Objective: To determine if pancreaticoduodenectomy operative time can provide insight into surgeon performance and thus be considered for use as a quality indicator. Background: Case volume is the traditional quality metric for complex pancreatic surgery, with studies showing better outcomes for high-volume providers. However, there are surgeons performing fewer cases with good quality who are overlooked for referrals directed to high-volume “centers of excellence”. Additional quality metrics are needed. Methods: The ACS NSQIP database (2005-2011) was used to identify 4805 pancreaticoduodenectomy patients. Cases were divided at the mean operative time (ORtime) into those ≤373 (n = 2638, 54.9%) vs ≥373 minutes in duration. Complications and outcome measures were compared and predictors of 30-day mortality were assessed. Results: Age ≤ 65 years, male sex, prior chemotherapy, prior radiation, disseminated cancer, diabetes, recent MI, no prior TIA, lower bilirubin and platelet count, and higher prothrombin time were associated with ORtime > 373 minutes. Patients with ORtime > 373 minutes demonstrated more intra-abdominal and superficial infections, wound dehiscence, bleeding requiring transfusion, need for reintubation, septic shock, and returns to OR. ORtime > 373 minutes was associated with longer hospital stay and increased 30-day mortality. ORtime > 373 minutes was a significant and independent predictor in a stepwise model of 30-day mortality. Conclusions: Shorter pancreaticoduodenectomy operative time is associated with fewer complications, shorter hospital stays and lower 30-day mortality after adjusting for patient factors. This may imply that shorter operative time is associated with superior surgical outcome. Operative time may provide insight into surgeon performance and be considered for use as a quality metric.
Compelling evidence suggests that improved outcomes in pancreatic and other complex surgeries can be achieved through centers of excellence [
Data for this study was obtained from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). ACS NSQIP is a prospective, multi-institutional, clinical registry created by the Veterans Health Administration in 1994 for quality improvement purposes. Over 130 pre-operative through 30-day post-operative variables are collected on a randomly selected sample of patients, including patient demographics, surgical profile, preoperative risk assessment, laboratory values, operative information, and 30-day morbidity and mortality rates. A highly trained Surgical Clinical Reviewer (SCR) collects the data. All reviewers receive extensive initial training prior to starting data collection and ongoing training via continuing education. ACS NSQIP monitors accrual rates and data sampling methodologies and conducts audits on a random basis, ensuring highly reliable data [
ACS NSQIP participant files for the years 2005-2011 were reviewed and Current Procedure Terminology (CPT) codes were used to identify all patients who underwent pancreatic procedures (48100-48999). We then narrowed down these codes to include only those codes that clearly identified a pancreaticoduodenectomy:
CPT code 48150: Pancreatectomy proximal subtotal with total duodenectomy, partial gastrectomy, choledochoenterostomy and gastrojejunostomy (Whipple procedure) with pancreatojejunostomy
CPT code 48153: Pancreatectomy proximal subtotal with near total duodenectomy, choledochoenterostomy and duodenojejunostomy (pylorus sparing), Whipple-type procedure with pancreaticojejunostomy
(2013 CPT Professional Edition, American Medical Association)
After inclusion of these two codes, the “principal treatments” listed with each of the procedures were reviewed. Only those procedures with principal treatments listed as “pancreatectomy with pancreaticojejunostomy” or “pancreatectomy, proximal with pancreaticojejunostomy” were included in our analysis. Cases listing “pancreatectomy” and “partial removal of pancreas” were excluded to ensure there was no miscoding of other types of pancreatic resections such as distal pancreatic resections or enucleations.
Patient demographics included sex, age, smoking, and alcohol use. The comorbidities considered were diabetes, chronic obstructive pulmonary disease (COPD), myocardial infarction (MI) within 6 months, congestive heart failure (CHF), hypertension requiring medications, disseminated cancer, and transfusions within 3 days prior to surgery. Post-operative complications of interest were superficial surgical site infection, deep surgical site infection, organ space surgical site infection, wound disruption, pneumonia, urinary tract infections, unplanned intubation, pulmonary embolism, deep vein thrombosis, cardiac arrest requiring cardiopulmonary resuscitation, myocardial infarction, intraoperative or postoperative transfusions, sepsis and septic shock.
Operative time was defined as the time between start of the surgery (incision) and the finish of surgery (closure of the skin). Room times and anesthetic times were not included in this definition. Operative times were noted and all patients who had operative time listed as less than 120 minutes were excluded to avoid any possible data entry errors. Mean operative time of the remaining patients was 373 minutes and this was the chosen cut-off value for establishing the groups. This study thus analyzed two groups: 1) Operative time equal to or less than 373 minutes and 2) Operative time greater than 373 minutes. All of the demographics, laboratory values, and post-operative complications were compared between the 2 groups in each of the analyses.
