Abstract
INTRODUCTION
Hospital volume has been correlated with improved outcomes in oncologic care and complex surgical procedures. Similarly, successful free-flap breast reconstruction requires multidisciplinary care coordination and subspecialty surgical training. We sought to determine the relationship between overall annual hospital volume and perioperative outcomes following free-flap breast reconstruction.
METHODS
Free-flap breast reconstruction patients (n=7,991) were identified at 1,907 centers using the HCUP-NIS database. Logistic regression with restricted cubic splines (RCS) characterized the association of hospital volume (total discharges/year) with systemic, surgical, and microsurgical complications. Patients were categorized as being treated at low versus high-volume hospitals based on identified threshold volumes, and the association with the incidence of complications was estimated.
RESULTS
Patients were predominately White (64.7%), privately insured (77.6%), received care at urban teaching hospitals (86.9%), underwent delayed reconstruction (63.2%), and had no post-operative complications (80%). Initially, RCS analysis suggested potential threshold volumes of 13,018 (95% CI:7,468-14,512) and 7,091 (95% CI:5,396–9,918) discharges/year, at which the risk for developing systemic and microsurgical complications may change, respectively. However, further patient-level evaluation of treatment at low versus high-volume hospitals demonstrated that hospital volume did not predict the risk of developing perioperative systemic (OR,1.28; 95% CI:0.75-2.18, p=0.36) or microsurgical complications (OR,1.06; 95% CI:0.78-1.44, p=0.73).
CONCLUSION
Perioperative complications after free-flap breast reconstruction did not differ between patients treated at low versus high-volume hospitals after in-depth multi-pronged analysis. Patient outcomes are more likely associated with surgeon and programmatic experience. Overall annual hospital volume should not serve as a proxy for high quality breast free-flap care.
Keywords: Breast reconstruction, Hospital-Volume, Microsurgery, Free Flap, Outcomes
INTRODUCTION
Prior literature has established a relationship between hospital volume and cancer outcomes, highlighting improvements in treatment-related complications and survival with greater experience of care.1–7 In breast cancer, high volume centers have been associated with an 11% reduction in mortality and improved 5 and 10-year overall survival when compared to low volume centers;4,5 a finding likely attributed to the infrastructure required to provide tailored multidisciplinary care.4,5 Similarly, significant reductions in operative mortality have been demonstrated when complex surgical procedures (e.g. carotid endarterectomy, pancreatectomy, abdominal aortic aneurysm repair) are performed at high volume hospitals.8 This volume-outcome relationship has prompted the development of national initiatives to regionalize care for patients undergoing complex surgical procedures.8,9
For the 5-15% of post-mastectomy patients pursuing breast reconstruction, high quality outcomes depend upon complex interdisciplinary care, individual surgeon expertise, and a resource-intensive infrastructure.10–13 Although surgeon and hospital procedural volumes have been associated with a lower incidence of perioperative complications amongst women who undergo implant or free-flap breast reconstruction;10–13 case-specific numbers may not adequately account for the broader programmatic expertise required for successful reconstructive surgery. In actuality, large volume hospitals may have more access to flap-trained operating room nurses, flap monitoring devices, fellows/residents, and equipment to assess flap perfusion.10,14 Thus, overall annual hospital volume may better reflect the components of care required for high quality outcomes following free-flap breast reconstruction.15,16 We sought to determine the association of overall annual hospital volume on perioperative complications following free-flap breast reconstruction, and to determine if a hospital-level threshold volume exists that improves clinical outcomes for women after mastectomy.
METHODS
Data Source
The data source for this study was the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) database. This database represents a 20% stratified random sample of all inpatient community hospital encounters (short-term, non-Federal, general, and other hospitals, excluding hospital units of other institutions) in the United States, and is designed to provide a representative sample of overall health care use.17
Study Design
Adult female breast cancer patients who underwent free-flap breast reconstruction from 2012-2016 were identified. Patients who underwent treatment between 2012 through the third quarter of 2015 were identified using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and the ICD-9-CM procedure codes. Patients treated during the fourth quarter of 2015-2016 were identified using the ICD-10-CM diagnosis codes and the ICD-10-CM procedure codes. Figure 1 outlines exclusion and inclusion criteria. Prior to, and while performing the statistical analyses, this study was confirmed to adhere to published guidelines regarding the appropriate use of the HCUP-NIS dataset.18,19
Figure 1:
Patient Selection Chart with Inclusion and Exclusion Criteria
Definition of Variables
Independent variables analyzed included: age, length of hospital stay, race/ethnicity, insurance status, year of surgery, facility location, facility type, medical comorbidities, timing of reconstruction (i.e. delayed vs. immediate), hospital size, and the total number of discharges/year at a given facility. Thirty-day complications were grouped into systemic, surgical, and microsurgical complications based on previously published criteria from studies utilizing the HCUP-NIS dataset.10,14 Systemic complications were defined as those involving the urinary, digestive or respiratory systems and/or a deep vein thrombosis or stroke. Surgical complications included wound dehiscence, wound infection, flap revision, hemorrhage/hematoma, microvascular complications, the need for transfusion, and seroma. Microsurgical complications were defined as in-house microvascular complications and flap revisions requiring exploration/anastomotic revision. The ICD-9 and ICD-10 codes used to define these individual variables are listed in Appendix, Supplemental Digital Content 1, INSERT HYPER LINK. The Charlson Comorbidity Index (CCI) was used to classify patient comorbidities.20,21 Annual hospital volume was defined as the total number of patient discharges/year, regardless of procedure or diagnosis. HCUP-NIS does not track hospitals across years, therefore annual volume was estimated separately for each hospital for each included year. The primary endpoint for this study was the incidence of systemic, surgical, or microsurgical complications following surgery.
