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Original Article |

National Variation in Operative Mortality Rates for Esophageal Resection and the Need for Quality Improvement FREE

Justin B. Dimick, MD; John A. Cowan Jr, MD; Gorav Ailawadi, MD; Reid M. Wainess, BS; Gilbert R. Upchurch Jr, MD
[+] Author Affiliations

From the Department of Surgery, University of Michigan Medical Center, Ann Arbor.


Arch Surg. 2003;138(12):1305-1309. doi:10.1001/archsurg.138.12.1305.
Text Size: A A A
Published online

Hypothesis  Operative mortality rates for esophageal resection vary across hospital volume groups in a nationally representative sample of hospitals.

Design  Cross-sectional study of all adult patients in the Nationwide Inpatient Sample who underwent esophageal resection from 1995 through 1999 (N = 3023). Operative mortality was determined for hospital volume quartiles (low, <3 per year; medium, 3-5 per year; high, 6-16 per year; very high, >16 per year). Multiple logistic regression of in-hospital mortality was used for case-mix adjusted analyses.

Setting  Hospitals performing at least 1 esophageal resection from 1995 through 1999 in the Nationwide Inpatient Sample.

Patients  Patients having esophageal resection from 1995 through 1999 in the Nationwide Inpatient Sample.

Results  Overall mortality was 8.2% and varied 3-fold from 11.8% to 3.7% across hospital volume groups (P<.001). In the case-mix–adjusted multivariate analysis, having surgery at a low-volume hospital (odds ratio, 2.9; 95% confidence interval, 1.7-4.9; P<.001) or medium-volume hospital (odds ratio, 2.4; 95% confidence interval, 1.4-4.3; P = .002) was associated with an increased risk of mortality compared with the reference group of very high–volume hospitals. The effect of volume on mortality was significant for both malignant and benign disease. Given the absolute risk difference of 8.1% between very high– and low-volume hospitals, only 12 patients would need to be referred to prevent 1 death after esophageal resection.

Conclusions  The operative mortality rate for esophageal resection varies across hospitals in the United States. To improve the quality of care and reduce operative mortality rates for patients in need of esophageal surgery, patients should either be referred to higher-volume hospitals, or quality improvement should be directed at lower-volume hospitals.

Figures in this Article

THE QUALITY of surgical care varies depending on where a patient chooses to have an operation. Several previous studies from state and national databases have shown superior outcomes for complex vascular, cardiac, and general surgical procedures when performed at high-volume compared with low-volume hospitals.113 Currently, there are active health policy initiatives aiming to concentrate several of these surgical procedures in high-volume hospitals.14,15

Most of the previous studies that have investigated the effect of volume on outcomes for esophageal resection have been from single states; thus, their findings may not represent the true relationship across the United States.2,3,5,6 For example, a given state may have a single high-volume hospital of excellence with extensive experience in performing the procedure, causing overestimation of the effect of volume on outcomes for the entire state.16,17 Other previous studies on the effect of volume on outcome for esophageal resection have been taken from the national Medicare database, which includes only patients older than 65 years.11 Such a limited population may not represent all patients having esophageal resection, given that mortality has a strong relationship to patient age.13 The current study was designed to investigate the variation in operative mortality across hospital volume groups in a nationally representative sample of US hospitals.

DATA SOURCE

Patient data were derived from 5 years of the Nationwide Inpatient Sample (NIS). This database is maintained by the Agency for Health Care Policy and Research as part of the Healthcare Cost and Utilization Project.18 The NIS is a 20% sample of all hospital discharges in the United States, stratified by geographic region, hospital size, urban vs rural location, and teaching vs nonteaching to be representative of the United States. Any adult patient discharged from an NIS hospital from 1995 to 1999 with an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) primary procedure code for esophageal resection was included in the study.19 Patient demographic information (age, race, and sex), nature of admission (elective, urgent, or emergent), in-hospital mortality, length of stay (LOS), and primary and secondary ICD-9-CM diagnostic codes were obtained from the NIS database. Indication for surgery and comorbid diseases were determined using the secondary ICD-9-CM codes. The Romano modification of the Charlson comorbidity score was used to determine comorbid diseases from the ICD-9-CM codes.2022

OUTCOME VARIABLES

The primary outcome variable was operative mortality (in-hospital mortality). A secondary outcome variable used to represent relative resource utilization was prolonged LOS. Prolonged LOS was defined as greater than the 75th percentile of 20 days and was encoded as a dichotomous variable. Prolonged LOS was used rather than comparing the median LOS, because using the latter method would minimize the effect of outliers. In the setting of high-risk surgery, patients with prolonged LOS are generally those who encounter complications. These outliers are therefore important, and their impact should not be minimized.

