Mortality as a Risk of Anesthesia
Early "Anesthesia Deaths"
In 1848, only 15 months after William Morton's successful demonstration of clinical anesthesia in Boston, an Edinburgh medical journal reported the death of Hannah Greener, a 15-year-old British girl who had a toenail removed under chloroform anesthesia, some 4 months after she had a similar procedure performed with diethyl ether anesthesia.104 This first report of an anesthesia-related death initiated a decades-long debate over the relative safety of ether versus chloroform and also indicated that surgical anesthesia could be fatal. Similar case reports followed.
Were these "anesthesia deaths" rare, occasional, or perhaps common? Although case reports and subsequent case series provided descriptions of the fatal events and even clues to possible etiologies, they could not quantitate the frequency of these catastrophes and allow an estimate of the risk in undergoing anesthesia. Anesthesia risk measurement benefited from the early epidemiologic work of anesthesiologist John Snow, who applied to an analysis of 50 deaths during chloroform anesthesia the epidemiologic logic that enabled him to identify the mode of transmission of cholera related to colonization of the Broad Street water pump.105
Cohort Studies of Anesthesia-Associated Mortality
Table 25-3 presents the principal results of international studies in which the mortality risk of surgical anesthesia was studied. These studies were major, often pioneering efforts, with most performed before the advent of modern information technology that facilitates aggregating and analyzing vast data and well before epidemiology and biostatistics had reached their current level of sophistication. Despite these limitations, their reports provide estimates for surgery-related, anesthesia-attributable mortality and, in many, morbidity.
++ Table Graphic Jump Location Table 25-3 Estimates of Mortality Attributable to Surgical Anesthesia Care ||Download (.pdf)
Table 25-3 Estimates of Mortality Attributable to Surgical Anesthesia Care
|Investigator(s)||Time Period||Location||No. of Hospitals||No. of Anesthetics||Primary Cause||Primary and Associated Causes|
|Dornette and Orth58||1943-1954||Madison, Wisconsin||1||63,105||1:2427||1:1343|
|Beecher and Todd8||1948-1952||United States||10||599,548||1:2680||1:1560|
|Dripps et al46||1949-1957||Philadelphia||1||33,224||1:852||1:415|
|Schapira et al107||1952-1956||New York, New York||1||22,177||1:1232||1:821|
|Phillips et al108||1953-1959||Baltimore, Maryland||Multiple||Unstated||(1:7692)||(1:2500)|
|Clifton and Hotten109||1952-1962||Australia||1||205,640||1:6048||1:3955|
|Greene et al110||1956-1959||Connecticut||Multiple||120,935||1:3901||1:3183|
|Harrison113||1956-1960||Cape Town, South Africa||1||177,928||1:3068|
|Holland66,114||1960-1968||New South Wales, Australia||Multiple||(300,000)||(1:5500)||—|
|Marx et al63||1965-1969||New York, New York||1||34,145||—||1:1265|
|Harrison56||1967-1976||Cape Town, South Africa||1||240,483||—||1:4537|
|Holland114||1970-1979||New South Wales, Australia||Multiple||(400,000)||(1:10,250)||—|
|Turnbull et al60||1973-1977||Vancouver, Canada||1||195,232||1:5138||—|
|Tiret et al57||1978-1982||France||460||198,103||1:13,207||1:3810|
|Chopra et al117||1978-1987||Leiden, The Netherlands||1||113,074||—||1:16,250|
|Holland114||1983-1985||New South Wales, Australia||Multiple||(550,000)||(1:26,000)||—|
|Tan and Delilkan119||1980-1992||Malaysia||1||155,000||—||1:25,833|
|Lunn and Devlin3||1987||United Kingdom||100||485,850||1:185,056||1:1351|
|Tikkanen and Hovi-Viander120||1987||Finland||69||325,585||1:66,700||1:16,279|
|Pedersen et al121||1986-1987||Herlev, Denmark||1||7306||—||1:2500|
|Cohen et al122||1988-1989||Toronto Canada||4||27,184||0||0|
|Warden et al123||1984-1990||New South Wales, Australia||Multiple||(493,000)||—||(1:20,000)|
|Coetzee124||1988-1992||Stellenbosch, South Africa||1||94,945||1:9090||1:2941|
|Eagle and Davis125||1990-1995||Western Australia||Multiple||(84,000)||—||(1:40,000)|
|Lagasse126||1992-1994||New York, New York||1b||37,924||—||1:12,641|
|1995-1999||New York, New York||1c||146,548||—||1:13,322|
|Arbous et al127||1995-1997||Netherlands||Multiple||869,483||—||1:7307|
|Lienhart et al128||1999||France||Multiple||(7,756,121)||(1:145,500)||(1:18,500)|
|Li et al1||1999-2005||United States||Multiple||(105,700,000)||(1:121,952)||—|
|Noordzij et al130||1991-2005||Netherlands||102||3,667,875||—||1:54|
|Gibbs and Borton4||2000-2002||Australia||Multiple||(7,650,000)||(1:182,143)||(1:56,000)|
|Charuluxananan et al131||2003-2005||Thailand||Multiple||163,403||1:5882||1:2500|
In surveying the unadjusted or crude mortality estimates listed in Table 25-3, we are immediately impressed by a wide variation in rates. Undoubtedly much of the variation is due to different study designs and other factors listed in Box 25-1. Yet there is a suggestion of improved outcome over time, especially in well-developed countries.