Statistical Analysis
Patient characteristics were summarized with N (%) for categorical variables, and median (interquartile range, IQR) for continuous variables. Logistic regression models examined the association between total annual discharges, as a continuous variable, and the aforementioned complications after adjustment for known covariates. These models were built in the Generalized Estimating Equations (GEE) framework with an exchangeable correlation structure to account for the correlation of patients treated at the same hospital in a given year. Logistic regression models with restricted cubic splines (RCS) were created to characterize the functional association of hospital volume with systemic, surgical, and microsurgical complications.22 This method allows for a flexible multivariable model that accounts for the nonlinear relationship of total annual discharges with the odds of a complication without assuming the existence of potential cut points.23 Three-, 4-, and 5-knot models were examined and the best models were selected based on the Akaike Information Criteria.24 If the association between overall annual hospital volume and complications was found to be non-linear, a critical point of overall annual discharge volume was estimated with a Monte Carlo Markov Chain Procedure that corresponded to a change in the risk for developing a systemic, surgical, or microsurgical complication. A bootstrap simulation was used with 1,000 iterations and the mean critical point was estimated from all iterations, along with the bootstrap 95% confidence interval. Hospital volume groups were then created based on these estimated critical point values and additional logistic regression models were applied to estimate the association of being treated at a low or high-volume facility with the development of these complications. If instead the association was found to be linear, no threshold volume was identified that corresponded to a change in the risk for developing a complication, and no further categorization was conducted.
All statistical analyses were conducted using SAS, version 9.4 (SAS Institute, Cary, NC). No adjustments were made for multiple comparisons. The significance value was set at p<0.05 for all statistical tests. Only patients with complete data were included in each analysis.
RESULTS
Patient Characteristics
Overall, 7,991 patients and 1,907 facilities were included in this study. The baseline unadjusted patient demographics and surgical characteristics are shown in Table 1. Breast reconstruction patients predominately displayed the following characteristics: White race, private insurance status, a CCI score of ≥ 2, care at an urban teaching hospital, and breast reconstruction surgery in the South. Most patients underwent a delayed reconstructive procedure (n=5,050, 63.2%) and did not experience a post-operative complication (n=6,395, 80%).
Table 1:
Characteristics of the Patient Cohort
| All Patients | |
|---|---|
| N=7,991 (100%) | |
| Age – Median (IQR) | 51 (44 - 57) |
| Length of Stay – Median (IQR) | 4 (3 - 5) |
| Race/Ethnicity | |
| White | 5,167 (64.7%) |
| Black | 1,142 (14.3%) |
| Asian/Pacific Islander | 292 (3.7%) |
| Hispanic | 732 (9.2%) |
| Other/Unknown | 658 (8.2%) |
| Insurance | |
| Private | 6,205 (77.6%) |
| Medicaid | 717 (9%) |
| Medicare | 687 (8.6%) |
| Self-pay | 126 (1.6%) |
| Other/unknown | 256 (3.2%) |
| Charlson Comorbidity Index | |
| 0 | 3,411 (42.7%) |
| 1 | 666 (8.3%) |
| ≥2 | 3,914 (49%) |
| Reconstruction Type | |
| Delayed | 5,050 (63.2%) |
| Immediate | 2,941 (36.8%) |
| Systemic Complications | |
| Stroke | 0 (0%) |
| Urinary | 11 (0.1%) |
| DVT | 203 (2.5%) |
| Digestive | 49 (0.6%) |
| Respiratory | 117 (1.5%) |
| Surgical Complications | |
| Wound Dehiscence | 38 (0.5%) |
| Wound Infection | 50 (0.6%) |
| Flap Revision | 48 (0.6%) |
| Hemorrhage/Hematoma | 272 (3.4%) |
| Microvascular complication | 264 (3.3%) |
| Transfusion | 687 (8.6%) |
| Seroma | 61 (0.8%) |
| Incidence of Any Systemic Complication | 636 (8%) |
| Incidence of Any Microsurgical Complication | 274 (3.4%) |
| Incidence of Any Surgical Complication | 1,157 (14.5%) |
| Incidence of Any Complication | 1,596 (20%) |
| Hospital Type | |
| Urban nonteaching/Rural | 1,046 (13.1%) |
| Urban teaching | 6,945 (86.9%) |
| Hospital Location | |
| Midwest | 1,210 (15.1%) |
| Northeast | 1,968 (24.6%) |
| South | 3,505 (43.9%) |
| West | 1,308 (16.4%) |
Impact of Annual Hospital Volume on Systemic Complications Following Free-Flap Breast Reconstruction
As shown in Table 2, annual hospital volume was not independently predictive of an increased risk for developing systemic complications after surgery (OR,1.00; 95% CI:0.98-1.03; p=0.82). Multivariate regression did reveal that on a patient level, a CCI score of 1 vs. 0 (OR, 1.90; 95% CI:1.47-2.46, p<0.001), longer length of hospital stay (OR,1.32; 95% CI:1.25-1.39, p<0.001), and older age (OR,1.01; 95% CI:1.00-1.02, p=0.01) were associated with an increased risk for developing systemic complications that involved the urinary, digestive, or respiratory systems and/or a deep vein thrombosis or stroke.
