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Anesthesia & Analgesia:
doi: 10.1213/ANE.0000000000000187
Critical Care, Trauma, and Resuscitation: Research Report

The Association Between ASA Status and Other Risk Stratification Models on Postoperative Intensive Care Unit Outcomes

Lupei, Monica I. MD*; Chipman, Jeffrey G. MD; Beilman, Gregory J. MD; Oancea, S. Cristina PhD; Konia, Mojca R. MD, PhD§

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From the *Anesthesiology Critical Care Medicine Department, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Surgery Department, University of Minnesota School of Medicine, Minneapolis, Minnesota; Master of Public Health Program, Department of Family & Community Medicine, University of North Dakota, Grand Forks, North Dakota; and §Anesthesiology Department, University of Minnesota School of Medicine, Minneapolis, Minnesota.

Funding: Not applicable.

The authors declare no conflicts of interest.

Reprints will not be available from the authors.

Address correspondence to Monica I. Lupei, MD, Anesthesiology Critical Care Medicine Department, University of Minnesota, B515 Mayo Memorial Building, 420 Delaware St., SE Minneapolis, MN 55455. Address e-mail to lupei001@umn.edu.

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Abstract

BACKGROUND: There is limited medical literature investigating the association between perioperative risk stratification methods and surgical intensive care unit (SICU) outcomes. Our hypothesis contends that routine assessments such as higher ASA physical status classification, surgical risk as defined by American College of Cardiology/American Heart Association guidelines, and simplified Revised Cardiac Index (SRCI) can reliably be associated with SICU outcomes.

METHODS: We performed a chart review of all patients 18 years or older admitted to the SICU between October 1, 2010, and March 1, 2011. We collected demographic and preoperative clinical data: age, sex, ASA physical status class, surgical risk, and SRCI. Outcome data included our primary end point, SICU length of stay, and secondary end points: mechanical ventilation and vasopressor treatment duration, number of acquired organ dysfunctions (NOD), readmission to the intensive care unit (ICU) within 7 days, SICU mortality, and 30-day mortality. Regression analysis and nonparametric tests were used, and P < 0.05 was considered significant.

RESULTS: We screened 239 patients and included 220 patients in the study. The patients’ mean age was 58 ± 16 years. There were 32% emergent surgery and 5% readmissions to the SICU within 7 days. The SICU mortality and the 30-day mortality were 3.2%. There was a significant difference between SICU length of stay (2.9 ± 2.1 vs 5.9 ± 7.4, P = 0.007), mechanical ventilation (0.9 ± 2.0 vs 3.4 ± 6.8, P = 0.01), and NOD (0 [0–2] vs 1 [0–5], P < 0.001) based on ASA physical status class (≤ 2 vs ≥ 3). Outcomes significantly associated with ASA physical status class after adjusting for confounders were: SICU length of stay (incidence rate ratio [IRR] = 1.79, 95% confidence interval [CI], 1.35–2.39, P < 0.001), mechanical ventilation (IRR = 2.57, 95% CI, 1.69–3.92, P < 0.001), vasopressor treatment (IRR = 3.57, 95% CI, 1.84–6. 94, P < 0.001), NOD (IRR = 1.71, 95% CI, 1.46–1.99, P < 0.001), and readmission to ICU (odds ratio = 3.39, 95% CI, 1.04–11.09, P = 0.04). We found significant association between surgery risk and NOD (IRR = 1.56, 95% CI, 1.29–1.89, P < 0.001, and adjusted IRR = 1.31, 95% CI, 1.05–1.64, P = 0.02). SRCI was not significantly associated with SICU outcomes.

CONCLUSIONS: Our study revealed that ASA physical status class is associated with increased SICU length of stay, mechanical ventilation, vasopressor treatment duration, NOD, readmission to ICU, and surgery risk is associated with NOD.

The medical literature pertaining to the assessment of perioperative risks and the correlation with the surgical intensive care unit (SICU) outcomes is limited. Increased intensive care unit (ICU) length of stay has been shown to be associated with increased social, psychological, and economical burden on patients and their family,1 poor patient outcome,2,3 and increased health care costs.4 Many models have been developed to risk-stratify surgical patients and predict their outcomes, such as morbidity, mortality, cost, and patient satisfaction. The challenge of many of the risk stratification models is in their complexity and impracticality. There are a few easy-to-use assessments such as the ASA physical status class, American College of Cardiology/American Heart Association (ACC/AHA) surgical risk, and simplified Revised Cardiac Index (SRCI).

