomes was about the same. However, emergency surgery was
associated with a much greater odds ratio for mortality (5.70) than for
very long ICU and PostOp LOS (2.84 and 2.61). A grade 4 left ventricle
was also associated with a higher odds ratio for mortality (3.64)
relative to that for very long ICU and PostOp LOS (2.18 and 2.18).
Repeat operation was a very significant risk factor for mortality (odds
ratio, 3.22; P<.001), whereas it did not quite reach
statistical significance for long ICU stays (P=.064) or long
postoperative stays (P=.054). These latter results are
compatible with the hypothesis that repeat operation sharply increases
the odds of early postoperative death but that once such patients
survive this period, their postoperative course is only slightly
prolonged compared with that of primary procedure patients. All three
logistic regression models passed Hosmer-Lemeshow goodness-of-fit tests
(P=.45 for mortality, P=.14 for ICU LOS, and
P=.34 for PostOp LOS; P
.05 indicates a poor
fit).15
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Risk Index
The risk index that was developed is shown in
Table 3. Tables 4 and 5 show the
observed mortality rate, mean ICU LOS, and mean PostOp LOS at each of
the different risk score levels in both the 1991 derivation and 1992
validation sets. Increasing risk scores were associated with greater
risks for mortality and prolonged ICU and PostOp LOS in both the
derivation and validation data sets. The index validated very well,
with similar outcome results at nearly all risk score levels. The mean
risk score was 3.11 in 1991 and 3.06 in 1992.
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Figs 1 through
3 show
graphically the observed mortality rates, mean ICU LOS, and mean PostOp
LOS in both the 1991 derivation and 1992 validation sets. The 95% CIs
for the observed outcomes are also shown for the 1991 derivation set.
The observed outcomes in the 1992 validation set lie within the 95%
CIs predicted by the 1991 derivation set with two exceptions. In the
highest risk group (8), the mortality rate fell significantly from
20.62% in 1991 to 13.22% in 1992. The mean ICU LOS was also
significantly lower at 3.68 days in 1992 compared with 4.61 days in
1991 for patients with a risk score of 6. These results show that the
overall improvement in outcomes noted between the 2 years was
predominantly in those at highest risk.
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Table 6 shows
the performance of the
risk index at each of the nine adult cardiac surgery centers in Ontario
for each of three different risk score categories in the 1992
validation set. Risk scores of 0 to 3 could be considered a low-risk
group; risk scores of 4 to 7, an intermediate-risk group; and risk
scores of 8, a high-risk group. Despite considerable differences in
case mix among the nine adult cardiac surgery institutions in Ontario,
the risk index validated very well, with higher risk scores being
associated with greater risks for all three outcomes, regardless of the
institution. Similar results were also observed in the 1991 derivation
set. The mean risk score at each hospital ranged between a low of 2.64
and a high of 3.49 in the 1992 validation set.
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Areas Under the ROC Curve
The areas under the ROC curve for
the risk index were 0.75, 0.67,
and 0.71 for mortality, very long ICU LOS, and very long PostOp LOS
predictions in the 1991 derivation set. For the outcome-specific
logistic regression models, the corresponding areas were 0.76, 0.68,
and 0.72. The areas using outcome-specific integer risk scores were
0.76, 0.67, and 0.71, respectively. These results demonstrate that very
little predictive performance was lost by using the mean of the odds
ratios from the three logistic models to determine the risk scores in
the risk index.
In the independent 1992 validation set, the areas under the ROC curve were 0.75, 0.66, and 0.69 for mortality, very long ICU LOS, and very long PostOp LOS predictions using the risk index, demonstrating that the index validated very well. The index predicted mortality significantly better than very long PostOp LOS (P<.05) in both the derivation and validation sets and very long PostOp LOS better than very long ICU LOS (P<.05) in the derivation set. These results suggest that differences in patient case mix explain variations in mortality outcomes better than they explain variations in resource use, where other factors, such as different practice styles, play a role.
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Discussion |
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TopAbstractIntroductionMethods Results Discussion References |
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In the present study, we have developed and validated a simple six-variable risk index for mortality, ICU LOS, and overall PostOp LOS after cardiac surgery using data from all nine adult cardiac surgery institutions in Ontario. This simple index yields predictive performance for mortality that is comparable to other cardiac surgery risk models that have been developed.3 4 Our index also predicts resource use (as measured by ICU and PostOp LOS) after cardiac surgery. Thus, the index has multiple potential applications, including comparing patient outcomes and resource use among different surgeons and hospitals, counseling patients about the risks of cardiac surgery, and use in patient and staff scheduling when resources are limited.9
Our index is being used to conduct comparisons of risk-adjusted cardiac surgical outcomes (mortality, ICU LOS, PostOp LOS) among the nine adult cardiac surgical institutions in Ontario. Hospitals are given their risk-adjusted outcomes so that they can evaluate their relative performance with the goal of continuous quality improvement. Expected hospital-level outcomes calculated using the risk index have been compared with those determined using logistic and linear regression models, and the overall results have been essentially the same (unpublished data). The index is also being used to provide resource-intensity adjustments of funding of cardiac surgical services in Ontario under the government-sponsored healthcare system. The aim is to ensure that hospitals with sicker surgical patients (ie, higher risk scores) receive greater reimbursement for their surgical cases so that they are not financially penalized for accepting more resource-intensive cases.