Table 2:
Logistic model predicting any systemic complication (N=7991, event=636), surgical complication (N=7991, event=1157), or microsurgical complication (N=7991, event=274)
| Systemic | Surgical | Microsurgical | ||||
|---|---|---|---|---|---|---|
| Odds Ratio (95% CI) | P | Odds Ratio (95% CI) | P | Odds Ratio (95% CI) | P | |
| Annual Discharge /1k | ||||||
| Continuous | 1.00 (0.98 - 1.03) | 0.82 | 1.00 (0.98 - 1.02) | 0.71 | 1.01 (0.97 - 1.05) | 0.77 |
| CDCC Score | ||||||
| 0 | -REF- | -REF- | -REF- | |||
| 1 | 1.90 (1.47 - 2.46) | <0.001 | 1.00 (0.79 - 1.28) | 0.97 | 1.39 (0.91 - 2.10) | 0.12 |
| ≥ 2 | 1.02 (0.85 - 1.23) | 0.83 | 0.98 (0.85 - 1.12) | 0.77 | 1.06 (0.81 - 1.39) | 0.67 |
| Reconstruction Type | ||||||
| Not delayed | -REF- | -REF- | -REF- | |||
| Delayed | 1.11 (0.93 - 1.34) | 0.25 | 1.13 (0.98 - 1.30) | 0.08 | 1.47 (1.11 - 1.94) | 0.01 |
| Length of Stay | ||||||
| Continuous | 1.32 (1.25 - 1.39) | <0.001 | 1.48 (1.40 - 1.55) | <0.001 | 1.25 (1.17 - 1.34) | <0.001 |
| Age | ||||||
| Continuous | 1.01 (1.00 - 1.02) | 0.01 | 1.00 (0.99 - 1.00) | 0.47 | 0.98 (0.97 - 1.00) | 0.03 |
| Race | ||||||
| White | -REF- | -REF- | -REF- | |||
| Asian/PI | 0.57 (0.33 - 0.99) | 0.05 | 1.23 (0.89 - 1.70) | 0.20 | 0.63 (0.28 - 1.42) | 0.26 |
| Black | 1.04 (0.81 - 1.33) | 0.76 | 1.28 (1.06 - 1.54) | 0.01 | 1.00 (0.70 - 1.44) | 0.99 |
| Hispanic | 1.00 (0.73 - 1.38) | 0.98 | 0.94 (0.75 - 1.17) | 0.59 | 0.74 (0.46 - 1.21) | 0.24 |
| Other/Unknown | 0.94 (0.69 - 1.28) | 0.71 | 1.02 (0.80 - 1.31) | 0.86 | 0.88 (0.56 - 1.40) | 0.60 |
| Insurance Status | ||||||
| Private | -REF- | -REF- | -REF- | |||
| Medicaid | 0.87 (0.64 - 1.18) | 0.37 | 0.90 (0.71 - 1.13) | 0.36 | 0.76 (0.48 - 1.18) | 0.22 |
| Medicare | 1.09 (0.81 - 1.47) | 0.58 | 1.04 (0.81 - 1.34) | 0.75 | 1.05 (0.66 - 1.67) | 0.83 |
| Other/Unknown | 1.58 (1.04 - 2.42) | 0.03 | 1.21 (0.86 - 1.72) | 0.27 | 0.84 (0.40 - 1.74) | 0.63 |
| Self-pay | 0.37 (0.13 - 1.03) | 0.06 | 0.91 (0.51 - 1.60) | 0.74 | 0.88 (0.31 - 2.51) | 0.81 |
| Facility Type | ||||||
| Urban teaching | -REF- | -REF- | -REF- | |||
| Urban nonteaching/Rural | 0.80 (0.60 - 1.07) | 0.13 | 1.34 (1.08 - 1.66) | 0.01 | 0.88 (0.58 - 1.32) | 0.54 |
| Facility Location | ||||||
| West | -REF- | -REF- | -REF- | |||
| Midwest | 1.16 (0.86 - 1.57) | 0.34 | 1.01 (0.79 - 1.31) | 0.91 | 1.12 (0.72 - 1.75) | 0.61 |
| Northeast | 1.00 (0.76 - 1.33) | 0.98 | 0.95 (0.75 - 1.21) | 0.68 | 0.76 (0.48 - 1.21) | 0.25 |
| South | 0.89 (0.68 - 1.15) | 0.37 | 1.03 (0.85 - 1.27) | 0.74 | 1.21 (0.84 - 1.73) | 0.30 |
All models account for the correlation of patients treated at the same hospital.
