- Email: [email protected]
Impact of cardiac intraoperative precursor events on adverse outcomes
Impact of cardiac intraoperative precursor events on adverse outcomes Daniel R. Wong, MD, MPH,a David F. Torchiana, MD,b Thomas J. Vander Salm, MD,c Arvind K. Agnihotri, MD,b Richard M. J. Bohmer, MBChB, MPH,d and Imtiaz S. Ali, MD,a Halifax, NS, Canada, Boston and Salem, Mass
Background. Although extensive study has been directed at the influence of patient factors and comorbidities on cardiac surgical outcomes, less attention has been focused on process. We sought to examine the relationship between intraoperative precursor events (those events that precede and are requisite for the occurrence of an adverse event) and adverse outcomes themselves. Methods. Anonymous, prospectively collected intraoperative data was merged with database outcomes for 450 patients undergoing major adult cardiac operations. Precursor events were categorized by type, person most affected, severity, and compensation. Number and categories of precursor events were analyzed as predictors of a composite outcome combining death or near miss complications (DNM), using logistic regression. Results. Precursor events occurred more frequently in cases with a DNM outcome than in those with no adverse event (2.7 ⫾ 2.4 vs 2.0 ⫾ 2.3/procedure, P ⫽ .005). After adjustment for other patient characteristics, the number of precursor events remained an independent predictor of DNM (RR, 1.14 per event [1.04 to 1.24]). Of 990 events, 35.6% related to management, 28.8% were technical, and 22.8% were environment-related. The surgeon was most affected in 40.8%, and 16.5% were of major severity. When categories of precursor events were analyzed, major severity events and those most affecting the surgeon were independent predictors of DNM. Conclusions. More detailed study of process in complex operations may lead to improved quality of care and patient safety. Special attention must be paid particularly to high risk patients and high risk precursor events. (Surgery 2007;141:715-22.) From the Maritime Heart Centre,a Halifax, NS, Canada; Massachusetts General Hospital,b Boston; North Shore Medical Center,c Salem; and Harvard Business School,d Boston, Mass
in recent years, the call to improve quality and safety in health care and reduce medical errors has been gaining prominence and credence.1,2 To this end, in the field of cardiac surgery, intensive interest has been drawn to the analysis, reporting, and prediction of outcomes, including efforts backed by government, industry-led, and hospital-based initiatives.3-5 Although attention
Supported by a Massachusetts General Hospital Rosetti Research Fellowship and a Dalhousie University Killam Scholarship (D.R.W.). Accepted for publication January 27, 2007. Reprint requests: Imtiaz S. Ali, MD, FRCSC, Division of Cardiac Surgery, Queen Elizabeth II Health Science Centre, 1796 Summer St., Suite 2269, Halifax, NS, Canada B3H 3A7. E-mail: [email protected] 0039-6060/$ - see front matter © 2007 Mosby, Inc. All rights reserved. doi:10.1016/j.surg.2007.01.017
to outcomes per se is important, additional and valuable insight can be gained from a better understanding of the structure and process of the operative procedure itself, to use Donabedian’s structure/process/outcome (SPO) model.6,7 Only a few studies have begun to analyze process in cardiac operation. In a pioneering effort, de Leval et al8,9 linked intraoperative observations by human factors observers to subsequent poor outcomes after the arterial switch operation for congenital transposition of the great arteries. We wished to extend their findings to the adult cardiac operative population to examine the association between process and outcomes. Specifically, we prospectively recorded and analyzed precursor events—that is, deviations from the normal process of cardiac operation that are prerequisite to the development of an adverse event—and then assessed the relationship between the number and characteristics of these precursor events and patient outcomes. SURGERY 715
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MATERIALS AND METHODS Study population. Ethics approval was obtained from the institutional research ethics boards of three participating university-affiliated teaching hospitals in the United States and Canada. Between April 2003 to January 2004 at 2 hospitals, and from February to May 2004 at a third site, detailed data was collected prospectively during 464 major adult cardiac operations, using methods described previously that are described below in brief.10 Otherwise, standard operative techniques and management were used according to the surgeons’ judgment and were not altered for this study. Precursor event definitions. The study of precursor events has been established and linked to the occurrence of adverse events in numerous areas of human endeavor, such as aviation and seismology.11,12 Precursor events are defined as incidents and circumstances that deviate from the expected, normal, optimal course of a process and must be present in advance for an adverse or catastrophic outcome to occur.13 Within the bounds of this definition, the specific inclusion criteria for what constituted precursor events were determined by 6 professional members of the operating room team (circulating nurse, perfusionist, anesthesiologist, surgical resident, nurse or physician assistant [PA], and surgeon) permitting capture of a broad range of problems relevant to each individual scope of practice. Examples include equipment failures, scheduling mix-ups, missing diagnostic test results, delays in lab reporting, medication errors, and technical operative problems. Problems directly relating to patient characteristics and premorbid medical status, such as the presence of adhesions, comorbidities, or lack of conduit, were purposely not recorded. Assessments of severity, timing, and compensation, and a free text description were collected on each precursor event, using definitions consistent those of de Leval et al.9 For example, “major” severity designated a potential for events to cause injury, as opposed to being mere nuisances or causing delay. Data collection. The 6 individual members of the operating team independently identified precursor events that occurred during the procedure, using an anonymous, standardized data collection.10 Insofar as possible given the limits imposed by the sterile technique for some of the observers, this data was collected in real-time during the procedure to minimize hind-sight bias for cases in which an adverse event occurred subsequently. For this reason, and for logistic considerations, we focused only on intraoperative precursor events, and
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we did not include postoperative events during transfer, in the intensive care unit, or on the ward. Patient characteristics and medical status were obtained from prospectively collected, audited institutional cardiac operative databases, in which the data was collected by a separate group of trained personnel blinded to the intraoperative precursor event data. Coding of precursor events. We collected 1,627 reports of precursor events, although more than one report pertaining to a single precursor event was often obtained independently from different team members. By combining duplicate reports together, we identified 990 unique precursor events. To resolve discrepancies between reports and to standardize free text and other descriptors, all reports for each unique precursor event were reviewed independently by 2 adjudicators blinded to outcomes, who categorized the events according to specified criteria as to: type of event (technical, relating to the performance of a physical task; environment, involving instruments, equipment, staffing, distractions, etc; management, information gathering and decision-making, including diagnostic testing, prescribing medication, etc; communication, and patient factors, pertaining directly to the medical or psychologic condition of the patient); person most affected (anesthesiologist, nurse, perfusionist, and surgeon [including resident and assistant], with no intention of ascribing fault); severity (3-point scale: minor, 1; equivocal, 2; and major, 3); and compensation (3-point scale: well compensated, 1; equivocal, 2; and poorly or not compensated, 3). Disagreements in the type and person categories were resolved by a third, blinded adjudicator. The latter 2 scores were averaged, with a mean score of greater than 2 signifying major and uncompensated events, respectively. The overall inter-adjudicator agreement for type and person was 80.2% (19.8% disagreement) and 85.5% (14.5% disagreement), respectively. Although perfect agreement on the three-point scales for severity and compensation was 76.4% and 83.5%, respectively, the scores differed by more than one point in only 5.5% and 3.1% of cases, respectively. (Note that condensing and adjudicating reports into individual events caused the proportion of reports described by team members as being of major severity [32.0%] to be greater than the proportion of major severity events in this study [16.5%]; this effect occurred largely because major severity events tended to be reported in duplicate by multiple team members more so than minor events, although occasionally the free-text or other descriptions may have prompted adjudicators to con-
