STRUCTURED ABSTRACT
Objective:
To associate surgeon-anesthesiologist team familiarity with cardiac surgery outcomes.
Background:
Team Familiarity (TF), a measure of repeated team member collaborations, has been associated with improved operative efficiency; however, examination of its relationship to clinical outcomes has been limited.
Methods:
This retrospective cohort study included Medicare beneficiaries undergoing coronary artery bypass grafting (CABG), surgical aortic valve replacement (SAVR), or both (CABG+SAVR) between 01/01/2017–09/30/2018. Team familiarity was defined as the number of shared procedures between the cardiac surgeon and anesthesiologist within six months of each operation. Primary outcomes were 30- and 90-day mortality, composite morbidity, and 30-day mortality or composite morbidity, assessed before and after risk adjustment using multivariable logistic regression.
Results:
The cohort included 113,020 patients (84,397 CABG; 15,939 SAVR; 12,684 CABG+SAVR). Surgeon-anesthesiologist dyads in the highest [31631 patients, TF median(interquartile range)=8(6,11)] and lowest [44307 patients, TF=0(0,1)] TF terciles were termed familiar and unfamiliar, respectively. The rates of observed outcomes were lower among familiar versus unfamiliar teams: 30-day mortality (2.8% vs 3.1%, p=0.001), 90-day mortality (4.2% vs 4.5%, p=0.023), composite morbidity (57.4% vs 60.6%, p<0.001), and 30-day mortality or composite morbidity (57.9% vs 61.1%, p<0.001). Familiar teams had lower overall risk-adjusted odds of 30-day mortality or composite morbidity [aOR 0.894(0.868,0.922), p<0.001], and for SAVR significantly lower 30-day mortality [aOR 0.724(0.547,0.959), p=0.024], 90-day mortality [aOR 0.779(0.620,0.978), p=0.031], and 30-day mortality or composite morbidity [aOR 0.856(0.791,0.927), p<0.001].
Conclusions:
Given its relationship with improved 30-day cardiac surgical outcomes, increasing TF should be considered among strategies to advance patient outcomes.
MINI ABSTRACT
Patients undergoing coronary artery and/or aortic valve operations by high versus low tercile team familiarity surgeon-anesthesiologist dyads had an associated 11% decrease in adjusted 30-day mortality or composite morbidity [adjusted odds ratio (CI95%): 0.89(0.87,0.92)]. Institutions may consider promoting operative team familiarity as one of several mechanisms to enhance postoperative outcomes.
INTRODUCTION
Successful performance in the cardiac surgery operating room relies on contributions from team members representing multiple specialties (e.g., surgery, anesthesiology, nursing, perfusion). A collaborative operating room environment is established via leadership, communication, and other non-technical skills which may vary between team members.1,2 There have been multiple calls for increasing the consideration of human factors, teamwork, and systems processes as targeted improvement areas for enhancing patient care.3–5 Teamwork behaviors can contribute to operating room performance such as recognition of errors,3,6 and may relate to patient outcomes including mortality.7 One team characteristic of interest is team familiarity (repeated collaboration amongst team members), which is associated with decreased operative times and contributes to improved response to errors in cardiac surgery.5,8,9 However, the associated impact of team familiarity on postoperative outcomes remains unclear.
Although multiple team members within distinct specialties are essential for successfully performing cardiac surgery, the relationship between surgeon and anesthesiologist may be particularly important. Both specialties are directly responsible for intraoperative clinical decision-making and managing patient physiology. Up to 15% of teamwork failures may be attributed to the surgeon-anesthesiologist dyad.10 Futher, while surgeons are particularly important for setting the tone in the operating room,11 they may interpret the degree of collaboration within the operating room differently than other team members.12,13 Despite its importance, relatively little work has directly examined the surgeon-anesthesiologist relationship, and it therefore remains an area in need of active research.14
In order to clarify a potential area of intervention for quality patient care, the present study examines the relationship between surgeon-anesthesiologist team familiarity (TF) and patient outcomes. Specifically, it was hypothesized that increased TF is associated with reduced perioperative mortality and morbidity following commonly performed cardiac surgeries, including coronary artery bypass grafting (CABG) and surgical aortic valve replacement (SAVR) either in isolation or performed concomitantly (CABG+SAVR).
METHODS
Institutional Review Board approval was obtained with waived consent on 3/27/2022 (Protocol #HUM00193225, DUA RSCH-2022-58037) from the University of Michigan. Data use agreements restrict the distribution of raw study-related data files. Requests for summary statistics will be reviewed by the study team.
Study Population
The study included patients with full Medicare Parts A and B coverage (six months prior to and 90-days following surgery) undergoing CABG, SAVR, or CABG+SAVR between 01/01/2017 and 09/30/2018 identified using International Classification of Diseases, Tenth Revision (ICD-10) codes (Supplemental Table 1). Given their limited frequency, patients undergoing concomitant major cardiac surgery including tricuspid valve, mitral valve, aortic, carotid, or mechanical device interventions were excluded. Only the index procedure was included for patients undergoing multiple qualifying surgeries.