Holland66,114 documented a 5-fold decrease in anesthesia-attributable mortality in 1 Australian region over 25 years, much of which he attributed to enhanced risk management education of practitioners. Ever greater progress in Australia was documented by Mackay129 and Gibbs and Borton.4 Similarly, Lienhart et al128 noted an 11-fold decrease in anesthesia mortality over 15 years in France when repeating (albeit with different methodology) the study by Tiret et al,57 which noted poor outcomes in relation to lack of postanesthesia care units.
Moreover, although we might regard the study results listed in Table 25-3 akin to a collection of mixed fruit, a plot of the primary-cause mortality estimates in those studies span almost 2 orders of magnitude (or a near 100-fold decrease) over 6 decades,1 and the putative improvement trend predates the vigorous US patient safety initiatives of the mid-1980s (Fig. 25-6), even if we might be uncomfortable concluding that any specific amount of "improvement" has occurred.
Figure 25-6.Graphic Jump Location
Rate of perioperative mortality primarily attributable to anesthesia care by the midpoint year of various studies undertaken in developed countries, from 1948 to 2005, as listed in Table 25-3.
However, even if an apparent trend is wholly an artifact of differing methodologies, as some argue,126 the clinical terrain has changed dramatically over the past half century. Concerned in 1959 that mortality statistics were unchanged from those decades earlier, the National Academy of Sciences' Committee on Anesthesia invited Chauncey Starr from the National Academy of Engineering to comment. "Well, that is exactly the way it is in farming," he related as he began to discuss the tractor principle132: The rate of farming accidents remained high despite many safety improvements in tractors (eg, wider wheel base, roll bars, seat belts). Seeking an understanding, Starr made site visits during which he learned that improved tractor design enabled farmers to plow on steeper inclines! The same is true in medicine, where we now routinely perform major "high-risk" operations on patients who as recently as 35 years ago were "too sick" for surgery. Thus even stable mortality rates in the face of increasing severity of illness reflects improved clinical outcome.
Sources of Mortality and Morbidity Risk
Our literature abounds with attempts to allocate overall perioperative mortality and morbidity risk to specific characteristics of the patient, anesthetic, operation, or clinical setting. An important reference point is the perioperative mortality rate that was estimated to be 1.43% (or 1 in 70) for US hospital–based surgery in 2004133 and has been relatively stable over several decades. Comparable mortality estimates based on individual cohort studies have ranged from 1 in 256 to 1 in 53,8,59,51,46,63,134 without any apparent temporal trend.
The earliest attempt to identify specific sources of mortality risk appeared in Beecher and Todd's mid–20th-century study,8 which noted a perioperative mortality rate of 1 in 75 and ascribed 78% of deaths (1 in 95) to the patient's disease, 18% (1 in 420) to surgical management, and 3% (1 in 2680) solely to anesthesia management. They attributed approximately 5% (1 in 1560) to anesthesia when considering it as both a contributing and a primary cause. Subsequent studies attributed somewhat greater proportions of mortality to surgical and anesthesia causes, as understanding of the pathophysiology of disease and the physiologic effects of anesthesia and surgical intervention increased.3,59,51,46
However, substantial confounding underlies such simple categorizations. Being male, elderly, and in poorer condition (ie, ASA physical status ≥3), each individually augments the risk of a given surgical procedure. An early (and still valid) effort to model perioperative mortality is that of Cohen et al,59 who identified the relative importance of patient characteristics (advanced age and ASA physical status, male gender), surgery-related factors (invasiveness, whether emergent), and anesthesia management (method and drug choice; Table 25-4).