ASA physical status class was introduced for research purposes in 1941.5 ASA physical status class was revised and simplified in 1961 by Dripps et al.6 and found to be associated with increased surgical mortality. Subsequent studies reiterated the association between ASA physical status class and perioperative mortality.7,8 Despite initial introduction as a research tool, ASA physical status class has been used worldwide as a perioperative assessment tool for more than 60 years.9 A group of German researchers demonstrated in 1993 that a higher ASA physical status class is associated with increased hospital and ICU admission.10 Another large prospective study showed that ASA physical status class is associated with increased mortality and a decline in morbidity perioperative outcomes, including ICU length of stay.11 ASA physical status class and Goldman cardiac risk index were studied as predictors of mortality and their combination was found to be more accurately associated with perioperative mortality.12 In 1999, Lee et al.13 published the study that validated the Revised Cardiac Index (RCI) including 6 independent predictors for assessing cardiac risk before elective noncardiac surgery. A retrospective study published in 2011 revealed that RCI, ASA physical status class IV, and emergency surgery were independent predictors for 30-day mortality.14 The ACC/AHA stratified the risk of surgical procedure in a different way than the RCI: low, intermediate, and high risk with an estimated 30-day major cardiac event rate of <1%, 1% to 5%, and >5%, respectively.15

The goal of our study was to assess whether ASA physical status class, SRCI, and surgical risk as defined by AHA/ACC could be associated with SICU outcomes. Because the association with mortality outcomes has already been widely studied, we focused on SICU length of stay and morbidity outcomes.

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METHODS

Our study was a retrospective, observational, single-center study conducted at the 20-bed SICU of the University of Minnesota Fairview Hospital. The IRB of the University of Minnesota approved this study, and written consent was obtained from all subjects or a legal surrogate. Patients 18 years or older admitted to the SICU after surgery from October 1, 2010, to March 1, 2011, who gave consent for research on admission were included in the study, excluding neurosurgery and cardiac surgery patients.

Data retrieved from the patient electronic and paper medical records included demographic and preoperative clinical data (age, sex, ASA physical status class, SRCI, surgical risk, and emergent surgery) and outcome data (SICU length of stay, mechanical ventilation and vasopressor treatment duration, number of acquired organ dysfunctions, readmission to the SICU within 7 days, SICU mortality, and 30-day mortality). Our primary end point was SICU length of stay. The secondary end points included mechanical ventilation duration, vasopressor treatment duration, number of acquired organ dysfunctions, readmission to the SICU within 7 days, SICU mortality, and 30-day mortality.

ASA physical status class is defined according to the American Society of Anesthesiologists’ Relative Value Guidea that is published each year. There are 6 ASA physical status classes and the letter E is added if the surgery is emergent: I—a normal healthy patient, II—a patient with mild systemic disease, III—a patient with severe systemic disease, IV—a patient with severe systemic disease that is a constant threat to life, V—a moribund patient who is not expected to survive without the operation, and VI—a declared brain-dead patient whose organs are being removed for donor purposes. The SRCI definition includes 5 of the 6 categories of the RCI introduced and validated by Lee et al.13 in 1999 without including the high-risk surgery criteria: history of ischemic heart disease (mild angina pectoris or previous myocardial infarction by history or Q wave), history of congestive heart failure, history of cerebrovascular disease, diabetes mellitus insulin dependent, and renal insufficiency with creatinine >2 mg/dL. The surgical risk was defined using the ACC/AHA guidelines revised in 200915 and was classified as: low (<1%)—endoscopic, superficial procedures, cataract surgery, breast surgery, and ambulatory surgery; intermediate (1%–5%)—intraperitoneal and intrathoracic procedures, carotid endarterectomy, head and neck surgery, orthopedic surgery, and prostate surgery; and high (>5%)—major vascular surgery and peripheral vascular surgery. We considered the surgery risk 1 if low, 2 if intermediate, and 3 if high, based on ACC/AHA guidelines. Acquired organ dysfunction was evaluated according to the Multiple Organ Dysfunction Score16 and Sequential Organ Failure Assessment score17 definitions. We assigned 1 point for each organ system dysfunction with a maximum of 6 points.