Use of an additive model allows clinicians to see how well they are performing relative to others at different levels of patient risk (eg, low, medium, or high risk), and it provides a summary measure (ie, the mean risk score) of a hospital's case mix severity. By using population-based data from multiple institutions to determine significant risk factors and to define a reference standard for expected outcomes, we have avoided potential biases that might exist when risk models are developed and validated at only a single institution. Overall cardiac surgical outcomes have been improving in Ontario over time, and thus expected outcomes at different risk score levels are determined annually to reflect temporal improvements in the quality of surgical care.
Several other risk stratification models have been developed for
predicting morbidity and mortality at cardiac surgery, including those
shown in Table 7. Many different variables have been
found to be associated with increased risk at cardiac surgery, but only
a few variables have consistently been found to be major risk factors
across multiple and very diverse study
settings.1 2 3 4 5 6
These
include the six risk factors contained in our index (age, female
gender, left ventricular function, type of surgery, urgency of surgery,
and repeat operation).
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The major difference between our model and others that have been
developed is its lack of inclusion of comorbid diseases. Although
intuitively one might expect that certain noncardiac diseases would be
major predictors of mortality, different investigators have found
different comorbid diseases to be significant risk factors and no
diseases to be consistent risk factors, with the possible exceptions of
renal dysfunction and diabetes, as shown in Table
7
.1 2 3 4 5 6
Renal dysfunction has recently been shown to be an important risk
factor for surgical mortality in patients who have this
condition.1 2 4 5 6
However, the spectrum of what
constitutes renal dysfunction is broad, with some models defining it as
elevated creatinine levels and others defining it as dialysis
dependency.1 2 4 5 The
incidence of dialysis dependency in
the cardiac surgical population is so low (eg, 0.5% in New York State)
that it is very unlikely to change the results of any population-based
comparisons.2 The association between diabetes and
mortality at cardiac surgery has been inconsistent, with some studies
supporting and other studies not supporting an
association.1 2 4 5 6 18
We have observed that noncardiac comorbidity as determined using HMRI administrative data does not usually affect overall hospital-level results once we have adjusted for the six risk factors found in our index. Other investigators have also found that adjustment for comorbidity had no effect on CABG hospital rankings once clinical risk factors were adjusted for.19 However, these findings could be a reflection of undercoding of comorbid diseases in administrative databases, a phenomenon noted by researchers elsewhere.20
The level of predictive performance for mortality achieved using our index (area under the ROC curve, 0.75 in both the 1991 derivation and 1992 validation sets) is comparable to that achieved using other models that have more risk factors (eg, ROC area, 0.74 in the Cleveland Clinic model developed by Higgins et al4 ; ROC area, 0.76 in the Northern New England model developed by O'Connor et al3 ). Given that the overall predictive performance is similar, we have elected to use our simpler model as a method for risk-adjusted outcome comparisons and funding determinations. For any risk factor not presently in the index to significantly affect overall results it would have to have several properties: it would have to be a statistically significant predictor of the outcome of interest, it would have to have a moderately high prevalence in the cardiac surgical population, it would have to be distributed unevenly between the various hospitals, and it would have to be uncorrelated with any of the variables currently in the index.
The Society of Thoracic Surgeons (STS) National Cardiac Surgery Database model developed by Edwards et al6 is the most widely used model in the United States and is an unparalleled effort in terms of its size and comprehensiveness. The current bayesian equation that is being used incorporates 23 risk factors. However, it is possible that the STS model includes some nominally significant mortality predictors of uncertain clinical impact, given the enormous size of the available database and the resultant statistical power. Some institutions may not have the resources necessary to undertake the significant data collection effort required to use this model; this is certainly a concern in Canada. We could not directly compare our index with other models that have been developed because we did not have information on all of the same risk factors. However, other investigators have compared different cardiac surgery risk models and have found that their overall performance is similar, independent of the level of complexity.7 8
The present study has certain limitations. The performance of our index outside Ontario remains to be determined, and other models that have been developed may have better predictive performance in other settings since the prevalence and importance of certain risk factors may be different. Other models may also provide more precise stratification of risk at the individual patient level. The major advantage associated with our model is its ability to predict three outcomes (mortality, ICU LOS, PostOp LOS) using only six variables that are already being collected in most existing cardiac surgery databases. Clinicians elsewhere may prefer the simplicity of our model or they may prefer to use other more comprehensive models that have been developed.
In conclusion, we have demonstrated that a simple six-variable risk index can be used to predict the risks of in-hospital mortality, prolonged ICU LOS, and prolonged PostOp LOS at all nine adult cardiac surgery institutions in Ontario. Patient age, sex, left ventricular function, type of surgery, urgency of surgery, and repeat operation have been combined into an easy-to-use six-variable risk-stratifying index, and this has been validated using a large population-based sample in Ontario. These risk factors are found in virtually all cardiac surgery databases, so our index could easily be used by practitioners in other centers to compare their patient outcomes and resource use with those observed in Ontario. Further case mix and outcome comparison studies are being planned using our risk index as a framework.
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Acknowledgments |
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Dr Tu is supported by a Health Research Personnel Development Program Fellowship (04544) from the Ontario Ministry of Health. Dr Jaglal was supported by an Ontario Heart and Stroke Foundation Post-doctoral Fellowship. Dr Naylor is supported by a Career Scientist Award (02377) from the Ontario Ministry of Health. The results and conclusions are those of the authors, and no official endorsement by the sponsoring agencies is intended or should be inferred. The authors would like to thank the many cardiologists and cardiac surgeons of the Provincial Adult Cardiac Care Network of Ontario, the institutional and regional coordinators who are responsible for data collection and patient follow-up, and the staff of the PACCN coordinating office at Victoria Hospital, London, Ontario.