Patient and Operative Characteristics
Demographic information was extracted from the MedPAR data file. Baseline patient comorbidities were identified using ICD-10 codes (Supplemental Table 2). Comorbidities were used to calculate the Charlson comorbidity index for each patient. Procedure-specific characteristics, including the number of bypasses and use of endoscopic vein harvest for CABG, were identified with Current Procedural Terminology (CPT®) codes (Supplemental Table 2).
Team Familiarity Calculation
The primary surgeon and anesthesiologist were identified for each procedure through a combination of provider specialty and CPT® billing codes (algorithm depicted in Supplemental Figure 1). In cases with multiple anesthesiologists the provider billing for the greatest number of time units was considered the primary anesthesiologist. Team familiarity was calculated as a count of the number of shared major open cardiac surgeries between the primary surgeon and anesthesiologist within the six months prior to a given surgery (Supplemental Figure 1). Terciles of TF were created: tercile 1 “unfamiliar”, tercile 2 “less familiar”, tercile 3 “familiar”.
Unadjusted Postoperative Outcomes
Postoperative outcomes were identified using ICD-10 codes (Supplemental Table 4). Primary outcomes included 30-day mortality, 90-day mortality, composite morbidity (bleeding, renal failure, stroke, pneumonia, prolonged ventilation, cardiac arrest, myocardial infarction, wound infection, sepsis), and 30-day mortality or composite morbidity. Secondary outcomes included the components of composite morbidity as well as 30-day and 90-day readmission, 30-day and 90-day reoperation, intensive care unit length of stay (LOS), and total LOS. Outcomes were compared across TF terciles. Continuous variables were generally found to be non-normally distributed via the Kolmgorov-Smirnov test and therefore all were compared across terciles using the nonparametric Kruskal-Wallis rank sum test and presented as median with interquartile range (IQR). Categorical variables were compared across terciles using Chi-Square or Fisher’s exact tests.
All analyses were conducted using SAS software, Version 9.4 (SAS Institute, Cary, NC) or R version 3.4.1 (R Foundation, Vienna, Austria). A two-sided p-value <0.05 defined significance.
Adjusted Postoperative Outcomes
Multivariable logistic regression was performed to account for confounding. Based on known relationships to cardiac surgery outcomes and a priori expert consensus, pertinent patient (age, sex, Charlson comorbidity index, race), surgical (specific surgery, use of endoscopic vein harvest for CABG or CABG+SAVR, urgent/emergent status), and hospital characteristics (annual cardiac surgical volume, teaching status, trauma center designation) were chosen (Table 3). Charlson comorbidity index was employed due to its previously documented association with outcomes after CABG15 and SAVR16. Individual surgeon and anesthesiologist annualized volumes were omitted from the model due to collinearity with TF. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated for the primary TF exposure. Model fit was assessed via the area under the receiver-operator curve (AUC).
Table 3:
Unadjusted Outcomes for Familiar and Unfamiliar Teams
| Team Familiarity |
|||||
|---|---|---|---|---|---|
| Outcome, N (%) | Overall | Unfamiliar | Less Familiar | Familiar | p-value |
|
| |||||
| PRIMARY OUTCOMES | |||||
| 30-Day Mortality | 3241 (2.9) | 1372 (3.1) | 992 (2.7) | 877 (2.8) | 0.001 |
| 90-Day Mortality | 4878 (4.3) | 2004 (4.5) | 1550 (4.2) | 1324 (4.2) | 0.023 |
| 30-Day Mortality or Composite Morbidity | 67405 (59.6) | 27076 (61.1) | 22026 59.4) | 18303 (57.9) | <0.001 |
| Composite Morbidity | 66889 (59.2) | 26867 (60.6) | 21862 (58.9) | 18160 (57.4) | <0.001 |
|
| |||||
| COMPOSITE MORBIDITY COMPONENTS | |||||
| Bleeding | 56396 (49.9) | 22557 (50.9) | 18535 (50.0) | 15304 (48.4) | <0.001 |
| Renal Failure | 16890 (14.9) | 6862 (15.5) | 5509 (14.9) | 4519 (14.3) | <0.001 |
| Stroke | 1738 (1.5) | 753 (1.7) | 551 (1.5) | 434 (1.4) | 0.001 |
| Pneumonia | 3332 (2.9) | 1394 (3.1) | 1013 (2.7) | 925 (2.9) | 0.002 |
| Prolonged Ventilation | 9497 (8.4) | 4008 (9.0) | 2988 (8.1) | 2501 (7.9) | <0.001 |
| Cardiac Arrest | 1059 (0.9) | 424 (1.0) | 340 (0.9) | 295 (0.9) | 0.836 |
| Myocardial Infarction | 676 (0.6) | 279 (0.6) | 207 (0.6) | 190 (0.6) | 0.419 |
| Wound Infection | 571 (0.5) | 241 (0.5) | 176 (0.5) | 154 (0.5) | 0.329 |
| Sepsis | 2031 (1.8) | 854 (1.9) | 626 (1.7) | 551 (1.7) | 0.026 |
|
| |||||
| 30-Day Readmission | 15800 (14.0) | 6315 (14.3) | 5242 (14.1) | 4243 (13.4) | 0.003 |
| 90-Day Readmission | 24663 (21.8) | 9787 (22.1) | 8179 (22.1) | 6697 (21.2) | 0.004 |
| 30-Day Reoperation | 487 (0.43) | 202 (0.46) | 147 (0.40) | 138 (0.44) | 0.430 |
| 90-Day Reoperation | 544 (0.48) | 226 (0.51) | 163 (0.44) | 155 (0.49) | 0.340 |
| ICU Length of Stay, median[IQR] | 3 [1,6] | 3 [1,7] | 3 [1,6] | 3 [1,6] | <0.001 |
| Total Length of Stay, median[IQR] | 7 [6,11] | 8 [6,11] | 7 [6,11] | 7 [5,10] | <0.001 |
ICU = intensive care unit; IQR = interquartile range.