++ Table Graphic Jump Location Table 25-4 Factors Associated with Mortality Within 7 Days of Surgery ||Download (.pdf)
Table 25-4 Factors Associated with Mortality Within 7 Days of Surgery
|Factora||Odds Ratiob||95% Confidence Interval|
|Age, 60-79 vs <60 y||2.32||1.70-3.17|
|Age, ≥80 vs <60 y||3.29||2.18-4.96|
|Gender, female vs male||0.77||0.59-1.00|
|ASA physical status score, 3-4 vs. 1-2||10.65||7.59-14.85|
|Major vs minor procedure||3.82||2.50-5.93|
|Intermediate vs minor procedure||1.76||1.24-2.50|
|Length of anesthesia, ≥2 vs <2 h||1.08||0.77-1.50|
|Emergent vs elective procedure||4.44||3.38-5.83|
|Years of operation, 1975-1979 vs 1980-1984||1.75||1.32-2.31|
|Complication in operating room vs none||1.42||1.06-1.89|
|Experience of anesthesia-provider, >600 cases for ≥8 y vs <600 procedures for <8 y||1.06||0.82-1.37|
|Inhalation agent with narcotic vs inhalation alone||0.76||0.51-1.55|
|Narcotic anesthesia alone vs inhalation alone||1.41||1.01-2.00|
|Narcotic with inhalation vs inhalation alone||0.79||0.47-1.32|
|Spinal anesthesia vs inhalation alone||0.53||0.29-0.98|
|Number of anesthetic drugs, 1-2 vs ≥3||2.94||2.20-3.84|
Although definitive allocation of perioperative risk, and specifically of anesthesia risk, is lacking, what follows is an attempt to dissect some of these relationships, with the goal of offering guidance to practitioners as well as identifying improvement opportunities.
Importance of Comorbidity
Intuition tells us that sick patients tend to do poorly. Among the many studies8,34,46,48-50,52,57,59,62,63,65,74,75,78-81,89 documenting this truism is Pedersen's prospective cohort study51 of 6307 patients, in which the extent of comorbidity was associated with greater complication and mortality rates (Table 25-5).
Table Graphic Jump Location Table 25-5 Factors Associated with Perioperative Complications and Death ||Download (.pdf)
Table 25-5 Factors Associated with Perioperative Complications and Death
|Cardiovascular Complications||Pulmonary Complications||In-hospital Mortality|
|Ischemic heart disease||29.1a||6.5||8.7||1.9||2.9||2.5|
| >1 y since||20.8a||7.7||4.0a|
| ≤1 y since||38.5a||10.4a||7.7a|
|Chronic heart failure||35.2a||15.1a||3.8||9.0a||9.9|
|Hypotension (SBP ≤90 mm Hg)||16.5a||2.5||17.3a||4.0||9.4a||9.8|
|Chronic obstructive lung disease||12.4a||2.1||12.4||3.0||5.0a||4.7|
|Duration of anesthesia (min)|
|Total study population||6.3||4.8||1.2|
Modeling Outcome with Patient Characteristics
Although the general principle is now well established, capturing the precise quantitative relationship between comorbidity and clinical outcome remains an active focus of research. The venerable ASA physical status classification, never designed as a risk metric, has long been known to correlate with perioperative outcome,49,59,63,126,134 with mortality rising sharply with advanced physical status (Table 25-4 and Table 25-6). ASA physical status interacts with age, becoming a much more potent predictor of major complications beyond middle age (Fig. 25-7).
++ Table Graphic Jump Location Table 25-6 Relationship of ASA Physical Status Classification to Perioperative Mortality ||Download (.pdf)
Table 25-6 Relationship of ASA Physical Status Classification to Perioperative Mortality
|Author(s)||Vacanti et al134||Marx et al63||Cohen et al59||Lagasse126|
|Surveillance Period||48 h||7 d||7 d||48 h||48 h|
|ASA Physical Status Class||1||1:1179||1:1665||1:1389||0||1:8756|
Figure 25-7.Graphic Jump Location
Probability of a severe perioperative respiratory or cardiovascular complication as a function of both American Society of Anesthesiologists physical status class and patient age, computed from the logistic regression equation developed in the clinical trial of inhalation anesthesia drugs conducted among 17,201 patients by Forrest et al.50 [From Muravchick S. Anesthesia for the elderly. In: Healy TEJ, Knight PR, eds. Wylie and Churchill-Davidson's A Practice of Anesthesia. 7th ed. London, UK: Arnold; 2003:990. Reproduced with permission of Edward Arnold, Ltd.]
