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. Author manuscript; available in PMC: 2026 Feb 5.
Published in final edited form as: Surg Endosc. 2025 Oct 3;39(12):8607–8621. doi: 10.1007/s00464-025-12244-9

Comparison of Utilization Trends, Outcomes, and Costs Between Open and Minimally Invasive Esophagectomy

Steven Tohmasi 1, Yifei Xu 2, Jingxia Liu 2, Nikki E Rossetti 1, Whitney S Brandt 1, Bryan F Meyers 1, Varun Puri 1, Benjamin D Kozower 1
PMCID: PMC12870180  NIHMSID: NIHMS2119606  PMID: 41042345

Abstract

Background:

Many surgeons have adopted minimally invasive esophagectomy (MIE) as an alternative to open esophagectomy (OE). However, limited population-level data exist comparing clinical outcomes and costs by surgical approach. This study evaluated contemporary utilization trends, outcomes, and costs between MIE and OE using real-world data.

Methods:

We conducted a retrospective cohort study of patients who underwent MIE or OE using data from the Healthcare Cost and Utilization Project Florida State Inpatient Database from 2016 to 2021. Utilization trends were analyzed using Cochran-Armitage tests. Multivariable regression models evaluated the association of surgical approach with postoperative outcomes and hospitalization costs.

Results:

Of 2,550 total patients, 1,218 (47.8%) and 1,332 (52.2%) underwent MIE and OE, respectively. Utilization of MIE increased significantly over time, as MIE grew from 43.4% of annual esophagectomy volume in 2016 to 57.7% by 2021 (trend p<0.001). MIE patients had a higher prevalence of esophageal or esophagogastric junction cancer compared to OE patients (75.7% vs. 60.1%; p < 0.001), but exhibited a comparable overall comorbidity burden (e.g., 2–3 comorbidities: 44.1% vs. 43.5%; p = 0.061). MIE patients had significantly shorter hospital stays (median: 8 vs. 10 days, p<0.001). MIE was associated with reduced risk-adjusted odds of postoperative complications (adjusted odds ratio 0.560, confidence interval 0.474–0.661, p<0.001). Operating room costs were higher with MIE compared to OE (median: $13,964 vs. $10,618, p<0.001), whereas intensive care unit costs were lower (median: $2,325 vs. $5,706, p<0.001). Index hospitalization (median: $41,795 vs. $40,289, p=0.340) and 90-day costs (median: $46,509 vs. $45,408, p=0.550) were comparable between groups. In subgroup analyses, in-hospital mortality was significantly lower with MIE at low-volume (<20 esophagectomies annually) hospitals (2.5% vs. 5.3%, p=0.010). However, this difference was not statistically significant at high-volume (≥20 esophagectomies annually) hospitals (2.9% vs. 5.0%, p=0.072).

Conclusions:

MIE has had rapid growth in utilization. MIE appears to provide a viable, cost-effective alternative to OE, with fewer postoperative complications, shorter hospital stays, and comparable overall costs.

Keywords: esophagectomy, minimally invasive, open, outcomes, cost, utilization

Introduction

Despite recent advancements in surgical techniques and perioperative management, esophagectomy remains a technically complex and highly morbid operation [1–7]. Among major oncologic surgeries, esophagectomy is associated with some of the highest perioperative mortality (ranging from 3–11% in the contemporary literature) and complication rates (up to 62%), even at experienced centers [1–5, 7]. These persistently high rates of postoperative adverse events are particularly concerning, as an analysis of over 23,000 admissions in the Nationwide Readmissions Database from 2010 to 2018 revealed a continued rise in both the incidence of esophagectomy and median institutional caseload across the United States (US) [2].

Over the past two decades, minimally invasive esophagectomy (MIE) has emerged as an effective and safe alternative to open esophagectomy (OE) for treating malignant and benign esophageal diseases [8, 9]. Multiple studies have shown that MIE is associated with improved short-term clinical and health-related quality of life outcomes as well as equivalent long-term oncologic outcomes to OE in select patients [7, 9–17]. However, concerns regarding steep learning curves, potentially higher costs, and longer operative times have posed challenges to its broader adoption [9, 18–21]. Additionally, the financial implications of MIE remains incompletely understood [22]. While MIE is thought to incur higher initial operative costs due to use of specialized surgical devices (e.g., robotic surgical platforms) and longer operative times, these upfront expenses may be offset by reduced complication rates, shorter intensive care unit (ICU) stays, and expedited recovery [9, 23]. However, existing cost analyses of MIE, the majority of which originate from single institutions outside of the US, have yielded conflicting results [19, 20, 23–28]. Furthermore, long-term costs, including those incurred within 90 days after surgery, remain understudied in the contemporary literature, leaving a critical gap in understanding whether MIE ultimately provides a cost advantage when factoring in postoperative complications and downstream health care utilization.

To address these gaps, we conducted a population-based study using real-world, multi-institutional data from the Florida State Inpatient Database (SID). Our objectives were to examine contemporary utilization trends of MIE and OE, compare postoperative outcomes between these approaches, and assess their associated hospitalization and 90-day costs. Additionally, we sought to determine whether postoperative complications are a driver of increased costs and whether their financial impact differs between MIE and OE. Understanding the relationship between surgical approach, complications, and costs can aid surgeons, hospital administrators, and policymakers in efforts to optimize both clinical and economic outcomes following esophagectomy.

