Annals of Surgical Oncology Sign the Guestbook
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kotwall, C. A.
Right arrow Articles by Covington, D. L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kotwall, C. A.
Right arrow Articles by Covington, D. L.
Related Collections
Right arrow Surgery
Annals of Surgical Oncology 9:847-854 (2002)
© 2002 Society of Surgical Oncology

ORIGINAL ARTICLES

National Estimates of Mortality Rates for Radical Pancreaticoduodenectomy in 25,000 Patients

Cyrus A. Kotwall, MD, J. Gary Maxwell, MD, Carla C. Brinker, BS, Gary G. Koch, PhD and Deborah L. Covington, DrPH

From the Coastal Area Health Education Center (CAK, JGM, CCB, DLC), New Hanover Health Network (CAK, JGM), Wilmington, North Carolina; and the University of North Carolina–Chapel Hill (CAK, JGM, CGK, DLC), Chapel Hill, North Carolina.

Correspondence: Address correspondence and reprint requests to: C. A. Kotwall, MD, Coastal AHEC, PO Box 9025, Wilmington, NC 28402-9025; Fax: 910-763-4630; E-mail: canuck{at}med.unc.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Recent publications suggest an inverse relationship between mortality rates in the Whipple procedure for periampullary cancer and hospital volume/teaching status.

Methods: The Nationwide Inpatient Sample database from 1988 to 1995, containing 24,926 patients undergoing pancreatectomy for periampullary cancer, was used.

Results: The mean number of procedures per hospital per year was 1.5, and the overall mortality was 14%. The volume of procedures per year increased from the rural to the urban nonteaching hospitals to the urban teaching hospitals (.6, 1.1, and 2.7, respectively), with a steady decrease in mortality among the three hospital types (18%, 15%, and 11%). A multiple logistic regression model with mortality odds ratios (ORs) showed that male sex (OR, 1.3), increasing age (OR, 1.6 to 6.7 in decades from 50 to >=80 vs. <50 years), emergency admission (OR, 1.5), and hospital volume (less than one vs. one or more cases per year; OR, 1.5) were significantly predictive for increased in-hospital mortality.

Conclusions: In-hospital mortality in the low-volume hospital setting is prohibitive, and review of each institution’s mortality rates must occur before these procedures are performed in those institutions. In addition, patients over the age of 60 years, male patients, and those with an urgent admission are at a significant risk of in-hospital death, and consideration should be given toward transfer to an experienced institution.

Key Words: Mortality rate • Whipple procedure • Pancreaticoduodenectomy • Hospital volume


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In the year 2002, there will be an estimated 30,300 new pancreatic cancer cases and an estimated 29,700 pancreatic cancer deaths in the United States, making pancreatic cancer the fourth leading cause of death in both men and women.1 Surgery remains the only curative modality, with 5-year survival rates of 15%,2 15%,3 10.2%,4 and 6.8%5 in four recent reports. These studies, each having more than 100 patients, are from institutions that offer considerable expertise in pancreaticobiliary surgery, and these are some of the best available survival figures in the literature.

Because of these modest 5-year survival figures, it is imperative that postoperative mortality be minimized. A postoperative mortality of 10% would almost completely negate any long-term survival benefit and would not justify a radical and costly operation. In the previously quoted series, operative mortality rates of 5.3%, 3.7%, 3.4%, and 3%, respectively, are quoted. These rates are from four well-established, dedicated centers, and it is unlikely that these figures could be duplicated at other institutions. Because of this, several investigators have examined mortality rates after pancreatic cancer surgery in relation to institutional volume. Studies from the Netherlands,6 Canada,7 the United Kingdom,8 and the United States9–14 clearly support a decreased postoperative mortality with increased surgical volume. Most of these studies are from single institutions with small numbers of patients or from single states or provinces (Maryland, New York, and Ontario), with patient numbers ranging from 496 to 1972. There are three relatively large national studies addressing mortality and patient volume, one from the United Kingdom8 and two from the United States,10,11 with patient numbers of 1026, 7229, and 742, respectively. The largest American study, of more than 7000 patients, is from a Medicare claims database and includes only patients older than 65 years of age.10 Therefore, the study is not applicable to patients of all ages having pancreatic resection.

