Mortality after cardiac bypass surgery: Prediction from administrative versus clinical data

Research output: Contribution to journalArticle

Jane M. Geraci, Michael L. Johnson, Howard S. Gordon, Nancy J. Petersen, A. Laurie Shroyer, Frederick L. Grover, Nelda Wray

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

Original languageEnglish (US)
Pages (from-to)149-158
Number of pages10
JournalMedical Care
Volume43
Issue number2
DOIs
StatePublished - Feb 1 2005

PMID: 15655428

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Mortality after cardiac bypass surgery : Prediction from administrative versus clinical data. / Geraci, Jane M.; Johnson, Michael L.; Gordon, Howard S.; Petersen, Nancy J.; Shroyer, A. Laurie; Grover, Frederick L.; Wray, Nelda.

In: Medical Care, Vol. 43, No. 2, 01.02.2005, p. 149-158.

Research output: Contribution to journalArticle

Harvard

Geraci, JM, Johnson, ML, Gordon, HS, Petersen, NJ, Shroyer, AL, Grover, FL & Wray, N 2005, 'Mortality after cardiac bypass surgery: Prediction from administrative versus clinical data' Medical Care, vol. 43, no. 2, pp. 149-158. https://doi.org/10.1097/00005650-200502000-00008

APA

Geraci, J. M., Johnson, M. L., Gordon, H. S., Petersen, N. J., Shroyer, A. L., Grover, F. L., & Wray, N. (2005). Mortality after cardiac bypass surgery: Prediction from administrative versus clinical data. Medical Care, 43(2), 149-158. https://doi.org/10.1097/00005650-200502000-00008

Vancouver

Geraci JM, Johnson ML, Gordon HS, Petersen NJ, Shroyer AL, Grover FL et al. Mortality after cardiac bypass surgery: Prediction from administrative versus clinical data. Medical Care. 2005 Feb 1;43(2):149-158. https://doi.org/10.1097/00005650-200502000-00008

Author

Geraci, Jane M. ; Johnson, Michael L. ; Gordon, Howard S. ; Petersen, Nancy J. ; Shroyer, A. Laurie ; Grover, Frederick L. ; Wray, Nelda. / Mortality after cardiac bypass surgery : Prediction from administrative versus clinical data. In: Medical Care. 2005 ; Vol. 43, No. 2. pp. 149-158.

BibTeX

@article{4524fa1f3ee4496abdae6f770b3236c0,
title = "Mortality after cardiac bypass surgery: Prediction from administrative versus clinical data",
abstract = "Background: Risk-adjusted outcome rates frequently are used to make inferences about hospital quality of care. We calculated risk-adjusted mortality rates in veterans undergoing isolated coronary artery bypass surgery (CABS) from administrative data and from chart-based clinical data and compared the assessment of hospital high and low outlier status for mortality that results from these 2 data sources. Study Population: We studied veterans who underwent CABS in 43 VA hospitals between October 1, 1993, and March 30, 1996 (n = 15,288). Methods: To evaluate administrative data, we entered 6 groups of International Classification of Diseases (ICD)-9-CM codes for comorbid diagnoses from the VA Patient Treatment File (PTF) into a logistic regression model predicting postoperative mortality. We also evaluated counts of comorbid ICD-9-CM codes within each group, along with 3 common principal diagnoses, weekend admission or surgery, major procedures associated with CABS, and demographic variables. Data from the VA Continuous Improvement in Cardiac Surgery Program (CICSP) were used to create a separate clinical model predicting postoperative mortality. For each hospital, an observed-to-expected (O/E) ratio of mortality was calculated from (1) the PTF model and (2) the CICSP model. We defined outlier status as an O/E ratio outside of 1.0 (based on the hospital's 90{\%} confidence interval). To improve the statistical and predictive power of the PTF model, selected clinical variables from CICSP were added to it and outlier status reassessed. Results: Significant predictors of postoperative mortality in the PTF model included 1 group of comorbid ICD-9-CM codes, intraortic balloon pump insertion before CABS, angioplasty on the day of or before CABS, weekend surgery, and a principal diagnosis of other forms of ischemic heart disease. The model's c-index was 0.698. As expected, the CICSP model's predictive power was significantly greater than that of the administrative model (c = 0.761). The addition of just 2 CICSP variables to the PTF model improved its predictive power (c = 0.741). This model identified 5 of 6 high mortality outliers identified by the CICSP model. Additional CICSP variables were statistically significant predictors but did not improve the assessment of high outlier status. Conclusions: Models using administrative data to predict postoperative mortality can be improved with the addition of a very small number of clinical variables. Limited clinical improvements of administrative data may make it suitable for use in quality improvement efforts.",
keywords = "Administrative data, Coronary artery bypass surgery, Mortality, Outlier, Risk adjustment",
author = "Geraci, {Jane M.} and Johnson, {Michael L.} and Gordon, {Howard S.} and Petersen, {Nancy J.} and Shroyer, {A. Laurie} and Grover, {Frederick L.} and Nelda Wray",
year = "2005",
month = "2",
day = "1",
doi = "10.1097/00005650-200502000-00008",
language = "English (US)",
volume = "43",
pages = "149--158",
journal = "Medical care",
issn = "0025-7079",
publisher = "Lippincott Williams and Wilkins",
number = "2",