Yet there is substantial subjectivity and variability in use of middle ASA physical status categories, even in settings where payment for services is unrelated to this classification.135-138 Prediction improves when this metric is used in combinations in multivariable modeling with other covariates, such as age and/or the presence of specific morbidities.18,25,34,48-51,59,65,74,80-83,89,122,139,140 Concerns about subjectivity of the ASA physical status metric may also be addressed in risk modeling studies by including the Charlson Comorbidity Score,52 a morbidity-specific metric that has been at least as good an outcome predictor in several studies18,98,141-143; the Acute Physiology Score (APS) of the Acute Physiology and Chronic Health Evaluation (APACHE) II score, a metric commonly used in critical care144; or perhaps the Sickness at Admission Scale Score.145
Alternatively, there have been embryonic efforts to reduce the subjectivity in ASA physical status scoring by enhancing its specificity, thereby enhancing its precision and predictive power: Barbeito et al138 suggested adding a "G" to the scoring of parturients (eg, "2-G" to indicate the gravid status), and Holt and Silverman146 proposed a superscripted notion to indicate specific morbidity (eg, "3RESP" to indicate the affected organ system). Emphasizing the confounding of physical status by surgical complexity, Pasternak147 suggested a preoperative assessment scale that includes both. These await further development and validation. The Charlson Comorbidity Score was developed to predict 1-year mortality among hospitalized medical patients,52 and, although it has taken several decades, similar morbidity-specific scoring systems for predicting surgical mortality are emerging. For example, Sessler's group has developed a risk metric using age and Medicare comorbidity and surgical procedure codes to predict duration of hospitalization 30-day postoperative mortality and morbidity.92
Several simple scoring systems for organ- or operation-specific complications are available. The Cardiac Risk Index of Goldman et al48provided an early model that was improved by Detsky et al,148 by adding angina to the risk factors. Lee et al149 developed and validated a Revised Cardiac Index that specified 6 independent predictors: history of ischemic heart disease, history of congestive heart failure, history of cerebrovascular disease, preoperative treatment with insulin, preoperative serum creatinine more than 2.0 mg/dL, and high-risk surgical procedure. Similar clinical prediction rules have been developed for early outcome after coronary artery bypass surgery.150 Glance et al151 have studied the substantially enhanced postoperative mortality risk associated with the metabolic syndrome (obesity, hypertension, and diabetes).
Even in the absence of simple, comprehensive risk scoring systems, multivariable modeling of several large cohorts has identified a common set of independent risk factors for severe complications and death (Tables 25-4 and 25-5, and Box 25-9). Differences in the risk associated with specific morbidities among studies probably are explained, in part, by differences in how disorder-specific acuity of illness is measured.152 Racial and socioeconomic characteristics, possibly proxies for unadjusted underlying comorbidity and/or problems with access to care, also influence outcome.89,153,154
++ Table Graphic Jump Location Box 25-9 ||Download (.pdf)
Independent Risk Factors for Severe Complications and Death
Advanced American Society of Anesthesiologists physical status class48,50,51,59,65,80
Moderate and severe specific comorbidity25,48,50,51,65,80,148,149
Building on the relationship between health status and clinical outcome, Silber et al155,156 developed a novel outcome metric, failure to rescue. Using multivariable modeling, they showed that patient characteristics (eg, age, gender, comorbidities) predict complications better than they predict death; whether a complication turns into a death reflects the capability of the facility to rescue the patient. The rescue capability itself depends on hospital characteristics that include proportion of board-certified anesthesiologists and ratios of nurses to patients.88,155,157 Subsequently validated in many studies, this metric is included among quality indicators used by the Agency for Healthcare Quality and Research, National Quality Forum, University HealthSystem Consortium, and independent health care researchers.
The Health Care Setting
Unexplained Variation in Outcome
Long before investigators had tools to identify the source of risk differences, they began to document variations in patient outcome for ostensibly similar surgery.16 Buried in the mid 20th-century Beecher-Todd study8 is an unexplained 3-fold difference in mortality across the 10 participating university hospitals. A decade later, the mortality variation was so great in the mid-1960s 34-hospital National Halothane Study (0.27%-6.40%) that its statisticians concluded, "Such variation in so important an outcome of surgery compels attention."158 Even after adjusting their data for age, ASA physical status, and surgical procedure, an unexplained, 3-fold variation remained.
Emergence of an Ill-Defined "Provider"
Intrigued by unexplained mortality variation in the National Halothane Study, one of its investigators joined with sociologists and statisticians to explore nonclinical factors, including structural characteristics of hospitals, as potential explanatory variables for the postoperative mortality variation among 1224 hospitals using a chart abstracting service in the early 1970s.159,160 Despite case-mix adjustments, 3- to 4-fold outcome variations remained for the 15 major surgical procedures studied in this Institutional Differences Study. Probing deeper in a 17-hospital subset with more detailed institutional data, they explored the influence of the individual surgeon's experience, anesthesia-provider type, and various measures of hospital structure and medical staff organization.161,162 Although such factors were weak predictors of outcome, the mere presence of such relationships heralded the beginning of our understanding of the multifaceted role of an ill-defined "provider" in surgical outcomes. (Provider here is a comprehensive term indicating not only the clinicians but also the facility, its associated staff, organizational structure, and policies.)