The purpose of this study was to provide a national perspective of in-hospital mortality rates for radical pancreaticoduodenectomy and to relate mortality to hospital volume and hospital teaching status. We selected a national database that would be representative of all cases in the United States, to make a firm statement about the pancreatic resection in-hospital mortality rate and its relationship to hospital volume and status. Results from this study may promote a change in referral patterns to institutions performing this complex surgical procedure with sufficient volume to minimize postoperative mortality.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
To address mortality rate with hospital volume/status, we selected the Nationwide Inpatient Sample (NIS), one of the databases of the Healthcare Cost and Utilization Project (HCUP), which is a federal/state/industry partnership designed to build a standardized, multistate health data system (http://www.ahrq.gov/data/hcup). The HCUP is maintained and sponsored by the Agency for Healthcare Research and Quality. These data are available to public and private users and serve to assist in health services research and health policy analysis, such as describing patterns of care, analyzing hospital procedures and outcomes, and comparing trends at national, regional, and state levels. The HCUP data contain more than 100 variables, such as diagnoses, procedures, admission/discharge status, patient demographics, payment source, charges, length of stay, and hospital characteristics. The State Inpatient Database, which is one of the HCUP databases, contains the State’s non-Federal hospital discharge abstracts and represents approximately 60% of all US hospital discharges. The NIS is a stratified probability sample of hospitals drawn from the State Inpatient Database, is designed to approximate a 20% sample of US community hospitals, including academic medical centers, and is the largest hospital inpatient database in the United States. To ensure hospital representativeness, hospitals are stratified by geographic region, ownership, location (urban or rural), teaching status, and bed size.

The years selected for this study were from 1988 to 1995, inclusive, and 19 states were included (California, Colorado, Florida, Illinois, Iowa, Maine, New Jersey, Washington, Arizona, Pennsylvania, Wisconsin, Connecticut, Kansas, Maryland, New York, Oregon, South Carolina, Missouri, and Tennessee), which submitted their inpatient hospital discharge data to the NIS. This includes almost 50 million inpatient records from 759 to 938 hospitals, depending on the year studied. Sample weights were developed for the NIS to obtain national estimates of hospital and inpatient parameters. Each hospital’s weight is equal to the number of hospitals it represents during that particular calendar year and allows weighted analysis of a parameter (such as length of stay) based on averages or regression estimates.

For this study, we selected the following procedures from the International Classification of Diseases, 9th Revision, Clinical Modification: procedure codes 52.7 (radical pancreaticoduodenectomy; n = 21,860), 52.6 (total pancreatectomy; n = 1,791), 52.51 (proximal pancreatectomy; n = 784), and 52.53 (radical subtotal pancreatectomy; n = 490), in conjunction with diagnostic codes 157.0 (cancer of the head of the pancreas; n = 15,049), 156.2 (cancer of the ampulla of Vater; n = 5,592), 152.0 (cancer of the duodenum; n = 2,002), and 156.1 (cancer of the extrahepatic bile ducts; n = 2,241).

The specific outcome of the study was postoperative in-hospital mortality (the total number of in-hospital deaths divided by the total number of patients). We analyzed mortality in relation to hospital volume (number of procedures performed in a hospital divided by the number of years that hospital participated in the sample) and hospital type (rural, urban nonteaching, and urban teaching). We defined in-hospital mortality as any death occurring before the patient was discharged from the hospital on the admission in which the patient had the radical pancreaticoduodenectomy (patient died during hospitalization). We analyzed the data by using commonly applied statistical software.

To evaluate the statistical association between the in-hospital mortality and procedure volume, we conducted a Spearman rank correlation analysis with hospital as the unit of analysis. We conducted comparisons of hospital volume for the three hospital types in an overall manner with the Kruskal-Wallis test, and we conducted pairwise comparisons among hospital types with a Wilcoxon rank sum test, both with hospital as the unit of analysis.