}

RIS

TY - JOUR

T1 - Mortality after cardiac bypass surgery

T2 - Medical care

AU - Geraci, Jane M.

AU - Johnson, Michael L.

AU - Gordon, Howard S.

AU - Petersen, Nancy J.

AU - Shroyer, A. Laurie

AU - Grover, Frederick L.

AU - Wray, Nelda

PY - 2005/2/1

Y1 - 2005/2/1

N2 - Background: Risk-adjusted outcome rates frequently are used to make inferences about hospital quality of care. We calculated risk-adjusted mortality rates in veterans undergoing isolated coronary artery bypass surgery (CABS) from administrative data and from chart-based clinical data and compared the assessment of hospital high and low outlier status for mortality that results from these 2 data sources. Study Population: We studied veterans who underwent CABS in 43 VA hospitals between October 1, 1993, and March 30, 1996 (n = 15,288). Methods: To evaluate administrative data, we entered 6 groups of International Classification of Diseases (ICD)-9-CM codes for comorbid diagnoses from the VA Patient Treatment File (PTF) into a logistic regression model predicting postoperative mortality. We also evaluated counts of comorbid ICD-9-CM codes within each group, along with 3 common principal diagnoses, weekend admission or surgery, major procedures associated with CABS, and demographic variables. Data from the VA Continuous Improvement in Cardiac Surgery Program (CICSP) were used to create a separate clinical model predicting postoperative mortality. For each hospital, an observed-to-expected (O/E) ratio of mortality was calculated from (1) the PTF model and (2) the CICSP model. We defined outlier status as an O/E ratio outside of 1.0 (based on the hospital's 90% confidence interval). To improve the statistical and predictive power of the PTF model, selected clinical variables from CICSP were added to it and outlier status reassessed. Results: Significant predictors of postoperative mortality in the PTF model included 1 group of comorbid ICD-9-CM codes, intraortic balloon pump insertion before CABS, angioplasty on the day of or before CABS, weekend surgery, and a principal diagnosis of other forms of ischemic heart disease. The model's c-index was 0.698. As expected, the CICSP model's predictive power was significantly greater than that of the administrative model (c = 0.761). The addition of just 2 CICSP variables to the PTF model improved its predictive power (c = 0.741). This model identified 5 of 6 high mortality outliers identified by the CICSP model. Additional CICSP variables were statistically significant predictors but did not improve the assessment of high outlier status. Conclusions: Models using administrative data to predict postoperative mortality can be improved with the addition of a very small number of clinical variables. Limited clinical improvements of administrative data may make it suitable for use in quality improvement efforts.

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

KW - Administrative data

KW - Coronary artery bypass surgery

KW - Mortality

KW - Outlier

KW - Risk adjustment

UR - http://www.scopus.com/inward/record.url?scp=13544266214&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=13544266214&partnerID=8YFLogxK

U2 - 10.1097/00005650-200502000-00008

DO - 10.1097/00005650-200502000-00008

M3 - Article

VL - 43

SP - 149

EP - 158

JO - Medical care

JF - Medical care

SN - 0025-7079

IS - 2

ER -

ID: 3312950