Role of Sociomanagerial Factors
The Institutional Differences Study160,162 identified diverse sociomanagerial factors that are associated with better surgical outcomes, including teaching hospital status, greater number of residency programs, higher hospital expense per day, stringent medical staff admission requirements, power over senior surgeons, higher proportion of surgeons' practices at the study hospital, and board certification of physicians. Subsequent studies have validated the influence of teaching hospital status and board certification,155,163-165 although research has not explored the full array of care.166,167 Silber et al155 specifically identified a low proportion of board-certified anesthesiologists on the anesthesia staff as an important hospital characteristic associated with "failure to rescue" the surgical patient who experiences a complication. Generally, hospital characteristics are stronger predictors of clinical outcome than are surgeon's characteristics.161,162
As a natural extension, investigators have explored whether mortality after high-risk surgery is influenced by provider experience. Studying procedures of varying complexity in the mid-1970s in 1498 hospitals using a chart abstracting service, Luft et al168 found an inverse relationship between mortality and procedure volume for high-risk procedures; a volume–outcome relationship existed for lesser risk surgery but was weaker at low volumes. What underlies this phenomenon, a team effect in the hospital, expertise of individual surgeons, or regional referral patterns? Research has been more equivocal, in part, because of technical issues, such as low statistical power in comparisons of lower total case volume and a statistical association (collinearity) affecting hospital and surgeon-specific volumes.169,170 Yet accruing literature documents better outcomes for high-risk surgery performed at high-volume sites and suggests that a more experienced team may underlie the phenomenon.171-173 As a result, performing high-risk surgery at high-volume sites has become a principle that guides contracting for services of some organizations (eg, the Leapfrog Group) and third-party payers.
Certain aspects of critical care medicine are also associated with better surgical outcomes. Pronovost et al174,175 showed that the presence of a dedicated intensive care physician making daily rounds, a nurse-to-patient ratio of at least 1.2, a monthly case conference, and tracheal extubation in the critical care unit rather than the operating room are associated with better outcomes after abdominal aortic surgery. The implications of not having a dedicated critical care physician are so grave (OR for in-hospital mortality: 3.0; 95% CI, 1.9-4.9) that this has also become a Leapfrog Group standard. The contribution of nurse staffing ratios for good outcomes echoes the studies of general hospital nurse staffing by Aiken et al.157
Delivering good outcomes in complex settings also requires effective team functioning, an essential part of an effective patient safety climate.2 Higher scores on the Safety Attitudes Questionnaire are associated with fewer medication errors, lower ventilator-associated pneumonia rates, and decreased risk-adjusted mortality.176 Makary et al177 developed a "teamwork culture" metric that is sensitive to operating room caregivers' perceptions and beginning to be used in improvement work. Awad et al178 showed that a preprocedural operating room briefing improves teamwork climate, including coordination among caregivers, and Haynes et al have demonstrated a 44% reduction in surgical mortality in association with use of an immediate presurgical checklist that fosters enhanced communication among team members.179 (See Chapter 3 for a more thorough discussion of team performance.)
Mortality risk appears to be influenced also by the facility in which the procedure is performed (ie, a "provider" effect). Fleisher et al89 studied outcomes of Medicare patients having surgery in a hospital outpatient unit, freestanding surgery center, or surgeon's office. Although death rates on the day of operation were not different, there were differences among the rates for 7-day mortality (1 in 2000, 1 in 4000, and 1 in 2856, respectively) and 7-day hospital admission (1 in 48, 1 in 119, and 1 in 110, respectively). The readmission rates resembled those in other studies, but the authors could not exclude the likelihood that the differences across sites in both readmission and mortality rates reflected patient referral patterns (selection bias), with sicker patients treated in the more intensive settings (Fig. 25-4B).
The Surgeon and the Operation
The Surgeon's Characteristics
The surgeon's board certification,155,162,164 experience,3,162,164 and case volume162,164,169,171,172 is each associated with clinical outcome when comparing different hospitals, although these relationships may be weak when comparing individual surgeons at the same site. However, outcome variation has been demonstrated among individual surgeons for complex procures.180 Lunn and Devlin3 called attention to the association of poor outcomes from surgery and anesthesia care provided by unsupervised and undersupervised trainees at night, particularly in emergent care and sicker patients, as have others more recently.4,127,129
Outcomes associated with individual surgeons are confounded by diverse hospital characteristics, such as teaching-hospital status,162,166,167 total hospital expenditures,162 total case volume,162,169,172,173 team culture,176,177 critical care organizational characteristics,174,175 and general nurse-to-patient ratio.157
The surgeon's influence on outcome is confounded by important characteristics of the operation. In most cohort studies, invasiveness of the surgical procedure (ie, "major" vs "minor" surgery) and whether the operation is undertaken emergently are potent independent determinants of both mortality and morbidity (Tables 25-4 and 25-5).34,48,51,59,148 Underlying these relationships undoubtedly are the implications of the extent of physiologic derangement and the limited preparatory care before emergent surgery. Procedure invasiveness interacts with age, becoming a much more potent predictor of major complications beyond middle age, particularly for the more invasive procedures (Fig. 25-8).50 Invasiveness (or complexity) of operation is so potent a predictor of outcome that it has been included in several proposals for modifications of the ASA physical status classification or new approaches to preoperative patient assessment.82,147
Figure 25-8.Graphic Jump Location
Probability of a severe perioperative respiratory or cardiovascular complication as a function of both type of surgical procedure and patient age, computed from the logistic regression equation developed in the clinical trial of inhalation anesthesia drugs conducted among 17,201 patients by Forrest et al.50 ASA PS, American Society of Anesthesiologists physical score. [From Muravchick S. Anesthesia for the elderly. In: Healy TEJ, Knight PR, eds. Wylie and Churchill-Davidson's A Practice of Anesthesia. 7th ed. London, UK: Arnold; 2003:990. Reproduced with permission of Edward Arnold, Ltd.]
