Surgical stapling is ubiquitous in abdominal surgery. Surgical staplers have advanced through the years and have now become automated. They now include built in safety features as well as the capability to sense tissue thickness and resistance to the device. This feedback in real time can allow the surgeon to modify staple cartridge size to accommodate thicker or thinner tissue. The safety profile of these staplers is very high, with an estimated failure rate of 1 in 8000 firings [1]. Even then, most of those events are not clinically significant, and are usually just an equipment failure with no risk to the patient. We previously reported on the safety of stapling devices and found that the most common adverse event reported to the Food and Drug Administration was failure to fire for both Medtronic and Ethicon. Failure to fire can be time consuming and frustrating to the surgeon but has little chance of harming the patient. The efficacy of these staplers is proven daily, and data from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) demonstrates very low leak and bleeding rates [2]. The MBSAQIP does not have the granularity to determine if stapling devices are directly responsible for leaks or bleeding, but with the rise of the sleeve gastrectomy (SG), which is a purely stapling procedure, most surgeons recognize the role of the stapler in these two complications.

In the last decade, a third company has entered the arena of surgical stapling. Intuitive Surgical introduced the robotic platforms stapler in 2012 [3]. The first iteration of this robotic stapler (RS) was the Endowrist™ and later it has been refined to the Sureform™. There is little data reported on this stapler, but by evaluating the MBSAQIP, suppositions about its safety can be made. According to the latest American Society of Metabolic and Bariatric Surgeons estimates, robotic-assisted cases made up 30% of total bariatric operations in 2022 [4]. There is no statistically significant increase in leakage in these cases compared to non-robotic, again demonstrating equivalent safety. However, these observations are muddled by the fact that not all robotic surgeons use the robotic stapler. The type of stapler used is not reported in the MBSAQIP, so a robotic case may or may not have used a robotic stapler. There are a few reasons for this, including surgeon comfort level with their traditional bedside staplers and distrust in the first-generation robotic stapler.

Few studies have evaluated results of non-robotic bedside staplers (BS) used in robotic procedures [5,6,7]. In addition, a previous study showed that robotic staplers need more reloads to complete the gastric pouch and the overall stapling costs were higher than bedside staplers [8]. The objective of this study is to evaluate the effectiveness of bedside staplers compared with robotic staplers during bariatric robotic-assisted procedures using a nationwide hospital-based database.

Materials and methods

Data sources

Data were extracted from the PINC AI™ Healthcare Data (PHD). The PHD comprises U.S. hospital-based, service-level, all-payor information on inpatient discharge [9]. More than 1400 hospitals/healthcare systems contribute data to the PHD with more than 9 million visits per year since 2012, representing approximately 25% of annual United States inpatient admissions. The PHD contains information on hospital and visit characteristics, admitting and attending physician specialties, healthcare payers, and patient data, including demographics, disease states, diagnoses, costs, medications, and device details from standard hospital discharge billing files. The PHD is de-identified in accordance with the HIPAA Privacy Rule. This study was determined exempt from full board review by Sterling IRB.

Study population

Patients who underwent primary sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) with a robotic system in the inpatient setting between 1/1/2021 and 12/31/2021 were obtained from PINC AI™ Healthcare Data. Inclusion criteria were patients whose procedure used BS and RS, had all the key variables, and had non-zero costs (Fig. 1). Robotic procedures were defined as an inpatient claim with a secondary procedure code (International Classification of Diseases version-10 [ICD 10 PCS]) or any claim with a Current Procedure Terminology (CPT) code indicated as a robotic procedure or patients charged with robotic supplies. These included CPT code S2900, ICD-10 code 8E0W4CZ.

Fig. 1
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Study cohort selection flow chart

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Study design

This study is based on Donabedian A4. “Structure-Process-Outcomes Quality Framework” to evaluate outcomes (clinical outcomes and healthcare resource utilization) effectiveness of bariatric robotic procedures (Fig. 2) [10].

Fig. 2
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Donabedian’s structure-process-outcomes quality framework [10]

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Clinical outcomes and healthcare resource utilization

Clinical outcomes included the rates of blood transfusion, bleeding, anastomotic leak, intensive care unit (ICU) visits, and 30-day urgent and emergency room readmission. We used ICD-10 diagnosis and procedures codes and CPT codes to identify blood transfusion, bleeding, and anastomotic leak from patients’ inpatient claim file. Premier ‘READMIT’ file and charge master files were used to identify ICU and readmission. Healthcare resource utilization included operating room (OR) time in minutes obtained from the hospital charge file, costs (in US dollars), and length of stay (in days).

