EJCTS Click here to locate an Ethicon representative
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Personal Folders
Right arrow Download to citation manager
Right arrow Author home page(s):
Daniel R. Wong
Thomas J. Vander Salm
Imtiaz S. Ali
Arvind K. Agnihotri
David F. Torchiana
Right arrow Permission Requests
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wong, D. R.
Right arrow Articles by Torchiana, D. F.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wong, D. R.
Right arrow Articles by Torchiana, D. F.
Related Collections
Right arrow Cardiac - other

Eur J Cardiothorac Surg 2006;29:447-455
© 2006 Elsevier Science NL

Prospective assessment of intraoperative precursor events during cardiac surgery

Daniel R. Wong a , Thomas J. Vander Salm b , Imtiaz S. Ali a , * , Arvind K. Agnihotri c , Richard M.J. Bohmer d , David F. Torchiana c

a Maritime Heart Centre, Halifax, NS, Canada
b North Shore Medical Center, Salem, MA, United States
c Massachusetts General Hospital, Boston, MA, United States
d Harvard Business School, Boston, MA, United States

Received 12 September 2005; received in revised form 30 December 2005; accepted 3 January 2006.

* Corresponding author. Address: Division of Cardiac Surgery, Queen Elizabeth II Health Science Centre, 1796 Summer Street, Suite 2269, Halifax, NS, Canada B3H 3A7. Tel.: +1 902 473 3808; fax: +1 902 473 4448. (Email: imtiaz.ali{at}dal.ca).


   Abstract
Top
 Abstract
1. Introduction
 2. Materials and methods
 3. Results
 4. Discussion
 Appendix A
 Appendix B
 References
 
Objective: Increasing attention has been afforded to the ubiquity of medical error and associated adverse events in medicine. There remains little data on the frequency and nature of precursor events in cardiac surgery, and we sought to characterize this. Methods: Detailed, anonymous information regarding intraoperative precursor events (which may result in adverse events) was collected prospectively from six key members of the operating team during 464 major adult cardiac surgical cases at three hospitals and were analyzed with univariable statistical methods. Results: During 464 cardiac surgical procedures, 1627 reports of problematic precursor events were collected for an average of 3.5 and maximum of 26 per procedure. 73.3% of cases had at least one recorded event. One-third (33.3%) of events occurred prior to the first incision, and 31.2% of events occurred while on bypass. While 68.0% of events were regarded as minor in severity (e.g., delays and missing equipment), a substantial proportion (32.0%) was considered major and included anastomotic problems, pump failure, and drug errors. Most problems (90.4%) were reported as being compensated for, although many (30.9%) were never discussed among the team. Major events were more likely to be discussed (p < 0.0001) and less likely to have been previously encountered (p = 0.0005). Perceptions of the severity and compensation of events varied across the team, as did temporal patterns of reporting (p < 0.0001). Conclusions: A wide range of problematic precursor events occurs during the majority of cardiac surgery procedures. Attention to causes and ways of preventing these precursor events could have an impact on the rate of significant errors and improve the safety of cardiac surgery.

Key Words: Surgical errors • Cardiac surgery • Frequency


   1. Introduction
Top
 Abstract
 1. Introduction
2. Materials and methods
 3. Results
 4. Discussion
 Appendix A
 Appendix B
 References
 
Large scale disasters like the partial meltdown of the Three Mile Island Unit 2 nuclear reactor core in 1979 and the loss of the Space Shuttle Columbia in 2003 have awakened the public consciousness to the importance of safety and reducing errors and have brought about sweeping changes in industry and government. Events like these have spawned a revolution of changes in aviation, energy, and other sectors that have made them some of the safest endeavors in human society today [1]. Health care delivery suffers from similar inadequacies when it comes to safety, with an estimated 44,000–98,000 patients dying from medical errors annually in the United States [2]. As awareness of this issue has increased within health care circles, and led by pioneering work in anesthesiology [3], the safety revolution has now also begun to impact medicine. Nevertheless, ‘lack of awareness of the extent to which errors occur daily in all health care settings and organizations’ has been cited by the Institute of Medicine as a major obstacle to improving patient safety [4]. While an abundance of outcome data exists in the cardiac surgery literature, little is known about the frequency and nature of medical errors and the factors that are at play in their occurrence. Two groundbreaking studies by de Leval et al. [5,6] have begun to delve into this area by studying arterial switch procedures for transposition of the great vessels in a pediatric population. Following the initial report of a cluster of closely timed deaths, de Leval et al. [5,6] undertook a prospective assessment of intraoperative events for this operation in the UK, as recorded by human factors observers. We wished to extend these findings to an adult cardiac surgical population using a novel approach to the prospective data collection.