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clude the severity of an event was different from that reported by a team member.) Finally, the number of precursor events per procedure was counted, as well as the number within each of the adjudicated categories, and these constituted the exposures of interest in this study. Outcomes. An aggregate outcome of death or near miss (DNM) was created by combining operative mortality with near misses (serious complications and morbidity, and markers of significant but non-fatal cardiorespiratory, hemodynamic, or other compromise). Intraoperative near miss complications included intra-aortic balloon pump (IABP) insertion, reinstitution of cardiopulmonary bypass (CPB), cardiac arrest, and profound hypotension, all of which have been previously shown to be associated with an increased risk of mortality. Short-term (in-hospital) outcomes were defined according to Society of Thoracic Surgeons (STS) database definitions and included cardiac re-operation during the index hospitalization, permanent stroke, deep sternal wound infection, acute renal failure, new postoperative dialysis, and prolonged ventilation (⬎48 h). Statistical analysis. The patient, precursor event, and outcomes data were electronically linked, and all patient and provider identifying data were then stripped from the records. In one hospital, in which a random number had been used as the sole identifier of the intraoperative reports, 14 patients were lost because the random number link had not been established correctly at operation. A total of 450 patients were available for analysis and constitute the study population. Data was analyzed with SAS software (SAS version 8, SAS Institute, Cary, NC). Standard univariable analyses included the 2 and Fisher exact test for discrete variables and t test and Wilcoxon rank sum test for continuous variables. Multivariable stepwise logistic regression with manual oversight was used, considering significant univariable predictors of DNM as candidate variables, with entry and stay criteria of 0.10 and 0.05, respectively, and using a complete case analysis, as few candidate data (only 7 entries for NYHA class) were missing. One model was constructed with the total number of precursor events adjusting for other patient and procedure characteristics. A second model was constructed with the total numbers of precursor events in each adjudicated category (type, person, severity, and compensation) that were significant on an unadjusted basis as candidate variables, adjusting for other characteristics. These 2 parsimonious models were tested with the Hosmer-Lemeshow test and c statistic. Predicted probabilities for the DNM outcome were calculated
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using these 2 models using hypothetical, arbitrary values for the covariates, and graphed to illustrate the influence of precursor events on DNM in representative clinical scenarios. A P value of less than .05 was considered significant, with no adjustment for multiple testing. RESULTS We analyzed 450 major adult cardiac operations at 3 hospitals, including 277 isolated coronary artery bypass grafting (CABG), 56 isolated valve, 56 CABG plus valve, and 61 other procedures. The composite outcome of death or near miss (DNM) occurred in 126 (28.0%) patients, including: 20 in-hospital deaths (none intraoperative), 37 patients with intraoperative near miss events (IABP insertion, 4; profound hypotension, 7; reinstitution of CPB, 31; and cardiac arrest, 4), 67 with postoperative near misses (prolonged ventilation, 43; deep sternal infection, 3; permanent stroke, 9; renal failure, 37; and dialysis, 14), and 42 reoperations. There were 324 patients in whom none of these outcomes occurred. There were significant differences between patients with and without DNM adverse outcomes with regard to age, comorbidity, urgency, and complexity of operation, with fewer isolated CABG procedures, more redo operations, and the requirement for a greater duration of cardiopulmonary bypass (CPB) (Table I). Precursor event case report. There were a total of 990 precursor events, and at least 1 occurred in 73.1% of procedures. As one dramatic example, a patient with a NYHA class 4 symptoms secondary to 3-vessel disease and significant mitral regurgitation underwent urgent MVR and CABG. Before induction, the patient suffered a respiratory arrest, a precursor event of major severity, resulting from possibly a combination of excess sedation, the early administration of muscle relaxant, or distraction while teaching another member of the team. During the case, other minor precursor events included problems with the cord connecting the TEE to the display, unavailability of the correct light cord for the endoscopic vein harvest, difficulty releasing the mitral retractor, dropping the cell saver and pump suction, and failure of the cardioplegia pump system while on bypass. The patient required intraoperative insertion of an IABP due to hemodynamic instability, and at the conclusion of the procedure the tubing was cut accidentally while undraping. Nine separate precursor events (plus 1 additional patient-related problem) were reported intraoperatively, with reporting by all or nearly all of the team members, particularly the 3 events
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Table I. Baseline patient characteristics stratified by DNM* DNM (N ⫽ 126)
No adverse outcome (N ⫽ 324)
P
Age, mean ⫾ SD 68.4 ⫾ 11.7 64.8 ⫾ 11.2 .003 Male 92 (73.0) 237 (73.1) .98 Smoking 79 (62.7) 209 (64.5) .72 Hypertension 89 (70.6) 211 (65.1) .27 Myocardial infarction 66 (52.4) 138 (42.6) .06 Unstable angina 36 (28.6) 54 (16.7) .005 Atrial fibrillation 32 (25.4) 45 (13.9) .004 Peripheral vascular 32 (25.4) 58 (17.9) .07 disease Cerebrovascular 24 (19.0) 42 (13.0) .10 disease Diabetes 44 (34.9) 89 (27.5) .12 Chronic renal failure 16 (12.7) 16 (4.9) .004 COPD 30 (23.8) 47 (14.5) .02 Preoperative IABP 21 (16.7) 16 (4.9) ⬍.0001 NYHA class, 3.1 ⫾ 0.9 2.6 ⫾ 0.9 ⬍.0001 mean ⫾ SD (N ⫽ 445) Left main coronary 26 (21.1) 63 (20.1) .80 disease .73 No. of diseased vessels (N ⫽ 438) 0 20 (16.1) 42 (13.4) 1 15 (12.1) 40 (12.7) 2 15 (12.1) 49 (15.6) 3 74 (59.7) 183 (58.3) Redo operation 15 (12.0) 18 (5.6) .02 (n ⫽ 448) Procedure Isolated CABG 66 (52.4) 211 (65.1) .01 Isolated valve 20 (15.9) 36 (11.1) .17 Valve ⫹ CABG 18 (14.3) 38 (11.7) .46 Other 22 (17.5) 39 (12.0) .13 Preoperative medications Beta-blocker 95 (75.4) 240 (74.1) .77 Calcium channel 29 (23.0) 78 (24.1) .81 blocker ACE inhibitor 62 (49.2) 160 (49.4) .97 Aspirin 78 (61.9) 235 (72.5) .03 Warfarin 19 (15.1) 30 (9.3) .08 CPB time, 138.0 ⫾ 82.5 120.2 ⫾ 52.6 .03 mean ⫾ SD Cross-clamp time, 88.6 ⫾ 56.0 86.3 ⫾ 42.7 .68 mean ⫾ SD CABG, coronary artery bypass grafting; COPD, chronic obstructive pulmonary disease; CPB, cardiopulmonary bypass; DNM, death or near miss; IABP, intra-aortic balloon pump; NYHA, New York Heart Association; SD, standard deviation. *Results are presented as N (%), unless otherwise stated.