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Footnotes |
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Reprint requests to Dr C. David Naylor, Institute for Clinical Evaluative Sciences, Rm G-106, 2075 Bayview Ave, North York, Ontario, Canada M4N 3M5.
1 List of Steering Committee members is provided in "Appendix." 
Steering Committee of the Provincial Adult Cardiac Care
Network of
Ontario
Steering Committee members are Alnoor Abdulla, MD, FRCPC,
Sudbury Memorial Hospital, Sudbury, Ontario; Glenn Bartlett, MD, FRCSC
(1991 to 1993), London, Ontario; Donald S. Beanlands, MD, FRCPC,
University of Ottawa Heart Institute, Ottawa Civic Hospital, Ottawa,
Ontario; Robert Chisholm, MD, FRCPC, St Michael's Hospital, Toronto,
Ontario; Martin Goldbach, MD, FRCSC, Victoria Hospital, London,
Ontario; Neil McKenzie, MB, BCh, FRCSC, University Hospital, London,
Ontario; Christopher D. Morgan, MD, FRCPC, Sunnybrook Health Science
Centre, Toronto, Ontario; John Pym, MB, BCh, FRCSC, Kingston General
Hospital, Kingston, Ontario; Hugh Scully, MD, FRCSC, Toronto Hospital,
Toronto, Ontario; B. William Shragge, MD, FRCSC, Hamilton Civic
Hospitals-General Division, Hamilton, Ontario; and James Swan, MD,
FRCPC, Scarborough Centenary Health Centre, Scarborough, Ontario.
Received December 16, 1994; accepted December 16, 1994.
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References |
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TopAbstractIntroductionMethods Results Discussion References |
|---|
1. Parsonnet V, Dean D, Bernstein AD. A method of uniform stratification of risk for evaluating the results of surgery in acquired adult heart disease. Circulation. 1989;79(suppl I):I-3-I-12.
2.
Hannan EL, Kilburn H Jr, O'Donnell JF, Lukacik G, Shields
EP. Adult open heart surgery in New York State: an analysis of risk
factors and hospital mortality rates. JAMA. 1990;264:2768-2774.
3. O'Connor GT, Plume SK, Olmstead EM, Coffin LH, Morton JR, Maloney CT, Nowicki ER, Levy DG, Tryzelaar JF, Hernandez F, et al, for the Northern New England Cardiovascular Disease Study Group. Multivariate prediction of in-hospital mortality associated with coronary artery bypass graft surgery. Circulation. 1992; 85:2110-2118.
4.
Higgins TL, Estafanous FG, Loop FD, Beck GJ, Blum JM,
Paranandi L. Stratification of morbidity and mortality outcome by
preoperative risk factors in coronary artery bypass patients: a
clinical severity score. JAMA. 1992;267:2344-2348.
5.
Tuman KJ, McCarthy RJ, March RJ, Najafi H, Ivankovich AD.
Morbidity and duration of ICU day after cardiac surgery: a model for
preoperative risk assessment. Chest. 1992;102:36-44.
6. Edwards FH, Clark RE, Schwartz M. Coronary artery bypass grafting: the Society of Thoracic Surgeons National Database experience. Ann Thorac Surg. 1994;57:12-19. [Abstract]
7. Bryson G, Mundi C, Dupuis JY, Bourke M. Can multifactorial classifications predict outcome after cardiac surgery in patients with preoperative renal dysfunction? Can J Anaesth. 1994;41:A13. Abstract.
8. Orr RK, Maini BS, Sottile F, Dumas E. Severity adjusted models fail to accurately predict mortality in coronary artery bypass graft surgery. Med Decis Making. 1994;14:438. Abstract.
9. Tu JV, Mazer CD, Levinton C, Armstrong PW, Naylor CD. A predictive index for length of stay in the intensive care unit following cardiac surgery. Can Med Assoc J. 1994;151:177-185. [Abstract]
10. Naylor CD, Baigrie RS, Goldman BS, Basinski A. Revascularisation Panel and Consensus Methods Group: assessment of priority for coronary revascularisation procedures. Lancet. 1990;335: 1070-1073.
11. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613-619. [Medline] [Order article via Infotrieve]
12. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40:373-383. [Medline] [Order article via Infotrieve]
13. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982; 143:29-36.
14. STATA. Santa Monica, California: Computing Resource Center; 1992.
15. Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons; 1989.
16. Pierpont GL, Kruse M, Ewald S, Weir EK. Practical problems in assessing risk for coronary artery bypass grafting. J Thorac Cardiovasc Surg. 1985;89:673-682. [Abstract]
17.
Hanley JA, McNeil BJ. A method of comparing the areas under
receiver operating characteristic curves derived from the same cases.
Radiology. 1983;148:839-843.
18. Higgins TL, Starr NJ. Risk stratification and outcome assessment of the adult cardiac surgical patient. Semin Thorac Cardiovasc Surg. 1991;3:88-94. [Medline] [Order article via Infotrieve]
19.
Hartz AJ, Kuhn EM, Kayser KL, Pryor DP, Green R, Rimm AA.
Assessing providers of coronary revascularization: a method for peer
review organizations. Am J Public Health. 1992;82:1631-1640.