Materials and Methods

Study Design and Data Source

We conducted a retrospective cohort study using data from the Healthcare Cost and Utilization Project (HCUP) Florida SID 2016 to 2021. The Florida SID, sponsored by the Agency for Healthcare Research and Quality, captures approximately 97% of all hospital discharges within the state [29]. This all-payer, administrative database includes patient demographics, primary and secondary diagnosis codes, procedure codes, admission and discharge dates, and hospitalization charges. Additionally, each patient is assigned a unique identifier, allowing for the linkage of multiple admissions across different hospitals within the state. This feature enables the analysis of patient readmissions at both index and non-index hospitals as well as associated hospitalization charges [30, 31]. The study protocol was reviewed and deemed exempt by the Washington University in St. Louis Institutional Review Board due to the use of deidentified, retrospective data. In compliance with the HCUP data use agreement and privacy protection requirements, cell sizes <11 have been excluded from this article.

Patient Population

We included all adult patients who underwent esophagectomy, either through an open or minimally invasive approach, at hospitals in Florida between January 1, 2016 and August 31, 2021. The study end date (August 31, 2021) was selected to allow for 90 days of follow up for all patients. Operations were categorized as MIE or OE using previously validated International Classification of Diseases, 10th Edition, Procedure Coding System (ICD-10-PCS) codes (Supplementary Table 1) [32, 33]. Since ICD-10-PCS codes do not reliably differentiate between video-assisted and robotic-assisted thoracic surgeries, both approaches were categorized as MIE [32]. Patients were excluded if they were <18 years old, had missing demographic data or hospital length of stay (LOS), or resided out-of-state (as they were unlikely to be readmitted to a hospital in Florida) (Figure 1).

Figure 1.

Figure 1.

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Consolidated Standards of Reporting Trials (CONSORT) diagram.

Variable Definitions and Outcome Measures

We collected patient-level data on age, sex, urban/rural residence, insurance type, median household income by ZIP code, comorbidities, and postoperative outcomes. Comorbidities were identified using the Elixhauser classification, based on ICD-10-Clinical Modification (CM) diagnosis codes from inpatient visits within the 12 months preceding the index admission. Intraoperative conversion from a MIE to OE was defined as the presence of dual ICD-10-PCS codes indicating both surgical approaches were used or an ICD-10-CM code documenting conversion from a thoracoscopic (Z53.32) or laparoscopic (Z53.31) approach to an open approach during the index admission. We performed this analysis using an intention-to-treat principle, categorizing operations with an intraoperative conversion in the MIE group (n=86/1,218, 7.1%).

The primary endpoint of the study was 90-day costs. Secondary endpoints included utilization trends of MIE and OE, in-hospital mortality, postoperative complications, prolonged hospitalization (defined as hospital LOS ≥14 days), 30-day readmission, operative costs, and index hospitalization costs. In-hospital postoperative complications were identified using previously validated ICD-10-CM diagnosis codes absent at admission and included respiratory failure, chylothorax, myocardial infarction, pneumonia, pulmonary embolism, deep vein thrombosis, acute posthemorrhagic anemia, wound complications, pneumothorax, paralytic ileus, bowel obstruction, acute kidney injury, vocal cord paralysis, mediastinitis, and empyema (Supplementary Table 1) [33–35]. Anastomotic leaks were not included due to inconsistent and unreliable ICD-10-CM coding for this complication, as previously described in the HCUP literature [2]. Hospitalization charges were converted to costs from the hospital payer perspective using center-specific cost-to-charge ratios and adjusted for inflation based on the 2021 Consumer Price Index [36].

In subgroup analyses by hospital volume, each hospital’s annual esophagectomy volume was calculated. Hospitals performing ≥20 esophagectomies per year were classified as high-volume, while those performing <20 were considered low-volume. These thresholds were selected based on the minimum volume standards recommended by the Leapfrog Group, a national advocacy group dedicated to improving healthcare quality and patient safety [37].

Statistical Approach

Summary statistics are presented as medians with interquartile ranges (IQR) for continuous data and as frequencies with corresponding proportions for categorical data. Mann-Whitney U tests were used to compare non-parametric continuous variables, while categorical variables were compared using Pearson’s chi-squared or Fisher’s exact tests (utilized if one or more groups had too few observations). We used Cochran-Armitage tests to assess trends in MIE and OE utilization over the study period. Multivariable logistic regression was used to evaluate the association between surgical approach and postoperative outcomes, including in-hospital mortality, prolonged hospitalization, postoperative complications, and 30-day readmission. Multivariable linear regression was performed to examine relationships between surgical approach and hospitalization costs. Given the skewed, non-normal distribution of hospitalization costs (Kolmogorov-Smirnov test for normality: p<0.001), cost data were log-transformed before inclusion in the linear regression model. Covariates for risk-adjustment were selected a priori based on clinical relevance and included age, sex, race, income quartile, insurance type, urban/rural residence, comorbidity burden (i.e., 0–1, 2–3, ≥4 comorbidities), and diagnosis of esophageal or esophagogastric junction cancer. Regression results are presented as adjusted odds ratios (aOR) with 95% confidence intervals (CI). All statistical tests were two-sided, with significance defined as p<0.05. Statistical analyses were performed using R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Utilization Trends

The number of hospitals performing at least one esophagectomy per year ranged from 55 to 75 between 2016 and 2021 (Figure 2). Fewer than 11 hospitals met the high-volume threshold (≥20 esophagectomy procedures annually) during each year of the study period.

Figure 2.

Figure 2.

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Annual trends in esophagectomy volume.