To evaluate patient and hospital factors that may influence in-hospital mortality, we constructed a multiple logistic regression model. The analysis accounted for the correlation structure of multiple patients from the same hospital having similar outcomes through the use of methods for generalized estimating equations.15 The dependent variable was in-hospital mortality, and the main explanatory variable was volume (less than one patient per year, one or more patients per year). Other explanatory variables that had control in the model were type of hospital (rural, urban nonteaching, or urban teaching), patient sex, age (<50, 50–59, 60–69, 70–79, and >=80 years), year of admission (1988–1989, 1990–1992, 1993–1995), and hospital admission status (elective/missing or urgent/emergency, with the combination of missing hospital admission status with elective hospital admission status being supported by background analyses indicating that missing were similar to elective with regard to in- hospital mortality). Hospital type was found to be nonsignificant in the original model. Therefore, we constructed a simplified model, which omitted hospital type. These analyses generated odds ratios and associated 95% confidence intervals so as to shed light on the predictive factors associated with in-hospital mortality in the three hospital types.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
On the basis of the 720 hospitals in which pancreatic procedures were performed, the estimated total number of procedures for the national population between 1988 and 1995 was 24,926, and the overall mean procedure volume was 1.5 (±3.6) per hospital per year. As listed in Table 1, the mean number of procedures increased from .62 (±.77) to 2.7 (±4.2) in ascending order from the rural to the urban nonteaching to the urban teaching hospitals. The mean age of the patients was 65.2 years (±11 years), 54% were men, and 49% were classified as emergency/urgent admissions. The distribution of these parameters was such that the rural hospital patients were a little older and had a higher number of emergency admissions (Table 2).


View this table:
[in this window]
[in a new window]
  		TABLE 1. Procedure volume by hospital type
 

View this table:
[in this window]
[in a new window]
  		TABLE 2. Demographics and admission status by hospital typea
 
The in-hospital mortality in the rural hospitals was significantly higher than that in the urban teaching institutions (17.9% vs. 11.3%), with an overall mean mortality in hospitals of 14.1%. The in-hospital mortality was inversely related to the mean hospital procedure volume per year, as shown in Figs. 1 and 2 (correlation coefficient, .156; P < .0001). Hospital volumes differed significantly among the three hospital types (P < .0001), both in an overall way and pairwise (P < .0001). There was an overall progressive decrease in mortality from 12.6% to 9% during the 8-year period of the study (Fig. 3). Likewise, there was a progressive decrease in mortality in all three hospital categories, with the smallest mortality in the urban teaching institutions (Fig. 4). There was a progressive increase in mortality by age group, with the patients under 50 years and those over 80 years having an in-house mortality rate of 3.9% and 22.2%, respectively (Fig. 5). The mortality in the rural hospitals increased precipitously from 12.6% in the under-70 age group to 38.3% in the over-80 age group, whereas the mortality in the urban teaching institutions had a much more modest increase from 5.4% in the under-70 to 13.8% in the over-80 age group (Fig. 6).



View larger version (47K):
[in this window]
[in a new window]
  		FIG. 1. Scatter plots of mortality and hospital volume by hospital type.

 


View larger version (42K):
[in this window]
[in a new window]
  		FIG. 3. Mortality by year of operation.

 


View larger version (31K):
[in this window]
[in a new window]
  		FIG. 4. Mortality by year and hospital type.

 


View larger version (27K):
[in this window]
[in a new window]
  		FIG. 5. Mortality by age group in decades.

 


View larger version (36K):
[in this window]
[in a new window]
  		FIG. 6. Mortality by age group and hospital type.

 


View larger version (23K):
[in this window]
[in a new window]
  		FIG. 2. Mortality and hospital volume by hospital type.

 
With adjustment for all variables in the multiple logistic regression model (Table 3), those patients having their procedure performed at a hospital with a volume of less than one patient per year had 1.5 times higher odds for death in the hospital (P = .003) compared with patients having their procedure performed in an institution with a volume of one or more patients per year. Male sex was predictive for mortality with an odds ratio of 1.3 (P = .009). Age was more strongly predictive for mortality, with patients over age 80 having an odds ratio of 6.7 compared with patients under the age of 50 (P < .0001). Emergency/urgent admissions were predictive for mortality, with an odds ratio of 1.5 compared with elective/missing admissions (P = .0003). Hospital type (P = .15) and year of admission (P = .18) were not significantly predictive for mortality. For the model in Table 4, with hospital type removed, the effect of volume was more strongly evident, with an odds ratio of 1.6 and a P value of .0008. A third model, which did not include volume but did include hospital type, was constructed. Hospital type was significant in this model (P = .04) once volume was excluded. The previous two models (Tables 3 and 4) show that any role for hospital type was explained by procedure volume.