20. Hannan EL, Kilburn H Jr, Lindsey ML, Lewis R. Clinical versus administrative data bases for CABG surgery: does it matter? Med Care. 1992;30:892-907.[Medline] [Order article via Infotrieve]
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|
|
 |
|
 J. Poelaert, P. Depuydt, A. De Wolf, S. Van de Velde, I. Herck, and S. Blot Polyurethane cuffed endotracheal tubes to prevent early postoperative pneumonia after cardiac surgery: A pilot study. J. Thorac. Cardiovasc. Surg., April 1, 2008; 135(4): 771 - 776. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Merello, E. Riesle, J. Alburquerque, H. Torres, E. Aranguiz-Santander, O. Pedemonte, and B. Westerberg Risk Scores Do Not Predict High Mortality After Coronary Artery Bypass Surgery in the Presence of Diastolic Dysfunction Ann. Thorac. Surg., April 1, 2008; 85(4): 1247 - 1255. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. D'Errigo, F. Seccareccia, S. Rosato, V. Manno, G. Badoni, D. Fusco, C. A. Perucci, and the Research Group of the Italian CABG Outcome Pro Comparison between an empirically derived model and the EuroSCORE system in the evaluation of hospital performance: the example of the Italian CABG Outcome Project Eur. J. Cardiothorac. Surg., March 1, 2008; 33(3): 325 - 333. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Ranucci, A. Pazzaglia, C. Bianchini, G. Bozzetti, and G. Isgro Body Size, Gender, and Transfusions as Determinants of Outcome After Coronary Operations Ann. Thorac. Surg., February 1, 2008; 85(2): 481 - 486. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 V. A. Ferraris, F. H. Edwards, D. M. Shahian, and S. P. Ferraris Risk Stratification and Comorbidity Card. Surg. Adult, January 1, 2008; 3(2008): 199 - 246. [Full Text]
|
|
|
|
 |
|
 P. E. Antunes, L. Eugenio, J. Ferrao de Oliveira, and M. J. Antunes Mortality risk prediction in coronary surgery: a locally developed model outperforms external risk models Interactive CardioVascular and Thoracic Surgery, August 1, 2007; 6(4): 437 - 441. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
O. Staudinger, H. Ostermann, G. Laufer, R. Schistek, B. Staudinger, and B. Tilg Evaluation of cardiac scoring models for an Austrian cardiac register Interactive CardioVascular and Thoracic Surgery, August 1, 2007; 6(4): 470 - 473. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. S. Adabag, T. Rector, S. Mithani, J. Harmala, H. B. Ward, R. F. Kelly, J. T. Nguyen, E. O. McFalls, and H. E. Bloomfield Prognostic Significance of Elevated Cardiac Troponin I After Heart Surgery Ann. Thorac. Surg., May 1, 2007; 83(5): 1744 - 1750. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Salem, A. Y. Denault, P. Couture, S. Belisle, A. Fortier, M.-C. Guertin, M. Carrier, and R. Martineau Left ventricular end-diastolic pressure is a predictor of mortality in cardiac surgery independently of left ventricular ejection fraction Br. J. Anaesth., September 1, 2006; 97(3): 292 - 297. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Nilsson, L. Algotsson, P. Hoglund, C. Luhrs, and J. Brandt Comparison of 19 pre-operative risk stratification models in open-heart surgery Eur. Heart J., April 1, 2006; 27(7): 867 - 874. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
O. V. Hein, J. Birnbaum, K. Wernecke, M. England, W. Konertz, and C. Spies Prolonged Intensive Care Unit Stay in Cardiac Surgery: Risk Factors and Long-Term-Survival. Ann. Thorac. Surg., March 1, 2006; 81(3): 880 - 885. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. S. Hillis, B. L. Croal, K. G. Buchan, H. El-Shafei, G. Gibson, R. R. Jeffrey, C. G.M. Millar, G. J. Prescott, and B. H. Cuthbertson Renal Function and Outcome From Coronary Artery Bypass Grafting: Impact on Mortality After a 2.3-Year Follow-Up Circulation, February 28, 2006; 113(8): 1056 - 1062. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Berman, A. Stamler, G. Sahar, G. P. Georghiou, E. Sharoni, R. Brauner, B. Medalion, B. A. Vidne, and A. Kogan Validation of the 2000 Bernstein-Parsonnet Score Versus the EuroSCORE as a Prognostic Tool in Cardiac Surgery Ann. Thorac. Surg., February 1, 2006; 81(2): 537 - 540. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Guru, S. E. Fremes, P. C. Austin, E. H. Blackstone, and J. V. Tu Gender Differences in Outcomes After Hospital Discharge From Coronary Artery Bypass Grafting Circulation, January 31, 2006; 113(4): 507 - 516. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J.-Y. Dupuis Clinical Predictions and Decisions to Perform Cardiac Surgery on High-Risk Patients Seminars in Cardiothoracic and Vascular Anesthesia, June 1, 2005; 9(2): 179 - 186. [Abstract] [PDF]
|
|
|
|
|
|
M. M. Lalu, E. Pasini, C. J. Schulze, M. Ferrari-Vivaldi, G. Ferrari-Vivaldi, T. Bachetti, and R. Schulz Ischaemia-reperfusion injury activates matrix metalloproteinases in the human heart Eur. Heart J., January 1, 2005; 26(1): 27 - 35. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S.-A. Hassantash, K. Mirpoor, and M. Afrakhteh Cardiac Surgery in an Iranian Teaching Hospital: Outcome and Risk Factors Asian Cardiovasc Thorac Ann, December 1, 2004; 12(4): 312 - 315. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C.-Y. Ng, M. F. Ramli, and Y. Awang Coronary Bypass Surgery in Patients Aged 70 Years and Over: Mortality, Morbidity, Length of Stay and Hospital Cost Asian Cardiovasc Thorac Ann, September 1, 2004; 12(3): 218 - 223. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. M. Clark, R. Jamieson, I. N. Findlay, M. T. McKenna, P. Wingo, J. J. Gibson, G. Haidinger, C. Vutuc, M. Maier, J. V. Tu, et al. Registries and Informed Consent N. Engl. J. Med., August 5, 2004; 351(6): 612 - 614. [Full Text] [PDF]