Sensitivity and Subgroup Analyses
The window for defining TF was extended to one year for conducting both adjusted and unadjusted sensitivity analyses. The associated impact of TF was also examined specifically for each type of surgery in the cohort and for elective versus urgent/emergent cases, given the likelihood that TF may vary significantly in these scenarios. Finally, a subgroup analysis including Black and White patients only was conducted to investigate possible disparities in access to familiar teams. Tukey style comparison test for proportions17,18 was used to adjust for multiple comparisons, with statistical significance defined by a q statistic greater than the critical value [q(0.05)].
RESULTS
Cases and Team Familiarity
The relationship between TF and postoperative outcomes was evaluated for a cohort of 113,020 cardiac surgical operations (84,397 CABG, 15,939 SAVR, 12,684 CABG+SAVR) (Supplemental Figure 2). Team familiarity for these operations was calculated from the number of collaborations between surgeon and anesthesiologist during the previous six months based on 255,104 major cardiac surgeries (Supplemental Table 3) performed between 6/30/2016 and 9/30/2018. Overall, median TF was 2 with an interquartile range of 1–5 and a maximum of 34 (Table 1; Supplemental Figure 3). Hospital volume was significantly, though weakly, positively correlated with TF on univariate linear regression (p<0.001; R2 = 0.023) (Supplemental Figure 4). Cases were divided into terciles by TF; unfamiliar [median(IQR) TF = 0(0,1); 44,307 patients], less familiar [TF = 3(2,3); 37,082 patients], and familiar [TF = 8(6,11); 31,631 patients].
Table 1:
Patient Baseline Characteristics, Overall and by Surgeon-Anesthesiologist Familiarity
| Team Familiarity |
|||||
|---|---|---|---|---|---|
| Overall N=113020 | Unfamiliar N=44307 | Less Familiar N=37082 | Familiar N=31631 | p-value | |
|
| |||||
| Team Familiarity, median (IQR) | 2 (1,5) | 0 (0,1) | 3 (2,3) | 8 (6,11) | <0.001 |
|
| |||||
| DEMOGRAPHIC INFORMATION | |||||
| Age, median (IQR) | 72 (68,77) | 72 (68,77) | 72 (68,77) | 73 (68,77) | <0.001 |
| Female, N(%) | 32379 (28.6) | 12706 (28.7) | 10509 (28.3) | 9164 (29.0) | 0.186 |
| Race, N(%) | <0.001 | ||||
| Asian | 1407 (1.2) | 581 (1.3) | 461 (1.2) | 365 (1.2) | |
| Black | 6325 (5.6) | 2793 (6.3) | 2028 (5.5) | 1504 (4.8) | |
| Hispanic | 1543 (1.4) | 618 (1.4) | 448 (1.2) | 477 (1.5) | |
| North American Native | 722 (0.6) | 275 (0.6) | 219 (0.6) | 228 (0.7) | |
| Other | 1726 (1.5) | 652 (1.5) | 585 (1.6) | 489 (1.5) | |
| Unknown | 2000 (1.8) | 785 (1.8) | 679 (1.8) | 536 (1.7) | |
| White | 99297 (87.9) | 38603 (87.1) | 32662 (88.1) | 28032 (88.6) | |
|
| |||||
| COMORBIDITIES, N (%) | |||||
| Myocardial Infarction | 41857 (37.0) | 17054 (38.5) | 13651 (36.8) | 11152 (35.3) | <0.001 |
| Congestive Heart Failure | 40712 (36.0) | 16443 (37.1) | 13447 (36.3) | 10822 (34.2) | <0.001 |
| Peripheral Vascular Disease | 18348 (16.2) | 7060 (15.9) | 5977 (16.1) | 5311 (16.8) | 0.005 |
| Cerebrovascular Disease | 454 (0.4) | 183 (0.4) | 137 (0.4) | 134 (0.4) | 0.476 |
| Dementia | 1866 (1.7) | 742 (1.7) | 590 (1.6) | 534 (1.7) | 0.537 |
| Chronic Pulmonary Disease | 26466 (23.4) | 10429 (23.5) | 8627 (23.3) | 7410 (23.4) | 0.656 |
| Connective Tissue Disease | 3182 (2.8) | 1283 (2.9) | 1010 (2.7) | 889 (2.8) | 0.335 |
| Peptic Ulcer Disease | 925 (0.8) | 368 (0.8) | 286 (0.8) | 271 (0.9) | 0.434 |
| Mild Liver Disease | 2248 (2.0) | 943 (2.1) | 762 (2.1) | 543 (1.7) | <0.001 |
| Diabetes Without Complications | 37688 (33.3) | 15022 (33.9) | 12340 (33.3) | 10326 (32.6) | 0.001 |
| Diabetes With Complications | 24813 (22.0) | 10175 (23.0) | 8124 (21.9) | 6514 (20.6) | <0.001 |
| Paraplegia and Hemiplegia | 1229 (1.1) | 491 (1.1) | 400 (1.1) | 338 (1.1) | 0.857 |
| Renal Disease | 27941 (24.7) | 11021 (24.9) | 9208 (24.8) | 7712 (24.4) | 0.251 |
| Cancer | 2966 (2.6) | 1195 (2.7) | 963 (2.6) | 808 (2.6) | 0.442 |
| Moderate or Severe Liver Disease | 313 (0.3) | 129 (0.3) | 102 (0.3) | 82 (0.3) | 0.709 |
| Metastatic Carcinoma | 315 (0.3) | 130 (0.3) | 106 (0.3) | 79 (0.2) | 0.505 |
| AIDS/HIV | 162 (0.1) | 71 (0.2) | 53 (0.1) | 38 (0.1) | 0.354 |
| Charlson Comorbidity Index, median (IQR) | 2 (1,3) | 2 (1,3) | 2 (1,3) | 2 (1,3) | <0.001 |
AIDS/HIV = acquired immunodeficiency syndrome/human immunodeficiency virus; IQR = interquartile range.