Most of the patients admitted to the SICU had surgery under general anesthesia as per our institution’s standard of practice. The patients were admitted to the SICU directly from the operating room if they were already SICU patients who had emergent surgery; otherwise, they were admitted from the postanesthesia care unit.

Data were analyzed using SAS version 9.2 (SAS Institute Inc., Cary, NC). We calculated the mean and standard deviation (SD) for continuous variables, median and range for discrete variables, and number and percentage for categorical variables. We grouped patients according to ASA physical status class into 1 group consisting of patients with ASA physical status class ≤ 2 and a second group consisting of patients with ASA physical status class ≥ 3 for univariable analysis. We performed power analysis and calculated a sample size of 200 to obtain a P value of 0.01, a power of 80%, a mean difference of 3, and an SD difference of 4 between the SICU length of stay as a function of ASA physical status class. We divided the patients in groups according to their surgical risk, SRCI, and emergent surgery status for univariable analysis.

Statistical analysis included the nonparametric Kolmogorov-Smirnov and Kruskal-Wallis tests for continuous and ordinal variables considering the skewed data distribution. For categorical variables, we used χ2 test and Fisher exact test as necessary. All tests were 2-tailed with P < 0.05 considered significant. We used logistic regression for dichotomous outcomes (readmission to ICU within 7 days), Poisson regression for count outcomes that have means and variances approximately the same (number of acquired organ dysfunctions), and negative binomial regression for count outcomes that are overdispersed (SICU length of stay, mechanical ventilation, and vasopressor treatment duration). To assess if the regression models’ estimates fit the data, we used goodness-of-fit χ2 test and Hosmer and Lemeshow goodness-of-fit test. We reported incidence rate ratio (IRR), odds ratio, and 95% 2-sided confidence interval (CI) for the corresponding outcomes. To account for confounding effects, we included possible confounders in our multivariable regression models based on existing literature and clinical expertise: age, sex, ASA physical status class, SRCI, surgical risk, and emergent surgery.

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RESULTS

We screened 239 patients admitted to SICU during a 5-month time frame; 19 were excluded based on missing consent for research before the ICU admission. Two hundred twenty patients with complete data were included in our statistical analysis. Table 1 summarizes the demographic characteristics, the preoperative clinical data, and the outcome data of all patients. Of 220 patients, 3 (1.5%) were ASA physical status class I, 23 (10%) ASA physical status class II, 158 (72%) ASA physical status class III, 33 (15%) ASA physical status class IV, and 3 (1.5%) ASA physical status class V. The patient distribution according to SRCI criteria was as follows: 0 corresponded to 125 patients (57%), 1 to 50 patients (22.5%), 2 to 33 patients (15%), 3 to 11 patients (5%), and 4 to 1 patient (0.5%). The surgical risk was 1 in 16 (7%), 2 in 178 (81%), and 3 in 26 (12%) patients.

Table 1
Table 1
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We performed univariable analysis and divided patients in 2 groups according to the ASA physical status class: 1 group included patients with ASA physical status class ≤2, and the second group included patients with ASA physical status class ≥3 (Table 2). There was a significant difference between SICU length of stay (2.9 ± 2.1 vs 5.9 ± 7.4, P = 0.007), mechanical ventilation (0.9 ± 2.0 vs 3.4 ± 6.8, P = 0.01), and number of acquired organ dysfunctions (0 [0–2] vs 1 [0–5], P < 0.001) based on ASA physical status class (≤2 vs ≥3). We divided patients into 3 groups based on their surgical risk 1, 2, or 3 (Table 3). There was a significant difference between groups regarding the emergent surgery percentage and ASA physical status class. The difference was statistically significant between surgery risk 1 and 2 and surgery risk 1 and 3 regarding SICU length of stay and mechanical ventilation duration. We also found a statistically significant difference between surgery risk 1 and 3 and surgery risk 2 and 3 regarding sex distribution and number of organ dysfunctions (Table 3).

Table 2
Table 2
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Table 3
Table 3
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We found no differences between the SICU outcomes by univariable analysis while dividing the patients based on their emergent surgery status and SRCI. There was no significant association between the preoperative clinical variables (ASA physical status class, SRCI, and surgery risk) and mortality outcomes.