   2. Materials and methods
Top
 Abstract
 1. Introduction
 2. Materials and methods
3. Results
 4. Discussion
 Appendix A
 Appendix B
 References
 
2.1 Study population
With the approval of each institutional research ethics board, three university-affiliated teaching hospitals in two countries participated in this study, with each site actively collecting data during a total of 464 major adult cardiac surgical procedures for consecutive patients by participating surgeons. Two of the institutions had well-established cardiac surgery programs, and one was a new program. Two hospitals participated from April 2003 until January 2004, and another from February to May 2004. Data were collected from up to six key members of the operative team. For each procedure, a nurse, perfusionist, anesthesiologist, and surgeon were present; a surgical resident was present in many cases, as was a physician assistant (PA) or nurse assistant. The role and presence of a resident, PA, and nurse assistant varied between hospitals and cases, and there was no attempt made to alter standard operating protocols at each site where differences occurred.

2.2 Definitions
The nomenclature describing medical errors, and the events that lead up to adverse outcomes, is not well established. Gaba [7,8] proposed a schema for the evolution of adverse events in which triggers may cause problems which may then become incidents (called critical if they have the potential to harm the patient) that may lead to the adverse outcome. In this study, we have grouped all of Gaba's stages proximate to the adverse outcome and designated them as problematic ‘precursor events.’ Precursor events have the potential to lead to adverse outcomes and may constitute medical errors per se. Precursor events can take the form of both active failures and latent conditions, and they may be inextricably and immediately linked to an adverse outcome (e.g., a slip while inserting a chest tube leads to a perforation of the IVC and causes inherent injury and bleeding) or cause one in a delayed manner (e.g., conduit injury may lead to graft occlusion and myocardial infarction). Furthermore, multiple precursor events may be required to ‘align’ for an adverse outcome to result, as per Reason's [9] Swiss cheese model of adverse events.

The six key operative team participants were instructed to record information regarding any precursor events that led up to the time of surgery or occurred during the operation. The precise limits for what ought to be recorded were left to the professional judgment of these key members; however, they were requested to record events or situations that disrupted surgical flow or fell outside of the range of normal practice, whether or not they ultimately led to an adverse event. We based many of these definitions and variables on the work of de Leval et al. [6] in the pediatric cardiac population. Participants designated events as ‘major’ if they perceived a potential to harm the patient. Events relating only to patient characteristics (such as a high number of comorbidities, or the need for a complex operation) were not solicited; this data was available from the medical record or from institutional cardiac surgical databases, as defined by STS criteria.

Precursor events were assigned to have occurred in one of the five phases of the procedure: preoperative (before the patient entered the operating room), anesthesia (once the patient entered the room but prior to the incision, and not necessarily limited to events relating to the anesthetic), prebypass (from the first incision until cardiopulmonary bypass was instituted), surgery (the main portion of the operation on bypass, with the obvious exception of OPCAB which then referred to the time during which anastomoses were constructed), and postbypass (until transferred from the OR).

For each event, information requested included: whether an event was compensated for (the appropriate actions were taken to negate any attendant consequences); the duration of time required to detect, decide (how) to correct, and correct the event; whether one had the required skills and/or equipment to do so; and whether one discussed the problem with another member of the team.

2.3 Data collection
Precursor event data were collected prospectively and independently by each of the team members present for the procedure using a simple questionnaire (Appendix A). As far as possible, these events were recorded promptly in real time or during the temporal phase of the operation in which they occurred, as defined above. This was done to try to limit the bias arising from retrospective assessment of events after the occurrence of adverse outcomes. Health care providers participated in a voluntary, confidential, and anonymous basis, with no data to identify a particular team member, which is essential for effective reporting [10]. Data collection forms were collated at the end of the surgery and stored in locked quarters, and the data were then transferred to a secure database. Patient-identifying data were also stripped from the records and/or patients were coded by a random number which was later removed once data processing was complete, as per individual institutional requirements. The temporary data collection forms were destroyed, with the exception of one hospital which had required a random number method of data linkage. In this hospital, the link was subsequently destroyed once data processing was accomplished.