Baseline Patient Characteristics
Overall, patients had a median age of 72 years (IQR 68–77), were predominantly male (74.6%), White (87.9%), and presented with significant comorbidities (Charlson comorbidity index 2[1,3]). Patients operated on by familiar teams were older (median 73 vs 72 years) with more peripheral vascular disease (16.8% vs 15.9%) (Table 1, p<0.001). Conversely, patients operated on by familiar teams had less myocardial infarction (35.3% vs 38.5%), congestive heart failure (34.2% vs 37.1%), mild liver disease (1.7% vs 2.1%), and diabetes with (20.6% vs 23.0%) or without (32.6% vs 33.9%) complications (Table 1, all p<0.001). Operative characteristics also differed significantly across TF terciles. Familiar teams had fewer CABG (73.4% vs 76.2%), fewer cases performed in 2018 (39.5% vs 43.6%), fewer emergent cases (19.4% vs 22.4%), and less use of endoscopic saphenous vein harvest among bypass procedures (71.9% vs 73.0%) (Table 2, all p<0.001). Familiar teams also performed more CABG+SAVR (12.1% vs 10.3%) and elective (61.3% vs 56.6%) cases (Table 2, all p<0.001).
Table 2:
Operative Characteristics, Overall and by Surgeon-Anesthesiologist Familiarity
| Team Familiarity |
|||||
|---|---|---|---|---|---|
| Overall N=113020 | Unfamiliar N=44307 | Less Familiar N=37082 | Familiar N=31631 | p-value | |
|
| |||||
| Procedure Type, N(%) | <0.001 | ||||
| CABG | 84397 (74.7) | 33743 (76.2) | 27422 (73.9) | 23232 (73.4) | |
| SAVR | 15939 (14.1) | 6000 (13.5) | 5357 (14.4) | 4582 (14.5) | |
| SAVR+CABG | 12684 (11.2) | 4564 (10.3) | 4303 (11.6) | 3817 (12.1) | |
| Procedure Year, N(%) | <0.001 | ||||
| 2017, N(%) | 65550 (57.9) | 24979 (56.4) | 21421 (57.8) | 19150 (60.5) | |
| 2018, N(%) | 47470 (42.0) | 19328 (43.6) | 15661 (42.2) | 12481 (39.5) | |
| Admission Status, N(%) | <0.001 | ||||
| Emergent | 23706 (21.0) | 9936 (22.4) | 7629 (20.6) | 6141 (19.4) | |
| Urgent | 22629 (20.0) | 9104 (20.5) | 7537 (20.3) | 5988 (18.9) | |
| Elective | 66280 (58.6) | 25098 (56.6) | 21792 (58.8) | 19390 (61.3) | |
| Trauma Activation | 93 (0.1) | 29 (0.1) | 35 (0.1) | 29 (0.1) | |
| Unknown | 312 (0.3) | 140 (0.3) | 89 (0.2) | 83 (0.3) | |
| Number of Bypasses (CABG and SAVR+CABG Only), N(%) | <0.001 | ||||
| 1 | 27230 (28.0) | 10741 (28.0) | 9026 (28.5) | 7463 (27.6) | |
| 2 | 40478 (41.7) | 16167 (42.1) | 13176 (41.5) | 11135 (41.2) | |
| 3 | 23220 (23.9) | 9166 (23.9) | 7533 (23.7) | 6521 (24.1) | |
| ≥4 | 6153 (6.3) | 2233 (5.8) | 1990 (6.3) | 1930 (7.1) | |
| Endoscopic Saphenous Vein Harvest, N(%) | 70554 (72.6) | 28018 (73.0) | 23084 (72.8) | 19452 (71.9) | <0.001 |
CABG = coronary artery bypass grafting; SAVR = surgical aortic valve replacement.