Table II. Independent predictors of DNM No. of precursor events Preop IABP NYHA class Isolated CABG Atrial fibrillation
OR
95% CI
1.14 per event 2.53 1.76 per class 0.63 1.77
1.04–1.24 1.18–5.43 1.36–2.29 0.39–0.99 1.01–3.10
CABG, coronary artery bypass grafting; CI, confidence interval; DNM, death or near miss; IABP, intra-aortic balloon pump; NYHA, New York Heart Association; OR, odds ratio.
judged to be of major severity (pre-induction management, pump failure, and cutting the IABP tubing). All precursor events were compensated for appropriately in a timely manner. The patient survived the hospitalization and was discharged after 9 days. Association with DNM outcomes. Precursor events occurred more frequently among DNM patients (2.7 ⫾ 2.4 per procedure; 340 events/126 patients) compared with patients experiencing no adverse outcome (2.0 ⫾ 2.3; 650 events/324 patients, P ⫽ .005). The DNM group had a lower proportion of cases with zero reported precursor events (19.0% vs 29.9%, P ⫽ .02). After adjustment for other patient and procedural characteristics, the number of precursor events per procedure remained an independent predictor of DNM (RR, 1.14 per event, P ⫽ .005) (Table II; c statistic 0.70; Hosmer-Lemeshow P ⫽ .79). Figure 1 illustrates the effect of the total number of precursor events for low, medium, and high risk patients as predicted by the model in Table 2. In post hoc analysis, there was a strong association between number of precursor events and risk of intraoperative DNM (RR, 1.22 per event; 95% confidence interval [CI], 1.09 to 1.37) after adjustment for the other independent predictor of this outcome, CPB time (1.006/min [1.001 to 1.011]). Categories of precursor events. Precursor events were categorized as being related to management (35.6%), technical (28.8%), environmental (22.8%), communication (5.4%), or patient (7.5%) issues. The most-affected person was the surgeon in 404 events (40.8%), followed by the anesthesiologist (28.4%), nurse (21.2%), and perfusionist (9.5%). There were 163 (16.5%) precursor events of major severity, and 3.1% were uncompensated. Certain kinds of precursor events occurred more frequently in the DNM group (Table III). The number of major precursor events per procedure and the number of surgeon precursor events per procedure were independent predictors of DNM (Table IV; c statistic 0.73, HosmerLemeshow, P ⫽ .66). The marked influence of major
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Table IV. Independent predictors of DNM OR
95% CI
No. of major precursor events 1.78/event 1.21–2.63 No. of surgeon precursor events 1.25/event 1.01–1.55 Preoperative IABP 2.71 1.25–5.86 NYHA class 1.73/class 1.33–2.26 Isolated CABG 0.59 0.37–0.94 CABG, coronary artery bypass grafting; CI, confidence interval; DNM, death or near miss; IABP, intra-aortic balloon pump; NYHA, New York Heart Association; OR, odds ratio; SD, standard deviation.
Fig 1. Predicted probability of death or near miss (DNM) based on number of precursor events for three patients: (A) low risk patient (isolated CABG, NYHA class 1); (B) medium risk patient (non-CABG procedure, NYHA class 2, atrial fibrillation); and (C) high risk patient (non-CABG procedure, NYHA class 4, preoperative intra-aortic balloon pump). Dashed lines represent 70% confidence intervals surrounding the predicted probability (solid line).
Table III. Number of precursor events per procedure Mean no. of precursor events ⫾ SD
Total Type Technical Management Communication Environment Patient factor Severity Major Minor Person most affected Surgeon Perfusionist Anesthesiologist Nurse Compensation Uncompensated Uncompensated and major severity Uncompensated and minor severity
DNM (N ⫽ 126)
No adverse outcome (N ⫽ 324)
P
2.7 ⫾ 2.4
2.0 ⫾ 2.3
.005
0.9 ⫾ 1.1 1.0 ⫾ 1.1 0.2 ⫾ 0.5 0.5 ⫾ 0.8 0.3 ⫾ 0.6
0.5 ⫾ 0.9 0.7 ⫾ 1.1 0.1 ⫾ 0.4 0.5 ⫾ 0.9 0.1 ⫾ 0.4
.004 .04 .31 .65 .02
0.6 ⫾ 0.9 2.1 ⫾ 2.1
0.3 ⫾ 0.5 1.7 ⫾ 2.2
⬍.0001 .14
1.2 ⫾ 1.4 0.2 ⫾ 0.4 0.8 ⫾ 1.1 0.5 ⫾ 0.9
0.8 ⫾ 1.0 0.2 ⫾ 0.5 0.6 ⫾ 0.9 0.4 ⫾ 0.9
.0006 .09 .04 .47
0.13 ⫾ 0.38 0.03 ⫾ 0.18
0.05 ⫾ 0.22 0.01 ⫾ 0.10
.03 .18
0.09 ⫾ 0.31
0.04 ⫾ 0.20
.11
DNM, death or near miss; SD, standard deviation.
precursor events on predicted risk of DNM is shown in Fig 2 across a variable number of surgeon precursor events. DISCUSSION Tremendous strides in documenting and analyzing cardiac operative outcomes have occurred, with many initiatives on a local to international scale.14,15 Of the few initiatives that have examined process in this specialty, O’Connor et al16 from the Northern New England Cardiovascular Disease Study Group, for example, has shown promising results from efforts to collectively improve process. We have described previously the great frequency and variety of precursor events that occurred during adult cardiac operations.10 In this report, we examine precursor events as a marker of process— or more precisely, of failures in process— and show an independent association between precursor events and DNM outcome, with a 14% increase in risk per additional precursor event (Fig 1). To put this into perspective, STS data produced the identical odds ratios of 1.14 for both triple-vessel disease and diabetes mellitus on oral medications, when modeling a composite outcome of 30-day death, cardiac reoperation, permanent stroke, renal failure requiring dialysis, deep sternal wound infection, and prolonged ventilation among isolated CABG patients.17 Thus, the impact of just one additional precursor event on short-term morbidity and mortality in our results is of a magnitude similar to that relating to triple-vessel disease in the STS CABG model, and 2 precursor events are comparable to the presence of triple-vessel disease plus diabetes together. Furthermore, events categorized as being of major severity or pertaining to the surgeon were even more strongly predictive of DNM, and this parallels the results of de Leval et al9 in a pediatric population. In addition to the impact of precursor events, the risk of the composite outcome of DNM was related to several better characterized risk factors: the procedure (lower risk for isolated CABG), urgency (preoperative IABP),
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Fig 2. Predicted probability of death or near miss (DNM) based on number of major severity precursor events (along the horizontal axis, drawn to similar scale as Fig 1) and based on number of precursor events most affecting the surgeon: (a) 0, (b) 5, and (c) 10 surgeon precursor events. The model assumes a low risk patient (isolated CABG, NYHA class 1). Dashed lines represent 70% confidence intervals surrounding the predicted probability (solid line).