Finally, the operative times in the mentioned groupings were compared in terms of other outcome measures including hospital length of stay (LOS), 30-day mortality and time from operation to death in those patients who expired in the perioperative period.
Statistical AnalysisThe association between patient characteristics, pre-operative laboratory values, and surgical complications were compared by operative time groups. Categorical and dichotomous variables were compared using the chi-square test, and continuous variables were compared using the t-test. The laboratory tests were log-transformed to meet the requirements of the t-test, and geometric means are displayed. To understand whether operative time is an independent predictor of the outcome measures, stepwise regression models of 30-day mortality and length of hospital stay were performed where all pre-operative factors and operative time were eligible for entry. Entry of operative time was considered a reflection of its importance as a predictor. A stepwise logistic regression was performed for 30-day mortality, with associated risks expressed as odds ratios (OR) with 95% confidence intervals (CIs). A stepwise linear regression was performed for hospital stay. For the stepwise tests, the laboratory tests were entered as indicator variables signifying low and high values, as listed in MedLine Plus (http://www.nlm.nih.gov/medlineplus/ency/article/003646.htm). All reported p values are two-tailed, and for all tests, p < 0.05 was considered statistically significant.
In this analysis of the ACS-NSQIP database, 11,148 patients had CPT codes 48150 or 48153. Of these patients, 6308 patients were listed with the principal procedures “pancreatectomy” or “partial pancreas resection” and were excluded from the study. This was to ensure that the data included only pancreaticoduodenectomies and excluded distal pancreatic resections, enucleations and central pancreatectomies. Of the remaining 4840 patients, 35 patients had an operative time listed as 0 to 120 minutes and were also excluded from the study. Our study population thus included 4805 patients.
In the overall cohort of 4805 patients, mean age was 63.9 years and 51.6% were males.
The mean operative time (ORtime) was 373.0 minutes (SD 130.3 minutes) with a range of 121 to 1295 minutes. Median operative time was 358 minutes. Distribution of operative times is shown in
In comparing patient characteristics, shorter operative times, ORtime < 373 minutes, were more likely to be among patients 65 years or older and female. Patients in the longer operative group were more likely to have diabetes, history of MI within 6 months of surgery, disseminated cancer, no history of TIA and prior radiation or chemotherapy (
In terms of post-operative complications, the details are noted in
Operative time > 373 minutes was found to be a significant predictor of 30-day mortality, by entering the stepwise logistic regression, along with the following preoperative factors: age 65 or higher, history of COPD
Total n = 4805 | Op Time < 373 n = 2638 | Op time ≥ 373 n = 2167 | p-value | |
---|---|---|---|---|
Mean age (SD) | 63.9 (12.5) | 64.4 (12.7) | 63.3 (12.1) | 0.001 |
Age 65 or older (%) | 2459 (51.2%) | 1406 (53.3%) | 1053 (48.6%) | 0.001 |
Males (%) | 2481 (51.6%) | 1250 (47.4%) | 1231 (56.8%) | <0.0001 |
Diabetes | 1118 (23.3%) | 567 (21.5%) | 551 (25.4%) | 0.001 |
History of smoking | 1008 (21.0%) | 552 (20.9%) | 456 (21.0%) | 0.66 |
History of alcohol use | 93 (1.9%) | 57 (2.2%) | 36 (1.7%) | 0.06 |
History of COPD | 209 (4.3%) | 120 (4.6%) | 89 (4.1%) | 0.45 |
Myocardial infarction within 6 mo. | 8 (0.2%) | 2 (0.1%) | 6 (0.3%) | 0.03 |
Hypertension requiring medications | 2614 (54.4%) | 1420 (53.8%) | 1194 (55.1%) | 0.38 |
Congestive heart failure | 11 (0.2%) | 7 (0.3%) | 4 (0.2%) | 0.56 |
Transient ischemic attack | 62 (1.3%) | 40 (1.5%) | 22 (1.0%) | 0.03 |
Cerebrovascular disease | 37 (0.8%) | 19 (0.7%) | 18 (0.8%) | 0.13 |
Currently on steroids | 93 (1.9%) | 58 (2.2%) | 35 (1.6%) | 0.14 |
Bleeding disorder | 103 (2.1%) | 59 (2.2%) | 44 (2.0%) | 0.62 |
Disseminated cancer | 112 (2.3%) | 51 (1.9%) | 61 (2.8%) | 0.04 |
Prior radiation therapy | 140 (2.9%) | 55 (2.1%) | 85 (3.9%) | <0.0001 |
Prior chemotherapy | 146 (3.0%) | 53 (2.0%) | 93 (4.3%) | <0.0001 |
Transfusion before surgery | 63 (1.3%) | 28 (1.1%) | 35 (1.6%) | 0.09 |
Data are number (% of group total), unless otherwise indicated. COPD indicates chronic obstructive pulmonary disease.