HOSPITAL VOLUME

Each hospital included in the NIS has a unique hospital identification number that was used to calculate the number of esophageal resections performed for each year of the study period (1995-1999). Hospital volume thresholds were determined by dividing the patients into 4 approximately equal sized groups based on quartiles of volume. The number of procedures for each quartile are the following: low, fewer than 3 per year; medium, 3 to 5 per year; high, 6 to 16 per year; and very high, more than 16 per year.

STATISTICAL ANALYSIS

Univariate analyses were performed using the χ2 test, the t test, and the Wilcoxon rank sum test where appropriate. Multiple logistic regression was used for the risk-adjusted multivariate analysis for both operative mortality and prolonged LOS. Independent variables used for risk-adjustment included demographics (age, sex, race), indication for surgery (malignant vs benign), extent of resection, admission type (elective, urgent, or emergent), and comorbid diseases. Independent variables with P<.1 in the univariate analysis was included in the multivariate analysis. The multivariate models were tested for goodness of fit using the Hosmer-Lemeshow test, and the area under the receiver operating characteristic curve was calculated.23 All statistical analyses were performed using STATA 7.0 (Stata Corp, College Station, Tex). P<.05 was considered significant in all final analyses.

HOSPITAL CHARACTERISTICS

A total of 3023 adult patients underwent esophageal resection in hospitals included in the NIS from 1995 through 1999. Each year, approximately 200 hospitals performed esophageal resection, with relatively consistent hospital volume profiles (Table 1). For example, during 1999, most hospitals were low-volume (138 hospitals [72%]) or medium-volume (31 hospitals [16%]) with a smaller proportion of high-volume (17 hospitals [9%]) and very high–volume (6 hospitals [3%]).

Table Graphic Jump LocationTable 1. Annual Hospital Volume for Esophageal Resection in the United States From 1995 Through 1999*
PATIENT CHARACTERISTICS

Patients were similar with respect to age and sex across hospital volume groups (Table 2). Patients having their operation at low-volume hospitals were more likely to be of nonwhite race (15% vs 11%) and have an urgent (17% vs 7%) or emergent (10% vs 6%) nature of admission compared with very high–volume hospitals (Table 2). With respect to comorbid diseases, patients at lower-volume hospitals had a higher incidence of chronic obstructive pulmonary disease (15% vs 6%) and diabetes mellitus (11% vs 7%). Patients were generally similar across volume groups with respect to other demographics and comorbid diseases (Table 2).

Table Graphic Jump LocationTable 2. Characteristics of Patients Undergoing Esophageal Resection in a Representative Sample of US Hospitals From 1995 Through 1999*
OPERATIVE MORTALITY

The overall operative mortality for esophageal resection in NIS hospitals from 1995 to 1999 was 8.2%. The operative mortality rate varied more than 3-fold across hospital volume groups (11.8% to 3.7%; P = .001), with a stepwise decrease in mortality with increasing volume (Figure 1). The variation across hospital volume groups was apparent for both benign and malignant indications for esophageal resection (Table 3). Given the absolute risk difference of 8.1% between very high– and low-volume hospitals, only 12 patients would need to be referred to prevent 1 death after esophageal resection.

Place holder to copy figure label and caption

Operative mortality rates after esophageal resection across hospital volume groups in the United States, 1995-1999. There was a significant inverse relationship between hospital volume and mortality (P<.001 for trend).

