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*Department of Anesthesiology and Intensive Care Medicine, University of Ulm, Ulm, Germany; and
Department of Medical Informatics, University of Utah, Salt Lake City, Utah
Address correspondence and reprint requests to Ulrich Bothner, MD, Department of Medical Informatics, University of Utah, School of Medicine AB193, Salt Lake City, UT 84132. Address e-mail to ulrich.bothner{at}m.cc.utah.edu
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Abstract |
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Implications: It is desirable to know how anesthesia-related incidents, events, and complications influence postanesthesia care. Analyses of standardized and routine perioperative outcome data, as proposed by the German anesthesia quality project, can show that even minor events consume relevant resources and are thus important to measure and follow.
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Introduction |
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Individual patient condition, as well as type of anesthesia and surgery, dictates requirements for postanesthesia treatment and monitoring (3,4). In addition, it is appealing to measure the impact of immediate anesthesia-related adverse events. For this purpose, all irregularities must be addressed; not only major and fatal events, but especially minor and frequent everyday occurrences. It would also be desirable to know which incidents, events, and complications (IECs) are trivial and without consequence to further therapy and observation, and minor IECs that frequently and significantly consume anesthesia department capacities.
Following the quality assurance and cost-containment regulations imposed by German healthcare law, the German Society of Anesthesiology and Intensive Care Medicine (Deutsche Gesellschaft für Anästhesiologie und Intensivmedizin [DGAI]) has launched a long-term study project (5). The aim of the project is to implement and evaluate a methodology for the standardized routine reporting of perioperative anesthesia-related IECs. Starting in 1992, our department was one of the first volunteer centers in this nationwide program.
In this article, we attempt to (a) describe results from measuring distinct minor and severe outcome characteristics of the immediate perioperative anesthesia patient care process; and (b) calibrate minor IECs as a significant source of PACU utilization requirement and validate the IEC severity grading against the DGAI project definitions.
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Methods |
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Patients can bypass the PACU after surgery under two conditions: (a) if no further anesthetic coverage is anticipated e.g., after a very short procedure with short-acting anesthesia; or (b) after long and extended procedures when patients are typically transferred either extubated to an intermediate care unit (operated by surgeons) or possibly endotracheally intubated for further ventilatory and intensive care treatment on an ICU (operated by anesthesiologists).
Patients can be dismissed from the PACU to the intermediate care unit or general ward if they are awake, fully conscious, hemodynamically and respiratory stable, and have adequate treatment for analgesia, nausea, and vomiting. For patients who have undergone regional anesthesia, a sufficient reduction in the level of the neurologic blockade must be discernible before transfer.
Vital signs are continuously monitored with central alarming and supplemental oxygen, but no ventilator therapy is usually available in the intermediate care unit. In the patient rooms on the general wards, nurses perform intermittent monitoring of vital signs (heart rate, blood pressure, and body temperature); there is usually no supplemental oxygen, no oximetry, and no continuous electrocardiograph monitoring available.
Bivariate statistics for categorical data were computed by using the Pearson 
2 test for contingency tables. If >10% of the cells had expected values <5 or if there were expected cells with values <1, categories were combined with the nearest lower level. Multivariate statistics were calculated by using the multiple linear regression module of the SPSS statistical package (SPSS Inc., Chicago, IL) using a stepwise variable selection algorithm and a dummy variable approach (8). Outlier characteristics for the continuous variables age, duration of anesthesia in the anesthetizing locations, and time in the PACU were considered above mean plus two times the standard deviation of the raw data. The current study was approved by the institutional review board of the University of Utah.
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Results |
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Mean (± SD) patient age was 48.3 ± 19.5 yr. Mean duration of anesthesia and PACU length of stay were 100.0 ± 50.8 min and 78.9 ± 49.5 min, respectively. After removal of outliers, data were assumed to fulfill requirements for parametric statistics. For patients treated in the PACU, mild (I or II), moderate (III), and severe (IV or V) IECs occurred in 13.8%, 1.9%, and 1.2% of patients, respectively. For patients treated in the PACU, mild, moderate, and severe IECs occurred in 20.5%, 1.6%, and 0.2% of patients, respectively.
As a first step, possible confounding of IEC incidence and PACU length of stay with additional risk and case severity variables (covariables) was considered. Therefore, bivariate analyses with contingency tables were performed in two dimensions: (a) between the maximal grade of IEC occurring in each procedure and all covariables and (b) between the PACU length of stay and all covariables. Figures 1 and 2 summarize the associations broken down into distinct categories of the covariables as included in the study scheme (Table 2).