p<0.05 indicates statistical significance
Restricted cubic spline analysis was then used to assess the relationship of overall annual hospital volume with the probability of developing systemic complications after surgery, and to identify a threshold volume associated with an increased risk for developing systemic complications. Initially, RCS analysis revealed an association between annual hospital volume and the development of systemic complications (non-linear association, p<0.001) (Figure 2, Top). An overall annual hospital volume of 13,018 (95% CI:7,468-14,512) discharges/year was estimated as the value threshold at which there was a significant change in the risk for developing systemic complications. However, when patients were categorized as receiving care at facilities with a low-volume (<13,018 discharges/year) and a high-volume (≥13,018 discharges/year) no difference in the odds of experiencing systemic complications were found (Table 3).
Figure 2:
Restricted cubic spline analysis displaying the association of total hospital volume with the odds of developing systemic (top), surgical (middle), and microsurgical (bottom) complications, after adjustment for known covariates
Table 3:
Logistic model predicting any systemic complication (N=7991, event=636) or microsurgical complication (N=7991, event=274) with annual volume categorized based on estimated thresholds
| Systemic | Microsurgical | |||
|---|---|---|---|---|
| Odds Ratio (95% CI) | P | Odds Ratio (95% CI) | P | |
| Annual Discharge /1k | ||||
| <Threshold* | -REF- | -REF- | ||
| ≥Threshold* | 1.28 (0.75 - 2.18) | 0.36 | 1.06 (0.78 - 1.44) | 0.73 |
| CDCC Score | ||||
| 0 | -REF- | -REF- | ||
| 1 | 1.9 (1.468 - 2.458) | <0.001 | 1.39 (0.91 - 2.10) | 0.13 |
| ≥ 2 | 1.02 (0.85 - 1.23) | 0.83 | 1.06 (0.81 - 1.39) | 0.68 |
| Reconstruction Type | ||||
| Not delayed | -REF- | -REF- | ||
| Delayed | 1.11 (0.93 - 1.34) | 0.25 | 1.47 (1.11 - 1.95) | 0.007 |
| Length of Stay | ||||
| Continuous | 1.315 (1.247 - 1.386) | <0.001 | 1.25 (1.17 - 1.35) | <0.001 |
| Age | ||||
| Continuous | 1.01 (1.00 - 1.02) | 0.01 | 0.99 (0.97 - 1.00) | 0.03 |
| Race | ||||
| White | -REF- | -REF- | ||
| Asian/PI | 0.58 (0.34 - 1.00) | 0.05 | 0.63 (0.28 - 1.42) | 0.26 |
| Black | 1.05 (0.82 - 1.34) | 0.72 | 1.00 (0.70 - 1.44) | 1.00 |
| Hispanic | 1.01 (0.74 - 1.39) | 0.94 | 0.74 (0.46 - 1.21) | 0.23 |
| Other/Unknown | 0.95 (0.70 - 1.30) | 0.75 | 0.88 (0.56 - 1.39) | 0.59 |
| Insurance Status | ||||
| Private | -REF- | -REF- | ||
| Medicaid | 0.87 (0.64 - 1.18) | 0.36 | 0.76 (0.49 - 1.19) | 0.23 |
| Medicare | 1.09 (0.81 - 1.47) | 0.58 | 1.05 (0.66 - 1.67) | 0.84 |
| Other/Unknown | 1.59 (1.04 - 2.43) | 0.03 | 0.84 (0.40 - 1.74) | 0.64 |
| Self-pay | 0.37 (0.13 - 1.04) | 0.06 | 0.88 (0.31 - 2.52) | 0.82 |
| Facility Type | ||||
| Urban teaching | -REF- | -REF- | ||
| Urban nonteaching/Rural | 0.80 (0.61 - 1.05) | 0.11 | 0.87 (0.59 - 1.29) | 0.50 |
| Facility Location | ||||
| West | -REF- | -REF- | ||
| Midwest | 1.16 (0.86 - 1.57) | 0.32 | 1.12 (0.72 - 1.75) | 0.62 |
| Northeast | 0.99 (0.75 - 1.31) | 0.94 | 0.76 (0.48 - 1.20) | 0.24 |
| South | 0.88 (0.67 - 1.14) | 0.34 | 1.21 (0.84 - 1.73) | 0.32 |
Threshold defined as 7,091 for microsurgical complications and 13,018 for systemic complications.