Unadjusted Primary Outcomes
Overall, unadjusted 30-day mortality was 2.9%, 90-day mortality was 4.3%, composite morbidity occurred in 59.2% of patients, and 30-day mortality or composite morbidity was 59.6%. Relative to unfamiliar teams, familiar teams had lower associated 30-day mortality (2.8% vs 3.1%, p=0.001), 90-day mortality (4.2% vs 4.5%, p=0.023), composite morbidity (57.4% vs 60.6%, p<0.001), and combined 30-day mortality or composite morbidity (57.9% vs 61.1%, p<0.001) (Table 3). These findings were robust when evaluating TF over a one-year time horizon (Supplemental Figure 5).
Unadjusted Secondary Outcomes
Patients operated on by familiar teams had significantly less associated postoperative bleeding (48.4% vs 49.9%), renal failure (14.3% vs 15.5%), stroke (1.4% vs 1.7%), pneumonia (2.7 vs 3.1%, p=0.002), prolonged ventilation (7.9 vs 9.0%), and sepsis (1.7 vs 1.9%) (Table 3, all p<0.001 unless indicated). There were no significant differences in cardiac arrest, myocardial infarction, or wound infection (Table 3, p>0.05). Additionally, familiar teams had less 30-day (13.4% vs 14.3%, p=0.003) or 90-day (21.1% vs 22.1%, p=0.004) readmission, shorter intensive care unit {3 (1,6) vs 3(1,7) days, p<0.001] and overall LOS [7(5,10) vs 7(6,11), p<0.001] without significant differences in 30-day or 90-day reoperation (Table 3).
Adjusted Primary Outcomes
After adjustment via multivariable logistic regression for patient, surgical, and hospital characteristics TF was significantly associated with reduced 30-day mortality or composite morbidity (aOR 0.988[0.985,0.991], p<0.001, Table 4). Figure 2 depicts the projected relationship between increasing TF and decreased adjusted 30-day mortality or composite morbidity based on the model. Comparing the adjusted primary outcomes of familiar versus unfamiliar teams within the overall cohort revealed significantly less composite morbidity (aOR 0.895[0.869,0.922]) and 30-day mortality or composite morbidity (aOR 0.894[0.868,0.922]; AUC 0.603) (both p<0.001), but not 30-day (aOR 0.918[0.841,1.002], p=0.056) or 90-day mortality (aOR 0.953[0.886,1.025], p=0.20) (Table 5). For SAVR specifically, familiar teams had significantly lower associated 30-day (aOR 0.724[0.547,0.959], p=0.024) and 90-day mortality (0.779[0.620,0.978], p=0.031). There was no difference in any primary outcome after CABG+SAVR (all p>0.05) (Table 5). The multivariable logistic regression model omitted surgeon and anesthesiologist annualized case volumes due to collinearity with TF, but the associated impact of TF on 30-day mortality or composite morbidity remained significant when they were included (Supplemental Table 5).
Table 4:
Multivariable Logistic Regression Model for 30-Day Mortality or Composite Morbidity
| Variable | aOR | 95% CI | p-value | |
|---|---|---|---|---|
|
| ||||
| TF (6 month) | 0.988 | [0.985,0.991] | <0.001 | |
|
| ||||
| Patient Factors | Age | 1.016 | [1.015,1.018] | <0.001 |
| Female vs male | 1.166 | [1.135,1.198] | <0.001 | |
| Charlson Score | 1.202 | [1.191,1.213] | <0.001 | |
| Race - Black vs White | 1.139 | [1.078,1.203] | <0.001 | |
| Race - Other vs White | 1.147 | [1.091,1.205] | <0.001 | |
| Preoperative LOS | 1.021 | [1.015,1.026] | <0.001 | |
|
| ||||
| Surgical Factors | SAVR vs CABG | 1.321 | [1.266,1.377] | <0.001 |
| SAVR+CABG vs CABG | 1.632 | [1.567,1.701] | <0.001 | |
| Endoscopic Saphenous Vein Harvest | 1.139 | [1.106,1.172] | <0.001 | |
| Urgent/Emergent vs Elective | 1.047 | [1.016,1.079] | 0.003 | |
|
| ||||
| Hospital Factors | Trauma Center and Other vs Elective | 0.973 | [0.795,1.190] | 0.789 |
| Annual Cardiac Surgical Volume (Per Additional 100 Cases) | 0.978 | [0.969,0.987] | <0.001 | |
| Teaching vs Non-Teaching | 1.208 | [1.173,1.244] | <0.001 | |
95% CI = 95% confidence interval; aOR = adjusted odds ratio; CABG = coronary artery bypass grafting; Charlson Score = Charlson comorbidity index; LOS = length of stay; SAVR = surgical aortic valve replacement; TF = team familiarity. TF, Charlson Score, and Annual Cardiac Surgical Volume are continuous variables.
Figure 2: Increasing Surgeon-Anesthesiologist Team Familiarity and Adjusted Relative Odds of 30-Day Mortality or Composite Morbidity.
Relationship between increasing values of team familiarity (x-axis) and the adjusted odds ratio (aOR) for 30-day mortality or morbidity (y-axis). Shaded area represents the 95% confidence interval for the aOR.