medical status (NYHA function); and diagnosis/ comorbidities (atrial fibrillation as a marker for valvular disease and additional disease burden). Classification of precursor events may provide useful insights. This analysis highlights the fact that a small but critical subset of events (only 16.5% were of major severity) can have a large impact on outcomes (OR, 1.78 per event), even in the low risk patient (Fig 2). Our results also emphasize the importance of technical skill and the role of the surgeon in this setting. These findings may apply to other specialties such as neurosurgery, in which similar proportions of major (15%) and technical (31%) problems have been reported.18 Rasmussen19 classified errors as skill-, knowledge-, or rulebased. Our classification system shares similarities with Rasmussen’s model: skill-based errors coincide with our technical category, and knowledge- and rule-based errors may be considered akin to management events. Furthermore, precursor events of
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the environment type may extend to include the Donabedian notion of structure. Our findings must be interpreted with a number of caveats in mind, some of which have been discussed previously.10 Foremost, comparison with other studies is difficult as a variety of methodologies for studying process have been used.20 Most studies were retrospective and assessed process using other metrics (medication or other medical errors, rates of prescribing, etc). They relied largely on medical record or administrative data and varied in their sensitivity for detecting events. We have attempted to translate an accepted and understood nomenclature and framework (the notion of precursor events) into the cardiac operative arena, and have used a standard, universal definition from the literature.13 Because the specific inclusion criteria for precursor events within this definition are therefore broad, we have relied on the professional judgment of “sharp end providers” who are involved intimately in the care of the patient as this permits collection of the most relevant and insightful data in real time or close to it. To concretize these concepts and also to analyze these data with statistical tools, we have attempted to deconstruct the entire universe of precursor events into more manageable categories, using handles such as type, severity, and compensation. This necessarily requires a certain degree of arbitrary assignment, although by using multiple blinded adjudicators, this process was as unbiased as possible. Furthermore, some difficulty lay in attempting to decompose complex, real-life events into a few discrete categories, although in most cases this process was fairly intuitive and consistent. Conceptually, precursor events in our study may be considered directly causative of an adverse outcome, or simply diagnostic of a process problem (symptomatic of, but not necessarily themselves the problems that led to the outcome). Even so, diagnostic events may have importance on their own and be indirectly implicated in the pathway, by eroding the team’s concentration or their ability to prevent and compensate for other precursor events. Furthermore, the timing of the outcome may be immediate or delayed. All these factors obfuscate causality and may dilute the observable impact of precursor events, but they do not diminish the importance of these events. Because only 3 hospitals participated in this study, our results may not be generalizable to other institutions with practice paradigms other than those represented by the 3 sites. Although we were able to obtain meaningful data from all 3 sites, suggesting the data collection tool was effective,
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intrinsic inter-hospital differences complicated any attempts at comparisons of precursor events between sites and rendered questions of inter-hospital validity moot. We also recognize the limitations in studying a heterogeneous population (different hospitals and a variety of procedures) and using a composite outcome incorporating softer near miss end points. For these reasons, it is often difficult to obtain a model with more robust explanatory strength (a c statistic of 0.73 is quite satisfactory). Finally, we selected identifiable, explicit death and near-miss outcomes and excluded more controversial and ill-defined outcomes such as postoperative myocardial infarction, left ventricular dysfunction, and type II neurologic outcomes (encephalopathy). This study is the first of its kind in the adult cardiac operative population and has implications for improving the quality of cardiac operative care. Although preoperative patient status may still be the single-most important driver of outcomes and has been studied extensively,20,21 these characteristics often hold limited opportunity for optimization. One important lesson from these data is that in patients known to be at high risk for adverse outcomes based solely on preoperative characteristics, particular attention on the part of the operative team to avoiding additional risk from intraoperative misadventures must be emphasized. This study shows that the impact of precursor events on adverse outcomes is independent of other patient characteristics, and as such represents a new horizon for improvement, especially in the most urgent, complex, and risky patients, where the potential for and consequences of precursor events may be increased even further. Nevertheless, a small subset of precursor events (major severity, in particular) may be highly associated with adverse outcomes, even in the low risk setting. Unlike mortality and even the more abundant near misses, precursor events are almost ubiquitous and offer a largely untapped potential for research. Quality improvement initiatives might be easier to target when viewed through the lens of precursor events, in particular those with a significant potential for harm or those occurring most frequently. Although an individual precursor event may not be able to discriminate with much precision between a patient who will suffer an adverse outcome and one who will not, we were able to identify characteristics of precursor events that were most predictive of adverse outcomes. Strategies for improving outcomes may lie on 2 fronts: eliminating precursor events by rote and standardization, and improving the team’s ability to compensate innovatively, including by the use of simulation. Although there is
a tension between these 2 strategies (surgeons who never deviate from the tried and true [and thus reduce precursor events] vs those who adapt their approach frequently depending on the situation [and may better compensate for precursor events]), the best practice may be a blend and this also warrants further study. Likewise, structure has been studied little but may represent another important portal for intervention.22,23 As a result of an increasing awareness as to the importance of structure and process, increasing interest in incorporating these measures into quality improvement initiatives has been garnered.24 Further work is needed to improve the usefulness and availability of current day metrics of process.25,26 Identifying specific high-impact areas of process may focus quality improvement efforts, provide important feedback for training and maintenance of competency among surgeons, and yield dividends in improving safety and health care delivery. Although much remains to be understood about the complexities of process in operations, the simple concept that operations need to go well for people to do well is a basic truism, especially when the patients are sick to begin with. The authors wish to thank Stephen Hinrichs, Susan April, Sandra deBronkart, Diane Davies, Eliza Gregory, Dimitri Kalavrouziotis, Maral Ouzounian; the nurses, perfusionists, physician assistants, residents, anesthesiologists, and surgeons who participated in this research; and the patients at the Maritime Heart Centre, Massachusetts General Hospital, and North Shore Medical Center. REFERENCES 1. Kohn LT, Corrigan JM, Donaldson MS. To err is human. Washington, DC: National Academy Press; 2000. 2. Committee on Quality of Health Care in America, Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2003. 3. Shahian DM, Normand SL, Torchiana DF, Lewis SM, Pastore JO, Kuntz RE, et al. Cardiac surgery report cards: comprehensive review and statistical critique. Ann Thorac Surg 2001;72:2155-68. 4. Milstein A, Galvin RS, Delbanco SF, Salber P, Buck CR. Improving the safety of health care: the leapfrog initiative. Eff Clin Pract 2000;3:313-6. 5. O’Connor GT, Plume SK, Wennberg JE. Regional organization for outcomes research. Ann N Y Acad Sci 1993;703:44-50. 6. Donabedian A. Evaluating the quality of medical care. Milbank Q 1966;44:166-203. 7. Brook RH, McGlynn EA, Cleary PD. Quality of health care. Part 2: measuring quality of care. N Engl J Med 1996; 335:966-70. 8. de Leval MR, Francois K, Bull C, Brawn W, Spiegelhalter D. Analysis of a cluster of surgical failures: application to a series of neonatal arterial switch operations. J Thorac Cardiovasc Surg 1994;107:914-24.
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9. de Leval MR, Carthey J, Wright DJ, Farewell VT, Reason JT. Human factors and cardiac surgery: a multicenter study. J Thorac Cardiovasc Surg 2000;119:661-72. 10. Wong DR, Vander Salm TJ, Ali IS, Agnihotri AK, Bohmer RMJ, Torchiana DF. Prospective assessment of intraoperative precursor events during cardiac surgery. Eur J Cardio-thor Surg 2006;29:447-55. 11. Precursor events point to system-wide shortcoming before crash. Air Safety Week 2003;17:1. 12. Hawkes AD, Scott DB, Lipps JH, Combellick R. Evidence for possible precursor events of megathrust earthquakes on the west coast of North America. Geol Soc Am Bull 2005; 117:996-1008. 13. Corcoran WR. Defining and analyzing precursors. In: Phimister JR, Bier VM, Kunreuther, editors. Accident precursor analysis and management: reducing technological risk through diligence. Washington, DC: The National Academies Press; 2004. p. 79-88. 14. Hannan EL, Kilburn H, Racz M, Shields E, Chassin MR. Improving the outcomes of coronary artery bypass surgery in New York State. JAMA 1994;271:761-6. 15. Khuri SF, Daley J, Henderson WG. The comparative assessment and improvement of quality of surgical care in the Department of Veterans Affairs. Arch Surg 2002; 137:20-7. 16. O’Connor GT, Plume SK, Olmstead EM, Morton JR, Maloney CT, Nugent WC, et al. A regional intervention to improve the hospital mortality associated with coronary artery bypass graft surgery. The Northern New England Cardiovascular Disease Study Group. JAMA 1996;275:841-6. 17. Shroyer ALW, Coombs LP, Peterson ED, Eiken MC, DeLong ER, Chen A, et al. The Society of Thoracic Surgeons:
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30-day operative mortality and morbidity risk models. Ann Thorac Surg 2003;75:1856-65. Bernstein M, Massicotte M. Prospective error recording in surgery: a feasibility study in 500 neurosurgical cases. Evid Based Surg 2003;1:57-62. Rasmussen J. Skills, rules and knowledge: signals, signs and symbols and other distinctions in human performance models. IEEE Trans Syst Man Cybern 1983;13:257-66. Battles JB, Lilford RJ. Organizing patient safety research to identify risks and hazards. Qual Saf Health Care 2003;12:2-7. Jamieson WRE, Edwards FH, Schwartz M, Bero JW, Clark RE, Grover FL, for the Database Committee of The Society of Thoracic Surgeons. Risk stratification for cardiac valve replacement. National cardiac surgery database. Ann Thorac Surg 1999;67:943-51. Gaba DM. Structural and organizational issues in patient safety: a comparison of health care and other high-hazard industries. Calif Manage Rev 2000;43:83-102. Shortell SM, Jones RH, Rademaker AW, Gillies RR, Dranove DS, Hughes EF, et al. Assessing the impact of total quality management and organizational culture on multiple outcomes of care for coronary artery bypass graft surgery patients. Med Care 2000:38:207-17. Guru V, Anderson GM, Fremes SE, O’Connor GT, Grover FL, Tu JV, et al. The identification and development of Canadian coronary artery bypass graft surgery quality indicators. J Thorac Cardiovasc Surg 2005;130:1257-64. Rich JB. Quality indicators, performance measures, and accountability: the right thing, at the right time, for the right reason. J Thorac Cardiovasc Surg 2006;131:4-8. Christian CK, Gustafson ML, Betensky RA, Daley J, Zinner MJ. The Leapfrog volume criteria may fall short in identifying high-quality surgical centers. Ann Surg 2003;238:447-57.