The information obtained by univariable analysis was used to perform regression analysis. In the multivariable regression analysis, we considered sex, age, emergent surgery, SRCI, and surgery risk as possible confounders. After adjusting for confounders, the multivariable regression analysis revealed that ASA physical status class is significantly associated with SICU length of stay (IRR = 1.79, 95% CI, 1.35–2.39, P < 0.001), mechanical ventilation duration (IRR = 2.57, 95% CI, 1.69–3.92, P < 0.001), vasopressor treatment duration (IRR = 3.57, 95% CI, 1.84–6.94, P < 0.001), number of acquired organ dysfunction (IRR=1.71, 95% CI, 1.46–1.99, P < 0.001), and readmission to SICU within 7 days (odds ratio = 3.39, 95% CI, 1.04–11.09, P = 0.02). The interpretation of the multivariable analysis (Table 4) reveals that for each unit increase in ASA physical status class, there was a 79% increase in the rate of SICU length of stay, 157% increase in the rate of mechanical ventilation duration, 257% increase in the rate of vasopressor treatment duration, and 71% increase in the rate of number of acquired organ dysfunction. The univariable and multivariable regression analyses revealed significant association between surgery risk and number of organ dysfunction (IRR = 1.56, 95% CI, 1.29–1.89, P < 0.001, and adjusted IRR = 1.31, 95% CI, 1.05–1.64, P = 0.02).

Table 4
Table 4
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DISCUSSION

We used univariable analysis information to guide the regression analysis. Our study revealed an association between ASA physical status class and important SICU outcomes: SICU length of stay, duration of mechanical ventilation, duration of vasopressor treatment, number of acquired organ dysfunctions, and readmission to the SICU. There was a significant difference between sex, emergency surgery status, ASA physical status class, SICU length of stay, mechanical ventilation duration, and number of acquired organ dysfunctions based on surgical risk. SRCI was not significantly associated with SICU outcomes. The emergent nature of the procedure was not associated with worse SICU outcomes in our population, by univariable and regression analysis. There was no association of any preoperative clinical data with mortality.

ASA physical status class is an assessment measure based on physical status and has been associated with increased postoperative mortality6,7 and postoperative complications18,19 including increased ICU length of stay.11 ASA physical status class accuracy was challenged by a historical survey that tested the consistency of ASA physical status classification by different anesthesia providers based on hypothetical scenarios.20 ASA physical status class is a very simple assessment measure based on physical status that has been associated with postoperative mortality and postoperative complications despite the question of inconsistency across different anesthesia providers. Our present study was adequately powered to show a difference of 3 days between the mean in SICU length of stay based on ASA physical status class by univariable analysis, which supports good association between ASA physical status class and SICU length of stay. In addition, our study also demonstrated association between ASA physical status class and most of the other assessed SICU outcomes, such as duration of mechanical ventilation, duration of vasopressor treatment, number of acquired organ dysfunctions, and readmission to the SICU. The readmission to SICU was found to be associated with increased hospital mortality in a prospective observational study.21 In our study, readmission to the ICU is not associated with increased mortality, perhaps because of the lower readmission rate (5%) and SICU mortality (3.2%) in our patient population.

Our study did not find a significant association of SRCI with SICU outcomes, probably because most of our patients (79.5%) had 0 or 1 criteria which correlated with <1% cardiac complications in the study by Lee et al.13 When patients were stratified by these criteria, the risk of major cardiac complications with 0, 1, 2, and 3+ criteria was 0.4% to 0.5%, 0.9% to 1.3%, 4% to 7%, and 9% to 11%, respectively.13 The RCI validated by Lee et al.13 in 1999 is considered the “gold standard” for prediction of perioperative cardiovascular major events, and 5 of its criteria are used in the ACC/AHA guidelines.15 Other studies contested the RCI prediction accuracy for cardiovascular complications and/or mortality perioperatively.22,23

We used a simple grading system for surgery based on ACC/AHA guidelines15 and found that surgical risk is associated with the SICU outcomes in univariable analysis. Most of our patients (81%) were stratified as intermediate risk. According to 1 large prospective study, the rate of serious adverse events postoperatively was 16.9%, and mortality was 7.1%.24 Efforts for surgery stratification and assessment of risks were studied by various teams, and scores such as POSSUM and IRIS use complex models to predict surgical outcomes.25,26 By dividing the patients based on surgical risk, we demonstrated significant differences not only between SICU outcomes but also between sex and emergency surgery status distribution. The patients who had vascular surgery were predominantly men and had emergent procedures. Furthermore, abdominal aortic aneurysms are more common in men.27 The likelihood of emergent surgery in patients with surgical risk 3 in our institution may be related to higher frequency of organ transplantation.