Table 5:
Odds Ratios for Primary Outcomes for Familiar vs Unfamiliar Teams after Adjustment via Multivariable Logistic Regression
| Outcome | aOR | 95% CI | p-value | |
|---|---|---|---|---|
|
| ||||
| All Surgeries | 30-Day Mortality | 0.918 | [0.841,1.002] | 0.056 |
| 90-Day Mortality | 0.953 | [0.886,1.025] | 0.20 | |
| Composite Morbidity | 0.895 | [0.869,0.922] | <0.001 | |
| 30-Day Mortality or Composite Morbidity | 0.894 | [0.868,0.922] | <0.001 | |
|
| ||||
| CABG | 30-Day Mortality | 0.969 | [0.874,1.073] | 0.54 |
| 90-Day Mortality | 0.993 | [0.912,1.081] | 0.866 | |
| Composite Morbidity | 0.896 | [0.865,0.927] | <0.001 | |
| 30-Day Mortality or Composite Morbidity | 0.894 | [0.863,0.925] | <0.001 | |
|
| ||||
| SAVR | 30-Day Mortality | 0.724 | [0.547,0.959] | 0.024 |
| 90-Day Mortality | 0.779 | [0.620,0.978] | 0.031 | |
| Composite Morbidity | 0.857 | [0.791,0.927] | <0.001 | |
| 30-Day Mortality or Composite Morbidity | 0.856 | [0.791,0.927] | <0.001 | |
|
| ||||
| SAVR+CABG | 30-Day Mortality | 0.866 | [0.702,1.068] | 0.18 |
| 90-Day Mortality | 0.919 | [0.770,1.096] | 0.348 | |
| Composite Morbidity | 0.921 | [0.839,1.011] | 0.08 | |
| 30-Day Mortality or Composite Morbidity | 0.929 | [0.845,1.020] | 0.12 | |
Adjusted for covariates enumerated in Table 3. 95% CI = 95% Confidence Interval; aOR = odds ratio after adjustment via multivariable regression; CABG = coronary artery bypass grafting; SAVR = surgical aortic valve replacement.
Adjusted Outcomes - Sensitivity Analyses
Three pre-specified sensitivity analyses were conducted. First, the primary results were robust when extending the look-back period for calculating TF to one year, with familiar (relative to unfamiliar) teams having lower associated 30-day mortality or composite morbidity (aOR 0.898[0.864,0.933], p<0.001) (Supplemental Table 6). Further, familiar (relative to unfamiliar) teams had a significantly larger associated reduction in 30-day mortality or composite morbidity after elective compared to urgent/emergent surgeries (aOR 0.863[0.830,0.897] vs 0.944[0.899,0.990]). Finally, when modeling hospital as a random effect, TF was no longer significantly associated with 30-day mortality or composite morbidity (aOR=0.999[0.995,1.003], p=0.60) or composite morbidity [aOR=0.999(0.995,1.003), p=0.74] (Supplemental Table 7).
Subgroup Analysis by Race
Initial results suggested a higher proportion of unfamiliar teams for Black patients - this trend was directly assessed via subgroup analysis with Black (N=6,325) and White (N=99,297) patients only to facilitate adjustment for multiple comparisons. Unfamiliar teams treated a significantly higher proportion of Black patients compared to familiar teams (6.8% vs 5.1%; q statistic=13.4, q(0.05)=3.3). Black patients also made up a higher proportion of urgent/emergent compared to elective cases (6.1% vs 5.0%), cases at teaching compared to non-teaching hospitals (6.5% vs 4.4%), and CABG compared to SAVR or SAVR+CABG (6.6% vs 5.0% vs 3.4%) [all q statistics >q(0.05)] (Supplementary Table 8).
DISCUSSION
This large, national study of Medicare beneficiaries demonstrates a significant, inverse relationship between increasing cardiac surgeon-anesthesiologist TF and decreased postoperative 30-day mortality or composite morbidity. Further, this study uniquely highlights that increasing TF has a stronger relationship with postoperative outcomes among: (1) SAVR versus CABG operations and (2) elective relative to urgent/emergent operations. These results emphasize an independent relationship between interdisciplinary non-technical skills and surgical outcomes, and suggest that efforts to enhance operative TF may contribute to improved postoperative outcomes.
Previous work examining intraoperative TF has primarily focused on a single professional discipline or single institutions with limited association with clinical outcomes.8,9,19–22 Examinations of familiarity between surgical attendings and their trainees during total knee replacement, mammoplasty, CABG, and colectomy demonstrated improved efficiency (decreased operative/turnover times) with increased familiarity.8,19–21 Within cardiac surgery specifically, increasing TF among either surgeon-trainee or overall operative team members was associated with decreased cardiopulmonary bypass and cross-clamp duration absent improvements in major morbidity or mortality outcomes.8,9 Only recently has TF been associated with significant improvements in clinical outcomes, with Hallet et al demonstrating a linear decrease in morbidity for each shared surgery between surgeon and anesthesiologist in the preceding four years for complex gastrointestinal surgeries.23 Given predominantly single institution experiences, prior studies may have been underpowered for detecting an association between TF and clinical outcomes. Additionally, the present study and that of Hallet et al focus on complex surgery and the surgeon-anesthesiologist relationship specifically rather than defining operative TF more broadly (i.e., including other team members). While the present study is unique in relating TF to operative mortality, the associated decrease in mortality was apparent only for SAVR operations.