2.4 Statistics
All statistical analyses were performed using SAS software (SAS version 8, SAS Institute, Cary, NC, USA). We analyzed a total of 1627 reports of precursor events. Based on the available hand-written descriptions of events, their timing, and other data, 37% of reports were duplicates of the same event but recorded by different team members, for a total of 1034 unique events. In this analysis, we describe the 1627 individual reports as they constituted the purest form of the data. Standard univariable analyses included the {chi} 2-test for discrete variables and t-test and ANOVA for continuous variables; the Kruskal–Wallis test was used to test for differences between several groups with a non-normally distributed continuous variable. A p-value of <0.05 was considered significant, with no adjustment for multiple testing.


   3. Results
Top
 Abstract
 1. Introduction
 2. Materials and methods
 3. Results
4. Discussion
 Appendix A
 Appendix B
 References
 
3.1 Frequency and reported severity of intraoperative precursor events
During the course of 464 major adult cardiac surgical procedures (99, 101, and 264 by site), a total of 1627 reports of precursor events were collected. The number of events per procedure ranged from 0 to 26, with a mean of 3.5 reports per case. At least one event was reported in the majority of cases (73.3%, Table 1 ). The number of cases without a single reported event differed quite significantly: for one hospital there were no events in 41.7% of cases compared to 7.1 and 6.9% for the other two sites (p < 0.0001). The average (2.0 (520/264), 4.8 (476/99), and 6.2 (631/101), respectively, p < 0.0001) and median number of reports per patient (1, 4, and 5, respectively, p < 0.0001) varied in the same manner.


View this table:
[in this window]
[in a new window]
  		Table 1. Characteristics of 1627 reports of intraoperative precursor events during cardiac surgery
 
The severity (minor vs major) of the reported events was assessed and recorded by the team members in 1418 reports. Sixty-eight percent of precursor events were reported as minor, and 32.0% were considered major. Examples of common minor events included: missing ID stickers, missing X-rays, contaminating a Swan–Ganz catheter, dropping an OPCAB retractor, monitors not working, pulse oximeter equipment difficulties, delayed starts due to lack of personnel or ICU beds or due to a blizzard, delays in obtaining lab results, a manufacturer's defect on a connector, incorrect booking, incorrect counts, cooling to too low a temperature, asking for the wrong valve, distraction by outside alarms, lack of assistants or other team members, and bleeding from the leg, among many others. Major events included perforating the IVC with a chest tube, making a vein graft too short, revising anastomoses for bleeding, significant bleeding from the CPB circuit or elsewhere, CPB pump failure, clot formation in the heart while on pump, carrier pump fluid for an inotrope running out unnoticed, giving a bolus of epinephrine instead of lidocaine, preparing an undiluted medication for infusion, accidental extubation when removing the TEE probe, and various other incidents.

Hospitals which reported lower number of cases per patient and greater proportions of event-free cases also reported higher proportions of minor severity events (73.4% and 70.8% vs 62.1%, p = 0.0003). Table 2 lists characteristics of events based on severity. Major events occurred more often during the bypass (p = 0.02) and postbypass (p < 0.0001) phases of surgery, and less frequently during the preoperative phase (p = 0.0001). While major precursor events may have been less frequently previously encountered than minor events (p = 0.0005), they were equally compensated (p = 0.61) and more often discussed among the team members (p < 0.0001).


View this table:
[in this window]
[in a new window]
  		Table 2. Characteristics of major and minor severity events
 
3.2 Compensation of precursor events
The majority of events (90.4%) were reported to have been compensated for, and this did not vary significantly by hospital. Minor and major events were equally likely to go uncompensated (p = 0.61). Uncompensated events were discussed less often when compared to compensated events (p = 0.0005). Health care providers felt they were less prepared to deal with uncompensated events as measured by a variety of indicators (Table 3 ).


View this table:
[in this window]
[in a new window]
  		Table 3. Characteristics of compensated and uncompensated events
 
3.3 Variation in reporting by surgical team member
The reported severity of events varied across the team (p < 0.0001, Table 4 ). Surgical residents reported major events more often—perhaps a combination of reporting fewer minor events and/or perceiving events to have been of a greater severity. In comparison, attending surgeons reported a lower proportion of events in the major category. Surgical residents who are still in training reported they felt they did not have the skills and equipment needed as often as their fully trained operating room team members. In addition, staffing allocations and responsibilities for nurses, perfusionists, and especially residents and PA/nurse assistants differed greatly from one institution to another, and so reporting also varied across hospitals for these team members.