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2 test: P < 0.001) with the incidence of IECs (Figure 1), except for patient gender (P = 0.29). Bivariate statistical association of all covariables was significant (P < 0.001) with PACU length of stay (Figure 2). Thus, all covariables had to be considered as confounders for which a multivariate approach must be used. For all procedures with minor IECs (grades I–III), a multiple linear regression model was built with PACU length of stay as the dependent variable. In addition to the continuous variables, discrete data were coded and entered as dummy variables (1 = present, 0 = not present) representing all categories. All covariables of the assessment scheme were found to be significant predictors, except for the patient’s age and ASA physical status II being indistinguishable from ASA physical status I. Thus, ASA physical status I and II were combined to form one reference category.
Estimation and significance of the regression model is shown in Figure 3, in which the regression coefficient value of a dummy variable can be interpreted as the mean difference of PACU length of stay of that category against a zero reference category. In addition, mean difference estimates were adjusted for the level of all covariables in the model. Occurrence of an IEC grade I thus predicts a significant but clinically short prolongation (6%) of mean PACU length of stay, whereas IEC grades II and III indicate significant and more relevant prolongation (26% and 23%) of mean PACU length of stay compared with the overall mean PACU length of stay.
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Discussion |
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Research in anesthesiology has always been concerned with severe or fatal perioperative events such as death, myocardial infarction, or pulmonary embolism. Although severe events are also covered by the IEC definitions, the project’s scope has been particularly broadened to study minor events. Minor events may better reflect the everyday challenge of anesthesia practice and the ability of the provider to meet the demands of a heterogeneous real-life patient population. There is growing experience that the traditional approach of conducting morbidity and mortality studies, which only investigate severe events, is not a sufficient outcome quality measure in anesthesia today. Minor IECs happen frequently and, therefore, probably have significant implications for anesthesia economics. However, the project has not advanced to the stage of directly associating IECs and costs. After collecting data from more than 100 sites in a national database, the nationwide IEC project has entered the phase of intersite comparisons. These data are not yet ready for publication. Our article is a first analysis of the data collected by our department. We studied the utilization of PACU services in regard to length of patient stay, which seemed to be more directly measurable than costs.
Fleisher et al. (9) explained the opportunities and limitations of conducting observational studies. Although these types of studies are supposed to present weaker evidence for particular questions, compared with randomized controlled trials, they fill knowledge gaps where information is not otherwise available. They better serve to generate hypotheses than to infer biological causality. If we discover, however, which variables are associated with better or worse outcomes, it might be worthwhile to consider changes in the long and complex chain of process steps in healthcare to improve outcomes. Constant evaluation, however, is essential to monitor outcome changes. In addition, focused clinical trials may still be applied to scrutinize new hypotheses.
We certainly never see the ideal "randomized" patient in practice, and we certainly never will have a totally uniform, standardized system for healthcare professionals to practice medicine, despite all guideline and practice policy efforts. Outcome quality studies must embrace the huge disparity of practice among healthcare professionals and the uniqueness of each patient’s condition. Their purpose is to detect problems not yet perceived or anticipated. New methods in epidemiology and medical informatics are required to process observable empirical data to extract new and relevant knowledge. Therefore, outcome quality research today is quite different from experimental research, in which conditions are strictly controllable and populations are highly constrained.
There is no general agreement on defining criteria for IECs, e.g., for hypotension. One could try to extract rules from dozens of pages of standard anesthesia textbooks. In practice, however, it still takes the complex interpretation of an experienced anesthesiologist to perceive a blood pressure problem in the clinical context. Therefore, the definitions of IECs are not simply related to numeric thresholds or measurements. Technical measurements, if available, certainly help to characterize the problem, but many problems cannot be measured with instruments at all (e.g., intubation problems). Thus, the ultimate classification of an IEC is solely based on its clinical consequence and is a product of the reasoning and decision-making process of the responsible clinician. That is why assessing interobserver agreement on IEC on those grounds is difficult.