Background. Although extensive study has been directed at the influence of patient factors and comorbidities on cardiac surgical outcomes, less attention has been focused on process. We sought to examine the relationship between intraoperative precursor events (those events that precede and are requisite for the occurrence of an adverse event) and adverse outcomes themselves. Methods. Anonymous, prospectively collected intraoperative data was merged with database outcomes for 450 patients undergoing major adult cardiac operations. Precursor events were categorized by type, person most affected, severity, and compensation. Number and categories of precursor events were analyzed as predictors of a composite outcome combining death or near miss complications (DNM), using logistic regression. Results. Precursor events occurred more frequently in cases with a DNM outcome than in those with no adverse event (2.7 ⫾ 2.4 vs 2.0 ⫾ 2.3/procedure, P ⫽ .005). After adjustment for other patient characteristics, the number of precursor events remained an independent predictor of DNM (RR, 1.14 per event [1.04 to 1.24]). Of 990 events, 35.6% related to management, 28.8% were technical, and 22.8% were environment-related. The surgeon was most affected in 40.8%, and 16.5% were of major severity. When categories of precursor events were analyzed, major severity events and those most affecting the surgeon were independent predictors of DNM. Conclusions. More detailed study of process in complex operations may lead to improved quality of care and patient safety. Special attention must be paid particularly to high risk patients and high risk precursor events. (Surgery 2007;141:715-22.) From the Maritime Heart Centre,a Halifax, NS, Canada; Massachusetts General Hospital,b Boston; North Shore Medical Center,c Salem; and Harvard Business School,d Boston, Mass
in recent years, the call to improve quality and safety in health care and reduce medical errors has been gaining prominence and credence.1,2 To this end, in the field of cardiac surgery, intensive interest has been drawn to the analysis, reporting, and prediction of outcomes, including efforts backed by government, industry-led, and hospital-based initiatives.3-5 Although attention
Supported by a Massachusetts General Hospital Rosetti Research Fellowship and a Dalhousie University Killam Scholarship (D.R.W.). Accepted for publication January 27, 2007. Reprint requests: Imtiaz S. Ali, MD, FRCSC, Division of Cardiac Surgery, Queen Elizabeth II Health Science Centre, 1796 Summer St., Suite 2269, Halifax, NS, Canada B3H 3A7. E-mail: [email protected] 0039-6060/$ - see front matter © 2007 Mosby, Inc. All rights reserved. doi:10.1016/j.surg.2007.01.017
to outcomes per se is important, additional and valuable insight can be gained from a better understanding of the structure and process of the operative procedure itself, to use Donabedian’s structure/process/outcome (SPO) model.6,7 Only a few studies have begun to analyze process in cardiac operation. In a pioneering effort, de Leval et al8,9 linked intraoperative observations by human factors observers to subsequent poor outcomes after the arterial switch operation for congenital transposition of the great arteries. We wished to extend their findings to the adult cardiac operative population to examine the association between process and outcomes. Specifically, we prospectively recorded and analyzed precursor events—that is, deviations from the normal process of cardiac operation that are prerequisite to the development of an adverse event—and then assessed the relationship between the number and characteristics of these precursor events and patient outcomes. SURGERY 715
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MATERIALS AND METHODS Study population. Ethics approval was obtained from the institutional research ethics boards of three participating university-affiliated teaching hospitals in the United States and Canada. Between April 2003 to January 2004 at 2 hospitals, and from February to May 2004 at a third site, detailed data was collected prospectively during 464 major adult cardiac operations, using methods described previously that are described below in brief.10 Otherwise, standard operative techniques and management were used according to the surgeons’ judgment and were not altered for this study. Precursor event definitions. The study of precursor events has been established and linked to the occurrence of adverse events in numerous areas of human endeavor, such as aviation and seismology.11,12 Precursor events are defined as incidents and circumstances that deviate from the expected, normal, optimal course of a process and must be present in advance for an adverse or catastrophic outcome to occur.13 Within the bounds of this definition, the specific inclusion criteria for what constituted precursor events were determined by 6 professional members of the operating room team (circulating nurse, perfusionist, anesthesiologist, surgical resident, nurse or physician assistant [PA], and surgeon) permitting capture of a broad range of problems relevant to each individual scope of practice. Examples include equipment failures, scheduling mix-ups, missing diagnostic test results, delays in lab reporting, medication errors, and technical operative problems. Problems directly relating to patient characteristics and premorbid medical status, such as the presence of adhesions, comorbidities, or lack of conduit, were purposely not recorded. Assessments of severity, timing, and compensation, and a free text description were collected on each precursor event, using definitions consistent those of de Leval et al.9 For example, “major” severity designated a potential for events to cause injury, as opposed to being mere nuisances or causing delay. Data collection. The 6 individual members of the operating team independently identified precursor events that occurred during the procedure, using an anonymous, standardized data collection.10 Insofar as possible given the limits imposed by the sterile technique for some of the observers, this data was collected in real-time during the procedure to minimize hind-sight bias for cases in which an adverse event occurred subsequently. For this reason, and for logistic considerations, we focused only on intraoperative precursor events, and
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we did not include postoperative events during transfer, in the intensive care unit, or on the ward. Patient characteristics and medical status were obtained from prospectively collected, audited institutional cardiac operative databases, in which the data was collected by a separate group of trained personnel blinded to the intraoperative precursor event data. Coding of precursor events. We collected 1,627 reports of precursor events, although more than one report pertaining to a single precursor event was often obtained independently from different team members. By combining duplicate reports together, we identified 990 unique precursor events. To resolve discrepancies between reports and to standardize free text and other descriptors, all reports for each unique precursor event were reviewed independently by 2 adjudicators blinded to outcomes, who categorized the events according to specified criteria as to: type of event (technical, relating to the performance of a physical task; environment, involving instruments, equipment, staffing, distractions, etc; management, information gathering and decision-making, including diagnostic testing, prescribing medication, etc; communication, and patient factors, pertaining directly to the medical or psychologic condition of the patient); person most affected (anesthesiologist, nurse, perfusionist, and surgeon [including resident and assistant], with no intention of ascribing fault); severity (3-point scale: minor, 1; equivocal, 2; and major, 3); and compensation (3-point scale: well compensated, 1; equivocal, 2; and poorly or not compensated, 3). Disagreements in the type and person categories were resolved by a third, blinded adjudicator. The latter 2 scores were averaged, with a mean score of greater than 2 signifying major and uncompensated events, respectively. The overall inter-adjudicator agreement for type and person was 80.2% (19.8% disagreement) and 85.5% (14.5% disagreement), respectively. Although perfect agreement on the three-point scales for severity and compensation was 76.4% and 83.5%, respectively, the scores differed by more than one point in only 5.5% and 3.1% of cases, respectively. (Note that condensing and adjudicating reports into individual events caused the proportion of reports described by team members as being of major severity [32.0%] to be greater than the proportion of major severity events in this study [16.5%]; this effect occurred largely because major severity events tended to be reported in duplicate by multiple team members more so than minor events, although occasionally the free-text or other descriptions may have prompted adjudicators to con-
