PURPOSE: The Lee risk index was developed to predict major cardiac complications in noncardiac surgery. We retrospectively evaluated its ability to predict cardiovascular death in the large cohort of patients who recently underwent noncardiac surgery in our institution. METHODS: The administrative database of the Erasmus MC, Rotterdam, The Netherlands, contains information on 108 593 noncardiac surgical procedures performed from 1991 to 2000. The Lee index assigns 1 point to each of the following characteristics: high-risk surgery, ischemic heart disease, heart failure, cerebrovascular disease, renal insufficiency, and diabetes mellitus. We retrospectively used available information in our database to adapt the Lee index calculated the adapted index for each procedure, and analyzed its relation to cardiovascular death. RESULTS: A total of 1877 patients (1.7%) died perioperatively, including 543 (0.5%) classified as cardiovascular death. The cardiovascular death rates were 0.3% (255/75 352) for Lee Class 1, 0.7% (196/28 892) for Class 2, 1.7% (57/3380) for Class 3, and 3.6% (35/969) for Class 4. The corresponding odds ratios were 1 (reference), 2.0, 5.1, and 11.0, with no overlap for the 95% confidence interval of each class. The C statistic for the prediction of cardiovascular mortality using the Lee index was 0.63. If age and more detailed information regarding the type of surgery was retrospectively added, the C statistic in this exploratory analysis improved to 0.85. CONCLUSION: The adapted Lee index was predictive of cardiovascular mortality in our administrative database, but its simple classification of surgical procedures as high-risk versus not high-risk seems suboptimal. Nevertheless, if the goal is to compare outcomes across hospitals or regions using administrative data, the use of the adapted Lee index, as augmented by age and more detailed classification of type of surgery, is a promising option worthy of prospective testing.
PURPOSE: The Lee risk index was developed to predict major cardiac complications in noncardiac surgery. We retrospectively evaluated its ability to predict cardiovascular death in the large cohort of patients who recently underwent noncardiac surgery in our institution. METHODS: The administrative database of the Erasmus MC, Rotterdam, The Netherlands, contains information on 108 593 noncardiac surgical procedures performed from 1991 to 2000. The Lee index assigns 1 point to each of the following characteristics: high-risk surgery, ischemic heart disease, heart failure, cerebrovascular disease, renal insufficiency, and diabetes mellitus. We retrospectively used available information in our database to adapt the Lee index calculated the adapted index for each procedure, and analyzed its relation to cardiovascular death. RESULTS: A total of 1877 patients (1.7%) died perioperatively, including 543 (0.5%) classified as cardiovascular death. The cardiovascular death rates were 0.3% (255/75 352) for Lee Class 1, 0.7% (196/28 892) for Class 2, 1.7% (57/3380) for Class 3, and 3.6% (35/969) for Class 4. The corresponding odds ratios were 1 (reference), 2.0, 5.1, and 11.0, with no overlap for the 95% confidence interval of each class. The C statistic for the prediction of cardiovascular mortality using the Lee index was 0.63. If age and more detailed information regarding the type of surgery was retrospectively added, the C statistic in this exploratory analysis improved to 0.85. CONCLUSION: The adapted Lee index was predictive of cardiovascular mortality in our administrative database, but its simple classification of surgical procedures as high-risk versus not high-risk seems suboptimal. Nevertheless, if the goal is to compare outcomes across hospitals or regions using administrative data, the use of the adapted Lee index, as augmented by age and more detailed classification of type of surgery, is a promising option worthy of prospective testing.
Authors: Michael R Mathis; Milo C Engoren; Hyeon Joo; Michael D Maile; Keith D Aaronson; Michael L Burns; Michael W Sjoding; Nicholas J Douville; Allison M Janda; Yaokun Hu; Kayvan Najarian; Sachin Kheterpal Journal: Anesth Analg Date: 2020-05 Impact factor: 5.108