View this table:
[in this window]
[in a new window]
  		TABLE 3. Logistic regression model—mortality odds ratios
 

View this table:
[in this window]
[in a new window]
  		TABLE 4. Logistic regression model—mortality odds ratios, hospital type omitted
 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
This study clearly documented that the postoperative mortality rate for periampullary cancer is primarily related to hospital volume and secondarily to hospital category. One of the first studies to address surgical volume and mortality was in 1979, and it found that a relationship existed with complex surgery, such as vascular surgery and coronary artery bypass, but did not exist for relatively uncomplicated procedures, such as cholecystectomy and vagotomy.16 Other investigators have documented a relationship between volume and outcome for cardiac17,18 and other vascular9 procedures. The same relationship was noted for colorectal cancer surgery19,20 and for breast, lung, and ovarian cancer patients.21

Our study had an overall mortality of 14%, which is considerably higher than the 3% to 5.3% rate in the previously quoted series in the four dedicated institutions (Johns Hopkins University, University of California–San Diego, Memorial Sloan-Kettering Cancer Center, and the Mayo Clinic). Indeed, there are several institutions without a single postoperative mortality, with the first one being a study of 41 patients in 1968,22 followed by a study of 118,23 145,24 and 54 patients.25 Our study did include a probability sample of all US community hospitals and academic medical centers, but it cannot identify whether those four specific dedicated institutions were included. Nevertheless, our study is representative of hospitals throughout the country and should provide national benchmarks.

In our study, slightly more than 50% of patients were men, and male sex was an independent factor contributing to a significantly higher postoperative mortality. This is borne out by other studies, with a 50%,7 37%,26 24%,10 and 23%14 decreased risk of death for female sex. There is no known reason for a higher postoperative mortality in men, but it may be due to a higher tumor stage at presentation. We were not able to determine this from our data.

Age is an independent factor contributing to postoperative mortality. There is a steady increase in mortality for every successive decade after age 50, and this applied to each of the three hospital categories. This is consistent with many other studies,6,7,10,13,14,26,27 all of which showed the same findings.

We did wish to examine patient comorbidity, such as hypertension, diabetes, coronary artery disease, malnutrition, chronic obstructive pulmonary disease, and renal failure. The comorbidity data that were submitted by many of the hospitals to the NIS database were incomplete; therefore, we did not use these data in our logistic regression model. Three prior studies have shown an increasing postoperative mortality with increasing comorbidities,7,10,11 which is what would be expected.

When we examined patient admission status, there was an increased death rate with those patients admitted in an emergent/urgent status versus an elective admission. Lieberman et al.14 showed a significantly increased mortality with unscheduled admissions (16.6% vs. 10.4%), and Simunovic et al.7 showed a nonsignificant 1.3 times increased mortality rate with urgent admission but had only 842 patients in the study. A positive relationship between mortality and nonelective admission is what would be predicted.

During the 8-year time period of the study, the in-hospital mortality decreased from 12.6% to 9%, and the overall trend of decreased mortality with time applied to all three hospital categories. Rios et al.28 had a mortality rate of 16%, which decreased to 1% over a 40-year period, and Gordon et al.13 had a mortality rate of 17%, which decreased to 5% over an 11-year period. Two other studies showed decreased death rates in periods as short as 5 years,6,27 indicating that historical comparison of series of patients is not valid. A consistent decreased mortality with time may be due to better patient selection, attention to intraoperative technique, and improved critical care.

The major finding of our study was an inverse relationship between mortality and hospital volume, which remained significant in the logistic regression model, whereas hospital type did not. This is supported by the fact that hospitals in the urban teaching category had 4.4 times the volume of cases compared with rural hospitals. The mean procedures per hospital per year in the urban teaching category would seem relatively low, at 2.7, and dedicated institutions with significantly more procedures per year have been able to achieve even lower mortality rates, providing further evidence that surgical volume is related to postoperative mortality. Similar findings were noted by Hutter et al.26 in a study of 5696 patients and in a study by the Commission on Cancer of the American College of Surgeons of 2263 resected patients.27

It should be noted that, on close inspection of the scatter plots of Fig. 1, whereas there is a linear relationship between volume and hospital status, several institutions in all three categories had mortality rates of both 0% and 100%. This means that there are several rural hospitals in the United States that are able to achieve excellent outcomes, and conversely, there are several urban teaching hospitals that have universally fatal outcomes. One cannot, therefore, infer that all rural hospitals have poor outcomes and that all urban teaching hospitals have excellent outcomes. Each hospital should determine its own mortality rate for this procedure, and, indeed, for any other complex surgical procedure.