An especially problematic procedure-related factor is duration of operation (or anesthesia). Consistent with the notion that increasing exposure to a potentially hazardous intervention risks a poorer outcome, duration of operation (or anesthesia) is associated with increased mortality and morbidity35,51,59 (Tables 25-4 and 25-5) and hospital admission after ambulatory surgery.34,35 Greater procedure duration may be a proxy for more extensive (perhaps unrecognized) surgical disease, lesser surgical skill, or the occurrence of intraoperative anesthesia and surgical complications, any of which may independently determine the postoperative outcome. Cohen et al59 specifically included occurrence of intraoperative complications among candidate predictor variables and found that such complications, rather than procedure duration, was an independent predictor of outcome (Table 25-4). Thus, unless the study has included occurrence of intraoperative complications in the outcome modeling, procedure duration alone should be regarded as a possibly "tainted" variable.
The Anesthesia Provider and the Anesthesia Care
The Anesthesiologist's Characteristics
Board certification seems an even more important predictor of outcome with the anesthesiologist than with the surgeon.88,155 Using sophisticated modeling, Silber et al155,156 showed that a lesser proportion of board-certified anesthesiologists on the anesthesia staff is associated with a greater likelihood of "failure to rescue." In a direct comparison of mortality among Medicare patients treated by midcareer anesthesiologists with and without board certification, Silber et al88 showed that absence of board certification is associated with greater likelihood of "failure to rescue" (OR: 1.13, 95% CI, 1.01-1.27) and higher mortality (OR: 1.13; 95% CI, 1.00-1.26). So confounded are the outcome predictors, however, that they noted that the poorer outcomes of noncertified practitioners may reflect hospital characteristics as well.
Apart from board certification, the effect of the anesthesiologist's experience on outcome is less clear. In a comparison of anesthesia providers at the Massachusetts General Hospital in the mid-1970s, Gilbert181 detected slightly better outcomes when anesthesia was administered directly by a senior anesthesiologist. However, Cohen et al59 were unable to detect an outcome difference among anesthesiologists with greater time in the specialty and greater case volumes (Table 25-4). Lunn and Devlin3 noted the association between poor outcomes of surgery and anesthesia care and unsupervised and undersupervised trainees at night, as have others.4,129 Exploring the linkage between myocardial ischemia and MI in anesthesia for coronary bypass, Slogoff and Keats182 famously identified Anesthesiologist 7, whose cases had a greater proportion of intraoperative ischemia and much greater postoperative infarction than did others in the group practice. They implied this was due to individual skill factors, but analysis was superficial and without risk adjustment of the clinical data.
Anesthesia Provider Type
More extensive efforts to explore outcome differences by type of anesthesia provider also have been indeterminant because of design flaws, principally failure to adequately address patient selection bias and/or unmeasured, potentially important clinical confounding variables.
Gilbert181 found no outcome differences when comparing anesthesiologists providing direct care, anesthesiology residents medically directed by faculty anesthesiologists, and nurse anesthetists similarly medically directed. Yet a fully trained anesthesiologist was involved in every case, the data were only partially risk adjusted, and patients were not randomly allocated to providers (Note: The physicians likely received the more difficult cases). Similar flaws are present in an analysis based on the Institutional Differences Study: In one portion of that study, clinical outcomes in " hospitals in which anesthesiologists primarily were the providers" were compared with those in " hospitals in which nurse anesthetists were primarily the providers."183 Outcomes were the same in both groups, yet anesthesiologists were involved in all care.
Multiple design flaws also plague an analysis of postoperative deaths in North Carolina during the period 1969-1976, in which half of the anesthetics were administered by nurse anesthetists medically directed by the surgeon and the other half by anesthesiologists working alone or a team of a nurse anesthetist and an anesthesiologist.184 Anesthesia-related death rates were similar across the 3 provider types; yet, unlike the studies by Gilbert181 and Forrest,183 there was no attempt to adjust the data for case-mix differences (eg, age, ASA physical status) and type of operation (eg, emergent vs elective, major vs minor). Because nurse anesthetists working without anesthesiologists (then and now) are located typically in smaller, often rural hospitals, the 2 other provider types (with anesthesiologists) likely were treating sicker patients having more complex procedures. Also, as with the other studies, there had been no random allocation of patients to provider type, resulting in likely selection bias.