A total of 2,550 patients met inclusion criteria, with 1,218 (47.8%) who underwent MIE and 1,332 (52.2%) who underwent OE. Utilization trends for MIE and OE changed significantly over the study period (p<0.001) (Figure 3). In 2016, MIE accounted for 43.4% (n=202/465) of annual esophagectomy volume, while OE comprised 56.6% (n=263/465). By 2021, MIE utilization increased to 57.7% (n=187/324) of annual esophagectomy volume, while OE usage declined to 42.3% (n=137/324).

Figure 3.

Figure 3.

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Annual trends in esophagectomy volume by surgical approach.

Patient Characteristics

Patients who underwent MIE were more often younger (66.0 vs. 67.0 years, p=0.020), male (73.2% vs. 69.4%, p=0.039), and from ZIP codes with higher median household incomes (e.g., quartile 4: 25.3% vs. 20.4%, p=0.019) (Table 1). Race (e.g., White: 79.4% vs. 77.5%, p=0.089), insurance type (e.g., Medicare: 56.9% vs. 59.1%, p=0.053), and overall comorbidity burden (e.g., 2–3 comorbidities: 44.1% vs. 43.5%, p=0.061) were comparable between groups. However, MIE patients had significantly lower rates of congestive heart failure (5.8% vs. 9.5%, p=0.001), chronic pulmonary disease (22.6% vs. 28.3%, p=0.001), diabetes (20.7% vs. 24.5%, p=0.023), and pulmonary circulation disease (1.9% vs. 3.5%, p=0.015). MIE patients had higher rates of obesity (21.2% vs. 15.8%, p=0.001), esophageal or esophagogastric junction cancer (75.7% vs. 60.1%, p<0.001), and history of chemotherapy or radiation therapy (40.3% vs. 28.3%, p<0.001).

Table 1.

Demographics of study population.

Open Esophagectomy, n=1,332 (%) Minimally Invasive Esophagectomy, n=1,218 (%) P-value
Age, median (interquartile range) 67.0 (58.8–74.0) 66.0 (59.0–73.0) 0.020*
Sex 0.039*
 Male 925 (69.4) 892 (73.2)
 Female 407 (30.6) 326 (26.8)
Race 0.089
 White 1032 (77.5) 967 (79.4)
 Black 97 (7.3) 62 (5.1)
 Hispanic 172 (12.9) 152 (12.5)
 Other 31 (2.3) 37 (3.0)
Insurance type 0.053
 Medicaid 97 (7.3) 70 (5.7)
 Medicare 787 (59.1) 693 (56.9)
 Private insurance 394 (29.6) 384 (31.5)
 Other 54 (4.1) 71 (5.8)
Median household income for patient’s ZIP code 0.019*
 Quartile 1 314 (23.6) 276 (22.7)
 Quartile 2 366 (27.5) 292 (24.0)
 Quartile 3 380 (28.5) 342 (28.1)
 Quartile 4 272 (20.4) 308 (25.3)
Urban/rural residence 0.794
 Metropolitan areas of ≥1 million in population 777 (58.3) 695 (57.1)
 Metropolitan areas of 50,000–999,999 in population 511 (38.4) 480 (39.4)
 Micropolitan and rural counties 44 (3.3) 43 (3.5)
Comorbidity burden 0.061
 0–1 comorbidities 402 (30.2) 406 (33.3)
 2–3 comorbidities 580 (43.5) 537 (44.1)
 ≥4 comorbidities 350 (26.3) 275 (22.6)
Comorbidities
 Alcohol or drug abuse 134 (10.1) 98 (8.0) 0.090
 Congestive heart failure 126 (9.5) 71 (5.8) 0.001*
 Chronic pulmonary disease 377 (28.3) 275 (22.6) 0.001*
 Chronic kidney disease 114 (8.6) 82 (6.7) 0.098
 Coagulopathy 163 (12.2) 127 (10.4) 0.169
 Diabetes 327 (24.5) 252 (20.7) 0.023*
 Hypertension 873 (65.5) 783 (64.3) 0.534
 Liver disease 99 (7.4) 102 (8.4) 0.419
 Metastatic cancer 246 (18.5) 231 (19.0) 0.787
 Obesity 210 (15.8) 258 (21.2) 0.001*
 Peripheral vascular disease 102 (7.7) 87 (7.1) 0.674
 Pulmonary circulation disease 47 (3.5) 23 (1.9) 0.015*
 Peptic ulcer disease 71 (5.3) 64 (5.3) 1.000
 Valvular disease 81 (6.1) 54 (4.4) 0.076
 Weight loss 462 (34.7) 400 (32.8) 0.347
Esophageal or esophagogastric junction cancer 801 (60.1) 922 (75.7) <0.001*
History of chemotherapy or radiation therapy 377 (28.3) 491 (40.3) <0.001*
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*

P-values <0.05 were considered statistically significant and are marked for clarity.

Postoperative Outcomes

The unadjusted in-hospital mortality rate was significantly lower for MIE patients, compared to OE patients (2.7% vs. 5.3%, p=0.001) (Table 2). On multivariable analysis, MIE was associated with lower risk-adjusted odds of in-hospital mortality (aOR 0.492, CI 0.319–0.760, p=0.001) (Table 3). A higher comorbidity burden (e.g., ≥4 vs. 0–1 comorbidities) was also associated with increased likelihood of in-hospital morality (aOR 7.608, CI 3.561–16.257, p<0.001).

Table 2.

Postoperative outcomes and hospitalization costs stratified by surgical approach.