All models account for the correlation of patients treated at the same hospital.
p<0.05 indicates statistical significance
Impact of Annual Hospital Volume on Surgical and Microsurgical Complications Following Free-Flap Breast Reconstruction
Annual hospital volume was not found to be independently predictive for developing surgical (OR,1.00; 95% CI:0.98-1.02, p=0.71) or microsurgical complications (OR,1.01; 95% CI:0.97-1.05, p=0.78) after surgery (Table 2). On a patient level, multivariate regression demonstrated that a longer length of hospital stay (OR,1.48; 95% CI:1.40-1.55, p<0.001), black race (OR,1.28; 95% CI:1.06-1.54, p=0.009), and care at an urban nonteaching or rural hospital (OR,1.34; 95% CI:1.08-1.66, p=0.008) were associated with an increased risk for experiencing surgical complications. In addition, delayed breast reconstruction (OR,1.47; 95% CI:1.11-1.94, p=0.007) and a longer length of hospital stay (OR,1.23; 95% CI:1.17-1.35, p<0.001) were found to be associated with an increased risk for experiencing microsurgical complications.
Restricted cubic spline analysis then assessed the relationship of overall annual hospital volume with the probability of developing surgical and microsurgical complications and identified a threshold volume associated with an increased risk for developing these complications. RCS did not reveal an association between annual hospital volume and the development of surgical complications (non-linear association, p=0.99), suggesting that the effect of overall annual hospital volume on the risk for developing surgical complications after free-flap breast reconstruction is linear and therefore no threshold value exists (Figure 2, Middle). With respect to microsurgical complications, RCS analysis demonstrated an association between hospital volume and the risk for developing microsurgical complications (p=0.01) (Figure 2, Bottom), and an overall annual hospital volume of 7,091 (95% CI:5,396–9,918) discharges/year was estimated as the threshold value where there was a significant change in the risk for developing microsurgical complications. However, when patients were categorized as having received care at low-volume (<7,091 discharges/year) vs. high-volume hospitals (≥7,091 discharges/year), no differences in the odds of experiencing microsurgical complications were found (Table 3). These findings further suggested that overall annual hospital volume does not influence the risk of microsurgical complications after free-flap breast reconstruction. See Figure, Supplemental Digital Content 2, which shows the representative ROC Curves for Table 2. Left: Any systemic complication; Middle: Any surgical complication; Right: Any microsurgical complication, INSERT HYPER LINK. See Figure, Supplemental Digital Content 3, which shows the representative ROC Curves for Table 3. Left: Any surgical complication; Right: Any microsurgical complication, INSERT HYPER LINK.
DISCUSSION
For women seeking reconstruction after mastectomy, the decision of where to receive surgical care is variable and often influenced by patient anecdotes.25,26 At the time of diagnosis, women with breast cancer face complex treatment decisions spanning choices for oncologic surgery and adjuvant treatments. Furthermore, women who choose post-mastectomy breast reconstruction must decide what type of reconstruction to pursue, and where to receive their reconstructive care. Thus, it is essential to define objective quality metrics that can be used to compare the processes and outcomes of breast reconstruction, providing patients with simple and accurate measures on where to receive complex surgical care.
In our attempt to account for the entire continuum of breast reconstruction, including subspecialty microsurgical expertise and multidisciplinary breast cancer care, we performed an in-depth and complex analysis comparing the risk of free-flap associated complications based on overall annual hospital volume. We found no functional association between overall annual hospital volume and surgical complications on both multivariable logistic regression and restricted cubic spline analysis. Although we identified threshold volumes that correlated with a suggested change in the risk for systemic (13,018 discharges/year) and microsurgical complications (7,091 discharges/year), further analysis demonstrated that when patients were categorized by these threshold values as receiving treatment at low versus high-volume hospitals, there was no difference in the patient-level risk of developing systemic or microsurgical complications.
Previous studies have analyzed the influence of surgeon and procedural volume on breast reconstruction outcomes; however, these studies have been limited in their definition of “high” and “low” volume, using arbitrary cut-points that may not be clinically relevant, and are limited in their analysis of microsurgical breast reconstruction.7,10,14 Albornoz et al. utilized the HCUP-NIS dataset to assess the relationship of procedural volume and outcomes following all forms of autologous breast reconstruction. The authors demonstrated an inverse relationship with hospital procedural volume and surgical and systemic complications. Furthermore, the authors emphasized that this relationship is stronger for surgery-specific complications. This finding may be attributed to autologous breast reconstruction patients representing a relatively healthy patient population that is at a lower risk for experiencing systemic complications as compared to higher risk patients who may instead be offered implant-based reconstruction or no breast reconstruction.14 Biling et al. conducted a similar analysis using the HCUP-NIS dataset focusing on free-flap breast reconstruction and supported the finding that higher procedural volume is associated with lower complications; however, as noted by the authors, the volume cutoffs used in this study were never validated.10
We hypothesized that overall annual hospital volume may better reflect the entire continuum of multidisciplinary care invested in the treatment of women undergoing post-mastectomy free-flap reconstruction. Thus, we focused on assessing the relationship between overall annual hospital volume and systemic, surgical, and microsurgical complications following surgery. Furthermore, an important challenge in accurately assessing the volume-outcome relationship relates to the lack of standardization around what constitutes “low” vs. “high” volume. Our study utilized RCS analysis to define hospital volume groups based on their relationship to complications, rather than imposing arbitrary cut-off points to define “high” and “low” volume. This minimizes bias associated with the use of arbitrary cut-off points and instead identifies specific volume thresholds that may be used to more accurately inform clinical decision-making.