|
|
|
|
|
|
S. Karthik, A. K. Srinivasan, A. D. Grayson, M. Jackson, D. A.C. Sharpe, D. J.M. Keenan, B. Bridgewater, and B. M. Fabri Limitations of additive EuroSCORE for measuring risk stratified mortality in combined coronary and valve surgery{star} Eur. J. Cardiothorac. Surg., August 1, 2004; 26(2): 318 - 322. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. Z. Omar, G. Ambler, P. Royston, J. Eliahoo, and K. M. Taylor Cardiac surgery risk modeling for mortality: a review of current practice and suggestions for improvement Ann. Thorac. Surg., June 1, 2004; 77(6): 2232 - 2237. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Hutfless, R. Kazanegra, M. Madani, M. A. Bhalla, A. Tulua-Tata, A. Chen, P. Clopton, C. James, A. Chiu, and A. S. Maisel Utility of B-type natriuretic peptide in predicting postoperative complications and outcomes in patients undergoing heart surgery J. Am. Coll. Cardiol., May 19, 2004; 43(10): 1873 - 1879. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Ugolini and L. Nobilio Risk adjustment for coronary artery bypass graft surgery: an administrative approach versus EuroSCORE Int. J. Qual. Health Care, April 1, 2004; 16(2): 157 - 164. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. N. Wijeysundera, W. S. Beattie, V. Rao, J. Ivanov, and K. Karkouti Calcium antagonists are associated with reduced mortality after cardiac surgery: a propensity analysis J. Thorac. Cardiovasc. Surg., March 1, 2004; 127(3): 755 - 762. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Johnston, J. R. Goss, J. A. Malmgren, and J. A. Spertus Health status and social risk correlates of extended length of stay following coronary artery bypass surgery Ann. Thorac. Surg., February 1, 2004; 77(2): 557 - 562. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Athanasiou, S. Al-Ruzzeh, P. Kumar, M.-C. Crossman, M. Amrani, J. R. Pepper, R. Del Stanbridge, R. Casula, and B. Glenville Off-pump myocardial revascularization is associated with less incidence of stroke in elderly patients Ann. Thorac. Surg., February 1, 2004; 77(2): 745 - 753. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 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]
|
|
|
|
|
|
R. V.H.P. Huijskes, P. M.J. Rosseel, and J. G.P. Tijssen Outcome prediction in coronary artery bypass grafting and valve surgery in the Netherlands: development of the Amphiascore and its comparison with the Euroscore Eur. J. Cardiothorac. Surg., November 1, 2003; 24(5): 741 - 749. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. Wang, J.-Y. Dupuis, H. Nathan, and K. Williams An Analysis of the Association Between Preoperative Renal Dysfunction and Outcome in Cardiac Surgery: Estimated Creatinine Clearance or Plasma Creatinine Level as Measures of Renal Function,* Chest, November 1, 2003; 124(5): 1852 - 1862. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B. Bridgewater, A. D Grayson, M. Jackson, N. Brooks, G. J Grotte, D. J M Keenan, R. Millner, B. M Fabri, and M. Jones Surgeon specific mortality in adult cardiac surgery: comparison between crude and risk stratified data BMJ, July 3, 2003; 327(7405): 13 - 17. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Loubani, S. Ghosh, and M. Galinanes The aging human myocardium: tolerance to ischemia and responsiveness to ischemic preconditioning J. Thorac. Cardiovasc. Surg., July 1, 2003; 126(1): 143 - 147. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. M. Burg, M. C. Benedetto, and R. Soufer Depressive Symptoms and Mortality Two Years After Coronary Artery Bypass Graft Surgery (CABG) in Men Psychosom Med, July 1, 2003; 65(4): 508 - 510. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Rao, M. C. Oz, M. A. Flannery, K. A. Catanese, M. Argenziano, and Y. Naka Revised screening scale to predict survival after insertion of a left ventricular assist device J. Thorac. Cardiovasc. Surg., April 1, 2003; 125(4): 855 - 862. [Abstract] [Full Text]
|
|
|
|
|
|
T. Bardell, J.F. Legare, K.J. Buth, G.M. Hirsch, and I.S. Ali ICU readmission after cardiac surgery Eur. J. Cardiothorac. Surg., March 1, 2003; 23(3): 354 - 359. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. W. C. ten Broecke, S. G. De Hert, E. Mertens, and H. F. Adriaensen Effect of preoperative {beta}-blockade on perioperative mortality in coronary surgery Br. J. Anaesth., January 1, 2003; 90(1): 27 - 31. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. H. Hulzebos, N. L. Van Meeteren, R. A De Bie, P. C Dagnelie, and P. J. Helders Prediction of Postoperative Pulmonary Complications on the Basis of Preoperative Risk Factors in Patients Who Had Undergone Coronary Artery Bypass Graft Surgery Physical Therapy, January 1, 2003; 83(1): 8 - 16. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. M. Burg, M. C. Benedetto, R. Rosenberg, and R. Soufer Presurgical Depression Predicts Medical Morbidity 6 Months After Coronary Artery Bypass Graft Surgery Psychosom Med, January 1, 2003; 65(1): 111 - 118. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. A. Ferraris and S. P. Ferraris Risk Stratification and Comorbidity Card. Surg. Adult, January 1, 2003; 2(2003): 187 - 224. [Full Text]