Outcome Open Esophagectomy, n=1,332 (%) Minimally Invasive Esophagectomy, n=1,218 (%) P-value
In-hospital mortality 70 (5.3) 33 (2.7) 0.001*
In-hospital complication 740 (55.6) 514 (42.2) <0.001*
 Respiratory failure 357 (26.8) 189 (15.5) <0.001*
 Chylothorax 30 (2.3) 23 (1.9) 0.579
 Myocardial infarction 13 (1.0) <11 0.669
 Pneumonia 171 (12.8) 122 (10.0) 0.030*
 Pulmonary embolism/deep vein thrombosis 26 (2.0) 15 (1.2) 0.159
 Acute posthemorrhagic anemia 365 (27.4) 263 (21.6) 0.001*
 Wound complication 31 (2.3) <11 <0.001*
 Pneumothorax 29 (2.2) 49 (4.0) 0.008*
 Paralytic ileus 57 (4.3) 47 (3.9) 0.617
 Bowel obstruction <11 <11 0.766
 Acute kidney injury 201 (15.1) 108 (8.9) <0.001*
 Vocal cord paralysis 55 (4.1) 11 (0.9) <0.001*
 Mediastinitis 39 (2.9) 18 (1.5) 0.015*
 Empyema 100 (7.5) 50 (4.1) <0.001*
Hospital length of stay, median (IQR) 10 (7–17) days 8 (6–12) days <0.001*
Prolonged hospitalization 489 (36.7) 253 (20.8) <0.001*
30-day readmission 249 (18.7) 200 (16.4) 0.146
Operating room cost, median (IQR) $10,618 ($7,143–$17,289) $13,964 ($7,829– $21,035) <0.001*
ICU cost, median (IQR) $5,706 ($2,563– $12,476) $2,325 ($941– $7,648) <0.001*
Index hospitalization cost, median (IQR) $40,289 ($26,335–$65,461) $41,795 ($29,402–$60,289) 0.340
90-day cost, median (IQR) $45,408 ($27,994–$77,536) $46,509 ($31,141–$69,004) 0.550
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*

P-values <0.05 were considered statistically significant and are marked for clarity.

Abbreviations used: ICU-intensive care unit; IQR-interquartile range.

In compliance with the HCUP data use agreement and privacy protection requirements, cell sizes <11 have been excluded.

Table 3.

Multivariable logistic regression results for postoperative outcomes.

Covariate In-hospital mortality, aOR (CI) P-value In-hospital complication, aOR (CI) P-value Prolonged hospitalization, aOR (CI) P-value 30-day readmission, aOR (CI) P-value
Age 1.013 (0.987–1.040) 0.337 1.004 (0.994–1.014) 0.440 1.002 (0.991–1.013) 0.686 1.000 (0.987–1.012) 0.970
Sex
 Male Reference - Reference - Reference - Reference -
 Female 0.951 (0.584–1.547) 0.839 0.899 (0.743–1.088) 0.273 1.024 (0.828–1.265) 0.828 1.048 (0.824–1.332) 0.702
Race
 Black Reference - Reference - Reference - Reference -
 White 1.126 (0.486–2.609) 0.781 0.701 (0.488–1.009) 0.056 0.708 (0.490–1.023) 0.066 1.112 (0.699–1.770) 0.654
 Hispanic 1.311 (0.493–3.486) 0.587 0.744 (0.492–1.123) 0.159 0.828 (0.541–1.267) 0.385 0.889 (0.515–1.535) 0.674
 Other 1.017 (0.198–5.216) 0.984 0.624 (0.340–1.144) 0.127 0.597 (0.303–1.176) 0.136 1.782 (0.874–3.634) 0.112
Insurance type
 Medicaid Reference - Reference - Reference - Reference -
 Medicare 0.779 (0.338–1.796) 0.557 0.719 (0.489–1.057) 0.094 0.584 (0.392–0.870) 0.008* 1.425 (0.858–2.366) 0.171
 Private insurance 0.420 (0.177–0.998) 0.049* 0.770 (0.537–1.105) 0.157 0.594 (0.409–0.863) 0.006* 0.860 (0.527–1.405) 0.548
 Other 0.817 (0.255–2.619) 0.734 0.731 (0.445–1.203) 0.218 0.682 (0.400–1.163) 0.160 1.866 (1.016–3.424) 0.044*
Median household income for patient’s ZIP code
 Quartile 1 Reference - Reference - Reference - Reference -
 Quartile 2 1.205 (0.706–2.055) 0.494 0.990 (0.780–1.256) 0.934 0.852 (0.662–1.098) 0.216 1.034 (0.764–1.401) 0.828
 Quartile 3 0.636 (0.348–1.161) 0.140 0.892 (0.705–1.129) 0.343 0.570 (0.440–0.739) <0.001* 1.072 (0.795–1.445) 0.648
 Quartile 4 0.826 (0.443–1.541) 0.549 0.936 (0.728–1.204) 0.608 0.658 (0.499–0.866) 0.003* 1.079 (0.784–1.485) 0.640
Urban/rural residence
 Metropolitan areas of ≥1 million in population Reference - Reference - Reference - Reference -
 Metropolitan areas of 50,000–999,999 in population 1.026 (0.665–1.584) 0.906 0.973 (0.815–1.162) 0.765 0.855 (0.702–1.042) 0.121 1.115 (0.895–1.390) 0.332
 Micropolitan and rural counties 0.473 (0.110–2.038) 0.315 0.831 (0.522–1.326) 0.438 0.348 (0.191–0.635) <0.001* 0.689 (0.355–1.340) 0.273
Comorbidity burden
 0–1 comorbidities Reference - Reference - Reference - Reference -
 2–3 comorbidities 3.593 (1.673–7.716) 0.001* 1.777 (1.468–2.150) <0.001* 1.875 (1.488–2.364) <0.001* 1.120 (0.876–1.433) 0.366
 ≥4 comorbidities 7.608 (3.561–16.257) <0.001* 3.884 (3.092–4.879) <0.001* 3.922 (3.050–5.043) <0.001* 1.168 (0.881–1.547) 0.280
Surgical approach
 Open Reference - Reference - Reference - Reference -
 Minimally invasive 0.492 (0.319–0.760) 0.001* 0.560 (0.474–0.661) <0.001* 0.444 (0.367–0.536) <0.001* 0.826 (0.669–1.020) 0.075
Esophageal or esophagogastric junction cancer
 No Reference - Reference - Reference - Reference -
 Yes 1.995 (1.194–3.332) 0.008* 1.499 (1.241–1.811) <0.001* 1.307 (1.060–1.613) 0.012* 1.200 (0.943–1.526) 0.138
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*