View this table:
[in this window]
[in a new window]
  		Table 4. Characteristics of events stratified by the reporting operative team member
 
3.4 Temporal patterns of reporting
One-third (33.3%) of reports were recorded prior to the first incision (Table 1). Of the events occurring after the incision, almost half (46.8% (508/1085)) of them occurred while on bypass. Significant temporal variation occurred by surgical team member coinciding with the normal activities of these providers (p < 0.0001, Table 4). The circulating nurse reported a relatively greater number of preoperative events; anesthesiologists recorded more reports than others during the anesthetic phase of the operation; the PA/nurse assistant noted reports largely during the prebypass and bypass phases when they were often harvesting conduit; and the perfusionist, surgeon, and resident reported predominantly during the bypass and postbypass phases. Reporting of events did not vary temporally across hospitals, with the exception of one hospital which reported slightly more preoperative events as a proportion of all reported events in that hospital (p = 0.002).


   4. Discussion
Top
 Abstract
 1. Introduction
 2. Materials and methods
 3. Results
 4. Discussion
Appendix A
 Appendix B
 References
 
A cultural shift has been gaining momentum in medicine focusing on patient safety. Like other highly specialized and technical disciplines, cardiac surgery could benefit from initiatives to improve safety and quality of care, but little is known about medical error in this field. We therefore prospectively gathered data from cardiac surgical operating teams to study precursor events, that is problematic incidents and circumstances that may lead to adverse events. This is the first report of its kind that we are aware of in the adult cardiac surgical literature.

In this study, we identified several important observations relating to the (1) frequency, (2) nature, and (3) compensation of precursor events.

4.1 Frequency of precursor events
Precursor events occur often both in terms of the number of events per case (mean of 3.5) and the proportion of cases with at least one event (73%), despite significant variation by case and hospital in both of these numbers. It is likely that factors specific to the patient, procedure, and institution contributed to this variation. Compared to some other studies in the literature, the event rate was higher in our study. Cooper et al. [11] reported that 18% of anesthetic cases experienced adverse (‘recovery-room impact’) events. Donchin et al. [12] reported that 1.7 errors occurred per patient daily in an intensive care setting. While both anesthesiology and critical care settings share similarities with cardiac surgery in that they are highly technical, involve many separate steps and personnel, and have the potential for a high level of risk, it is difficult to compare our results to other studies, since none of them used a continuous prospective monitoring protocol, and most captured adverse events and not the precursor events that may or may not have led to these outcomes [13,14]. In comparison, using prospective data collection by outside observers, major events were observed by de Leval et al. [6] in 78 of 173 patients (45%) and minor events in 170 of 173 cases (98%) (comparable to two of our sites), for an average of 1.4 events per case (slightly lower than what we reported). Like our report (see below), 31% of their events were major and 80% were compensated. The high concordance suggests that our prospective data collection tool captures a comparable dataset to their human factors observers.

4.2 Nature of precursor events
A broad spectrum in the type of precursor events was observed. Many different problems were encountered, either unique or recurring, with numerous minor events (68.0%) and a fair proportion of major incidents (32.0%), and cut across observations made by anesthesia, perfusion, nursing, surgeons, physician/nursing assistants, and residents in training. Events were divided approximately into three parts temporally—before surgery began (33.3%), during surgery but not on bypass (35.5%), and during cardiopulmonary bypass (31.2%)—suggesting that all components of the operation and its preparation are prone to the occurrence of these events. The high degree of variability of the events is in keeping with the highly complex nature of cardiac surgery and the patients encountered.

4.3 Compensation of precursor events
Lessons can be learned from the compensatory responses themselves, which appeared to be robust but not infallible. In our study, most events (90.4%) were reported as being compensated for. It is interesting that major and minor events were equally likely to go uncompensated, especially since they may go uncompensated for different reasons (for example, low priority vs low ability to compensate). Also, we did not distinguish between partial and complete compensation of events, which may differ for major and minor events. It is likely that depending on the completeness and effectiveness of any compensatory actions, the severity, type, permanence, and even detection of a resultant adverse outcome may be modified, and we did not attempt to measure this. Furthermore, the link with adverse outcomes is not always simple and direct, even with uncompensated major events, as other factors (additional precursor events like failed backups, weakened defenses, etc.) may be required for harm to occur, as described by Reason's [9] Swiss cheese model of adverse events.