Investigators have suggested statewide regionalization of pancreatic cancer. Sosa et al.12 documented significant increases in in-hospital death in those patients undergoing pancreatic interventional procedures in 496 patients with progression from high-volume to medium- and low-volume hospitals. In another study of 795 pancreaticoduodenectomies, one institution almost tripled its statewide share of this particular procedure, with mortality decreasing from 17% to 5%.13 The authors concluded that this study demonstrated the effectiveness of regionalization for this particular high-risk surgery. In another study of 1972 patients, there was a significant difference in mortality between the high-volume and the minimal-volume hospitals (4% vs. 22%), with the authors suggesting a defined minimum hospital experience for elective pancreatectomy.14

In-hospital death rate is only one measure of an institution’s success in managing a complex surgical procedure. Other measures, such as length of stay, cost, quality of life, local recurrence, and survival, are just as or more important and should be taken into account. In a Maryland study that compared 1 provider that performed 54% of all Whipple procedures in the state with 38 other Maryland hospitals, there was a significant decrease in in-hospital mortality (2% vs. 14%), mean length of stay (23 vs. 27 days), and mean total charges ($26,204 vs. $31,659).13 In a Canadian study, there was a similar significant decreased in-hospital mortality (3% vs. 11%) and length of stay (25 vs. 31 days) in high-volume centers compared with low-volume centers.7 These two studies further support reasons for performing Whipple procedures at high-volume institutions.

It is more important to note that disease-specific survival should be the primary measure of an institution’s ability to manage a complex disease. Birkmeyer et al.,29 in a study of 7229 patients, found a strong relationship between hospital volume and survival. Our study did not examine any other parameters aside from in-hospital mortality. In the NIS database, information is available on length of stay and cost, and these may be the focus of another study. Unfortunately, survival after discharge is not included in the database, because hospitals submit only their discharge abstracts to this database.

Drawbacks to our study included coding inaccuracies, lack of complete comorbidity data, no information on tumor stage, and assessment of only one measure of institutional success—that of in-hospital mortality. It is well known that large databases contain errors in coding, and we would assume that any coding errors were made uniformly among the three hospital categories. The data would have been strengthened by including comorbid diseases in the logistic regression model, because there could have been differences in the amount of comorbid disease among the three hospital categories. Regarding tumor stage, there is no information on tumor-node-metastasis classification or tumor stage in the NIS database, and, again, this would have strengthened the data, because there could well have been differences in tumor stage among the three hospital types. However, one may postulate that rural hospitals would operate on patients with lower tumor stage and less comorbid disease and would preferentially refer patients with more advanced disease and more comorbidities to larger teaching institutions, which would produce even more divergent mortality rates.

It is imperative that we as physicians examine our own data and be aware of our mortality rates so we can possibly refer patients with this complex surgical procedure to institutions that have a proven track record. If we do not, state and federal bodies, insurance companies, and policy-makers will mandate where we can and cannot perform these complex surgical procedures. As early as 1989, the New York State Department of Health proposed annual hospital volume thresholds, such as 5 partial gastrectomies and 40 colectomies per year.30 A survey by the Commission on Cancer suggested that pancreatic cancer operations be performed at institutions performing >=20 cases per year,27 and another New York study supported a defined minimum hospital experience for pancreatic surgery.14 As mentioned previously, other parameters, such as overall survival, must ideally be considered before widespread referral changes can and should be implemented. Before centralization of procedures is performed, more evidence is required in terms of length of stay, cost, quality of life issues, and survival, in addition to only in-hospital mortality data. In addition, this study shows that several rural hospitals have excellent outcomes, and "broad policy" statements should not malign rural hospitals as a group. Each institution should monitor its own outcomes, and if their morbidity and mortality are excessive, referral of those patients should then occur.

In summary, this study showed that in-hospital mortality in the low-volume hospital setting is prohibitive. Before a Whipple procedure is performed in that particular setting, review of in-house morbidity and mortality must occur. In addition, patients over the age of 60, male patients, and those operated on in a nonelective admission are at significantly increased risk of in-hospital death. If these parameters exist for a particular patient, consideration should be given toward transfer to an institution experienced in pancreaticobiliary surgery.