P-values <0.05 were considered statistically significant and are marked for clarity.

Abbreviations used: aOR-adjusted odds ratio; CI-95% confidence interval.

Overall, 1,254 (49.1%) patients experienced one or more in-hospital complications. MIE patients had lower rates of in-hospital complications compared to OE patients (42.2% vs. 55.6%, p<0.001), including respiratory failure (15.5% vs. 26.8%, p<0.001), pneumonia (10.0% vs. 12.8%, p=0.030), acute posthemorrhagic anemia (21.6% vs. 27.4%, p=0.001), wound complications (<11 vs. 2.3%, p<0.001), acute kidney injury (8.9% vs. 15.1%, p<0.001), vocal cord paralysis (0.9% vs. 4.1%, p<0.001), mediastinitis (1.5% vs. 2.9%, p=0.015), and empyema (4.1% vs. 7.5%, p<0.001). On multivariable analysis, MIE was associated with significantly lower risk of developing one or more complications, relative to OE (aOR 0.560, CI 0.474–0.661, p<0.001). Additionally, a higher comorbidity burden was associated with increased risk-adjusted odds of developing an complication (e.g., ≥4 vs. 0–1 comorbidities: aOR 3.884, CI 3.092–4.879, p<0.001).

MIE patients also had significantly shorter hospital LOS than OE patients (8 vs. 10 days, p<0.001). Multivariable-adjusted analysis showed that MIE was associated with a 56% reduction in odds of prolonged hospitalization (aOR 0.444, CI 0.367–0.536, p<0.001). A higher comorbidity burden was also associated with increased risk-adjusted odds of prolonged hospitalization (e.g., ≥4 vs. 0–1 comorbidities: aOR 3.922, CI 3.050–5.043, p<0.001).

The overall 30-day readmission rate for the study cohort was 17.6% (n=449/2,550). Readmission rates were similar between groups (16.4% vs. 18.7%, p=0.146). On multivariable analysis, surgical approach was not associated with 30-day readmission (aOR 0.826, CI 0.669–1.020, p=0.075).

Operative, Hospitalization, and 90-day Costs

Median operating room costs were significantly higher for MIE than OE ($13,964 vs. $10,618, p<0.001) (Table 2). Linear regression analysis demonstrated that operating room costs for MIE were 14% more expensive than OE (aOR 1.141, Cl 1.083–1.201, p<0.001) (Table 4). However, ICU costs were significantly lower for MIE ($2,325 vs. $5,706, p<0.001), with linear regression analysis demonstrating a 46% reduction in ICU expenses compared to OE (aOR 0.543, CI 0.484–0.608, p<0.001).

Table 4.

Multivariable linear regression results for hospitalization costs.