The results of our study did not reveal any meaningful difference in the risk of microsurgical complications between breast reconstruction patients treated at low versus high-volume hospitals. Microsurgical breast reconstruction is a complex procedure requiring institutional investment and a high level of technical proficiency for successful perforator flap reconstruction; thus, related complications are strongly associated with years of surgeon experience.27,28 These complications are likely driven by individual surgeon and programmatic volume as opposed to oncologic outcomes which reflect the multidisciplinary care afforded by high-volume hospitals.4,27,28 Similarly, no difference in the risk of experiencing systemic complications following reconstructive surgery was evident when comparing high to low-volume hospitals, as defined by our study. Our findings may be explained by perioperative protocols that mitigate the incidence of systemic complications. Implementation of these protocols is cost and resource-effective and is less likely to be influenced by overall hospital volume or size. Recent evidence suggests that free-flap breast reconstruction is becoming increasingly concentrated in urban cities, academic centers, and large size hospitals, creating possible barriers to access for reconstructive care.10,11,29 Our findings suggest, however, that patients and referring providers should consider that complications following free-flap breast reconstruction are likely less influenced by overall annual hospital volume and size, and more dependent on individual provider and reconstructive programmatic experience.
Prior studies consistently demonstrate the strength of the volume-outcome relationship when assessing the influence of hospital and surgeon volume on perioperative mortality and survival in oncologic surgery.4,8 Finks et al. and Reames et al. demonstrated that across eight different complex surgical procedures that included carotid endarterectomies, pancreatectomies, and abdominal aortic aneurysm repair, high-volume was associated with an 8-36% reduction in mortality.8,30 With respect to breast cancer outcomes, a similar relationship with hospital volume was found, with an 11% reduction in overall mortality for women who receive care at high-volume versus low-volume hospitals, and improved 5 and 10-year survival rate for breast cancer patients who are treated at high-volume centers.4,5,31,32 Our findings suggest, however, that this relationship is less pronounced for free-flap breast reconstruction in its relationship with systemic, surgical, and microsurgical complications. Some organizations have recommended that strict volume standards be instituted for certain surgical procedures, with an emphasis on regional referral systems to ensure care at high-volume centers.33 Plastic surgery is a unique field in that common evaluation of surgical outcomes, such as 30-day mortality, are less applicable and do not adequately reflect the quality of care.34 In this setting, relevant metrics of perioperative processes and experiences of care, and validated patient-reported outcome measures might collectively better assess the quality of free-flap breast reconstruction. The increasing recognition of breast reconstruction as an essential component to breast cancer care warrants further development of objective quality metrics that define improved outcomes following surgery.35
With respect to breast reconstruction, the implementation of a regional referral system to improve patient outcomes has the potential to further exacerbate existing disparities and increase the burden of reconstructive care.11,36 The majority of facilities analyzed in this study had a low number of annual discharges, suggesting that most free-flap breast reconstruction is occurring at lower volume hospitals. As a result, referring breast reconstruction patients to large volume centers may increase patient travel distance and reduce the rates of free-flap breast reconstruction.11 Our results suggest that unless patient referrals are to experienced surgeons, referral to a large hospital center is unlikely to mitigate complications following reconstructive surgery. Community-centric programs that facilitate and incentivize the high-level training of microsurgeons at lower-volume hospitals and rural areas is likely to have a more sustainable effect in reducing the rates of microsurgery specific complications while facilitating access to reconstructive surgery.
This study is not without its limitations. The complications reported within this study are from a single encounter. Thus, we were unable to capture long term complications (i.e. >30 days) such as infection, flap necrosis, and wound complications. We were also unable to assess chemotherapy and radiation therapy treatment regimens, as information regarding these items are not readily available in the dataset. Furthermore, the HCUP-NIS utilizes a systematic random sample of approximately 20% of representative discharges from U.S. community hospitals; however, it precludes an assessment of procedural volume for an individual facility. In addition, the HCUP-NIS dataset no longer provides unique hospital or provider identifiers, thus we could not estimate surgeon-specific volume. Lastly, beginning in 2012, the sampling design of the HCUP-NIS dataset precludes hospital level analyses. Thus, we cannot identify if a hospital that was sampled in one year was also sampled in multiple years. It is likely that individual hospitals were counted more than once across different years.18
CONCLUSION
Overall annual hospital volume was not associated with surgical complications following free-flap breast reconstruction; however, an association of hospital volume and the development of systemic and microsurgical complications was found. When patients were further categorized as receiving care at low versus high-volume hospitals based on identified volume thresholds, there was no difference in the risk for experiencing systemic or microsurgical complications despite an in-depth multipronged analysis. These results suggest that patient outcomes are more likely associated with surgeon or programmatic experience, and overall annual hospital volume should not serve as a proxy for quality breast free-flap care.