The mechanism of improved outcomes with increased TF remains unknown, though may be related to the high degree of teamwork necessitated by the complexity of cardiac surgery. Each surgery has multiple steps, including initiation of and separation from cardiopulmonary bypass for on-pump cases and dynamic management of a patient’s fluid status, that requires coordinated evaluation and expertise involving surgeons, anesthesiologists, perfusionists, and other operating room members. Repeated collaboration may improve relationship dynamics that beget enhanced performance. Human factors including leadership style and methods of communication are important contributors to operating room performance,4,24,25 and significant variation exists in relationship dynamics across operating room teams.26 Although there is limited direct study of surgeon and anesthesiologist TF, familiarity between other intraoperative team members is associated with fewer flow disruptions and improved surgeon focus.22,27 Disruptions in surgical flow are frequently related to miscommunication between team members,28 and communication across disciplines has been found to be less frequent and more prone to error.29 Team familiarity may be especially important in the setting of acute complications or intraoperative errors, as familiar teams may better identify and compensate for errors.4,5 Others have suggested that shared experience may facilitate collaborative thinking and transactive memory systems that enhance team performance.23,30 While the present study uses Medicare data that do not capture intraoperative communication and teamwork behaviors, differences in human factors in familiar and unfamiliar teams should be an ongoing topic of research.
In the present study, increased TF was associated with a 10% decrease in mortality or morbidity and may thereby present an opportunity for hospitals to enhance performance within value-based payment systems. The Centers for Medicare and Medicaid Services (CMS) has instituted several value-based care programs that adjust fee-for-service reimbursement based on hospital performance on standard metrics.31,32 Beginning in 2022 these measures included risk-standardized mortality and readmissions after CABG.33–35 The present analysis did not find an effect of TF on mortality after CABG specifically, though did find a significant decrease in 30- and 90-day readmission associated with more familiar surgeon-anesthesiologist teams. Therefore, increasing TF may serve as an effective strategy to improve hospital performance and increase value-based reimbursement within the setting of CABG surgery. Promoting increased operative team familiarity in surgery has not been studied prospectively, but encouragingly a recent trial randomized medical residents and nurses to work together consistently and found improved teamwork culture and performance in simulated scenarios,36 thus supporting the possible efficacy of such interventions. If value-based programs expand to additional cardiac surgeries, such as SAVR, increasing TF may become an even more attractive strategy to advance value.
Importantly, Black patients were disproportionately treated by unfamiliar teams compared to White patients. This finding echoes previous race disparities in cardiac surgery including in access to high-performing cardiac surgery hospitals,37 outcomes after CABG,38 and the diagnosis/treatment of aortic stenosis39. Black patients comprised a larger proportion of non-elective cases and cases at teaching hospitals, suggesting that structural factors may contribute to the observed disparity in team familiarity. Hospitals serving areas with a higher proportion of Black residents have significantly different referral patterns, in-network physician clustering, and repeated physician interactions.40,41 Additionally, hospitals with providers that treat more segregated patient populations have increased mortality after CABG for Black patients specifically.42 Future work will need to investigate the causes of the observed disparity in operative TF for Black patients, and interventions systematically incorporating TF considerations to improve patient care should be carefully designed to alleviate rather than exacerbate pre-existing inequities.
Finally, the present results raise questions surrounding TF that warrant additional investigation. First, the differential effect of TF within distinct surgeries deserves further attention, as evidenced by the greater associated decrease in mortality or composite morbidity for familiar versus unfamiliar teams in SAVR compared to CABG. The difference within the present study may be attributed to procedural variation (e.g., vein harvest during CABG), higher risk of anesthesia for patients with severe aortic stenosis, or the typically less homogenous, higher-risk patient population undergoing SAVR. Regardless, extension of similar analyses to additional subsets of cardiac surgery (e.g., aortic surgery, heart transplant) may highlight where TF offers the greatest value. Additionally, operating room performance is only one facet within the continuum of surgical care. It is possible that familiarity between operative and postoperative team members (e.g., intensivists) whose care is independently associated with improved outcomes,43–45 is similarly important. Finally, multiple barriers render prioritizing consistent intraoperative team collaboration challenging, including: (1) independent surgeon and anesthesiologist schedules, which are not typically managed by a single entity; (2) urgent/emergent and add-on cases, which preclude personnel foreplanning; and (3) wide variation in individual provider operative volumes. Therefore, observational studies are needed to identify specific behaviors or habits unique to familiar, highly functional teams. Extension of such behaviors to unfamiliar teams could be facilitated via interventions such as formalized operating room protocols or simulation training. Multiple examples of effective team-based interventions have already been described: an operating team training program implemented in the Veterans Health Administration reduced patient mortality,46 the TeamSTEPPS system can reduce operative team errors,47 and pre-operative interprofessional surgical checklists reduce communication errors.48
A number of limitations warrant discussion. First, while among the largest evaluations of surgical team familiarity within cardiac surgery, the study findings may not be generalizable outside of Medicare fee-for-service beneficiaries. Second, despite using commonly used administrative billing codes (e.g., to identify clinicians, surgical procedures, adverse sequelae), misclassification errors cannot be ruled out. Third, while this study leveraged multivariable regression to account for patient and procedural risk factors, residual unmeasured confounding may remain. In particular, use of the Society of Thoracic Surgeons Risk Calculator49 is common in the cardiac surgery literature to compare observed:expected ratios for morbidity and mortality, but Medicare claims data do not contain certain parameters (e.g. laboratory and echocardiographic data) necessary for this calculation, thus possibly contributing to residual confounding. Another possible source of confounding is surgeon and anesthesiologist experience, which could not be accounted for in our model due to (1) collinearity between TF and annual provider volume, (2) absence of a national registry for fellowship/residency graduation dates, and (3) only recent introduction of National Provider Identifiers, which limits its capacity to define experience. Additionally, the study may underestimate the impact of team familiarity on postoperative outcomes as it cannot include non-billing team team members (e.g., residents, nurses, perfusionists). Nonetheless, compared to previous studies our approach has the advantage of using national, multi-institutional data while emphasizing the two primary clinical decision makers in the operating room. Finally, the effect of TF is not significant when including hospital as a random effect. While this finding may suggest that the impact of TF is smaller than other sources of variation in outcomes between hospitals, it may also suggest that there is limited TF variation within single hospitals or that individual hospitals better facilitate repeated collaborations between surgeons and anesthesiologists.