Covariate Operating room cost, aOR (CI) P-value ICU cost, aOR (CI) P-value Index hospitalization cost, aOR (CI) P-value 90-day cost, aOR (CI) P-value
Age 0.996 (0.993–0.998) 0.003* 0.996 (0.989–1.002) 0.195 0.996 (0.993–0.999) 0.018* 0.995 (0.992–0.999) 0.008*
Sex
 Male Reference - Reference - Reference - Reference -
 Female 0.926 (0.872–0.982) 0.011* 1.059 (0.927–1.211) 0.397 0.974 (0.913–1.038) 0.415 0.997 (0.931–1.068) 0.927
Race
 Black Reference - Reference - Reference - Reference -
 White 0.971 (0.868–1.087) 0.611 0.734 (0.567–0.949) 0.019* 0.915 (0.810–1.033) 0.152 0.919 (0.807–1.047) 0.203
 Hispanic 0.913 (0.804–1.038) 0.164 0.825 (0.618–1.101) 0.191 0.951 (0.829–1.092) 0.479 0.930 (0.802–1.077) 0.332
 Other 1.014 (0.842–1.220) 0.885 0.818 (0.533–1.256) 0.358 0.949 (0.777–1.158) 0.604 0.946 (0.764–1.171) 0.609
Insurance type
 Medicaid Reference - Reference - Reference - Reference -
 Medicare 0.937 (0.834–1.054) 0.279 0.866 (0.671–1.118) 0.271 0.815 (0.717–0.925) 0.002* 0.858 (0.749–0.983) 0.027*
 Private insurance 0.945 (0.846–1.055) 0.311 0.778 (0.613–0.987) 0.039* 0.817 (0.725–0.921) <0.001* 0.817 (0.719–0.928) 0.002*
 Other 1.031 (0.885–1.200) 0.695 0.693 (0.486–0.988) 0.043* 0.874 (0.741–1.031) 0.109 0.939 (0.787–1.121) 0.488
Median household income for patient’s ZIP code
 Quartile 1 Reference - Reference - Reference - Reference -
 Quartile 2 0.994 (0.924–1.070) 0.877 0.891 (0.759–1.047) 0.161 0.991 (0.916–1.073) 0.831 1.016 (0.933–1.107) 0.710
 Quartile 3 1.030 (0.958–1.108) 0.419 0.807 (0.687–0.947) 0.009* 0.909 (0.841–0.984) 0.018* 0.936 (0.860–1.018) 0.124
 Quartile 4 1.009 (0.934–1.091) 0.819 0.840 (0.710–0.994) 0.043* 0.957 (0.880–1.041) 0.307 0.963 (0.880–1.053) 0.407
Urban/rural residence
  Metropolitan areas of ≥1 million in population Reference - Reference - Reference - Reference -
  Metropolitan areas of 50,000–999,999 in population 1.143 (1.082–1.208) <0.001* 0.866 (0.767–0.978) 0.021* 0.972 (0.916–1.031) 0.343 0.956 (0.897–1.019) 0.166
  Micropolitan and rural counties 1.186 (1.028–1.368) 0.019* 0.779 (0.561–1.081) 0.135 0.873 (0.747–1.019) 0.084 0.836 (0.708–0.986) 0.034*
Comorbidity burden
 0–1 comorbidities Reference - Reference - Reference - Reference -
 2–3 comorbidities 1.166 (1.099–1.237) <0.001* 1.423 (1.245–1.626) <0.001* 1.245 (1.168–1.328) <0.001* 1.269 (1.185–1.359) <0.001*
 ≥4 comorbidities 1.396 (1.303–1.496) <0.001* 1.989 (1.712–2.311) <0.001* 1.738 (1.612–1.873) <0.001* 1.737 (1.603–1.882) <0.001*
Surgical approach
 Open Reference - Reference - Reference - Reference -
 Minimally invasive 1.141 (1.083–1.201) <0.001* 0.543 (0.484–0.608) <0.001* 0.967 (0.914–1.022) 0.233 0.960 (0.904–1.019) 0.179
Esophageal or esophagogastric junction cancer
 No Reference - Reference - Reference - Reference -
 Yes 1.428 (1.347–1.514) <0.001* 1.081 (0.944–1.237) 0.259 1.632 (1.532–1.738) <0.001* 1.671 (1.562–1.788) <0.001*
Open in a new tab
*

P-values <0.05 were considered statistically significant and are marked for clarity.

Abbreviations used: aOR-adjusted odds ratio; CI-95% confidence interval; ICU-intensive care unit.

In both unadjusted and adjusted analyses, index hospitalization ($41,795 vs. $40,289, p=0.340; aOR 0.967, CI 0.914–1.022, p=0.233) and 90-day costs ($46,509 vs. $45,408, p=0.550; aOR 0.960, CI 0.904–1.019, p=0.179) were comparable between groups. In multivariable analyses, a higher comorbidity burden was associated with increased index hospitalization (e.g., ≥4 vs. 0–1 comorbidities: aOR 1.738, CI 1.612–1.873, p<0.001). and 90-day costs (e.g., ≥4 vs. 0–1 comorbidities: aOR 1.737, CI 1.603–1.882, p<0.001).

Relationship of Postoperative Complications with Outcomes and Costs

Among MIE patients with at least one postoperative complication (n=514, 42.2%), median hospital LOS was significantly greater compared to MIE patients without complications (11 vs. 7 days, p<0.001) (Supplementary Table 2). MIE patients with at least one postoperative complication also had significantly greater operating room ($16,214 vs. $10,674, p<0.001), ICU ($5,612 vs. $1,288, p<0.001), index hospitalization ($55,005 vs. $34,639, p<0.001) and 90-day costs ($62,114 vs. $37,620, p<0.001). Linear regression analyses demonstrated that MIE patients with complications had 57% higher index hospitalization costs (aOR 1.574, CI 1.474–1.680, p<0.001) and 59% higher 90-day costs (aOR 1.592, CI 1.481–1.711, p<0.001) than MIE patients without complications. Similar trends were observed in subgroup analyses of OE patients (Supplementary Table 2). Additionally, among all patients with one or more complications, index hospitalization ($55,005 vs. $56,025, p=0.573) and 90-day costs ($62,114 vs. $64,151, p=0.739) were comparable between MIE and OE (Supplementary Table 3).

Relationship of Hospital Volume with Utilization Trends, Outcomes, and Costs

At hospitals performing <20 esophagectomies annually, MIE utilization increased from 35.5% (n=100/282) in 2016 to 45.5% (n=81/178) in 2021 (p=0.010) (Supplementary Figure 1). Similarly, at hospitals performing ≥20 procedures annually, MIE use increased from 55.7% (n=102/183) to 72.6% (n=106/146) over the same period (p=0.023) (Supplementary Figure 2). At hospitals performing <20 esophagectomies annually, MIE patients had significantly lower in-hospital mortality (2.5% vs. 5.3%, p=0.010) and complication rates (47.8% vs. 54.8%, p=0.012) compared to OE patients (Supplementary Table 4). Median index hospitalization costs ($34,890 vs. $38,179, p=0.192) and 90-day costs ($40,327 vs. $41,505, p=0.411) were also similar between surgical approaches (Supplementary Table 4). Similar trends were observed among patients treated at hospitals performing ≥20 esophagectomies annually, except for in-hospital mortality, which was comparable between MIE and OE patients (2.9% vs. 5.0%, p=0.072) (Supplementary Table 5).