A more recent provider-type comparison, although much more sophisticated, still suffers from serious design flaws: Pine et al185 compared mortality of 404,194 Medicare patients having 1 of 8 common surgical procedures, whose anesthesia was provided by nurse anesthetists working without anesthesiologists, anesthesiologists providing direct care, or an anesthesia care team of a nurse anesthetist medically directed by an anesthesiologist. After stratification by procedure and adjustment for patient, institutional, and geographic factors, the mortality rates were similar across the 3 provider types. However, 80% of the cases in which nurse anesthetists worked without anesthesiologists were performed in rural hospitals, most assuredly caring for a lower-risk population (severe selection bias). Direct comparison of outcomes in this circumstance is likely to be misleading because patients in the 3 groups would be expected to differ markedly in measured and unmeasured risk factors and thus their likelihood of poor outcome. Just as Silber et al88 noted that the poorer outcomes of noncertified anesthesiologists may also reflect residual confounding by hospital characteristics, there is likely to be similar, unresolved facility-related confounding in the study by Pine et al,185 particularly without a propensity–score analysis.
In another study, Silber et al87 demonstrated in a matched-pair, propensity–score analysis (addressing selection bias) that lack of medical direction by an anesthesiologist is associated with a higher failure-to-rescue rate (OR: 1.10; 95% CI, 1.01-1.18) and higher 30-day postoperative mortality (OR: 1.08; 95% CI, 1.00-1.15). They estimated that the higher mortality rate engenders 2.5 excess deaths per 1000 patients, which represents a number needed to treat (NNT) of 400 (Table 25-1). Although a NNT of 400 reflects a modest effect, anesthesiologist's medical direction gains enormous importance because of the tens of millions of anesthetics administered each year.
Several recent comparisons of clinical outcomes associated with different anesthesia provider types undertaken with less analytical sophistication—principally the absence of a propensity–score analysis—undoubtedly were flawed by selection bias and/or unmeasured confounding variables. For example, a comparison of maternal outcomes following obstetric anesthesia care by the 3 anesthesia provider types for 1,141,641 patients in 369 hospitals in 6 states during the period 1999-2001 found no statistically significant differences186: Among sources of selection bias was excluding tertiary hospitals expected to receive referrals of high-risk patients (eg, level 3 obstetric units, facilities having neonatal intensive care, Council of Teaching Hospital membership) and patient populations with low prevalence of important chronic illnesses (eg, diabetes).
Another recent example, with particularly subtle flaws, is a study of surgery-related complications and deaths associated with the 3 anesthesia-provider types during the period 1999-2005 in the 14 states that opted out of the Medicare requirement that a physician oversee delivery of anesthesia care by a nurse anesthetist compared with those in states that had not opted out187: They specifically chose to focus on surgery-related adverse outcomes, given that those specifically related to anesthesia care are generally believed to occur at very low rates. Present is the previously mentioned selection bias (particularly in the absence of a propensity–score analysis) related to comparing outcomes from small, often rural hospitals in which nurse anesthetists treat healthier patients having simpler procedures with those from tertiary care centers where anesthesiologists alone or working with nurse anesthetists in an anesthesia care team provide complex care for patients with the highest acuity. In addition, opting out of the physician-supervision requirement had a minimal impact on anesthesia delivery in the 14 affected states because the opt-out provision does not mandate any change but rather gives each hospital the discretion to permit nurse anesthetists to function without physician oversight. Under this circumstance, anesthesia provider change (to unsupervised nurse anesthetists) would probably occur predominantly in the small rural hospitals where anesthesiologists are often not present. Although the presentation of their analysis is not sufficiently detailed to identify personnel shifts by hospital size, case complexity (as measured by mean ASA Relative Value base units) was noted to be significantly lower for solo nurse anesthetists than for either solo anesthesiologists or the anesthesia care team in both opt-out and non–opt-out states. The multivariate analyses, however seemingly exhaustive, did not include a propensity–score analysis or even variables characterizing the site of care. Finally, anesthesia risk (unmeasured here) is but a very small component of overall surgery risk. Hence the inability to demonstrate statewise outcome differences in relation to states opting out should not be unexpected.
Outcome comparisons of specific anesthesia drugs and methods have not identified a single ideal approach to anesthesia but rather have emphasized characteristics, typically pharmacologic, that can be regarded as tradeoffs among different options. Keats68 suggested that anesthesia agents are "inherently toxic" and that, as our knowledge grows, anesthesia risk diminishes. Past controversies about specific drugs were resolved by either learning how to use them safely or discarding those that were problematic, usually on a pharmacologic basis (eg, methoxyflurane due to dose-related nephrotoxicity). Thus Beecher and Todd8 attributed "intrinsic toxicity" to curare (a generic term applied to muscle relaxants) before it was appreciated that postanesthesia residual paralysis could be both hazardous and avoided with pharmacologic antagonism. That drug class now is a mainstay of anesthesia practice (see Chapter 34).