and MI, hypertension requiring medication, albumin < 3.4 gm/dL, creatinine > 1.3 mg/dL and prothrombin time > 13.5 seconds. The odds ratio for ORtime > 373 minutes was 1.73, with a 95% CI of 1.18 - 2.52 and a p-value of 0.005 (
Similarly, operative time > 373 minutes was found to be a significant predictor of longer hospital stays, by
Op time ≤ 373 n = 2638 | Op time > 373 min n = 2167 | p value | |
---|---|---|---|
Sodium (mmol/L) | 138.7 | 138.5 | 0.21 |
BUN (mg/dL) | 13.1 | 13.4 | 0.20 |
Creatinine (mg/dL) | 0.95 | 0.94 | 0.38 |
Albumin (gm/dL) | 3.68 | 3.68 | 0.99 |
Total bilirubin (mg/dL) | 1.36 | 1.26 | 0.04 |
Alanine aminotransferase (U/L) | 40.5 | 38.8 | 0.09 |
Alkaline phosphatase (U/L) | 142.8 | 145.2 | 0.47 |
WBC (×108/L) | 7.06 | 7.00 | 0.40 |
Hematocrit (%) | 37.1 | 37.2 | 0.58 |
Platelet count (×103/ml) | 252 | 246 | 0.04 |
Partial thromboplastin time (seconds) | 29.2 | 29.2 | 0.86 |
Prothrombin time (seconds) | 12.6 | 12.7 | 0.04 |
Total | Op time ≤ 373 min (n = 2638) | Op time > 373 min (n = 2167) | p value | |
---|---|---|---|---|
Superficial/skin infection | 469 (9.8%) | 225 (8.5%) | 244 (11.3%) | 0.002 |
Deep surgical site infection | 119 (2.5%) | 63 (2.4%) | 56 (2.6%) | 0.66 |
Intra-abdominal infection | 547 (11.4%) | 272 (10.3%) | 275 (12.7%) | 0.01 |
Wound dehiscence | 87 (1.8%) | 36 (1.4%) | 51 (2.4%) | 0.01 |
Post-operative pneumonia | 208 (4.3%) | 109 (4.1%) | 99 (4.6%) | 0.46 |
Need for reintubation | 247 (5.1%) | 116 (4.4%) | 131 (6.0%) | 0.01 |
Pulmonary embolism | 34 (0.7%) | 17 (0.6%) | 17 (0.8%) | 0.56 |
Urinary tract infection | 234 (4.9%) | 128 (4.9%) | 106 (4.9%) | 0.95 |
Myocardial infarction | 42 (0.9%) | 18 (0.7%) | 24 (1.1%) | 0.12 |
Cardiac arrest | 66 (1.4%) | 30 (1.1%) | 36 (1.7%) | 0.12 |
Cerebrovascular accident | 10 (0.2%) | 8 (0.3%) | 2 (0.1%) | 0.11 |
Deep venous thrombosis | 110 (2.3%) | 53 (2.0%) | 57 (2.6%) | 0.15 |
Bleeding requiring transfusion | 1289 (26.8%) | 589 (22.3%) | 700 (32.3%) | <0.0001 |
Sepsis | 491 (10.2%) | 250 (9.5%) | 241 (11.1%) | 0.06 |
Septic shock | 188 (3.9%) | 88 (3.3%) | 100 (4.6%) | 0.02 |
Return to operating room | 303 (6.3%) | 149 (5.6%) | 154 (7.1%) | 0.04 |
Op time ≤ 373 min (n = 2638) | Op time > 373 min (n = 2167) | p-value | |
---|---|---|---|
Hospital length of stay in days (SD) | 12.3 | 13.8 | 0.001 |
Days from operation to death within the perioperative period (SD) | 14.0 | 13.0 | 0.54 |
30-day mortality | 50 (1.9%) | 69 (3.2%) | 0.004 |
Step | Variable | Odds ratio (95% CI) | p value |
---|---|---|---|
1 | History of MI | 8.75 (1.68 - 45.53) | 0.0001 |
2 | Age 65 or higher | 2.20 (1.43 - 3.38) | 0.0001 |
3 | Albumin < 3.4 gm/dL | 2.01 (1.37 - 2.93) | 0.0001 |
4 | Hypertension w/meds | 2.14 (1.36 - 3.34) | 0.0002 |
5 | History of COPD | 2.76 (1.53 - 5.00) | 0.001 |
6 | Prothrombin time > 13.5 | 1.86 (1.23 - 2.81) | 0.002 |
7 | Op time > 373 min | 1.73 (1.18 - 2.52) | 0.005 |
8 | Creatinine > 1.3 mg/dL | 2.30 (1.28 - 4.14) | 0.004 |
Odds ratios and 95% confidence interval are based on the final logistic model in a stepwise regression of 30-day mortality, where the independent variables are mutually adjusted. The independent variables were factors that would be known prior to surgery and operative time.