We did not find an association between emergent surgery (32% of our patients) and ICU outcomes. Due to the small sample size, the regression analysis for emergent surgery revealed large 95% 2-sided CI for vasopressor treatment duration and readmission to SICU within 7 days, but narrow 95% CI for other variables, most likely due to the variance difference (Table 4). Emergent surgery status was associated with worse postoperative outcomes in numerous studies.7,19,26 One study found emergency status association with shorter hospital stay, with most of the patients having emergent surgery being ASA physical status I.28 We extrapolate that ASA physical status class is a more powerful risk factor than the emergency status alone.

There was no association between preoperative clinical variables and mortality in our study. ICU mortality rates vary widely among studies from 7% to 15%.29–31 SICU mortality is lower in our institution than in most of the published studies; therefore, we might not see a significant influence of the preoperative clinical data on mortality rates.

Our study has several limitations. The retrospective design of our study could have generated confounders that are not assessed in the study. We did not assess for comorbidities, such as respiratory disease or malignancy, but ASA physical status class is increased based on comorbidities severity. Nineteen of the 239 patients screened were excluded based on missing consent before ICU admission. The reason for autoexclusion might have been the severity of the illness, which impeded the patients’ ability to sign consent for research on admission. Our sample size was relatively small but adequately powered to show a clinically significant difference between the SICU lengths of stay based on ASA physical status class by univariable analysis. We acknowledge the limitation due to smaller sample size that did not allow us to perform a thorough investigation of the interaction effects by multivariable regression analysis. Neurosurgery patients were excluded, since they were treated by a different team, similar to the study by Lee et al.13 Other studies found that neurosurgery patients have a lower risk for serious adverse events preoperatively than thoracic or general surgery patients.21,24 Cardiac surgery patients were excluded as well considering the specifics of physiologic changes after cardiopulmonary bypass and its effects on ICU stay.

In conclusion, we evaluated a limited number of relatively easy-to-use risk assessment tools used frequently in everyday practice of an anesthesiologist. Our study results confirm the importance of ASA physical status class for the appreciation of postoperative complications and SICU outcomes. Our study further supports the use of ASA physical status class and surgical risk as important assessment tools for SICU risk stratification and association with serious nonfatal morbidity and resource utilization.

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DISCLOSURES

Name: Monica I. Lupei, MD.

Contribution: This author contributed to the design and the conduct of the study, data collection, data interpretation and analysis, and the manuscript preparation.

Attestation: Monica I. Lupei approved the final manuscript and attests to the integrity of the original data and the analysis reported in the manuscript. This author is the archival author who is responsible for maintaining the study records.

Name: Jeffrey G. Chipman, MD.

Contribution: This author helped design the study and prepare the manuscript.

Attestation: Jeffrey G. Chipman approved the final manuscript.

Name: Gregory J. Beilman, MD.

Contribution: This author helped design the study and prepare the manuscript.

Attestation: Gregory J. Beilman approved the final manuscript.

Name: S. Cristina Oancea, PhD.

Contribution: This author contributed to the data interpretation and analysis and the manuscript preparation.

Attestation: S. Cristina Oancea approved the final manuscript.

Name: Mojca R. Konia, MD, PhD.

Contribution: This author contributed to the study design, data collection, interpretation and analysis, and the manuscript preparation.

Attestation: Mojca R. Konia attests to the integrity of the original data and the analysis reported in the manuscript. This author approved the final manuscript.

This manuscript was handled by: Steven L. Shafer, MD.

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ACKNOWLEDGMENTS

The authors thank Dr. Ioanna Apostolidou, MD, PhD, Associate Professor of Anesthesiology, University of Minnesota, for providing advice and recommendations for the manuscript preparation.

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FOOTNOTE

a Available at: http://www.asahq.org/Home/For-Members/Clinical-Information/ASA-Physical-Status-Classification-System. Accessed January 17 2014. Cited Here...

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