Graphic Jump Location
Table Graphic Jump LocationTable 3. Variation in Operative Mortality Rates Across Hospital Volume Quartiles for Patient Subgroups Undergoing Esophageal Resection*

The urgency of admission was also a significant predictor of mortality, with an increase across elective (6.6%), urgent (13.8%), and emergent (17.3%) categories (P<.001). When considering only elective admissions (n = 2093), there remains a strong relationship between increasing volume and operative mortality (Table 3). Older age was associated with a higher operative mortality and varied 3-fold from 5.3% to 15.6% across patient age groups (P = .005) (Table 3). Other factors associated with mortality in the univariate analysis included female sex(P = .004), nonwhite race (P = .02), extent of resection (P = .005), history of chronic obstructive pulmonary disease (P = .08), chronic liver disease (P<.001), and chronic renal insufficiency (P = .002).

In the multivariate analysis of operative mortality, having surgery at a low-volume hospital (odds ratio, [OR], 2.9; 95% confidence interval [95% CI], 1.7-4.9; P<.001) or a medium-volume hospital (OR, 2.4; 95% CI, 1.4-4.3; P = .002) was associated with an increased risk of mortality compared with a very high–volume hospital (Table 4). Other significant variables in the multivariate analysis included age of 71 to 80 years (OR, 2.5; 95% CI, 1.6-2.5; P = .001), age greater than 80 years (OR, 3.2; 95% CI, 1.6-6.1; P = .001) urgent admission (OR, 1.9; 95% CI, 1.3-3.0; P = .003), emergent admission (OR, 2.3; 95% CI, 1.4-3.7; P = .001), and nonwhite race (OR, 1.9 95% CI, 1.2-3.0; P = .003).

Table Graphic Jump LocationTable 4. Independent Variables Associated With In-Hospital Mortality After Esophageal Resection in the United States From 1995 Through 1999
LENGTH OF STAY

The overall median LOS was 13 days (interquartile range, 10-20 days). Any patient who had an LOS greater than the 75th percentile of 20 days was considered to have a prolonged LOS. Patients having surgery at very high–volume hospitals were less likely to have prolonged LOS compared with low-volume hospitals (20% vs 28%; P<.001). Nature of admission was a significant univariate predictor of prolonged LOS, with 21% for elective, 33% for urgent, and 50% for emergent admissions (P<.001). Other factors associated with prolonged LOS include increasing age (P<.001), nonwhite race (P<.001), chronic obstructive pulmonary disease (P<.001), malignant indication for surgery (P = .02), and history of chronic renal insufficiency (P<.001).

In the multivariate analysis of prolonged LOS, having surgery at a low-volume hospital (OR, 1.5; 95% CI, 1.1-2.0; P = .01) or a medium-volume hospital (OR, 1.4; 95% CI, 1.0-1.9; P = .03) was associated with an increased risk of prolonged LOS compared with a very high–volume hospital. Independent variables associated with prolonged LOS in the multivariate analysis include ages 61 to 70 years (OR 1.4; 95% CI, 1.1-1.8; P = .01), 71 to 80 years (OR, 1.5; 95% CI, 1.1-2.0; P = .004), older than 80 years (OR, 1.7; 95% CI, 1.0-2.7; P = .03), urgent admission (OR, 2.0; 95% CI, 1.5-2.7; P<.001), emergent admission (OR, 3.6; 95% CI, 2.6-5.0; P<.001), nonwhite race (OR, 1.8; 95% CI, 1.4-2.5; P<.001), and chronic obstructive pulmonary disease (OR, 1.5; 95% CI, 1.1-2.0; P = .007).

In recent years, health care consumers, providers, and policy-makers have become increasingly concerned with the quality of health care delivered in US hospitals.24 For many surgical procedures, outcomes are not uniform across hospitals. Variation in patient outcomes, particularly operative mortality rates, has been shown to relate to hospital volume. The current study documents a 3-fold variation in risk-adjusted operative mortality across a representative sample of US hospitals. Such discrepancy in outcomes across hospitals should not be ignored. Two strategies exist for reducing the variation in mortality after complex surgical procedures. The first is to selectively refer patients to high-volume hospitals. The second is to study the structure and process variables at high-volume hospitals and use these to guide quality improvement at lower-volume hospitals.

The previous statewide investigations of the effect of volume on outcome for esophageal resection have all demonstrated significantly lower mortality rates at higher-volume hospitals, but the magnitude of the effect varies among reports. Dimick et al3 described the outcomes for esophageal resection for 1136 patients undergoing surgery in Maryland from 1984 through 1999. The mortality rates for the current study period were 0.6% at high-volume hospitals compared with 13.5% and 12.1% (P<.001) at medium- and low-volume hospitals, respectively. Overall, in the case-mix adjusted multivariate analysis, there was a 5-fold reduction in mortality at high-volume hospitals (OR, 0.21; 95% CI, 0.10-0.42; P<.001).3

In a similar recent report, Kuo and colleagues5 used data from the Massachusetts Health Data Consortium from 1992 through 2000 to investigate the effect of hospital volume on outcomes for 1193 patients undergoing esophageal resection. They demonstrated a 3.7-fold increase in mortality at low-volume hospitals relative to high-volume hospitals, which persisted after case-mix adjustment (9.2% vs 2.5%; P<.001).5 Maryland and Massachusetts are not representative states, since they both have high-quality providers that perform a large proportion of the states' high-risk surgical procedures. As such, relying on studies from these states would overestimate the effect of volume on outcome after esophageal resection. The present study demonstrates that, although volume is significantly associated with outcome, the magnitude of the volume-outcome effect for the entire United States is not as large as in states with these national referral centers.

Two other recent reports using subsets of the Medicare database provide additional evidence of the volume-outcome effect for esophagectomy. In a comparison of national cancer centers with a sample of community hospitals, Swisher and colleagues6 reported a significant difference in mortality rate between high-volume hospitals(3.0%) and low-volume hospitals (12.2%) for 340 patients in the Health Care Utilization Project. In a subset of the national Medicare database linked to the Surveillance, Epidemiology, and End Results database, Begg and colleagues1 showed a mortality rate of 17.3% at low-volume hospitals compared with 3.4% at high-volume hospitals (P<.001). Studies including only patients older than 65 years are likely to overestimate the effect of volume on outcome for esophageal resection, given the strong relationship between mortality and age.

Hospital volume is a complex variable, likely representing many aspects of the health care system. The quality of health care can be studied according to the structure, process, or outcome of a hospital-patient encounter.26 Structure represents the organization of health care systems in which care is delivered; process is what the health care system does to and for the patient; and outcome is to what extent the process allows the patient to achieve a desired health status. With respect to the volume-outcome relationship, hospital volume is an example of structure. Other structural variables that may contribute to the volume-outcome relationship for esophagectomy include intensive care unit staffing of physicians and nurses.27,28 Patients having surgery at hospitals with daily rounds by an intensive care unit physician– and intensive care unit nurse–to patient ratios of no more than 1:2 have a lower incidence of postoperative complications and decreased resource use.27,28 Further, this effect was independent of hospital volume, which was the most important predictor of in-hospital mortality. Individual surgeon volume and surgical subspecialty are other structural variables that may be related to outcomes and contribute to the hospital volume-outcome effect. Very little information exists regarding health care processes that differ between high- and low-volume hospitals. Future studies on the structure and process of care at high- and low-volume hospitals are needed.

Hospital volume is only a surrogate for the quality of health care. An alternative to selective referral would be to prospectively measure risk-adjusted outcomes and use these as a tool to compare and improve the quality of care across medical centers. Several regional and national quality improvement initiatives have been successful in improving outcomes for cardiac surgery in New York State and Northern New England.29,30 Additionally, morbidity and mortality for noncardiac surgery have declined significantly in Veterans Affairs hospitals as a result of the National Surgical Quality Improvement Project.31 These initiatives have improved quality by identifying hospitals with low risk-adjusted mortality rates and studying the processes of care that lead to superior outcomes at these hospitals. These practices are then shared with other medical centers to improve quality in the region of study.

For example, the primary focus of the National Surgical Quality Improvement Project is to provide the surgeons at each Veterans Affairs hospital with reliable outcomes data that can be used for benchmarking and quality improvement. Such efforts are carried out using the following tools: (1) feedback to each participating hospital regarding the hospital's ratio of observed to expected outcomes relative to other hospitals; (2) periodic assessment of the performance of high and low outlier institutions; (3) provision of self-assessment tools to help surgical leaders identify quality problems; (4) structured site visits to ensure accurate data collection; and (5) identification and dissemination of "best practices" (eg, processes of care that lead to superior outcomes).31

The utility of such quality improvement efforts for esophageal resection depends on whether improvement in the overall delivery of surgical services will carry over to specific procedures. Few operations are performed frequently enough to obtain statistical precision in determining procedure-specific morbidity and mortality rates. This is particularly so for esophageal resection, with some hospitals only performing a few operations per year. In this setting, outcomes measures are of little use in quality improvement for a single procedure. Future studies should determine whether summary quality measures (eg, hospital-level quality determinations) also identify hospitals with superior outcomes for specific procedures.

Using the NIS will yield a generalizable estimate of the volume-outcome effect. However, this database comprises administrative datasets from several different states and is subject to certain limitations. Specifically, the ability to adjust for differing severity of illness and comorbid disease is limited. In the present study, the case-mix adjustment included demographics (age, sex, and race), nature of admission (elective, urgent, or emergent), primary diagnosis, extent of resection, and several comorbid diseases. There is no doubt that using a clinical database with more clinical and physiologic variables would provide for more robust risk adjustment. The profound effect of volume on outcome, however, is unlikely to be affected significantly by such adjustment. Further, there currently does not exist a nationally representative clinical database for general thoracic surgical procedures.

Our study demonstrates that the operative mortality rate for esophageal resection varies across hospitals in the United States. Some of the variation is related to the hospitals' experience with the operation, with increasing volume associated with decreased mortality rates. To improve the quality of care for patients in need of esophageal surgery, patients should either be referred to higher-volume hospitals or quality improvement should be directed at lower-volume hospitals.

Corresponding author: Justin B. Dimick, MD, 1500 E Medical Center Dr, Taubman Center 2210, Ann Arbor, MI 48109-0329 (e-mail: jdimick@umich.edu).

Accepted for publication March 15, 2003.

Begg  CBCramer  LDHoskins  WJBrennan  MF Impact of hospital volume on operative mortality for major cancer surgery. JAMA. 1998;2801747- 1751
PubMed Link to Article
Patti  MGCorvera  CUGlasgow  REWay  LW A hospital's annual rate of esophagectomy influences the operative mortality rate. J Gastrointest Surg. 1998;2186- 192
PubMed Link to Article
Dimick  JBCattaneo  SLipsett  PAPronovost  PJHeitmiller  RF Hospital volume is related to clinical and economic outcomes of esophageal resection in Maryland. Ann Thorac Surg. 2001;72334- 340
PubMed Link to Article
van Lanschot  JJHulscher  JBBuskens  CJTilanus  HWten Kate  FJObertop  H Hospital volume and hospital mortality for esophagectomy. Cancer. 2001;911574- 1578
PubMed Link to Article
Kuo  EYChang  YWright  CD Impact of hospital volume on clinical and economic outcomes for esophagectomy. Ann Thorac Surg. 2001;721118- 1124
PubMed Link to Article
Swisher  SGDeford  LMerriman  KW  et al.  Effect of operative volume on morbidity, mortality, and hospital use after esophagectomy for cancer. J Thorac Cardiovasc Surg. 2000;1191126- 1132
PubMed Link to Article
Miller  JDJain  MKde Gara  CJMorgan  DUrschel  JD Effect of surgical experience on results of esophagectomy for esophageal carcinoma. J Surg Oncol. 1997;6520- 21
PubMed Link to Article
Gordon  TABowman  HMBass  EB  et al.  Complex gastrointestinal surgery: impact of provider experience on clinical and economic outcomes. J Am Coll Surg. 1999;18946- 56
PubMed Link to Article
Dudley  RAJohansen  KLBrand  RRennie  DJMilstein  A Selective referral to high-volume hospitals: estimating potentially avoidable deaths. JAMA. 2000;2831159- 1166
PubMed Link to Article
Hannan  ELO'Donnell  JFKilburn  H  JrBernard  HRYazici  A Investigation of the relationship between volume and mortality for surgical procedures performed in New York state hospitals. JAMA. 1989;262503- 510
PubMed Link to Article
Birkmeyer  JDSiewers  AEFinlayson  EV  et al.  Hospital volume and surgical mortality in the United States. N Engl J Med. 2002;3461128- 1137
PubMed Link to Article
Dimick  JBStanley  JCAxelrod  DA  et al.  Variation in death rate after abdominal aortic aneurysmectomy in the United States: impact of hospital volume, gender, and age. Ann Surg. 2002;235579- 585
PubMed Link to Article
Finlayson  EVBirkmeyer  JD Operative mortality with elective surgery in older adults. Eff Clin Pract. 2001;4172- 177
PubMed
Milstein  AGalvin  RSDelbanco  SFSalber  PBuck  CR  Jr Improving the safety of health care: the leapfrog initiative. Eff Clin Pract. 2000;3313- 316
PubMed
Birkmeyer  JDFinlayson  EVBirkmeyer  CM Volume standards for high-risk surgical procedures: potential benefits of the Leapfrog initiative. Surgery. 2001;130415- 422
PubMed Link to Article
Birkmeyer  JD High-risk surgery—follow the crowd. JAMA. 2000;2831191- 1193
PubMed Link to Article
Birkmeyer  JD Should we regionalize major surgery? potential benefits and policy considerations. J Am Coll Surg. 2000;190341- 349
PubMed Link to Article
Healthcare Cost and Utilization Project (HCUP-6), Nationwide Inpatient Sample, Release 6.  Rockville, Md Agency for Health Care Research and Quality1997;
Not Available, International Classification of Diseases, Ninth Revision, Clinical Modification  Washington, DC Public Health Service, US Dept of Health and Human Services1991;
Romano  PSRoos  LLJollis  JG Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;461075- 1079
PubMed Link to Article
Charlson  MEPompei  PAles  KLMacKenzie  CR A new method for classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40373- 383
PubMed Link to Article
Deyo  RACherkin  DCCiol  MA Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45613- 619
PubMed Link to Article
Lemeshow  SHosmer  DW  Jr A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982;11592- 106
PubMed
Chassin  MGalvin  RW The urgent need to improve health care quality: Institute of Medicine national roundtable on health care quality. JAMA. 1998;2801000- 1005
PubMed Link to Article
Hewitt  MPetitti  D Interpreting The Volume-Outcome Relationship in the Context of Cancer Care.  Washington, DC National Academy Press2001;
Donabedian  A The quality of care: how can it be assessed? JAMA. 1988;2601743- 1748
PubMed Link to Article
Dimick  JBPronovost  PJHeitmiller  RFLipsett  PA Intensive care unit physician staffing is associated with decreased length of stay, hospital cost, and complications after esophageal resection. Crit Care Med. 2001;29753- 758
PubMed Link to Article
Amaravadi  RDimick  JBPronovost  PJLipsett  PA ICU Nurse-to-patient ratio is associated with postoperative complications and resource use after esophagectomy. Intensive Care Med. 2000;261857- 1862
PubMed Link to Article
O'Connor  GTPlume  SKOlmstead  EM  et al.  A regional intervention to improve the hospital mortality associated with coronary artery bypass graft surgery. JAMA. 1996;275841- 846
PubMed Link to Article
Hannan  ELKilburn  H  JrRacz  MShields  EChassin  MR Improving the outcomes of coronary artery bypass surgery in New York State. JAMA. 1994;271761- 766
PubMed Link to Article
Khuri  SFDaley  JHenderson  WG The comparative assessment and improvement of quality of surgical care in the Department of Veterans Affairs. Arch Surg. 2002;13720- 27
PubMed Link to Article

Figures

Place holder to copy figure label and caption

Operative mortality rates after esophageal resection across hospital volume groups in the United States, 1995-1999. There was a significant inverse relationship between hospital volume and mortality (P<.001 for trend).

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Annual Hospital Volume for Esophageal Resection in the United States From 1995 Through 1999*
Table Graphic Jump LocationTable 2. Characteristics of Patients Undergoing Esophageal Resection in a Representative Sample of US Hospitals From 1995 Through 1999*
Table Graphic Jump LocationTable 3. Variation in Operative Mortality Rates Across Hospital Volume Quartiles for Patient Subgroups Undergoing Esophageal Resection*
Table Graphic Jump LocationTable 4. Independent Variables Associated With In-Hospital Mortality After Esophageal Resection in the United States From 1995 Through 1999