Patients and hospital characteristics, and type of staplers used

Patient characteristics included age group (< 40, 40–54, 55–64, and 65), gender, race and ethnicity (Non-Hispanic White, Non-Hispanic Black, Hispanic, and others), payer (Medicare, Medicaid, Private, and others), obese diagnosis, sleeve or bypass procedures, Charlson comorbidity index (0, 1–2, and 3+), and APR severity (Minor/Median vs Major/Extreme). Provider characteristics included region (Northeast, Midwest, South, and West), urban or rural, bed size (< 500 beds vs ≥ 500 beds), teaching status, and bariatric procedure annual volume.

The key variable evaluated in the study is the type of stapler. We grouped staplers into two groups: bedside staplers included Johnson and Johnson Ethicon™ or Echelon™ staplers and Medtronic Signia™, Endo Gia™, Tri-staple™ staplers; the robotic staplers included the latest Intuitive SUREFORM™ staplers. The manual/intelligent bedside staplers (MIBS) subgroup only included Medtronic beside staplers. All types of staplers were used product name/product number as text searching through the hospital change master file.

All study codes are listed in Supplementary Table 1.

Statistical analysis

Effectiveness was measured by clinical outcomes and healthcare resource utilization. The covariates to evaluate effectiveness included baseline patient and provider characteristics. We evaluated baseline balance in BS and RS and the bivariate association between covariates and outcomes using Chi-square or Fisher exact test and t-test or ANOVA. Multivariable general linear mixed models (GLMMs) with respective gamma or binomial distribution and log-link function were used to obtain the variations of adjusted outcomes and healthcare resources utilization between BS and RS. Additionally, a subgroup analysis was performed to analyze the effectiveness of manual/intelligent bedside staplers.

Sensitivity analysis

For testing the robustness of the results, two additional modeling approaches for sensitivity analyses are used. First, we used propensity score matching methods to adjust baseline imbalance for two types of staplers. We performed a 1:1 match between bedside staplers and robotic staplers with a maximum caliper width of 0.2 for the absolute probability using the nearest neighbor technique without replacement. All baseline patients and provider characteristics were input into a logistic regression model for propensity score matching. The chi-squared, fisher exact, or paired t-test was used to examine the outcomes of the post-matched cases [11]. Secondly, we used propensity scores covariate adjustment multivariable general linear mixed models to estimate the outcome variations between types of staplers used. The propensity score calculation was based on all baseline patients and provider characteristics. The statistical significance was determined if p-value < 0.05. All data management and analyses were conducted using SAS 9.4 software (SAS Institute Inc, Cary, NC) using 2-sided statistical tests.

Results

Baseline patients and hospital characteristics

A total of 7268 discharges met the inclusion criteria and included 1603 (22.1%) BS and 5665 (77.9%) RS cases. RS cases, compared to BS, consisted of a higher number of patients who were Hispanic (17.0% vs. 9.4%), had Medicaid (26.9% vs. 19.4%), and underwent sleeve gastrectomy (68.4% vs. 53.5%). Compared to BS, higher proportions of RS cases were done by providers in Northeast region (35.5% vs. 24.3%), urban (97.2% vs. 92.4%), smaller hospital size (< 500 beds; 71.1% vs. 52.3%), and teaching hospitals (59.4% vs. 39%). MIBS, a subgroup of BS, had similar patient and hospital characteristics distribution. Compared to MIBS, a higher proportion of RS cases were under age 40 (40.9% vs 35.1%), had less than three comorbidities (93.6% vs 91.3%), and were performed in lower volume hospitals (46.5% vs 27.7%) (Table 1).

Table 1 Patient and provider characteristics
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The effectiveness of clinical outcomes and healthcare resource utilization

The crude rates of blood transfusion and ICU stays were higher for RS compared to BS (1.2% vs 0.5%, p = 0.015; 0.64% vs 0.12%, p = 0.012). The proportion of ICU stays was also higher in the RS group compared to the MIBS group. (0.64% vs 0%, p = 0.011). For other clinical outcomes (bleeding, anastomotic leak, and readmission), bedside staplers had equivalent results as robotic staplers. The unadjusted OR time was higher when RS were used (152.3 ± 64.9 (mean ± standard deviation) minutes) compared to BS (137.9 ± 54.1 min) as well as MIBS (129.9 ± 51.3 min). Bedside staplers ($14,202 ± $5209) were less costly compared to robotic staplers ($15,156 ± $6141) (Table 2).