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clude the severity of an event was different from that reported by a team member.) Finally, the number of precursor events per procedure was counted, as well as the number within each of the adjudicated categories, and these constituted the exposures of interest in this study. Outcomes. An aggregate outcome of death or near miss (DNM) was created by combining operative mortality with near misses (serious complications and morbidity, and markers of significant but non-fatal cardiorespiratory, hemodynamic, or other compromise). Intraoperative near miss complications included intra-aortic balloon pump (IABP) insertion, reinstitution of cardiopulmonary bypass (CPB), cardiac arrest, and profound hypotension, all of which have been previously shown to be associated with an increased risk of mortality. Short-term (in-hospital) outcomes were defined according to Society of Thoracic Surgeons (STS) database definitions and included cardiac re-operation during the index hospitalization, permanent stroke, deep sternal wound infection, acute renal failure, new postoperative dialysis, and prolonged ventilation (⬎48 h). Statistical analysis. The patient, precursor event, and outcomes data were electronically linked, and all patient and provider identifying data were then stripped from the records. In one hospital, in which a random number had been used as the sole identifier of the intraoperative reports, 14 patients were lost because the random number link had not been established correctly at operation. A total of 450 patients were available for analysis and constitute the study population. Data was analyzed with SAS software (SAS version 8, SAS Institute, Cary, NC). Standard univariable analyses included the 2 and Fisher exact test for discrete variables and t test and Wilcoxon rank sum test for continuous variables. Multivariable stepwise logistic regression with manual oversight was used, considering significant univariable predictors of DNM as candidate variables, with entry and stay criteria of 0.10 and 0.05, respectively, and using a complete case analysis, as few candidate data (only 7 entries for NYHA class) were missing. One model was constructed with the total number of precursor events adjusting for other patient and procedure characteristics. A second model was constructed with the total numbers of precursor events in each adjudicated category (type, person, severity, and compensation) that were significant on an unadjusted basis as candidate variables, adjusting for other characteristics. These 2 parsimonious models were tested with the Hosmer-Lemeshow test and c statistic. Predicted probabilities for the DNM outcome were calculated
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using these 2 models using hypothetical, arbitrary values for the covariates, and graphed to illustrate the influence of precursor events on DNM in representative clinical scenarios. A P value of less than .05 was considered significant, with no adjustment for multiple testing. RESULTS We analyzed 450 major adult cardiac operations at 3 hospitals, including 277 isolated coronary artery bypass grafting (CABG), 56 isolated valve, 56 CABG plus valve, and 61 other procedures. The composite outcome of death or near miss (DNM) occurred in 126 (28.0%) patients, including: 20 in-hospital deaths (none intraoperative), 37 patients with intraoperative near miss events (IABP insertion, 4; profound hypotension, 7; reinstitution of CPB, 31; and cardiac arrest, 4), 67 with postoperative near misses (prolonged ventilation, 43; deep sternal infection, 3; permanent stroke, 9; renal failure, 37; and dialysis, 14), and 42 reoperations. There were 324 patients in whom none of these outcomes occurred. There were significant differences between patients with and without DNM adverse outcomes with regard to age, comorbidity, urgency, and complexity of operation, with fewer isolated CABG procedures, more redo operations, and the requirement for a greater duration of cardiopulmonary bypass (CPB) (Table I). Precursor event case report. There were a total of 990 precursor events, and at least 1 occurred in 73.1% of procedures. As one dramatic example, a patient with a NYHA class 4 symptoms secondary to 3-vessel disease and significant mitral regurgitation underwent urgent MVR and CABG. Before induction, the patient suffered a respiratory arrest, a precursor event of major severity, resulting from possibly a combination of excess sedation, the early administration of muscle relaxant, or distraction while teaching another member of the team. During the case, other minor precursor events included problems with the cord connecting the TEE to the display, unavailability of the correct light cord for the endoscopic vein harvest, difficulty releasing the mitral retractor, dropping the cell saver and pump suction, and failure of the cardioplegia pump system while on bypass. The patient required intraoperative insertion of an IABP due to hemodynamic instability, and at the conclusion of the procedure the tubing was cut accidentally while undraping. Nine separate precursor events (plus 1 additional patient-related problem) were reported intraoperatively, with reporting by all or nearly all of the team members, particularly the 3 events
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Table I. Baseline patient characteristics stratified by DNM* DNM (N ⫽ 126)
No adverse outcome (N ⫽ 324)
P
Age, mean ⫾ SD 68.4 ⫾ 11.7 64.8 ⫾ 11.2 .003 Male 92 (73.0) 237 (73.1) .98 Smoking 79 (62.7) 209 (64.5) .72 Hypertension 89 (70.6) 211 (65.1) .27 Myocardial infarction 66 (52.4) 138 (42.6) .06 Unstable angina 36 (28.6) 54 (16.7) .005 Atrial fibrillation 32 (25.4) 45 (13.9) .004 Peripheral vascular 32 (25.4) 58 (17.9) .07 disease Cerebrovascular 24 (19.0) 42 (13.0) .10 disease Diabetes 44 (34.9) 89 (27.5) .12 Chronic renal failure 16 (12.7) 16 (4.9) .004 COPD 30 (23.8) 47 (14.5) .02 Preoperative IABP 21 (16.7) 16 (4.9) ⬍.0001 NYHA class, 3.1 ⫾ 0.9 2.6 ⫾ 0.9 ⬍.0001 mean ⫾ SD (N ⫽ 445) Left main coronary 26 (21.1) 63 (20.1) .80 disease .73 No. of diseased vessels (N ⫽ 438) 0 20 (16.1) 42 (13.4) 1 15 (12.1) 40 (12.7) 2 15 (12.1) 49 (15.6) 3 74 (59.7) 183 (58.3) Redo operation 15 (12.0) 18 (5.6) .02 (n ⫽ 448) Procedure Isolated CABG 66 (52.4) 211 (65.1) .01 Isolated valve 20 (15.9) 36 (11.1) .17 Valve ⫹ CABG 18 (14.3) 38 (11.7) .46 Other 22 (17.5) 39 (12.0) .13 Preoperative medications Beta-blocker 95 (75.4) 240 (74.1) .77 Calcium channel 29 (23.0) 78 (24.1) .81 blocker ACE inhibitor 62 (49.2) 160 (49.4) .97 Aspirin 78 (61.9) 235 (72.5) .03 Warfarin 19 (15.1) 30 (9.3) .08 CPB time, 138.0 ⫾ 82.5 120.2 ⫾ 52.6 .03 mean ⫾ SD Cross-clamp time, 88.6 ⫾ 56.0 86.3 ⫾ 42.7 .68 mean ⫾ SD CABG, coronary artery bypass grafting; COPD, chronic obstructive pulmonary disease; CPB, cardiopulmonary bypass; DNM, death or near miss; IABP, intra-aortic balloon pump; NYHA, New York Heart Association; SD, standard deviation. *Results are presented as N (%), unless otherwise stated.