   Footnotes
 
Presented at the plenary session at the Society of Surgical Oncology’s 54th Annual Cancer Symposium, Washington, DC, March 15–18, 2001.

A national database assessing in-hospital mortality rate after the Whipple procedure for periampullary cancer showed that mortality in the low-volume hospital setting is prohibitive. Other factors predictive for increased mortality included male sex, increasing age, and nonelective admissions.

Received for publication March 16, 2001. Accepted for publication June 27, 2002.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Jemal A, Thomas A, Murray T, Thun M. Cancer statistics, 2002. CA Cancer J Clin 2002; 52: 23–47.[Abstract/Free Full Text]
  2. Yeo CJ, Cameron JL, Lillemoe KD, Pitt HA. Periampullary adenocarcinoma: analysis of 5-year survivors. Ann Surg 1998; 227: 821–31.[CrossRef][Medline]
  3. Bouvet M, Gamagami RA, Gilpin EA, et al. Factors influencing survival after resection for periampullary neoplasms. Am J Surg 2000; 180: 13–7.[CrossRef][Medline]
  4. Conlon KC, Klimstra DS, Brennan MF. Long-term survival after curative resection for pancreatic ductal adenocarcinoma: clinicopathologic analysis of 5-year survivors. Ann Surg 1996; 223: 273–9.[CrossRef][Medline]
  5. Nitecki SS, Sarr MG, Colby TV, van Heerden JA. Long-term survival after resection for ductal adenocarcinoma of the pancreas: is it really improving? Ann Surg 1995; 221: 59–66.[Medline]
  6. Gouma DJ, van Geenen RCI, van Gulik TM, et al. Rates of complications and death after pancreaticoduodenectomy: risk factors and the impact of hospital volume. Ann Surg 2000; 232: 786–95.[CrossRef][Medline]
  7. Simunovic M, To T, Theriault M, Langer B. Relation between hospital surgical volume and outcome for pancreatic resection for neoplasm in a publicly funded health care system. Can Med Assoc J 1999; 160: 643–8.[Abstract]
  8. Neoptolemos JP, Russell RCG, Bramhall S, Theis B. Low mortality following resection for pancreatic and periampullary tumours in 1026 patients: UK survey of specialist pancreatic units. Br J Surg 1997; 84: 1370–6.[CrossRef][Medline]
  9. Dudley RA, Johansen KL, Brand R, Rennie DJ, Milstein A. Selective referral to high-volume hospitals: estimating potentially avoidable deaths. JAMA 2000; 283: 1159–66.[Abstract/Free Full Text]
  10. Birkmeyer JD, Finlayson SRG, Tosteson ANA, Sharp SM, Warshaw AL, Fisher ES. Effect of hospital volume on in-hospital mortality with pancreaticoduodenectomy. Surgery 1999; 125: 250–6.[Medline]
  11. Begg CB, Cramer LD, Hoskins WJ, Brennan MF. Impact of hospital volume on operative mortality for major cancer surgery. JAMA 1998; 280: 1747–51.[Abstract/Free Full Text]
  12. Sosa JA, Bowman HM, Gordon TA, et al. Importance of hospital volume in the overall management of pancreatic cancer. Ann Surg 1998; 228: 429–38.[CrossRef][Medline]
  13. Gordon TA, Bowman HM, Tielsch JM, Bass EB, Burleyson GP, Cameron JL. Statewide regionalization of pancreaticoduodenectomy and its effect on in-hospital mortality. Ann Surg 1998; 228: 71–8.[CrossRef][Medline]
  14. Lieberman MD, Kilburn H, Lindsey M, Brennan MF. Relation of perioperative deaths to hospital volume among patients undergoing pancreatic resection for malignancy. Ann Surg 1995; 222: 638–45.[Medline]
  15. Stokes ME, Davis CS, Koch GG. Categorical Data Analysis Using the SAS System. 2nd ed. Cary, NC: SAS Institute, 2000.
  16. Luft HS, Bunker JP, Enthoven AC. Should operations be regionalized? The empirical relation between surgical volume and mortality. N Engl J Med 1979; 301: 1364–9.[Abstract]
  17. McGrath PD, Wennberg DE, Dickens JD, et al. Relation between operator and hospital volume and outcomes following percutaneous coronary interventions in the era of coronary stent. JAMA 2000; 284: 3139–44.[Abstract/Free Full Text]