A high proportion (30.4%) of events went without being discussed by anyone in the team, including 23.1% of major events. It is not clear whether this is because of the entirely obvious nature of the event, a reluctance to discuss problems, or some other reason. We believe that this represents an obvious area for improvement. Unlike numerous other industries, debriefing of important precursor events is often neglected in medicine unless an adverse outcome occurs, meriting discussion of the case in forums like morbidity and mortality rounds. Broadening the scope of these rounds has been entertained as a means of accomplishing this and would be another positive way to move forward from this study now that formal data collection has ceased. In one participating hospital, the team stated that recording events for the study sparked special weekly quality improvement meetings, and this may also be a worthwhile activity for all cardiac surgical OR teams to undertake in order to maintain safety awareness and to identify and rectify problem areas. Unfortunately, the sheer magnitude and variation in the nature of the precursor events means that an equally varied set of compensations must be orchestrated seamlessly during the case to counteract them, and that it may not be easily apparent how to do this. In this study, there were several recurring themes noted, however, which brought about anecdotal positive changes. For instance, delays in lab result reporting prompted the purchase of blood analyzers for the OR in one institution. Thus, the study of the compensatory actions at work during surgery may provide insights into how best to avoid precursor events, how to avert those that do occur, and what characteristics of teams or organizations may permit successful recovery from these events. The rapidity with which events are detected, decisions are made, and compensations are initiated and completed is part of this equation, and we documented a wide range in these response times. Other factors influencing the effectiveness of compensations and how to learn and improve on this skill require ongoing and more in-depth investigation. This has important implications for training surgeons, who must learn routine, mechanical reproducibility while also adapting to newly encountered situations.

4.4 Studying precursor events
The most distinctive feature of this study is the manner in which data were collected. Most other studies on medical errors and adverse events relied on retrospective data collection from chart reviews and this tends to limit reporting, particularly of mundane events [15,16]. The presence of the data collection infrastructure in this study likely prompted a greater level of reporting by acting as a tangible reminder. In addition, by collecting data in real time, we hoped to minimize hindsight bias that might increase reporting specifically of events that led to adverse events.

Some of the greatest challenges and limitations in this study concerned hospital culture, language, and definitions. Unfortunately, in health care, a culture of blame, fear of litigation, and an expectation of perfection have hampered efforts to promote full disclosure, despite numerous calls for such openness [17]. Participating providers were, therefore, assured complete anonymity for themselves, their colleagues, and their patients, and participated on an entirely voluntary basis (practitioners were still encouraged to engage the usual institutional and regulatory channels for reporting of errors and incidents as required by the state or province, and this study acted in some ways as an extension and refinement of these systems). We avoided the use of loaded terms like ‘error’ which may have undesirable effects [18] and instead propose using the broader designation ‘precursor events’ to refer to the universe of incidents and circumstances that may ultimately lead to an adverse event. Unfortunately, no framework or generally accepted nomenclature for the study of safety, errors, and adverse outcomes currently exists.

We debated the merits of using outside observers to record events, rather than asking the operating team members to do so, and this lack of expert observers might be considered a limitation of our study. We believe that the professionals involved in daily patient care would be much more aware of events and better able to discriminate events from the normal, expected flow of surgery, especially if they were simultaneous, highly specialized to a field of expertise, or subtle. For example, it might be difficult for an observer to determine whether an anesthesiologist had improperly mixed and almost administered a medication incorrectly, or to discern acts of omission such as forgetting to plug in critical equipment. While this has been accomplished before by de Leval et al. [6], we did not feel this was a feasible option for us logistically (de Leval et al.'s [6] study excluded 50 of 243 cases for this reason). Nevertheless, it is possible that observers may have identified other types of problems. Larger scale, latent precursor events relating to the hospital organization, ergonomics, the physical facility, staffing structure, team cooperation, finances, and other long-term deficiencies were probably underrepresented in this study (for example, only once did someone comment on the inconvenience and interference caused by storing bulky orthopedic equipment in a high traffic area, although this was present for all cases performed at that hospital). Therefore, there are theoretical advantages of combining reporting by providers with observation by outside experts, including correlating the two sets of data and so identifying the strengths and weaknesses of each, especially where no benchmark for measuring precursor events exists. Nevertheless, we propose that the judgment of the involved professionals, engaged consciously in quality of care research and unfettered by a blaming culture, is a near and useful approximation.