Table II. Independent predictors of DNM No. of precursor events Preop IABP NYHA class Isolated CABG Atrial fibrillation
OR
95% CI
1.14 per event 2.53 1.76 per class 0.63 1.77
1.04–1.24 1.18–5.43 1.36–2.29 0.39–0.99 1.01–3.10
CABG, coronary artery bypass grafting; CI, confidence interval; DNM, death or near miss; IABP, intra-aortic balloon pump; NYHA, New York Heart Association; OR, odds ratio.
judged to be of major severity (pre-induction management, pump failure, and cutting the IABP tubing). All precursor events were compensated for appropriately in a timely manner. The patient survived the hospitalization and was discharged after 9 days. Association with DNM outcomes. Precursor events occurred more frequently among DNM patients (2.7 ⫾ 2.4 per procedure; 340 events/126 patients) compared with patients experiencing no adverse outcome (2.0 ⫾ 2.3; 650 events/324 patients, P ⫽ .005). The DNM group had a lower proportion of cases with zero reported precursor events (19.0% vs 29.9%, P ⫽ .02). After adjustment for other patient and procedural characteristics, the number of precursor events per procedure remained an independent predictor of DNM (RR, 1.14 per event, P ⫽ .005) (Table II; c statistic 0.70; Hosmer-Lemeshow P ⫽ .79). Figure 1 illustrates the effect of the total number of precursor events for low, medium, and high risk patients as predicted by the model in Table 2. In post hoc analysis, there was a strong association between number of precursor events and risk of intraoperative DNM (RR, 1.22 per event; 95% confidence interval [CI], 1.09 to 1.37) after adjustment for the other independent predictor of this outcome, CPB time (1.006/min [1.001 to 1.011]). Categories of precursor events. Precursor events were categorized as being related to management (35.6%), technical (28.8%), environmental (22.8%), communication (5.4%), or patient (7.5%) issues. The most-affected person was the surgeon in 404 events (40.8%), followed by the anesthesiologist (28.4%), nurse (21.2%), and perfusionist (9.5%). There were 163 (16.5%) precursor events of major severity, and 3.1% were uncompensated. Certain kinds of precursor events occurred more frequently in the DNM group (Table III). The number of major precursor events per procedure and the number of surgeon precursor events per procedure were independent predictors of DNM (Table IV; c statistic 0.73, HosmerLemeshow, P ⫽ .66). The marked influence of major
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Table IV. Independent predictors of DNM OR
95% CI
No. of major precursor events 1.78/event 1.21–2.63 No. of surgeon precursor events 1.25/event 1.01–1.55 Preoperative IABP 2.71 1.25–5.86 NYHA class 1.73/class 1.33–2.26 Isolated CABG 0.59 0.37–0.94 CABG, coronary artery bypass grafting; CI, confidence interval; DNM, death or near miss; IABP, intra-aortic balloon pump; NYHA, New York Heart Association; OR, odds ratio; SD, standard deviation.
Fig 1. Predicted probability of death or near miss (DNM) based on number of precursor events for three patients: (A) low risk patient (isolated CABG, NYHA class 1); (B) medium risk patient (non-CABG procedure, NYHA class 2, atrial fibrillation); and (C) high risk patient (non-CABG procedure, NYHA class 4, preoperative intra-aortic balloon pump). Dashed lines represent 70% confidence intervals surrounding the predicted probability (solid line).
Table III. Number of precursor events per procedure Mean no. of precursor events ⫾ SD
Total Type Technical Management Communication Environment Patient factor Severity Major Minor Person most affected Surgeon Perfusionist Anesthesiologist Nurse Compensation Uncompensated Uncompensated and major severity Uncompensated and minor severity
DNM (N ⫽ 126)
No adverse outcome (N ⫽ 324)
P
2.7 ⫾ 2.4
2.0 ⫾ 2.3
.005
0.9 ⫾ 1.1 1.0 ⫾ 1.1 0.2 ⫾ 0.5 0.5 ⫾ 0.8 0.3 ⫾ 0.6
0.5 ⫾ 0.9 0.7 ⫾ 1.1 0.1 ⫾ 0.4 0.5 ⫾ 0.9 0.1 ⫾ 0.4
.004 .04 .31 .65 .02
0.6 ⫾ 0.9 2.1 ⫾ 2.1
0.3 ⫾ 0.5 1.7 ⫾ 2.2
⬍.0001 .14
1.2 ⫾ 1.4 0.2 ⫾ 0.4 0.8 ⫾ 1.1 0.5 ⫾ 0.9
0.8 ⫾ 1.0 0.2 ⫾ 0.5 0.6 ⫾ 0.9 0.4 ⫾ 0.9
.0006 .09 .04 .47
0.13 ⫾ 0.38 0.03 ⫾ 0.18
0.05 ⫾ 0.22 0.01 ⫾ 0.10
.03 .18
0.09 ⫾ 0.31
0.04 ⫾ 0.20
.11
DNM, death or near miss; SD, standard deviation.
precursor events on predicted risk of DNM is shown in Fig 2 across a variable number of surgeon precursor events. DISCUSSION Tremendous strides in documenting and analyzing cardiac operative outcomes have occurred, with many initiatives on a local to international scale.14,15 Of the few initiatives that have examined process in this specialty, O’Connor et al16 from the Northern New England Cardiovascular Disease Study Group, for example, has shown promising results from efforts to collectively improve process. We have described previously the great frequency and variety of precursor events that occurred during adult cardiac operations.10 In this report, we examine precursor events as a marker of process— or more precisely, of failures in process— and show an independent association between precursor events and DNM outcome, with a 14% increase in risk per additional precursor event (Fig 1). To put this into perspective, STS data produced the identical odds ratios of 1.14 for both triple-vessel disease and diabetes mellitus on oral medications, when modeling a composite outcome of 30-day death, cardiac reoperation, permanent stroke, renal failure requiring dialysis, deep sternal wound infection, and prolonged ventilation among isolated CABG patients.17 Thus, the impact of just one additional precursor event on short-term morbidity and mortality in our results is of a magnitude similar to that relating to triple-vessel disease in the STS CABG model, and 2 precursor events are comparable to the presence of triple-vessel disease plus diabetes together. Furthermore, events categorized as being of major severity or pertaining to the surgeon were even more strongly predictive of DNM, and this parallels the results of de Leval et al9 in a pediatric population. In addition to the impact of precursor events, the risk of the composite outcome of DNM was related to several better characterized risk factors: the procedure (lower risk for isolated CABG), urgency (preoperative IABP),
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Fig 2. Predicted probability of death or near miss (DNM) based on number of major severity precursor events (along the horizontal axis, drawn to similar scale as Fig 1) and based on number of precursor events most affecting the surgeon: (a) 0, (b) 5, and (c) 10 surgeon precursor events. The model assumes a low risk patient (isolated CABG, NYHA class 1). Dashed lines represent 70% confidence intervals surrounding the predicted probability (solid line).