entering the stepwise logistic regression, along with the following preoperative factors: age 65 or higher, hypertension requiring medication, bleeding disorders, albumin < 3.4 gm/dL, alkaline phosphatase ≤ 147 and hematocrit < 36.1%. The average number of days in the hospital was 13.9 for patients with ORtime > 373 minutes and 15.5 for those with ORtime ≤ 373 minutes (p < 0.0001), after adjustment for other important predictors (
Patients are increasingly referred to high-volume centers of excellence for PD based on early studies that suggested superior outcomes [
How do we currently measure a surgeon’s ability to perform PD? For credentialing committees and hospital employers, surgical skill is very difficult to determine from job applications, letters of reference or evaluation reports as there is a lack of objective measures [
Average hospital length of stay in days | ||||
---|---|---|---|---|
Step | Variable | No | Yes | p-value |
1 | Albumin < 3.4 gm/dL | 14.6 | 18.1 | <0.0001 |
2 | Operative time > 373 minutes | 15.5 | 17.1 | <0.0001 |
3 | Bleeding Disorders | 13.8 | 18.9 | <0.0001 |
4 | Age > 65 years | 15.8 | 16.8 | 0.0005 |
5 | Alkaline phosphatase > 147 | 16.9 | 15.8 | 0.0002 |
6 | Hematocrit < 36.1% | 15.8 | 16.9 | 0.0004 |
Means are based on the final linear model in a stepwise regression of hospital stay, where the independent variables are mutually adjusted. The independent variables were factors that would be known prior to surgery and operative time.
surgeon, such as operative times, may provide a better assessment of surgical skills. Experience requirements also discriminate against newly trained yet highly skilled surgeons and cannot be used to distinguish the best surgeons. While a certain number of cases are often needed to overcome the “learning curve” in complex cases such as PD, once a surgeon has surpassed this volume threshold, annual volume has not been shown to significantly impact outcome [
What are the other shortcomings of the volume metric? While there is an inverse relationship between PD operative volume and morbidity and mortality, studies have failed to define a precise volume cutoff that clearly distinguishes high-volume centers of excellence from other institutions [
Medical centers are increasingly constrained by cost and need for quality, yet they must balance this with the need for access to medical care and elimination of healthcare disparities. Hospital volume for complex procedures such as PD has governed referral patterns with the aim of improving outcomes through regionalization. However, there are significant variations in referral patterns to high-volume centers with fewer referrals of ethnic minorities, elderly and lower socio-economic groups [
While operative time may be used to recruit experienced surgeons, the concept may also be applied for recruiting newly trained surgeons. Many centers again rely on volume to determine credentialing and require a surgeon to perform a certain number of cases before granting them privileges to perform the procedure independently [
There are many limitations to this study regarding both the use of operative time as a quality measure as well as the use of the ACS NSQIP database. NSQIP does not have information on the specific diagnosis for which PD or the details of surgery. Factors such as obesity, prior chemo-radiation, previous abdominal surgery, anatomic abnormalities, an additional organ/vascular resections are not captured. These additional factors, such as prior chemo-radiation may create more tedious and difficult dissections thus prolonging the operative time beyond what can be compensated for by technical prowess [
One final limitation of the NSQIP database is the lack of information on the experience level of the surgeons performing the procedures and the degree of resident physician involvement in a case. Like operative time, resident involvement and education is a controversial subject when discussed in the realm of healthcare quality improvement initiatives. Resident involvement has been documented to prolong operative times though has not been shown to adversely affect outcomes and quality [
The use of any metric for determining quality is inadequate and potentially detrimental to efforts aimed at improving quality. While volume has been the surrogate quality metric for PD, operative time may be another measure of individual surgeon performance. Perhaps operative times in addition to volume may be used to assess quality, although specific criteria would require a large study in which identification of individual surgeons, operative times and surgical details are available. Additional studies are still needed to determine the accuracy of operative time as a quality metric for PD and other complex surgeries, and in identifying more accurate ways to define excellence.
Gwendolyn M.Garnett,LynneWilkens,WhitneyLimm,Linda L.Wong,11, (2015) Operative Time as a Measure of Quality in Pancreaticoduodenectomy: Is Faster Better? A Retrospective Review Using the ACS NSQIP Database. Surgical Science,06,418-426. doi: 10.4236/ss.2015.69060