Supplementary Material
Appendix, Supplemental Digital Content 1: ICD-9 and ICD-10 codes used to define individual variables.
Figure, Supplemental Digital Content 2: Representative ROC Curves for Table 2. Left: Any systemic complication; Middle: Any surgical complication; Right: Any microsurgical complication.
Figure, Supplemental Digital Content 3: Representative ROC Curves for Table 3. Left: Any surgical complication; Right: Any microsurgical complication.
Acknowledgments
Declaration of Funding: Dr. RA Greenup is supported by the National Institutes of Health Office of Research in Women’s Health and National Institute of Child Health and Human Development Building Interdisciplinary Research Careers in Women’s Health K12HD043446 (PI: Andrews). This work is also supported by the Duke Cancer Institute through NIH grant P30CA014236 (PI: Kastan).
REFERENCES
- 1.Adam MA, Thomas S, Youngwirth L, et al. Is There a Minimum Number of Thyroidectomies a Surgeon Should Perform to Optimize Patient Outcomes? Annals of surgery 2017;265:402–407. [DOI] [PubMed] [Google Scholar]
- 2.Kozower BD, Stukenborg GJ, Lau CL, Jones DR Measuring the quality of surgical outcomes in general thoracic surgery: should surgical volume be used to direct patient referrals? The Annals of thoracic surgery 2008;86:1405–1408. [DOI] [PubMed] [Google Scholar]
- 3.Birkmeyer JD, Siewers AE, Finlayson EVA, et al. Hospital Volume and Surgical Mortality in the United States. New England Journal of Medicine 2002;346:1128–1137. [DOI] [PubMed] [Google Scholar]
- 4.Greenup RA, Obeng-Gyasi S, Thomas S, et al. The Effect of Hospital Volume on Breast Cancer Mortality. Annals of surgery 2018;267:375–381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chen CS, Liu TC, Lin HC, Lien YC Does high surgeon and hospital surgical volume raise the five-year survival rate for breast cancer? A population-based study. Breast cancer research and treatment 2008;110:349–356. [DOI] [PubMed] [Google Scholar]
- 6.Hata T, Motoi F, Ishida M, et al. Effect of Hospital Volume on Surgical Outcomes After Pancreaticoduodenectomy: A Systematic Review and Meta-analysis. Annals of surgery 2016;263:664–672. [DOI] [PubMed] [Google Scholar]
- 7.Tuggle CT, Patel A, Broer N, Persing JA, Sosa JA, Au AF Increased hospital volume is associated with improved outcomes following abdominal-based breast reconstruction. Journal of Plastic Surgery and Hand Surgery 2014;48:382–388. [DOI] [PubMed] [Google Scholar]
- 8.Finks JF, Osborne NH, Birkmeyer JD Trends in hospital volume and operative mortality for high-risk surgery. The New England journal of medicine 2011;364:2128–2137. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Urbach DR Pledging to Eliminate Low-Volume Surgery. New England Journal of Medicine 2015;373:1388–1390. [DOI] [PubMed] [Google Scholar]
- 10.Billig JI, Lu Y, Momoh AO, Chung KC A Nationwide Analysis of Cost Variation for Autologous Free Flap Breast Reconstruction. JAMA Surgery 2017;152:1039–1047. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Albornoz CR, Cohen WA, Razdan SN, et al. The Impact of Travel Distance on Breast Reconstruction in the United States. Plastic and reconstructive surgery 2016;137:12–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Ilonzo N, Tsang A, Tsantes S, Estabrook A, Thu Ma AM Breast reconstruction after mastectomy: A ten-year analysis of trends and immediate postoperative outcomes. Breast (Edinburgh, Scotland) 2017;32:7–12. [DOI] [PubMed] [Google Scholar]
- 13.Alderman AK, Wei Y, Birkmeyer JD Use of breast reconstruction after mastectomy following the Women’s Health and Cancer Rights Act. Jama 2006;295:387–388. [DOI] [PubMed] [Google Scholar]
- 14.Cemal Y, Albornoz CR, Disa JJ, et al. A paradigm shift in U.S. breast reconstruction: Part 2. The influence of changing mastectomy patterns on reconstructive rate and method. Plastic and reconstructive surgery 2013;131:320e–326e. [DOI] [PubMed] [Google Scholar]
- 15.Carlson GW Trends in autologous breast reconstruction. Seminars in plastic surgery 2004;18:79–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kamali P, Paul MA, Ibrahim AMS, et al. National and Regional Differences in 32,248 Postmastectomy Autologous Breast Reconstruction Using the Updated National Inpatient Survey. Annals of plastic surgery 2017;78:717–722. [DOI] [PubMed] [Google Scholar]
- 17.Stulberg JJ, Haut ER Practical Guide to Surgical Data Sets: Healthcare Cost and Utilization Project National Inpatient Sample (NIS)Guide to Statistics and Methods: The National Inpatient SampleGuide to Statistics and Methods: The National Inpatient Sample. JAMA Surgery 2018;153:586–587. [DOI] [PubMed] [Google Scholar]
- 18.HCUP-NIS. Checklist For Working with the NIS Available at: https://www.hcup-us.ahrq.gov/db/nation/nis/nischecklist.jsp. Accessed 12/22 2019.