As there is no gold standard, lower rates of reporting can be interpreted in two ways: fewer reported events may equate with truly fewer problems or alternatively may reflect lower levels of vigilance. The hospital with the highest reporting in our study was a small cardiac surgical team of experienced members who were starting to work together for the first time as a new team. The constancy of the same team members working together may have created a more cohesive and tightly functioning group, more in tune with the process and thus any deviations from the expected course. On the other hand, this new unit may have also been prone to more problems related to start up. Finally, slight variations in the data collection process and organizational differences in the three hospitals may have influenced the number of events that occurred or were reported.

In conclusion, a spectrum of precursor events occurred frequently during major adult cardiac surgical procedures; compensation of many of these events was documented by the operating team members. We hope that other investigators may be able to make use of this tool as a feasible means to further study the precursor events. By increasing awareness of these issues for all cardiac surgeons, the specialty as a whole will make inroads towards improving the safety and quality of care delivered [19]. We believe these results are compelling evidence highlighting the need to better understand precursor events, medical errors and their causes, and methods of preventing and mitigating these events in cardiac surgery.


   Appendix A
Top
 Abstract
 1. Introduction
 2. Materials and methods
 3. Results
 4. Discussion
 Appendix A
Appendix B
 References
 
Conference discussion

Dr G. Bolotin (Tel Aviv, Israel): I have two comments. A similar study at the University of Chicago done by Dr Basha. The results were similar, a very high frequency of minor and major errors. I think there are already enough data to support that it should not be a kind of study, that it should be implemented as routine work in any cardiac surgery department for reviewing the mistakes on a daily basis and have communication on a daily basis in trying to reduce the number, because data are there that the problems are already in significant numbers.

The other thing is whether you checked the influence of sleeping hours of the residents or the attendings. We know from aviation and from other industries that sleeping hours have a major impact on mistakes.

Dr Wong : Number one, I agree wholeheartedly that this is something that is quite important. We do a good job in cardiac surgery compared to many other disciplines in terms of measuring outcomes, but we don’t do a very good job of measuring the process of care, in particular the problems that occur and reporting them well. One of the three hospitals that participated was a brand new cardiac surgical center and they were very keen to do this so much so that they held weekly meetings based on the results of what they as a team discussed during the case and reported to our study. They used these meetings as part of their quality assurance measures. However, most hospitals don’t have anything like this implemented, and I think it would be a good step.

Your second point is regarding sleep and deprivation of sleep. Unfortunately we didn’t measure this, but I do believe that it would have an impact on the number of mistakes and problems that we see. The reason why we didn’t do this was that we weren’t addressing only residents, and sleep deprivation varies significantly among the team members. I think it would be valuable to do anyway, and perhaps the next time we should look into that further.

Dr M. Siepe (Freiburg, Germany): Did you do a correlation between the events on one hand and mortality and morbidity on the other hand in the patients?

Dr Wong : That work is still ongoing. I can tell you preliminarily that there is a correlation with certain outcomes. Similar to Marc de Leval, we have looked at death and near misses, which include major cardiac surgical outcomes, and certainly there is a correlation. We are still working on that at the present time. As you can imagine, the data are pretty rough in their present form. There were reports of all sorts of things, and to try to finesse that into data that you can analyze with statistical software does take a little bit of work and we’re still continuing that effort.


   Appendix B
Top
 Abstract
 1. Introduction
 2. Materials and methods
 3. Results
 4. Discussion
 Appendix A
 Appendix B
References
 
Operative data collection form


Formula


Formula


   Acknowledgments
 
The authors wish to acknowledge the invaluable contribution of Stephen Hinrichs towards the data management and collection; Susan April, Sandra deBronkart, Diane Davies, Eliza Gregory, Dr Dimitri Kalavrouziotis, and Dr Maral Ouzounian, for their assistance; the participating nurses, perfusionists, physician assistants, residents, anesthesiologists, and surgeons, who completed the data collection forms; and the patients at Salem Hospital, Massachusetts General Hospital, and the Maritime Heart Centre.


   Footnotes
 
{star} Presented at the joint 19th Annual Meeting of the European Association for Cardio-thoracic Surgery and the 13th Annual Meeting of the European Society of Thoracic Surgeons, Barcelona, Spain, September 25–28, 2005.