  18. Magid DJ, Calonge BN, Rumsfeld JS, et al. Relation between hospital primary angioplasty volume and mortality for patients with acute MI treated with primary angioplasty vs thrombolytic therapy. JAMA 2000; 284: 3131–8.[Abstract/Free Full Text]
  19. Schrag D, Cramer LD, Bach PB, Cohen AM, Warren JL, Begg CB. Influence of hospital procedure volume on outcomes following surgery for colon cancer. JAMA 2000; 284: 3028–35.[Abstract/Free Full Text]
  20. Harmon JW, Tang DG, Gordon TA, et al. Hospital volume can serve as a surrogate for surgeon volume for achieving excellent outcomes in colorectal resection. Ann Surg 1999; 230: 404–13.[CrossRef][Medline]
  21. Hillner BE, Smith TJ, Desch CE. Hospital and physician volume or specialization and outcomes in cancer treatment: importance in quality of cancer care. J Clin Oncol 2000; 18: 2327–40.[Abstract/Free Full Text]
  22. Howard JM. Pancreatico-duodenectomy. Forty-one consecutive Whipple resections without an operative mortality. Ann Surg 1968; 168: 629–40.[Medline]
  23. Trede M, Schwall G, Saeger HD. Survival after pancreaticoduodenectomy: 118 consecutive resections without an operative mortality. Ann Surg 1990; 211: 447–58.[Medline]
  24. Cameron JL, Pitt HA, Yeo CJ, Lillemoe KD, Kaufman HS, Coleman J. One hundred and forty-five consecutive pancreaticoduodenectomies without mortality. Ann Surg 1993; 217: 430–8.[Medline]
  25. Schwarz RE, Keny H, Ellenhorn JDI. A mortality-free decade of pancreatoduodenectomy: is quality independent of quantity? Am Surg 1999; 85: 949–54.
  26. Hutter MM, Glasgow RE, Mulvihill SJ. Does the participation of a surgical trainee adversely impact patient outcomes? A study of major pancreatic resections in California. Surgery 2000; 128: 286–92.[CrossRef][Medline]
  27. Janes RH, Niederhuber JE, Chmiel JS, et al. National patterns of care for pancreatic cancer: results of a survey by the Commission on Cancer. Ann Surg 1996; 223: 261–72.[CrossRef][Medline]
  28. Rios G, Conrad A, Cole D, et al. Trends in indications and outcomes in the Whipple procedure over a 40-year period. Am Surg 1999; 65: 889–93.[Medline]
  29. Birkmeyer JD, Warshaw AL, Finlayson SRG, Grove MR, Tosteson ANA. Relationship between hospital volume and late survival after pancreaticoduodenectomy. Surgery 1999; 126: 178–83.[Medline]
  30. Hannan EL, O’Donnell JF, Kilburn H, Bernard HR, Yazici A. Investigation of the relationship between volume and mortality for surgical procedures performed in New York state hospitals. JAMA 1989; 262: 503–10.[Abstract]



This article has been cited by other articles:


Home page
 Arch SurgHome page
C. M. Vollmer Jr, W. Pratt, T. Vanounou, S. K. Maithel, and M. P. Callery
Quality Assessment in High-Acuity Surgery: Volume and Mortality Are Not Enough
Arch Surg, April 1, 2007; 142(4): 371 - 380.
[Abstract] [Full Text] [PDF]

Home page
 Arch SurgHome page
J. K. Smith, J. T. McPhee, J. S. Hill, G. F. Whalen, M. E. Sullivan, D. E. Litwin, F. A. Anderson, and J. F. Tseng
National Outcomes After Gastric Resection for Neoplasm
Arch Surg, April 1, 2007; 142(4): 387 - 393.
[Abstract] [Full Text] [PDF]

Home page
 Ann. Surg. Oncol.Home page
J. D. Birkmeyer
Raising the Bar for Pancreaticoduodenectomy
Ann. Surg. Oncol., November 1, 2002; 9(9): 826 - 827.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kotwall, C. A.
Right arrow Articles by Covington, D. L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kotwall, C. A.
Right arrow Articles by Covington, D. L.
Related Collections
Right arrow Surgery

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS