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10.1245/s10434-007-9381-0
Annals of Surgical Oncology 14:1846-1852 (2007)
© 2007 Society of Surgical Oncology
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Original Article

Factors Influencing the Volume-Outcome Relationship in Gastrectomies: A Population-Based Study

David L. Smith, MD1, Linda S. Elting, Dr.PH2, Peter A. Learn, MD1, Chandrajit P. Raut, MD3 and Paul F. Mansfield, MD4

1 Department of Surgery, Wilford Hall Medical Center, 2200 Bergquist Drive/Ste 1, Lackland AFB, Texas 78236, USA
2 Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 447, Houston, Texas 77030, USA
3 Division of Surgical Oncology, Brigham and Women’s Hospital, 75 Francis Street, Boston, Massachusetts 02115, USA
4 Department of Surgical Oncology, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 447, Houston, Texas 77030, USA

Correspondence: Address correspondence and reprint requests to: Paul F. Mansfield, MD; E-mail: pmansfie{at}mdanderson.org


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCE
 
Background: A relationship between hospital procedural volume and patient outcomes has been observed in gastrectomies for primary gastric cancer, but modifiable factors influencing this relationship are not well elaborated.

Methods: We performed a population-based study of 1864 patients undergoing gastrectomy for primary gastric cancers at 214 hospitals. Hospitals were stratified as high-, intermediate-, or low-volume centers. Multivariate models were constructed to evaluate the effect of institutional procedural volume and other hospital- and patient-specific factors on the risk of inhospital mortality, adverse events, and failure to rescue, defined as mortality after an adverse event.

Results: High-volume centers attained an in-hospital mortality rate of 1.0% and failure-to-rescue rate of .7%, both less than one-fifth of that seen at intermediate- and low-volume centers, although adverse event rates were similar across the three volume tiers. In multivariate modeling, treatment at a high-volume hospital decreased the odds of mortality (odds ratio [OR], .22; 95% confidence interval [95% CI], .05–.89), whereas treatment at an institution with a high ratio of licensed vocational nurses per bed increased the odds of mortality (OR, 1.96; 95% CI, 1.04–3.75). Being treated at a hospital with a greater than median number of critical care beds decreased odds of mortality (OR, .46; 95% CI, .25–.81) and failure to rescue (OR, .53; 95% CI, .29–.97).

Conclusions: Undergoing gastrectomy at a high-volume center is associated with lower inhospital mortality. However, improving the rates of mortality after adverse events and reevaluating nurse staffing ratios may provide avenues by which lower-volume centers can improve mortality rates.

Key Words: Gastrectomies • Volume-outcome • Population based • Nursing staffing


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 REFERENCE
 
Operative mortality after gastric resection for cancer has decreased from approximately 15% in the 1970s to approximately half that more recently.1,2 Despite these overall improvements, mortality rates for gastric operations still vary greatly by hospital, surgeon, and geographic region.1,3–8 Much of this variance is attributed to the observed volume-outcome relationship, whereby mortality rates seem to be lower among patients treated by high-volume surgeons or in high-volume hospitals. This effect has been observed across a broad array of surgical procedures, data sets, and time periods.5,9–13 Although the data supporting this relationship are strong, the exact causes for the association remain the subject of speculation. Despite adjustments for case mix and institutional characteristics, volume has remained an important variable, possibly representing a crude surrogate for quality of care. By using these volume-outcome observations, many have advocated for regionalization of services for certain procedures to improve procedure-related mortality on a population scale. However, even these proponents have recognized the potential limitations of regionalization,8,14–16 leaving open the question of whether modification of other factors could reduce mortality rates in lower-volume hospitals.

In an effort to explore this question, we undertook a population-based study of gastrectomies performed for primary gastric cancer in Texas that used a state-administered discharge database for nonfederal hospitals. We hypothesized that a volume-outcome relationship exists at the hospital level but that this association may be influenced by institutional differences that extend beyond the adjusted volume measures. To specifically investigate actionable differences in care among hospitals, we explored not only inhospital mortality, but also the rates of adverse events and failure to rescue (defined as mortality after an adverse event), which have been endorsed as offering a more complete picture of hospital outcomes.17 Furthermore, we investigated the influence of not only well-documented, patient-specific factors, but also less-reported, hospital-specific factors, which might explain the observed differences between higher- and lower-volume institutions.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 REFERENCE
 
Study Design
Approval for this study was obtained from the institutional review board at the M. D. Anderson Cancer Center. By use of data collected from the Texas Hospital Discharge Public Use Data File, we identified a cohort of patients undergoing gastrectomy for primary gastric cancer from January 1, 1999, to December 31, 2001. The data file contains abstracted claims information from all nonfederal hospitals in Texas. The database provided patient-level information on site of treatment, acuity of admission, procedures performed, patient diagnoses at discharge (10 fields using ICD-9 codes), and demographics of age, sex, and race. Patients were screened on the basis of procedure codes for gastrectomy and were included in the data set provided there was a concurrent ICD-9-coded diagnosis of primary gastric cancer. Detailed procedure information was obtained on the type of resection, extent of node dissection, use of diversion, and concurrent splenectomy. Chronic comorbid conditions were coded from the diagnosis fields by the Dartmouth/Manitoba adaptation of the Charlson comorbidity score.18,19 To obtain indicators of socioeconomic status, demographic data were linked to data on education, income, and primary language by zip code from the 2000 United States Census. For each patient, data entered for these variables reflected the median from the zip code of the patient,s home address. For analysis, this information was dichotomized around the median national values obtained from the census (80.4% high school graduates, 82.1% primarily English speaking, $41,994 annual income).

Hospital-level information was obtained from the Center for Medicare and Medicaid Services, Hospital Cost Report Information System and Provider of Services files, as well as from the American Hospital Association Survey. These databases supplied information on number of regular and critical care beds, occupancy rates, annual number of surgical procedures, and teaching status. Ratios of staff to occupied beds were constructed by dividing reported numbers of professional staff (registered nurses, licensed vocational nurses, and respiratory therapists) by the mean annual number of occupied bed days for each hospital. For analysis, these data were dichotomized around the median ratios for each staff position.

Outcomes
Three primary outcome measures were obtained from the Public Use Data File: in-hospital mortality, adverse event rate, and failure to rescue. In-hospital mortality data was available directly from the database. Adverse events were identified by searching the 10 diagnosis fields for specific ICD-9 codes by using algorithms developed for administrative data sets.20 Diagnoses selected for inclusion in the analysis met two criteria. First, the event would need to have a logical relationship with the perioperative period. Second, because of the high false-negative rates that can occur with specific diagnosis codes, we chose complications that have been demonstrated to have a high sensitivity and specificity relative to data abstracted directly from medical records.21–24 This approach resulted in selection of 13 major complications: bacteremia; wound infections; pulmonary compromise; pneumonia; need to reopen the operative site; deep-vein thrombosis; pulmonary embolus; reoperation; postoperative coma or shock; acute myocardial infarction; arrhythmia; cardiac arrest or shock; and splenectomy performed with a subtotal gastric resection. Failure to rescue was coded as an inpatient death after one of the studied adverse events and was therefore defined relative to the other two outcomes.

Statistical Analysis
To categorize gastrectomy volume, institution-specific data on gastrectomies performed per year were plotted and natural cut points were identified. A clear cut point was identified at >15 gastrectomies per year, defining the high-volume hospitals (n = 2). In the absence of other apparent cut points, the remaining hospitals were divided roughly in half such that no group contained less than 10% of patients, thereby defining the intermediate-volume hospitals (3–15 gastrectomies per year, n = 78) and the low-volume hospitals (fewer than 3 gastrectomies per year, n = 134). In similar analyses, many authors have chosen to categorize volume,4,5,8,12,25,26 whereas others have studied it as a continuous variable.10,11 Although some statistical accuracy is sacrificed with categorization, we thought that this approach offered more concrete thresholds around which policy discussions may be conducted.

Multivariate models for each outcome defined risk, with the patient as the unit of analysis. The effect of patient- and hospital-specific factors on outcome measures was initially studied in univariate analysis by the Pearson {chi}2 test for categorical variables and the Student t-test for continuous variables. Variables with tests of association achieving a P value < .10 were included in the subsequent multivariate analysis. Random-effect logistic regression was used to adjust for clustering of outcomes within hospitals. All P values are two-sided. Statistical analyses were performed under the direction of the study biostatistician (L.S.E.) by Stata software (StataCorp, College Station, TX).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 REFERENCE
 
Over the 3-year study period, 1864 patients at 214 hospitals underwent a gastric resection for primary gastric cancer. Women comprised 38.9% of the cohort, and 28.0% were older than 75 years. Modified Charlson comorbidity scores were 0 for 67.7%, 1 for 25.8%, and ≥2 for the remaining 6.5% of patients. Most gastric operations (63.0%) were performed as partial resections, and of these, Billroth II reconstructions were most commonly performed. Node dissection of any degree was coded in only 10.2%, and the gastric procedure was accompanied by splenectomy in 11.5%.

By use of the stratification scheme described previously, the three outcome measures were calculated for each volume stratum and subdivided by patient comorbidity status (Table 1Go). In general, patients with preexisting comorbidities had higher rates of poor outcomes. With respect to volume, gastrectomy outcomes were similar between the low-and intermediate-volume hospitals, but hospitals in both strata differed from high-volume hospitals in rates of inpatient mortality and failure to rescue. These relationships persisted regardless of the patients’ comorbidities. Adverse event rates, however, were not markedly different among the strata. Lengths of stay were also similar across strata. Rates of transfer to other hospitals decreased from low- to high-volume strata, whereas rates of discharge with home health care demonstrated the opposite trend.


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  		TABLE 1. Outcomes of gastrectomy by hospital volumea
 
As shown in Table 2Go, patient demographics and some details of patient care varied greatly among strata. In general, low- and intermediate-volume hospitals performed gastrectomies in an older patient population with more comorbidities and more frequent need for emergent admission than high-volume hospitals. Conversely, total gastrectomies were performed far more frequently in high-volume hospitals (54.1%) than in either intermediate-volume (37.3%) or low-volume hospitals (25.9%, P < .001). Also, node dissections were performed more frequently in high-volume hospitals (38.1% vs. 6.5% at intermediate-volume and 5.4% at low-volume hospitals, P < .001). Splenectomy rates and presence of metastases were not greatly different between strata. It was also observed that most of the patients treated at low- and intermediate-volume centers lived within 100 miles of a high-volume center (64% and 62%, respectively), but less than half of those treated at high-volume centers did (38%, P < .001).


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  		TABLE 2. Characteristics of patients by location of treatment
 
Comparisons performed at the hospital level again revealed notable differences (Table 3Go). Moving from low-volume to high-volume strata, mean occupancy rates and surgical volumes increased, with high-volume centers supporting twice the number of total annual surgical procedures seen at intermediate-volume hospitals and over seven times the number of procedures seen at low-volume hospitals (P < .001). Additionally, high-volume hospitals were all teaching hospitals based in large metropolitan areas. Differences in staffing ratios were not pronounced, although high-volume hospitals tended to support more registered nurses per occupied bed (2.63 vs. 1.74 at intermediate-volume and 1.61 at low-volume hospitals, P = .07).


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  		TABLE 3. Characteristics of hospitalsa
 
By use of results from patient-level univariate analyses, models were constructed to estimate the contributions of patient and hospital characteristics to observed outcomes. Given the similarity in results between low-volume and intermediate-volume hospitals, the volume variable was dichotomized such that patients were observed to be treated in a high-volume hospital or not. Results of the analyses for each outcome are demonstrated on Table 4Go. For all three outcomes, event odds were increased by age older than 75 years and performance of a total gastrectomy. Being treated at a high-volume hospital strongly decreased the odds of inpatient mortality to .22 (95% confidence interval [95% CI], .05–.89), but it did not affect the likelihood of an adverse event. Treatment at a high-volume hospital also seemed to decrease the odds of failure to rescue, to .22 (95% CI, .04–1.13), although this relationship did not attain statistical significance (P = .07). Potential for an adverse event was increased by patient-specific factors such as comorbid conditions and emergency admission. In contrast, the chances of failure to rescue were affected less by patient-specific factors and more by hospital-specific factors, particularly the number of critical care beds (odds ratio [OR], .53; 95% CI, .29–.97). Reliance on high ratios of licensed vocational nurse to beds had not only a marked effect on the inpatient mortality (OR, 1.96; 95% CI, 1.04–3.75), but also trended strongly toward affecting failure to rescue (OR, 2.00; 95% CI, .99–4.05).


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  		TABLE 4. Results of multivariate models for factors associated with inpatient mortality, adverse event, and failure to rescue
 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
REFERENCE
 
Like previous studies, we have demonstrated that although lower-volume hospitals see an older, sicker, and more acutely presenting patient population, their inpatient mortality rates exceed those seen at high-volume hospitals even when these factors are accounted for in multivariate models. However, our findings extend beyond the usual identification of this volume-outcome relationship to identifying possible areas of improvement.

First, the inclusion of adverse event and failure-to-rescue rates as primary outcomes offers an expanded view of the issue. Although inpatient mortality rates differ among strata, the adverse event rates do not, at least for the complications for which we screened patients. As observed by Silber et al.,17 the adverse event rate seems driven more by patient characteristics, such as comorbidities, acuity of presentation, and age. However, despite similar complication rates, volume trends toward exerting a protective effect against failure to rescue. This observation suggests that a major component of the differences in mortality rates might be accounted for by the ability to identify and treat adverse events after surgery.

We identified two key hospital characteristics that influenced failure to rescue: critical care beds and nurse staffing. A higher number of critical care beds positively affected the odds of failure to rescue. The number of critical care beds may be a surrogate for the number of patients with complex conditions seen and treated at the institution, and this may therefore reflect the effect of experience on this outcome. Alternatively, the number of critical care beds may represent the degree of technological advancement and availability of expertise present at the institution (such as interventional radiology), similar to the way the presence of organ transplantation and cardiac surgery programs has been used as a surrogate for technological advancement.27,28 However, making this link definitive was beyond the scope of this study.

In contrast, the reasons for the effect of nurse staffing on failure to rescue are easier to infer. Our analysis suggests that heavy reliance on licensed vocational nurses to deliver patient care is associated with worse outcomes. Logically, nurses play an important role in the early identification of postoperative complications, and patterns of nurse staffing have been clearly demonstrated to influence mortality and failure to rescue in other studies.27,28 It is possible that postgastrectomy care provided by nurses with higher levels of training may improve outcomes in hospitals with high mortality rates. However, given projected nationwide nursing shortages,29 this may be an easier solution to name than to implement.

We also found that advanced age and undergoing a total gastrectomy strongly influenced all three outcomes. Age is a well-recognized risk factor for worse outcomes, and although it should not exclude patients from consideration for gastric resection, its effect should be recognized. Poorer outcomes from total gastrectomy are also not unanticipated, and they may be influenced by both the underlying process that require a larger resection and the inherent risks of a more complex operation. However, the observation that high-volume hospitals performed the highest percentage of total gastric resections is noteworthy because their mortality rates remained the lowest. Institutional variations in how the decision to perform a total gastrectomy is made may account for this finding and may be another potentially actionable difference.

Although we consider our findings reliable and instructive, we acknowledge some limitations to the design of this study. With concerns about the accuracy of coded information and the ability to control for case mix, the limitations of using administrative data sets are well documented, but they have nevertheless been demonstrated to provide valid conclusions that encourage further detailed assessments.20–24 We have used methods to limit biases inherent in the use of administratively coded data. Furthermore, our results are consistent with studies that use chart-derived data,2–4 thus encouraging confidence in our results. The database also limited our mortality analysis to inpatient mortality, which is clearly not as sensitive as 30-day mortality. Importantly, Jencks et al.30 observed that inpatient mortality rates are greatly influenced by length of stay in medical patients. However, that study, which stratified hospitals by death rates, also observed that differences initially seen in inpatient mortality rates remained conserved to 45 days. Similar results have been observed in surgical populations at hospitals stratified by actual surgical volume,11,31,32 suggesting that inpatient mortality, although not a perfect representative of postoperative mortality, remains a good indicator of mortality results. Finally, the adverse event analysis is limited to those that can be reliably linked to actual events and does not encompass the full range of potential postoperative complications. The results of this study might be inaccurate if the relative rates of complications not analyzed were to vary markedly from the rates of the studied events. However, the adverse event diagnoses we used represent many of the most common postoperative complications, are unlikely to have been present before the operation, and likely account for a preponderance of major complications expected after gastrectomy.

Ultimately, our results again raise questions about regionalization or selective referral practices in the delivery of certain types of health care, policies that have been discussed in great detail by others.5,15,33,34 However, many authors note that regionalization would likely increase health care costs, might be challenging to implement in the fragmented U.S. health care system, and may actually run counter to patient preference.8,14,16,35 We observed that although most patients at low- and intermediate-volume centers lived within 100 miles of a high-volume center, they still chose to obtain care at the lower-volume institutions. As a result, attention must necessarily be given to modifiable hospital-specific factors that can improve outcomes. To this end, we have demonstrated that postcomplication response is a key factor in mortality from gastric resections for cancer, and that nurse staffing may have an influence on the formulation of this response. Further investigation into the effect of staffing factors and other details in recognizing and treating postoperative complications may identify avenues for improvement. In addition, these data suggest that it may be possible to create a decision algorithm to decide which patients should be moved to high-volume centers.


   ACKNOWLEDGMENTS
 
Supported by William Randolph Hearst Foundations and the Carlos Cantu Foundation Fund for Research in Surgical Oncology.

Received for publication August 30, 2006. Accepted for publication January 31, 2007.


   REFERENCE
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCE
 

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