   References
Top
 Abstract
 1. Introduction
 2. Materials and methods
 3. Results
 4. Discussion
 Appendix A
 Appendix B
 References
 

  1. Slovic P, Fischoff B, Lichtenstein S. Facts and fears: understanding perceived risk. In: Schwing R, Albers WA, editors. Societal risk assessment: how safe is safe enough?. New York: Plenum; 1980.
  2. Committee on Quality of Health Care in America, Institute of Medicine, Kohn LT, Corrigan JM, Donaldson MS, editors. To err is human: building a safer health system. Washington, DC: National Academy Press; 1999..
  3. Cooper JB, Newbower RS, Long CD, McPeek B. Preventable anesthesia mishaps: a study of human factors. Anesthesiology 1978;49:399-406.[CrossRef][Medline]
  4. 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.
  5. 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;107:914-924.[Abstract/Free Full Text]
  6. de Leval MR, Carthey J, Wright DJ, Farewell VT, Reason JT. Human factors and cardiac surgery: a multicenter study. J Thorac Cardiovasc Surg 2000;119:661-672.[Abstract/Free Full Text]
  7. Gaba DM. Human error in anesthetic mishaps. Int Anesthesiol Clin 1989;27:137-147.[Medline]
  8. Gaba DM. Human error in medical domains. In: Bogers MS, editor. Human error in medicine. Hillsdale, NJ: Lawrence Erlbaum Associates Inc.; 1994.
  9. Reason JT. Human error. Cambridge: Cambridge University Press; 1990.
  10. Spencer FC. Human error in hospitals and industrial accidents: current concepts. J Am Coll Surg 2000;4:410-418.
  11. Cooper JB, Cullen DJ, Nemeskal R, Hoaglin DC, Gevirtz CC, Csete M, Venable C. Effects of information feedback and pulse oximetry on the incidence of anesthesia complications. Anesthesiology 1987;67:686-694.[Medline]
  12. Donchin Y, Gopher D, Olin M, Badihi Y, Biesky M, Sprung CL, Pizov R, Cotev S. A look into the nature and causes of human errors in the intensive care unit. Crit Care Med 1995;23:294-300.[CrossRef][Medline]
  13. Andrews LB, Stocking C, Krizek T, Gottlieb L, Krizek C, Vargish T, Siegler M. An alternative strategy for studying adverse events in medical care. Lancet 1997;349:309-313.[CrossRef][Medline]
  14. Gawande AA, Thomas EJ, Zinner MJ, Brennan TA. The incidence and nature of surgical adverse events in Colorado and Utah in 1992. Surgery 1999;126:66-75.[CrossRef][Medline]
  15. Gallagher TH, Waterman AD, Ebers AG, Fraser VJ, Levinson W. Patients’ and physicians’ attitudes regarding the disclosure of medical errors. J Am Med Assoc 2003;289:1001-1007.[Abstract/Free Full Text]
  16. Brennan TA. The Institute of Medicine report on medical errors—could it do harm?. N Engl J Med 2000;342:1123-1125.[Free Full Text]
  17. Brennan TA, Leape LL, Laird NM, Hebert L, Localio AR, Lawthers AG, Newhouse JP, Weiler PC, Hiatt HH. Incidence of adverse events and negligence in hospitalized patients. N Engl J Med 1991;324:370-376.[Abstract]
  18. Dovey SM, Meyers DS, Phillips RL, Green LA, Fryer GE, Galliher JM, Kappus J, Grob P. A preliminary taxonomy of medical errors in family practice. Qual Saf Health Care 2002;11:233-238.[Abstract/Free Full Text]
  19. Pairolero PC. Presidential address: quality, safety, and transparency: a rising tide floats all boats. Ann Thorac Surg 2005;80:387-395.[Free Full Text]




This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Personal Folders
Right arrow Download to citation manager
Right arrow Author home page(s):
Daniel R. Wong
Thomas J. Vander Salm
Imtiaz S. Ali
Arvind K. Agnihotri
David F. Torchiana
Right arrow Permission Requests
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wong, D. R.
Right arrow Articles by Torchiana, D. F.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wong, D. R.
Right arrow Articles by Torchiana, D. F.
Related Collections
Right arrow Cardiac - other

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
ANN THORAC SURG ASIAN CARDIOVASC THORAC ANN EUR J CARDIOTHORAC SURG
J THORAC CARDIOVASC SURG ICVTS ALL CTSNet JOURNALS