- 19.Khera R, Angraal S, Couch T, et al. Adherence to Methodological Standards in Research Using the National Inpatient Sample. Jama 2017;318:2011–2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Charlson ME, Pompei P, Ales KL, MacKenzie CR A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of chronic diseases 1987;40:373–383. [DOI] [PubMed] [Google Scholar]
- 21.Deyo RA, Cherkin DC, Ciol MA Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. Journal of clinical epidemiology 1992;45:613–619. [DOI] [PubMed] [Google Scholar]
- 22.Harrell FE Jr Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis: Springer; 2015. [Google Scholar]
- 23.Desquilbet L, Mariotti F Dose-response analyses using restricted cubic spline functions in public health research. Statistics in medicine 2010;29:1037–1057. [DOI] [PubMed] [Google Scholar]
- 24.Akaike H Information theory and an extension of the maximum likelihood principle Selected papers of hirotugu akaike: Springer; 1998:199–213. [Google Scholar]
- 25.Sheetz KH, Waits SA, Girotti ME, Campbell DA Jr., Englesbe MJ Patients’ perspectives of care and surgical outcomes in Michigan: an analysis using the CAHPS hospital survey. Annals of surgery 2014;260:5–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Fu R, Chang MM, Chen M, Rohde CH A Qualitative Study of Breast Reconstruction Decision-Making among Asian Immigrant Women Living in the United States. Plastic and reconstructive surgery 2017;139:360e–368e. [DOI] [PubMed] [Google Scholar]
- 27.Mahmoudi E, Lu Y, Chang SC, et al. Associations of Surgeon and Hospital Volumes with Outcome for Free Tissue Transfer by Using the National Taiwan Population Health Care Data from 2001 to 2012. Plastic and reconstructive surgery 2017;140:455e–465e. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Broome M, Juilland N, Litzistorf Y, et al. Factors Influencing the Incidence of Severe Complications in Head and Neck Free Flap Reconstructions. Plastic and reconstructive surgery Global open 2016;4:e1013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Albornoz CR, Bach PB, Mehrara BJ, et al. A paradigm shift in U.S. Breast reconstruction: increasing implant rates. Plastic and reconstructive surgery 2013;131:15–23. [DOI] [PubMed] [Google Scholar]
- 30.Reames BN, Ghaferi AA, Birkmeyer JD, Dimick JB Hospital volume and operative mortality in the modern era. Annals of surgery 2014;260:244–251. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Nomura E, Tsukuma H, Ajiki W, Ishikawa O, Oshima A Population-based study of the relationship between hospital surgical volume and 10-year survival of breast cancer patients in Osaka, Japan. Cancer science 2006;97:618–622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Skinner KA, Helsper JT, Deapen D, Ye W, Sposto R Breast cancer: do specialists make a difference? Annals of surgical oncology 2003;10:606–615. [DOI] [PubMed] [Google Scholar]
- 33.Birkmeyer JD, Finlayson EV, Birkmeyer CM Volume standards for high-risk surgical procedures: potential benefits of the Leapfrog initiative. Surgery 2001;130:415–422. [DOI] [PubMed] [Google Scholar]
- 34.Chung KC, Rohrich RJ Measuring quality of surgical care: is it attainable? Plastic and reconstructive surgery 2009;123:741–749. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.ICHOM Standard Sets. Available at: https://www.ichom.org/healthcare-standardization/. Accessed 11/14/2019 2019.
- 36.Sergesketter AR, Thomas SM, Lane WO, et al. Decline in Racial Disparities in Postmastectomy Breast Reconstruction: A Surveillance, Epidemiology, and End Results Analysis from 1998 to 2014. Plastic and reconstructive surgery 2019;143:1560–1570. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Appendix, Supplemental Digital Content 1: ICD-9 and ICD-10 codes used to define individual variables.
Figure, Supplemental Digital Content 2: Representative ROC Curves for Table 2. Left: Any systemic complication; Middle: Any surgical complication; Right: Any microsurgical complication.
Figure, Supplemental Digital Content 3: Representative ROC Curves for Table 3. Left: Any surgical complication; Right: Any microsurgical complication.