medical status (NYHA function); and diagnosis/ comorbidities (atrial fibrillation as a marker for valvular disease and additional disease burden). Classification of precursor events may provide useful insights. This analysis highlights the fact that a small but critical subset of events (only 16.5% were of major severity) can have a large impact on outcomes (OR, 1.78 per event), even in the low risk patient (Fig 2). Our results also emphasize the importance of technical skill and the role of the surgeon in this setting. These findings may apply to other specialties such as neurosurgery, in which similar proportions of major (15%) and technical (31%) problems have been reported.18 Rasmussen19 classified errors as skill-, knowledge-, or rulebased. Our classification system shares similarities with Rasmussen’s model: skill-based errors coincide with our technical category, and knowledge- and rule-based errors may be considered akin to management events. Furthermore, precursor events of
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the environment type may extend to include the Donabedian notion of structure. Our findings must be interpreted with a number of caveats in mind, some of which have been discussed previously.10 Foremost, comparison with other studies is difficult as a variety of methodologies for studying process have been used.20 Most studies were retrospective and assessed process using other metrics (medication or other medical errors, rates of prescribing, etc). They relied largely on medical record or administrative data and varied in their sensitivity for detecting events. We have attempted to translate an accepted and understood nomenclature and framework (the notion of precursor events) into the cardiac operative arena, and have used a standard, universal definition from the literature.13 Because the specific inclusion criteria for precursor events within this definition are therefore broad, we have relied on the professional judgment of “sharp end providers” who are involved intimately in the care of the patient as this permits collection of the most relevant and insightful data in real time or close to it. To concretize these concepts and also to analyze these data with statistical tools, we have attempted to deconstruct the entire universe of precursor events into more manageable categories, using handles such as type, severity, and compensation. This necessarily requires a certain degree of arbitrary assignment, although by using multiple blinded adjudicators, this process was as unbiased as possible. Furthermore, some difficulty lay in attempting to decompose complex, real-life events into a few discrete categories, although in most cases this process was fairly intuitive and consistent. Conceptually, precursor events in our study may be considered directly causative of an adverse outcome, or simply diagnostic of a process problem (symptomatic of, but not necessarily themselves the problems that led to the outcome). Even so, diagnostic events may have importance on their own and be indirectly implicated in the pathway, by eroding the team’s concentration or their ability to prevent and compensate for other precursor events. Furthermore, the timing of the outcome may be immediate or delayed. All these factors obfuscate causality and may dilute the observable impact of precursor events, but they do not diminish the importance of these events. Because only 3 hospitals participated in this study, our results may not be generalizable to other institutions with practice paradigms other than those represented by the 3 sites. Although we were able to obtain meaningful data from all 3 sites, suggesting the data collection tool was effective,
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intrinsic inter-hospital differences complicated any attempts at comparisons of precursor events between sites and rendered questions of inter-hospital validity moot. We also recognize the limitations in studying a heterogeneous population (different hospitals and a variety of procedures) and using a composite outcome incorporating softer near miss end points. For these reasons, it is often difficult to obtain a model with more robust explanatory strength (a c statistic of 0.73 is quite satisfactory). Finally, we selected identifiable, explicit death and near-miss outcomes and excluded more controversial and ill-defined outcomes such as postoperative myocardial infarction, left ventricular dysfunction, and type II neurologic outcomes (encephalopathy). This study is the first of its kind in the adult cardiac operative population and has implications for improving the quality of cardiac operative care. Although preoperative patient status may still be the single-most important driver of outcomes and has been studied extensively,20,21 these characteristics often hold limited opportunity for optimization. One important lesson from these data is that in patients known to be at high risk for adverse outcomes based solely on preoperative characteristics, particular attention on the part of the operative team to avoiding additional risk from intraoperative misadventures must be emphasized. This study shows that the impact of precursor events on adverse outcomes is independent of other patient characteristics, and as such represents a new horizon for improvement, especially in the most urgent, complex, and risky patients, where the potential for and consequences of precursor events may be increased even further. Nevertheless, a small subset of precursor events (major severity, in particular) may be highly associated with adverse outcomes, even in the low risk setting. Unlike mortality and even the more abundant near misses, precursor events are almost ubiquitous and offer a largely untapped potential for research. Quality improvement initiatives might be easier to target when viewed through the lens of precursor events, in particular those with a significant potential for harm or those occurring most frequently. Although an individual precursor event may not be able to discriminate with much precision between a patient who will suffer an adverse outcome and one who will not, we were able to identify characteristics of precursor events that were most predictive of adverse outcomes. Strategies for improving outcomes may lie on 2 fronts: eliminating precursor events by rote and standardization, and improving the team’s ability to compensate innovatively, including by the use of simulation. Although there is
a tension between these 2 strategies (surgeons who never deviate from the tried and true [and thus reduce precursor events] vs those who adapt their approach frequently depending on the situation [and may better compensate for precursor events]), the best practice may be a blend and this also warrants further study. Likewise, structure has been studied little but may represent another important portal for intervention.22,23 As a result of an increasing awareness as to the importance of structure and process, increasing interest in incorporating these measures into quality improvement initiatives has been garnered.24 Further work is needed to improve the usefulness and availability of current day metrics of process.25,26 Identifying specific high-impact areas of process may focus quality improvement efforts, provide important feedback for training and maintenance of competency among surgeons, and yield dividends in improving safety and health care delivery. Although much remains to be understood about the complexities of process in operations, the simple concept that operations need to go well for people to do well is a basic truism, especially when the patients are sick to begin with. The authors wish to thank Stephen Hinrichs, Susan April, Sandra deBronkart, Diane Davies, Eliza Gregory, Dimitri Kalavrouziotis, Maral Ouzounian; the nurses, perfusionists, physician assistants, residents, anesthesiologists, and surgeons who participated in this research; and the patients at the Maritime Heart Centre, Massachusetts General Hospital, and North Shore Medical Center. REFERENCES 1. Kohn LT, Corrigan JM, Donaldson MS. To err is human. Washington, DC: National Academy Press; 2000. 2. Committee on Quality of Health Care in America, Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2003. 3. Shahian DM, Normand SL, Torchiana DF, Lewis SM, Pastore JO, Kuntz RE, et al. Cardiac surgery report cards: comprehensive review and statistical critique. Ann Thorac Surg 2001;72:2155-68. 4. Milstein A, Galvin RS, Delbanco SF, Salber P, Buck CR. Improving the safety of health care: the leapfrog initiative. Eff Clin Pract 2000;3:313-6. 5. O’Connor GT, Plume SK, Wennberg JE. Regional organization for outcomes research. Ann N Y Acad Sci 1993;703:44-50. 6. Donabedian A. Evaluating the quality of medical care. Milbank Q 1966;44:166-203. 7. Brook RH, McGlynn EA, Cleary PD. Quality of health care. Part 2: measuring quality of care. N Engl J Med 1996; 335:966-70. 8. de Leval MR, Francois K, Bull C, Brawn W, Spiegelhalter D. Analysis of a cluster of surgical failures: application to a series of neonatal arterial switch operations. J Thorac Cardiovasc Surg 1994;107:914-24.
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9. de Leval MR, Carthey J, Wright DJ, Farewell VT, Reason JT. Human factors and cardiac surgery: a multicenter study. J Thorac Cardiovasc Surg 2000;119:661-72. 10. Wong DR, Vander Salm TJ, Ali IS, Agnihotri AK, Bohmer RMJ, Torchiana DF. Prospective assessment of intraoperative precursor events during cardiac surgery. Eur J Cardio-thor Surg 2006;29:447-55. 11. Precursor events point to system-wide shortcoming before crash. Air Safety Week 2003;17:1. 12. Hawkes AD, Scott DB, Lipps JH, Combellick R. Evidence for possible precursor events of megathrust earthquakes on the west coast of North America. Geol Soc Am Bull 2005; 117:996-1008. 13. Corcoran WR. Defining and analyzing precursors. In: Phimister JR, Bier VM, Kunreuther, editors. Accident precursor analysis and management: reducing technological risk through diligence. Washington, DC: The National Academies Press; 2004. p. 79-88. 14. Hannan EL, Kilburn H, Racz M, Shields E, Chassin MR. Improving the outcomes of coronary artery bypass surgery in New York State. JAMA 1994;271:761-6. 15. Khuri SF, Daley J, Henderson WG. The comparative assessment and improvement of quality of surgical care in the Department of Veterans Affairs. Arch Surg 2002; 137:20-7. 16. O’Connor GT, Plume SK, Olmstead EM, Morton JR, Maloney CT, Nugent WC, et al. A regional intervention to improve the hospital mortality associated with coronary artery bypass graft surgery. The Northern New England Cardiovascular Disease Study Group. JAMA 1996;275:841-6. 17. Shroyer ALW, Coombs LP, Peterson ED, Eiken MC, DeLong ER, Chen A, et al. The Society of Thoracic Surgeons:
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