Discussion

This observational study provides real-world evidence highlighting the increasing adoption of MIE. OE utilization declined from 57% of total esophagectomy volume in 2016 to only 42% by 2021. This trend was even more pronounced at high-volume hospitals, where OE utilization dropped from 44% in 2016 to just 28% in 2021. Our findings build upon previous studies using nationally representative data from earlier time periods. For example, Espinoza-Mercado and colleagues analyzed the National Cancer Database from 2010 to 2015 and reported a decrease in OE utilization from 64% to 44% over the study period [38]. Our findings reflect a consistent shift toward minimally invasive thoracic surgery techniques over recent years [15, 30, 39]. This trend may be driven by multiple factors, including technological advancements, increased patient awareness of these technologies, and greater surgeon experience with MIE approaches. Furthermore, educational changes could have contributed to this shift, as the American Board of Thoracic Surgery mandates training in video-assisted or robotic-assisted thoracic surgery, and many thoracic training programs have incorporated formal robotic surgery curricula [40, 41]. As a result, future generations of thoracic surgeons are becoming increasingly proficient and confident in using minimally invasive techniques.

Early analyses (i.e., prior to 2015) of surgical outcomes from the Society of Thoracic Surgeons National Database and the National Cancer Database demonstrated comparable morbidity and operative mortality rates between MIE and OE [9, 38]. However, our contemporary analysis revealed shorter hospital LOS and reduced short-term morbidity and mortality following MIE, aligning with findings from more recent cohorts [7, 15–17, 42]. These improvements over time may reflect the influence of the learning-curve effect, whereby patient outcomes continue to improve over time as surgeons gain more experience with MIE techniques. Previous studies have shown that MIE adoption can lead to significant learning associated morbidity (i.e., complications that might be avoidable if patients were treated by surgeons who had surpassed the learning curve) [21, 43]. As access to advanced minimally invasive surgical technologies and formal robotics training continues to improve, it is reasonable to expect further reductions in postoperative morbidity and mortality associated with MIE.

Despite the overall benefits, morbidity following MIE remains high (approximately 42% in our cohort). However, the major advantages of a minimally invasive versus open approach, as demonstrated by these data, appear to be significantly lower rates of select complications (e.g., pulmonary complications, wound infection) and a shorter hospital LOS. Notably, our subgroup analyses uniquely demonstrates that MIE is associated with lower postoperative morbidity at both high- and low-volume hospitals. Additionally, we observed a difference in in-hospital mortality between MIE and OE at low-volume hospitals, a trend not seen at high-volume centers, potentially due to lower failure to rescue rates following esophagectomy at those institutions [44].

With healthcare budgets becoming increasingly constrained, accurate cost measurement for MIE is essential for identifying opportunities to reduce healthcare costs and improve quality of surgical care. Identifying methods to reduce healthcare expenses is crucial for esophageal cancer treatment, which ranks fourth in average cost of care per patient in the first year after diagnosis among all cancers [45]. Our study found that while MIE had higher operating room costs, likely due to the use of specialized devices and disposable instruments, its index hospitalization costs were similar to OE. This may be due to the increased upfront operative expenses of MIE being offset by the lower postoperative ICU costs, shorter hospital LOS, and reduced complication rates. Notably, although OE patients experienced higher complication rates and longer hospital stays, their index hospitalization costs remained comparable to those of MIE patients. This cost similarity may reflect the extensive resources often available at these specialized centers, including the presence of house staff, dedicated ICU care teams, and/or subspecialty services, which likely facilitate the early recognition and management of complications in a cost-efficient manner [46–49]. Ultimately, 90-day costs were also comparable between the two surgical approaches, suggesting that while MIE may offer short-term clinical benefits, its overall financial impact remains neutral.

A key finding of our study was that preoperative risk factors (i.e., high comorbidity burden) and postoperative complications are significant drivers of hospital LOS and costs following esophagectomy, irrespective of the surgical approach used. Among esophagectomy patients, those who experienced at least one postoperative complication had substantially higher rates of prolonged hospitalization and increased healthcare utilization costs across all stages of care, including operating room, ICU, index hospitalization, and 90-day costs. Additionally, among all esophagectomy patients with one or more postoperative complications, index hospitalization and 90-day costs were similar between surgical approaches. These findings underscore the importance of complication prevention and perioperative risk reduction, which may have a greater impact on outcomes and cost than surgical approach alone. As such, surgical decision-making should prioritize clinical and oncologic appropriateness for the individual patient rather than cost considerations alone.

Our results support existing data that preoperative risk factors and postoperative complications are associated with increased hospital costs after major oncologic surgeries, including esophagectomy [25, 50, 51]. For instance, Fu et al. analyzed 80 patients at a single institution and found that complications, advanced age, American Society of Anesthesiologists class IV status, and coronary artery disease were independently associated with higher costs after esophagectomy [25]. Similarly, an institutional review of patients treated at the Mayo Clinic between 2008 and 2014 revealed that mean treatment costs were 2.6 times greater for esophagectomy patients with a grade III-IV anastomotic leak (additional median standardized cost of $68,296), primarily due to prolonged hospitalization [52]. Recently, perioperative optimization strategies, such as prehabilitation and evidence-based postoperative care pathways, have emerged as potential methods to expedite recovery and reduce complications following esophagectomy [53–57]. While these early results are promising, further research is needed to determine whether these targeted interventions can help reduce complication rates and healthcare utilization costs after esophagectomy in high-risk patients.