In conclusion, this national evaluation of Medicare beneficiaries undergoing cardiac surgery finds that increasing TF between the primary surgeon and anesthesiologist was associated with significantly lower adjusted odds of postoperative mortality or morbidity. These findings suggest that increasing TF should be included among other institutional strategies to enhance postoperative outcomes following cardiac surgery.
Supplementary Material
Figure 1: Schematic of Team Familiarity Calculation and Analysis.
Approach for calculating Team Familiarity (TF) prior to comparing outcomes. The primary surgeon and anesthesiologist for each case were identified via the algorithms depicted in Supplemental Figure 1. For the primary analysis TF equaled the number of shared surgeries between the surgeon and anesthesiologist in the six months preceding each case in the cohort.
ACKNOWLEDGEMENTS:
Dr. Xiaoting Wu and Dr. Jie Yang had full access to all the data in the study and assume responsibility for the integrity of the data and the accuracy of the data analysis.
This project is supported by grant number 1R01HL146619-01A1 from the National Institutes of Health.
The opinions, beliefs, and viewpoints expressed by authors do not necessarily reflect those of AHRQ, NIH or the U.S. Department of Health and Human Services, BCBSM, or its employees.
The authors acknowledge the support of the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative (MSTCVS-QC). Support for the MSTCVS-QC is provided by Blue Cross and Blue Shield of Michigan and Blue Care Network as part of the BCBSM Value Partnerships program. Although Blue Cross Blue Shield of Michigan and MSTCVS-QC work collaboratively, the opinions, beliefs, and viewpoints expressed by the authors do not necessarily reflect the opinions, beliefs, and viewpoints of BCBSM or any of its employees.
This study was accepted for a poster presentation at the 103rd American Association for Thoracic Surgery annual meeting, May 6–9, 2023.
Sources of Support:
This project is supported by grant number 1R01HL146619-01A1 from the National Institutes of Health.
Footnotes
Disclosures/Conflicts of Interest: Dr. Awtry - None. Dr Abernathy receives funding from the Agency for Healthcare Research and Quality; is a member of the Society of Cardiovascular Anesthesiologists Board of Directors; serves on the advisory board for Intelliport. Dr. Wu - None. Dr. Zhang - None. Ms. Hou - None. Dr. Kaneko is a consultant receiving advisor, speaking, or lecture fees from Edwards Lifesciences Corporation, Abbott Laboratories, and Medtronic Inc. Dr. de la Cruz is a consultant receiving advisor, speaking, or lecture fees from Edwards Lifesciences Corporation and Terumo Aortic. Ms. Stakich-Alpirez - None. Dr. Yule - receives consulting fees from Johnson & Johnson Institute. Dr. Cleveland is a consultant for Abbott Laboratories and Edwards Lifesciences. Dr. Shook is a consultant receiving consulting and speaking fees from Edwards Lifesciences. Dr. Fitzsimons - None. Dr. Harrington - None. Dr. Pagani is a non-compensated ad-hoc scientific advisor for Abbott, CH Biomedical, FineHeart, and Medtronic; non-compensated medical monitor for Abiomed; Member, Data Safety Monitoring Board for Carmat and the National Heart, Lung, and Blood Institute PumpKIN Study; receives grant funding from the National Heart, Lung, and Blood Institute and the Agency for Healthcare Research and Quality; and receives partial salary support from Blue Cross / Blue Shield of Michigan as Associate Director of the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative. Dr. Likosky has received a research grant from the National Institutes of Health (NHLBI R01HL146619). Outside of this work, Dr. Likosky: (1) received research funding from the Agency for Healthcare Research and Quality, and the National Institutes of Health; (2) served as a consultant for the American Society of Extracorporeal Technology; and (3) received partial salary support from Blue Cross Blue Shield of Michigan to advance quality in Michigan in conjunction with the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative.
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