We applied the proposed severity coding system to a five-year patient population with respect to the consumption of treatment and monitoring time in the PACU. The results suggest that PACU time prolongation due to grade I IECs, although statistically significant for a large population, is probably not clinically relevant given the small amount of the mean difference (
t = 3.3 min). PACU time prolongation due to grade II and II IECs was both significant and clinically relevant (t = 13.2 and 11.6 min, respectively). However, there was no discrimination between grade II and III. Because grade III are much more frequent in high-risk patients undergoing longer and more invasive surgical procedures, these patients were more likely to be transferred to the intermediate care unit after a short recovery period in the PACU. In the intermediate care unit, basic monitoring and treatment facilities were available, allowing a rapid and safe discharge from a busy PACU. This probably introduces a substantial bias in the direction of shorter PACU time for patients with grade III IECs. Today, only the actual discharge destination from the OR or the PACU is stored in the minimal data set. Grade IV indicates an IEC that was the reason for an unplanned transfer of the patient to the intermediate care unit or ICU. Thus, in addition to the minimal data set variables, it is desirable to have more data about the discrepancy between the planned and actual courses of the patient. Another problem is the time difference between readiness for discharge and the actual transfer to the next step in the care plan, which is influenced by other institutional variables (3). Readiness for discharge may be more rigorously defined with data according to the classic Aldrete score (10), a modified version of which is increasingly used in anesthesia for ambulatory surgery (11).
In an earlier phase of the DGAI project, our study group demonstrated the epidemiology of risk features and IEC incidence (12). We evaluated the phenomenon of IEC occurrence in populations at high risk (13,14) and studied the association of quality of process variables as viewed by an anesthesiologist versus outcome variables and satisfaction as perceived by the patient (15). In addition, we addressed the possibility of a broad perioperative risk assessment (16). In a recent study, we evaluated the long-term stability and documentation discipline of the IEC reporting process (17). The current study is a further step in validating the IEC methodology.
There is still a controversial and primarily nonacademic discussion about the perioperative outcome project in Germany. One opinion is that it is not necessary for an anesthesiologist to report complications, especially minor ones that do not lead to a definite unwanted outcome. The results of this study suggest the opposite. If we can show, and possibly predict, which patients are prone to IECs, and if we can quantify the impact of IECs on postanesthesia treatment requirements, we may have a better basis with which to make management and allocation decisions. This methodology operates on an aggregate or "macro" level (e.g., department or hospital staffing and reimbursement), whereas other methods address PACU utilization more directly, such as daily PACU staffing needs (2). There is continuing research on how particular conditions or procedures affect PACU utilization (3,4). Postoperative pain, nausea and vomiting, regional anesthesia, anesthesia with short-acting anesthetics, or rearrangements of OR schedules and PACU admissions (18) are only the most eminent examples. In contrast, the aim of the IEC methodology was twofold: (a) to document perioperative problems and their consequences on a comprehensive real-life population in a single institution and (b) to compare the results across different institutions. With respect to the first, the definitions of the IECs are quite similar to those of other anesthesia outcome research projects (19–21). After the first attempts to benchmark 70 national anesthesia departments, adjusting for provider levels of service proved to be a difficult task (22). Designing and evaluating interventions for quality improvement still lie ahead. At this point, the database may best be used for basic epidemiologic and economic associations.
Classic scientific medicine has always been driven by an ethical imperative to improve outcomes. Today, the paradigm seems to be shifted to the least expensive treatment option while hopefully not degrading outcomes. There is not much doubt about the benefits of the routine services of a PACU. However, highly equipped facilities and specifically trained personnel are inevitably expensive. Thus, anesthesiologists must sustain and provide data on outcomes when cost-reductions take place. The IEC methodology intends to provide routine means for doing this.
We conclude that the proposed methodology facilitates integration of epidemiologic information about perioperative outcome from individual routine anesthesia procedures. The results of this study indicate that minor but frequently occurring adverse events significantly affect postanesthesia care utilization and are thus important to measure and follow. Assessment of minor IECs is possible and worthwhile, and it provides valuable information about the perioperative anesthesia process outcome.
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Acknowledgments |
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We thank Susan D. Horn, PhD, and Reed M. Gardner, PhD, of Medical Informatics, University of Utah, for their help in critically discussing and improving the manuscript. In particular, we thank all our anesthesiology department staff and documentation team for assessing and validating a myriad of protocols.
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Footnotes |
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  X. Paoletti and J. Marty Consequences of running more operating theatres than anaesthetists to staff them: a stochastic simulation study Br. J. Anaesth., April 1, 2007; 98(4): 462 - 469. [Abstract] [Full Text] [PDF]
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S. Fasting and S. E. Gisvold Equipment problems during anaesthesia--are they a quality problem? Br. J. Anaesth., December 1, 2002; 89(6): 825 - 831. [Abstract] [Full Text] [PDF]
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