An unexpected finding in this study was the relatively low number of hospitals meeting the Leapfrog Group’s recommended volume criteria for esophagectomy (≥20 operations annually) [37]. In our analysis, which included data from the third most populated US state, <11 hospitals performed at least 20 esophagectomies annually during each year of the study period [58]. This finding aligns with prior literature reporting that only 7–11% of US hospitals performing esophagectomies meet the Leapfrog minimum-volume standard [2, 59, 60]. While some experts have advocated for referral to high-volume centers for complex operations, it is important to recognize that substantial variability exists in outcomes even among hospitals meeting the Leapfrog standards [5, 59, 61–63]. For instance, Varghese et al. demonstrated over fivefold variation in 90-day mortality (1.7%–10.2%), 2.5-fold variation in reinterventions (8%–20%), and fourfold variation in discharges to an institutional care facility (5.3%–19.8%) among Leapfrog hospitals performing esophagectomy in Washington state [59]. Therefore, a hospital’s annual esophagectomy volume may be a limited proxy measure for surgical quality [64].

This study has several strengths. Most notably, it utilizes population-level data from the Florida SID, which captures all readmissions statewide, including index and non-index hospital readmissions. Most clinical databases do not capture readmissions to non-index hospitals, limiting their ability to accurately measure downstream healthcare costs after a patient’s index admission. Second, it is one of the few studies to use multi-institutional data to examine contemporary trends, outcomes, and costs associated with MIE and OE.

There are some limitations to this work. First, as with nearly all observational research, unmeasured confounding and treatment selection bias may have influenced our findings. Second, the Florida SID lacks cancer-specific variables such as stage or tumor size. While these factors affect long-term survival in esophageal cancer, their impact on the short-term postoperative outcomes assessed in this study is likely limited [65, 66]. The dataset used in this study also does not capture overall survival, as it is limited to inpatient admissions and does not include mortality data beyond the hospitalization period. As such, we are unable to assess long-term survival outcomes in this analysis. Additionally, we were unable to distinguish between different esophagectomy techniques (e.g., Ivor Lewis vs. transhiatal), as Current Procedural Terminology codes were not available. Lastly, since this analysis utilized data from the Florida SID, differences in patient demographics or surgical practices may limit the generalizability of our findings to the broader US population. Further research is needed to validate our findings in a large, nationally representative patient cohort.

Conclusions

MIE has had rapid growth in utilization. MIE appears to provide a viable, cost-effective alternative to OE, with fewer postoperative complications, shorter hospital stays, and comparable overall costs. Preoperative risk factors and postoperative complications, rather than surgical approach, appear to be important drivers of costs following esophagectomy. Therefore, surgeons should prioritize minimizing perioperative risk and achieving the optimal oncologic outcome, rather than cost implications, when selecting between MIE and OE for a given patient. Future research should aim to identify targeted interventions that can help reduce complications rates and costs after esophagectomy, particularly in high-risk patients.

Supplementary Material

Supp File

Acknowledgements

Guarantor:

ST takes responsibility for the integrity of the research and its results.

Funding Sources:

This work was supported in part by National Cancer Institute (Grant T32CA009621 to ST, Award Number 3P30CA091842–22S4 to ST) and The Foundation for Barnes-Jewish Hospital Grant #6476 (to ST). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. This content is solely the responsibility of the authors and does not reflect the official views of the funding sources.

Abbreviations Used:

HCUP

Healthcare Cost and Utilization Project

MIE

minimally invasive esophagectomy

OE

open esophagectomy

SID

State Inpatient Database

US

United States

Footnotes

Financial Disclosures: Dr. Varun Puri has disclosed receiving honoraria from PrecisCa and that his spouse owns stock in Intuitive Surgical. Dr. Nikki Rossetti and Dr. Benjamin Kozower disclose receiving additional grants outside of the submitted work from the National Cancer Institute (R38CA255575 to NER, R01CA258681 to BDK). Dr. Steven Tohmasi, Yifei Xu, Dr. Jingxia Liu, Dr. Whitney Brandt, and Dr. Bryan Meyers have no conflicts of interest or financial ties to disclose.

Meeting Presentation: This work was presented at the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) 2025 Annual Meeting in Long Beach, California, March 12–15, 2025.

Institutional Review Board Statement: This study was deemed exempt from the Institutional Review Board at the Washington University in St. Louis due to the deidentified and retrospective nature of the analysis.

Data Availability Statement:

The data used in this study are maintained by the Agency for Healthcare Research and Quality. The State Inpatient Database used in this study is part of the family of databases developed for the Healthcare Cost and Utilization Project (HCUP). HCUP data are publicly available for purchase through the HCUP Central Distributor. For more information, visit https://hcup-us.ahrq.gov/tech_assist/centdist.jsp or contact the HCUP Central Distributor at HCUP@AHRQ.gov. All HCUP data users must complete the Data Use Agreement Training Tool and sign the Data Use Agreement for State Databases. Additional data inquiries can be directed to the corresponding author. We may balance the potential benefits and risks of each request and then provide the data that can be shared.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp File
NIHMS2119606-supplement-Supp_File.docx (1.3MB, docx)

Data Availability Statement

The data used in this study are maintained by the Agency for Healthcare Research and Quality. The State Inpatient Database used in this study is part of the family of databases developed for the Healthcare Cost and Utilization Project (HCUP). HCUP data are publicly available for purchase through the HCUP Central Distributor. For more information, visit https://hcup-us.ahrq.gov/tech_assist/centdist.jsp or contact the HCUP Central Distributor at HCUP@AHRQ.gov. All HCUP data users must complete the Data Use Agreement Training Tool and sign the Data Use Agreement for State Databases. Additional data inquiries can be directed to the corresponding author. We may balance the potential benefits and risks of each request and then provide the data that can be shared.

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