References

Begg  CBCramer  LDHoskins  WJBrennan  MF Impact of hospital volume on operative mortality for major cancer surgery. JAMA. 1998;2801747- 1751
PubMed Link to Article
Patti  MGCorvera  CUGlasgow  REWay  LW A hospital's annual rate of esophagectomy influences the operative mortality rate. J Gastrointest Surg. 1998;2186- 192
PubMed Link to Article
Dimick  JBCattaneo  SLipsett  PAPronovost  PJHeitmiller  RF Hospital volume is related to clinical and economic outcomes of esophageal resection in Maryland. Ann Thorac Surg. 2001;72334- 340
PubMed Link to Article
van Lanschot  JJHulscher  JBBuskens  CJTilanus  HWten Kate  FJObertop  H Hospital volume and hospital mortality for esophagectomy. Cancer. 2001;911574- 1578
PubMed Link to Article
Kuo  EYChang  YWright  CD Impact of hospital volume on clinical and economic outcomes for esophagectomy. Ann Thorac Surg. 2001;721118- 1124
PubMed Link to Article
Swisher  SGDeford  LMerriman  KW  et al.  Effect of operative volume on morbidity, mortality, and hospital use after esophagectomy for cancer. J Thorac Cardiovasc Surg. 2000;1191126- 1132
PubMed Link to Article
Miller  JDJain  MKde Gara  CJMorgan  DUrschel  JD Effect of surgical experience on results of esophagectomy for esophageal carcinoma. J Surg Oncol. 1997;6520- 21
PubMed Link to Article
Gordon  TABowman  HMBass  EB  et al.  Complex gastrointestinal surgery: impact of provider experience on clinical and economic outcomes. J Am Coll Surg. 1999;18946- 56
PubMed Link to Article
Dudley  RAJohansen  KLBrand  RRennie  DJMilstein  A Selective referral to high-volume hospitals: estimating potentially avoidable deaths. JAMA. 2000;2831159- 1166
PubMed Link to Article
Hannan  ELO'Donnell  JFKilburn  H  JrBernard  HRYazici  A Investigation of the relationship between volume and mortality for surgical procedures performed in New York state hospitals. JAMA. 1989;262503- 510
PubMed Link to Article
Birkmeyer  JDSiewers  AEFinlayson  EV  et al.  Hospital volume and surgical mortality in the United States. N Engl J Med. 2002;3461128- 1137
PubMed Link to Article
Dimick  JBStanley  JCAxelrod  DA  et al.  Variation in death rate after abdominal aortic aneurysmectomy in the United States: impact of hospital volume, gender, and age. Ann Surg. 2002;235579- 585
PubMed Link to Article
Finlayson  EVBirkmeyer  JD Operative mortality with elective surgery in older adults. Eff Clin Pract. 2001;4172- 177
PubMed
Milstein  AGalvin  RSDelbanco  SFSalber  PBuck  CR  Jr Improving the safety of health care: the leapfrog initiative. Eff Clin Pract. 2000;3313- 316
PubMed
Birkmeyer  JDFinlayson  EVBirkmeyer  CM Volume standards for high-risk surgical procedures: potential benefits of the Leapfrog initiative. Surgery. 2001;130415- 422
PubMed Link to Article
Birkmeyer  JD High-risk surgery—follow the crowd. JAMA. 2000;2831191- 1193
PubMed Link to Article
Birkmeyer  JD Should we regionalize major surgery? potential benefits and policy considerations. J Am Coll Surg. 2000;190341- 349
PubMed Link to Article
Healthcare Cost and Utilization Project (HCUP-6), Nationwide Inpatient Sample, Release 6.  Rockville, Md Agency for Health Care Research and Quality1997;
Not Available, International Classification of Diseases, Ninth Revision, Clinical Modification  Washington, DC Public Health Service, US Dept of Health and Human Services1991;
Romano  PSRoos  LLJollis  JG Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;461075- 1079
PubMed Link to Article
Charlson  MEPompei  PAles  KLMacKenzie  CR A new method for classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40373- 383
PubMed Link to Article
Deyo  RACherkin  DCCiol  MA Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45613- 619
PubMed Link to Article
Lemeshow  SHosmer  DW  Jr A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982;11592- 106
PubMed
Chassin  MGalvin  RW The urgent need to improve health care quality: Institute of Medicine national roundtable on health care quality. JAMA. 1998;2801000- 1005
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