Table 2 Unadjusted clinical outcomes and health care resources utilization by types of staplers
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After balancing baseline patient and provider variation between RS and BS, the adjusted outcomes variations between RS and BS groups showed that the RS group was significantly more likely to have blood transfusions odds ratio [(OR) (95% confidence interval (CI)): 2.4 (1.1, 5.0), p = 0.02], ICU stays [OR 8.5 (95% CI 1.9, 37.0)], increased operative time [OR 19 (95% CI 17.9, 20.2)], and costs [$1273 ($1191, $1355)] US dollars with a lower length of stay [OR − 0.26 (95% CI − 0.27, − 0.24) days, p < 0.001] compared to the BS group. Compared to RS, MIBS patients had 31.6 min shorter operating time (95% CI 29.4, 33.8) and $2670 less inpatient costs (95% CI $2518, $2822), but slightly longer length of stay (0.16 days, 95% CI 0.15, 0.16, p = 0.002) (Tables 3 and 4).

Table 3 Adjusted clinical outcomes variations between robotic staplers and the compared bedside staplers
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Table 4 Adjusted variations of health care resources utilization between robotic staplers and the compared bedside staplers manual/intelligent bedside staplers and robotic staplers
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Sensitivity analysis

Our sensitivity analysis–propensity score covariate adjustment GLMMs showed similar results except no significant differences in blood transfusion rates between BS and RS (Tables 3 and 4). The propensity scores matching (PSM) method did not obtain enough matched pairs for an unbiased estimation of outcomes variations, for BS versus RS (1517 pairs, 94.6% of cases were matched) and for MIBS versus RS (657 pairs, 76.1% of cases were matched). However, the results of PSM were similar to the other two approaches (Table 5).

Table 5 The results of the propensity score matched cohorta
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Discussion

The important findings of this study were that bedside staplers had better outcomes than robotic staplers in terms of lower rates of blood transfusion, less ICU stays, shorter operative time and lower costs. The difference in blood transfusion was not shown in the GLMM sensitivity analysis. It should be noted that these findings are from a single year (2021) using data from a hospital-based database. But they may be generalizable to the bariatric population as a whole, since this data is collected on a national level. This study helps to clarify outcomes related to the type of stapler in robotic-assisted cases. Up until now, it was difficult to determine if complications in robotic surgery were secondary to the type of stapler used. This is because most papers use the MBSAQIP database for short-term outcomes. But there is no way to determine what type of stapler was used in the MBSAQIP database, as that is not reported data. That is why the PINC AI™ was used to evaluate this, as each stapling company’s product could be individually evaluated. However, it is important to note that this data is from 2021, and in the last 3 years surgeon use of RS may have dramatically increased [4]. In fact, the ASMBS Task Force in bariatric volume reported a rate of 30% robotic-assisted cases in 2022.

Bedside stapling was the norm for robotic-assisted bariatric operation until Intuitive released their first generation of endoscopic staplers. But even then, many surgeons continued to use beside staplers. Intuitive has continuously improved their stapler and released a newer version. The robotic platform also can be modified with software updates to improve performance of the RS. The rate of bedside stapling will likely decrease over time in robotic-assisted cases as time goes by.

The question remains; however, will surgeons continue to use BS with the Intuitive robotic platform? Bedside staplers have a long track record and have improved continuously over the years. There is force feedback in these staplers and tissue sensing technology. Many surgeons have their assistant fire the stapler at the bedside and prefer to have a skilled assistant there. There is also a familiarity factor and mid to late career surgeons have used these staplers literally tens of thousands of times and may be resistant to changing from a product they are very comfortable with.

Study limitation

Due to small or zero events for ICU visits in the comparison of Medtronic staplers versus SUREFORM, we used a modified model (Firth or Fisher exact) to analyze and estimate the rate of ICU visits. As a result, the 95% confidence interval was wide. Another limitation is that the hospital-based database does not include the granularity of staple load (height) and staple line reinforcement. These factors can affect the formation of staples and outcomes and we were unable to account for these two factors.

Conclusion

Bedside staplers significantly reduce healthcare resource utilization with equivalent effectiveness and fewer ICU stays compared to robotic staplers.