Perhaps expectedly, well-conducted studies have failed to identify important risk differences among anesthesia options. Regional anesthesia (eg, epidural analgesia) may pose lower risk than general anesthesia for graft thrombosis and deep venous thrombosis, among other morbidity, in vascular, lower extremity, and pelvic surgery (see Chapter 55); however, the magnitude of such benefit is uncertain, and whether it results from neuraxial blockade or avoiding general anesthesia is also unknown.188 Cohen et al59 noted that anesthesia-related factors (eg, principal anesthesia method) added negligibly to the contributions of patient- and surgery-related factors in accounting for mortality risk (Table 25-4). However, they did find markedly greater mortality if a single-agent anesthetic rather than "balanced anesthesia" with multiple drugs was used (OR: 4.85; 95% CI, 1.97-11.96). Although their inability to identify benefits of specific anesthesia methods arguably could result from selection bias (confounding by indication), Forrest et al50 were unable to detect meaningful morbidity differences among inhalation agents in a clinical trial that would minimize biased selection.
However, in support of a long-held belief that it is more important how rather than specifically what one does, Arbous et al189 have shown that there are multiple opportunities to decrease anesthesia risk by adopting a set of good practices in the management of anesthesia care (Table 25-7). Although the efficacy of most of the practices identified by Arbous et al is well documented in the literature (eg, epidural opiates for postoperative pain management rather than intramuscular or intravenous narcotic administration; antagonism of nonmetabolized muscle relaxants at conclusion of anesthesia), these practices continue to require support and encouragement toward full adoption.
++ Table Graphic Jump Location Table 25-7 Anesthesia Management Factors Associated with 24-Hour Mortality and Coma ||Download (.pdf)
Table 25-7 Anesthesia Management Factors Associated with 24-Hour Mortality and Coma
|Factora||Odds Ratiob||95% Confidence Interval|
| Equipment check with protocol and checklist (vs none or incomplete)||0.640||0.432-0.948|
| Documentation of equipment check (vs none)||0.607||0.399-0.923|
| Availability of and access to attending anesthesiologist (direct vs indirect)||0.455||0.313-0.662|
| No intraoperative change of anesthesiologist (vs change)||0.444||0.199-0.990|
| Presence of full-time anesthesia nurse (vs part time)||0.408||0.236-0.704|
| Presence of attending anesthesiologist at emergence and termination of anesthesia (2 practitioners vs 1)||0.687||0.474-0.996|
| Reversal of opiates (vs none)||0.636||0.100-4.027|
| Reversal of muscle relaxants (vs none)||0.101||0.032-0.314|
| Reversal of opiates and muscle relaxants (vs none)||0.290||0.175-0.482|
| Postoperative pain medication: opiate (vs none)||0.165||0.108-0.254|
| Postoperative pain medication: local anesthetics (vs none)||0.061||0.009-0.400|
| Postoperative pain medication: combination (vs none)||0.324||0.140-0.752|
| Postoperative opiate route: epidural (vs IV)||0.226||0.057-0.887|
| Postoperative opiate route: intramuscular (vs IV)||0.130||0.074-0.335|
More recently, investigators have begun to probe deeper into specific aspects of anesthetic management to gain insights into additional ways to enhance patient outcome and safety: The identification of a relationship between cumulative deep hypnotic time (BIS <45) and greater 1-year mortality18-21 has been followed recently by recognition that outcomes worsen when low mean arterial pressure (MAP <75 mm Hg) and low anesthetic concentration (minimal anesthetic concentration <0.7) are also present.190 Moreover, the duration of such a "triple low" predicts worsening outcomes,190 and the promptness with which a vasopressor is administered attenuates the potential harm of the "triple low."191 These findings from Sessler's group, based on observational data from one large academic medical center, require validation in a prospective study; however, other evidence demonstrates that intraoperative parameters influence patient outcome: a surgical Apgar score—a composite reflecting intraoperative blood loss, lowest pulse rate, and lowest MAP—predicts 30-day complications and mortality rates.192,193
These results emphasize the inadequacy of viewing anesthesia care as merely the presence of certain drugs, anesthetic methods, and devices. We may infer that anesthesia risk studies that consider patient characteristics and their clinical outcomes—but omit intraoperative detail relating to the anesthesiologist's clinical practices—provide an incomplete and possibly misleading perspective. We also might speculate that the beneficial practices identified are likely to be among those that underlie the anesthesiologist's beneficial influence detected by the failure-to-rescue metric.