|
|
|
|
|
|
J. A. Fox, V. Formanek, A. Friedrich, and S. K. Shernan Intraoperative Echocardiography Card. Surg. Adult, January 1, 2003; 2(2003): 283 - 314. [Full Text]
|
|
|
|
 |
|
 C. Chen-Scarabelli Beating-Heart Coronary Artery Bypass Graft Surgery: Indications, Advantages, and Limitations Crit. Care Nurse, October 1, 2002; 22(5): 44 - 58. [Full Text] [PDF]
|
|
|
|
 |
|
 P. S. Myles, J. O. Hunt, H. Fletcher, J. Watts, D. Bain, A. Silvers, and M. R. Buckland Remifentanil, Fentanyl, and Cardiac Surgery: A Double-Blinded, Randomized, Controlled Trial of Costs and Outcomes Anesth. Analg., October 1, 2002; 95(4): 805 - 812. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Bainbridge and D. Cheng Initial Perioperative Care of the Cardiac Surgical Patient Seminars in Cardiothoracic and Vascular Anesthesia, September 1, 2002; 6(3): 229 - 236. [Abstract] [PDF]
|
|
|
|
|
|
G. Gatti, G. Cardu, A. M. Lusa, and P. Pugliese Predictors of postoperative complications in high-risk octogenarians undergoing cardiac operations Ann. Thorac. Surg., September 1, 2002; 74(3): 671 - 677. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. S Meharwal and N. Trehan Off-Pump Coronary Artery Surgery in the Elderly Asian Cardiovasc Thorac Ann, September 1, 2002; 10(3): 206 - 210. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. D. Peterson, L. P. Coombs, T. B. Ferguson, A. L. Shroyer, E. R. DeLong, F. L. Grover, and F. H. Edwards Hospital variability in length of stay after coronary artery bypass surgery: results from the Society of Thoracic Surgeon's National Cardiac Database Ann. Thorac. Surg., August 1, 2002; 74(2): 464 - 473. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. R. Williams, R. B. Wellner, E. A. Hartnett, B. Thornton, M. N. Kavarana, R. Mahapatra, M. C. Oz, and R. Sladen Long-term survival and quality of life in cardiac surgical patients with prolonged intensive care unit length of stay Ann. Thorac. Surg., May 1, 2002; 73(5): 1472 - 1478. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
I. J. Welsby, E. Bennett-Guerrero, D. Atwell, W. D. White, M. F. Newman, P. K. Smith, and M. G. Mythen The Association of Complication Type with Mortality and Prolonged Stay After Cardiac Surgery with Cardiopulmonary Bypass Anesth. Analg., May 1, 2002; 94(5): 1072 - 1078. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Hoefer, E. Ruttmann, M. Riha, W. Schobersberger, A. Mayr, G. Laufer, and J. Bonatti Factors influencing intensive care unit length of stay after surgery for acute aortic dissection type A Ann. Thorac. Surg., March 1, 2002; 73(3): 714 - 718. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. S. Kurki, O. Jarvinen, M. J. Kataja, J. Laurikka, and M. Tarkka Performance of three preoperative risk indices; CABDEAL, EuroSCORE and Cleveland models in a prospective coronary bypass database Eur. J. Cardiothorac. Surg., March 1, 2002; 21(3): 406 - 410. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Pinna-Pintor, M. Bobbio, S. Colangelo, F. Veglia, M. Giammaria, D. Cuni, F. Maisano, and O. Alfieri Inaccuracy of four coronary surgery risk-adjusted models to predict mortality in individual patients Eur. J. Cardiothorac. Surg., February 1, 2002; 21(2): 199 - 204. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T.S. Kurki, U. Hakkinen, J. Lauharanta, J. Ramo, and M. Leijala Evaluation of the relationship between preoperative risk scores, postoperative and total length of stays and hospital costs in coronary bypass surgery Eur. J. Cardiothorac. Surg., December 1, 2001; 20(6): 1183 - 1187. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Gatti, G. Maffei, A. M. Lusa, and P. Pugliese Tricuspid valve repair with the Cosgrove-Edwards annuloplasty system: early clinical and echocardiographic results Ann. Thorac. Surg., September 1, 2001; 72(3): 764 - 767. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. J. Novick, S. A. Fox, L. W. Stitt, S. A. Swinamer, K. R. Lehnhardt, R. Rayman, and W. D. Boyd Cumulative sum failure analysis of a policy change from on-pump to off-pump coronary artery bypass grafting Ann. Thorac. Surg., September 1, 2001; 72(3): S1016 - 1021. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. C. H. Cheng, M. F. Newman, P. Duke, D. T. Wong, B. Finegan, M. Howie, J. Fitch, T. A. Bowdle, C. Hogue, Z. Hillel, et al. The Efficacy and Resource Utilization of Remifentanil and Fentanyl in Fast-Track Coronary Artery Bypass Graft Surgery: A Prospective Randomized, Double-Blinded Controlled, Multi-Center Trial Anesth. Analg., April 1, 2001; 92(5): 1094 - 1102. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 British Cardiac Society Guidelines and Medical Pra and Royal College of Physicians Clinical Effectiveness Guideline for the management of patients with acute coronary syndromes without persistent ECG ST segment elevation Heart, February 1, 2001; 85(2): 133 - 142. [Full Text]
|
|
|
|
|
|
O. Pitkanen, M. Niskanen, S. Rehnberg, M. Hippelainen, and M. Hynynen Intra-institutional prediction of outcome after cardiac surgery: comparison between a locally derived model and the EuroSCORE Eur. J. Cardiothorac. Surg., December 1, 2000; 18(6): 703 - 710. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Mariani, R. Ou, M. Bailey, M. Rowland, P. Nagley, F. Rosenfeldt, and S. Pepe Tolerance to ischemia and hypoxia is reduced in aged human myocardium J. Thorac. Cardiovasc. Surg., October 1, 2000; 120(4): 660 - 667. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Abramov, M. G. Tamariz, J. Y. Sever, G. T. Christakis, G. Bhatnagar, A. L. Heenan, B. S. Goldman, and S. E. Fremes The influence of gender on the outcome of coronary artery bypass surgery Ann. Thorac. Surg., September 1, 2000; 70(3): 800 - 805. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. D. Boyd, N. D. Desai, R. J. Novick, F. N. McKenzie, D. F. DelRizzo, and A. H. Menkis Use of Cardiopulmonary Bypass in High-Risk Patients Is a Predictor of Adverse Outcome Seminars in Cardiothoracic and Vascular Anesthesia, July 1, 2000; 4(2): 86 - 91. [Abstract] [PDF]
|
|
|
|
|
|
J. Ivanov, M. A. Borger, T. E. David, G. Cohen, N. Walton, and C. D. Naylor Predictive accuracy study: comparing a statistical model to clinicians' estimates of outcomes after coronary bypass surgery Ann. Thorac. Surg., July 1, 2000; 70(1): 162 - 168. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. C. Erickson, P. H. Wise, E. F. Cook, A. Beiser, and J. W. Newburger The Impact of Managed Care Insurance on Use of Lower-Mortality Hospitals by Children Undergoing Cardiac Surgery in California Pediatrics, June 1, 2000; 105(6): 1271 - 1278. [Abstract] [Full Text]
|
|
|
|
|
|
H. J. Geissler, P. Holzl, S. Marohl, F. Kuhn-Regnier, U. Mehlhorn, M. Sudkamp, and E. R. de Vivie Risk stratification in heart surgery: comparison of six score systems Eur. J. Cardiothorac. Surg., April 1, 2000; 17(4): 400 - 406. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D R Lawrence, O Valencia, E E J Smith, A Murday, and T Treasure Parsonnet score is a good predictor of the duration of intensive care unit stay following cardiac surgery Heart, April 1, 2000; 83(4): 429 - 432. [Abstract] [Full Text]
|
|
|
|
|
|
A. D. Bernstein and V. Parsonnet Bedside estimation of risk as an aid for decision-making in cardiac surgery Ann. Thorac. Surg., March 1, 2000; 69(3): 823 - 828. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. M. Graham, R. J. Chambers, and R. F. Davies ANGIOGRAPHIC QUANTIFICATION OF DIFFUSE CORONARY ARTERY DISEASE: RELIABILITY AND PROGNOSTIC VALUE FOR BYPASS OPERATIONS J. Thorac. Cardiovasc. Surg., October 1, 1999; 118(4): 618 - 627. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. D. Boyd, N. D. Desai, D. F. Del Rizzo, R. J. Novick, F. N. McKenzie, and A. H. Menkis Off-pump surgery decreases postoperative complications and resource utilization in the elderly Ann. Thorac. Surg., October 1, 1999; 68(4): 1490 - 1493. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. E. Cohn, F. W. Sellke, C. Sirois, A. Lisbon, and R. G. Johnson Surgical ICU Recidivism After Cardiac Operations Chest, September 1, 1999; 116(3): 688 - 692. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
O. Godje, P. Lamm, K. Adelhard, A. Schutz, E. Kilger, A. Gotz, T. Lange, H. Mair, and B. Reichart Surgical versus medical care for postoperative cardiac surgical patients at the general ward Eur. J. Cardiothorac. Surg., August 1, 1999; 16(2): 222 - 227. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. Roques, S.A.M. Nashef, P. Michel, E. Gauducheau, C. de Vincentiis, E. Baudet, J. Cortina, M. David, A. Faichney, F. Gavrielle, et al. Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19030 patients Eur. J. Cardiothorac. Surg., June 1, 1999; 15(6): 816 - 823. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Martinez-Alario, I. D. Tuesta, E. Plasencia, M. Santana, and M. L. Mora Mortality Prediction in Cardiac Surgery Patients : Comparative Performance of Parsonnet and General Severity Systems Circulation, May 11, 1999; 99(18): 2378 - 2382. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Ivanov, J. V. Tu, and C. D. Naylor Ready-Made, Recalibrated, or Remodeled? : Issues in the Use of Risk Indexes for Assessing Mortality After Coronary Artery Bypass Graft Surgery Circulation, April 27, 1999; 99(16): 2098 - 2104. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. M. Weightman, N. M. Gibbs, M. R. Sheminant, E. G. Whitford, B. D. Mahon, and M. A. J. Newman Drug Therapy Before Coronary Artery Surgery: Nitrates Are Independent Predictors of Mortality and {beta}-Adrenergic Blockers Predict Survival Anesth. Analg., February 1, 1999; 88(2): 286 - 286. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Mozes, L. Olmer, N. Galai, and E. Simchen A national study of postoperative mortality associated with coronary artery bypass grafting in Israel Ann. Thorac. Surg., October 1, 1998; 66(4): 1254 - 1262. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. F. Del Rizzo, W. D. Boyd, R. J. Novick, F. N. McKenzie, N. D. Desai, and A. H. Menkis Safety and cost-effectiveness of MIDCABG in high-risk CABG patients Ann. Thorac. Surg., September 1, 1998; 66(3): 1002 - 1007. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Carrier, L. C. Pelletier, R. Martineau, M. Pellerin, and B. C. Solymoss In elective coronary artery bypass grafting, preoperative troponin T level predicts the risk of myocardial infarction J. Thorac. Cardiovasc. Surg., June 1, 1998; 115(6): 1328 - 1334. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B Bridgewater, H Neve, N Moat, T Hooper, and M Jones Predicting operative risk for coronary artery surgery in the United Kingdom: a comparison of various risk prediction algorithms Heart, April 1, 1998; 79(4): 350 - 355. [Abstract] [Full Text]
|
|
|
|
|
|
J. Ivanov, R. D. Weisel, T. E. David, and C. D. Naylor Fifteen-Year Trends in Risk Severity and Operative Mortality in Elderly Patients Undergoing Coronary Artery Bypass Graft Surgery Circulation, February 24, 1998; 97(7): 673 - 680. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. G. Holloway Jr, D. M. Witter Jr, A. I. Mushlin, K. B. Lawton, M. P. McDermott, and G. P. Samsa Carotid Endarterectomy Trends in the Patterns and Outcomes of Care at Academic Medical Centers, 1990 Through 1995 Arch Neurol, January 1, 1998; 55(1): 25 - 32. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. P. Lippmann and D. M. Shahian Coronary Artery Bypass Risk Prediction Using Neural Networks Ann. Thorac. Surg., June 1, 1997; 63(6): 1635 - 1643. [Abstract] [Full Text]
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|
|
|
 |
|
 R. K. Orr Use of a Probabilistic Neural Network to Estimate the Risk of Mortality after Cardiac Surgery Med Decis Making, April 1, 1997; 17(2): 178 - 185. [Abstract] [PDF]
|
|
|
|
 |
|
 R. A. Perugini, R. K. Orr, D. Porter, E. M. Dumas, and B. S. Maini Gastrointestinal Complications Following Cardiac Surgery: An Analysis of 1477 Cardiac Surgery Patients Arch Surg, April 1, 1997; 132(4): 352 - 357. [Abstract] [PDF]
|
|
|
|
|
|
E. Bennett-Guerrero, L. Ayuso, C. Hamilton-Davies, W. D. White, G. R. Barclay, P. K. Smith, S. A. King, L. H. Muhlbaier, M. F. Newman, and M. G. Mythen Relationship of Preoperative Antiendotoxin Core Antibodies and Adverse Outcomes Following Cardiac Surgery JAMA, February 26, 1997; 277(8): 646 - 650. [Abstract] [PDF]
|
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|
|
|
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J. V. Tu and C. D. Naylor Coronary Artery Bypass Mortality Rates in Ontario: A Canadian Approach to Quality Assurance in Cardiac Surgery Circulation, November 15, 1996; 94(10): 2429 - 2433. [Abstract] [Full Text]
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J.-L. Trouillet, A. Scheimberg, A. Vuagnat, J.-Y. Fagon, J. Chastre, and C. Gibert LONG-TERM OUTCOME AND QUALITY OF LIFE OF PATIENTS REQUIRING MULTIDISCIPLINARY INTENSIVE CARE UNIT ADMISSION AFTER CARDIAC OPERATIONS J. Thorac. Cardiovasc. Surg., October 1, 1996; 112(4): 926 - 934. [Abstract] [Full Text]
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T. S. O. Kurki and M. Kataja Preoperative Prediction of Postoperative Morbidity in Coronary Artery Bypass Grafting Ann. Thorac. Surg., June 1, 1996; 61(6): 1740 - 1745. [Abstract] [Full Text]
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V. Ferraris, S. Ferraris, and S. L. H. Edmunds Jr RISK FACTORS FOR POSTOPERATIVE MORBIDITY J. Thorac. Cardiovasc. Surg., April 1, 1996; 111(4): 731 - 741. [Abstract] [Full Text]
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C. D. Naylor, G. H. Guyatt, Evidence-Based Medicine Working Group, E. Bass, H. Gerstein, D. Heyland, A. Holbrook, V. Moyer, T. Newman, A. Oxman, et al. Users' Guides to the Medical Literature: X. How to Use an Article Reporting Variations in the Outcomes of Health Services JAMA, February 21, 1996; 275(7): 554 - 558. [Abstract] [PDF]
|
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|
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M. R. Chassin, E. L. Hannan, and B. A. DeBuono Benefits and Hazards of Reporting Medical Outcomes Publicly N. Engl. J. Med., February 8, 1996; 334(6): 394 - 398. [Full Text]
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D. E. Maziak, V. Rao, G. T. Christakis, K. J. Buth, J. Sever, S. E. Fremes, and B. S. Goldman Can Patients With Left Main Stenosis Wait for Coronary Artery Bypass Grafting? Ann. Thorac. Surg., February 1, 1996; 61(2): 552 - 557. [Abstract] [Full Text]
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W. J. Flameng, P. Herijgers, J. Szecsi, P. T. Sergeant, W. J. Daenen, and I. Scheys Determinants of Early and Late Results of Combined Valve Operations and Coronary Artery Bypass Grafting Ann. Thorac. Surg., February 1, 1996; 61(2): 621 - 628. [Abstract] [Full Text]
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K. E. Hammermeister Risk, Predicting Outcomes, and Improving Care Circulation, February 1, 1995; 91(3): 899 - 900. [Full Text]
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