Abstract

Laparoscopic staplers are used extensively to seal and transect tissue. These devices compress tissue between the stapler jaws to achieve a desired compressed tissue thickness in preparation for stapling. The extent and rate of compression are dependent on surgeon technique, tissue characteristics, and stapler type, all of which can impact stapling outcomes such as bleeding, staple line leaks, and tissue healing. Historically, surgeons have relied on their experience, training, and tactile feedback from the device to optimize stapling. In recent years, the transition to electromechanical and robotic staplers has greatly impacted the tactile feedback available to the surgeon. This raises new questions about the optimal rates of tissue compression and the resultant tissue forces. This study quantifies the transmural biomechanics of the porcine stomach wall. Multirate indentation tests were used to observe the effects of indentation rate on the viscoelastic behavior of the stomach tissue during indentation, stress relaxation, and unconstrained recovery. Results show that the stomach wall demonstrates higher stress relaxation (88% versus 80%) and greater strain recovery (52% versus 47%) when indented at high rates (37.5%/s) versus slow rates (7.5%/s). Additionally, water content analysis was used to study fluid flow away from indented regions. Unindented regions were found to have greater water content compared to indented regions (78% compared to 75%). This data generated in this study may be used to enable the development of constitutive models of stomach tissue, which in turn may inform the control algorithms that drive compressive surgical devices.

1 Introduction and Background

Obesity and obesity-related illness affect a substantial percentage of the US population and this percentage is on the rise [1,2]. According to the 2021 National Health and Nutrition Examination Survey (NHANES), the obesity prevalence in adults (above the age of 20) has grown from 30.5% to 41.9% from the year 2000 to 2020 [2]. During this period, morbid obesity has increased from 4.7% to 9.2% [2]. People diagnosed with obesity have an increased chance of heart disease, stroke, type 2 diabetes, and certain types of cancer, diseases that are well known to be the leading causes of preventable, premature death [3]. Obesity also incurs a large economic toll with $173 billion spent on obesity-related medical care costs in the United States in 2019 alone [4].

A number of prescription medications (Wegovy, Saxenda, Xenical™, Alli™, Qsymia™, Contrave™, etc.) have been approved by the FDA to target weight loss [5]. While these medications are effective for some people in their battle against obesity, surgical interventions are often required for those struggling with morbid obesity [6,7]. Bariatric surgeries surgically alter the mechanics and geometry of the gastrointestinal (GI) tract to achieve clinically desired weight loss and have emerged as an effective intervention for morbidly obese patients [1].

The most common bariatric procedure is the vertical sleeve gastrectomy (VSG) [8]. In 2019, of the 256,000 bariatric surgeries performed in the United States, sleeve gastrectomy accounted for nearly 60% of these procedures [9]. During a sleeve gastrectomy, surgeons mobilize the stomach and use surgical staplers to transect and remove 75–80% of the stomach. As shown in Fig. 1, this leaves behind a tube-shaped stomach with reduced food capacity [10], altered gastric motility, and numerous physiological and metabolic changes associated with the procedure. Electromechanical handheld laparoscopic staplers are the most commonly used devices in VSG procedures today. These handheld, powered staplers use battery power to drive the functions of the device ranging from deploying the staples and cutting the tissue, articulating the end effector, and closing the jaws of the stapler on tissue [11]. These powered staplers use unique tissue gripping techniques in combination with predefined compression cycles to reliably deploy the desired staple lines. The Echelon™ 3000 system (ECH) by Ethicon and the Signia™ Stapling System (SIG) by Medtronic represent the latest handheld powered stapling technologies used in VSG procedures [11]. These staplers employ different approaches to achieve reliable stapling and transection of the stomach.

Fig. 1
(a) Outcome of a vertical sleeve gastrectomy procedure highlighting the deployed staple line, (b) initial state of the tissue bi-layer prior to stapling, (c) intermediate state of the tissue bi-layer during the precompression phase of the stapling procedure, and (d) final state of the tissue bi-layer after the rapid compression during staple firing
Fig. 1
(a) Outcome of a vertical sleeve gastrectomy procedure highlighting the deployed staple line, (b) initial state of the tissue bi-layer prior to stapling, (c) intermediate state of the tissue bi-layer during the precompression phase of the stapling procedure, and (d) final state of the tissue bi-layer after the rapid compression during staple firing
Close modal

Handheld staplers such as the ECH and SIG staplers apply compression to the tissue when the jaws of the stapler are closed onto the tissue. These devices close to an instrument defined location which roughly establishes a displacement boundary condition or tissue gap which is defined by the final distance between the jaws, the associated geometry of the end effector jaws, and the relative compliance in the jaw closure system. Therefore, the actual initial tissue compression for a given device is dictated by the characteristics of the tissue (e.g., thickness, stiffness, etc.) between the jaws of the device. Different devices can therefore deliver different amounts of initial compression. For manually closed systems, the rate of compression of the tissue is determined by the surgeon. For devices with powered closure, the rate of initial closure is defined by the device. As tissue is viscoelastic, the amount of precompression will stabilize over time. Some device manufacturers recommend waiting 15 s for these viscoelastic changes to stabilize [12,13]. During this phase, it is expected that the compressed tissue undergoes viscoelastic stress relaxation. Once compressed, the surgeon can fire the device at their discretion. Both the ECH and SIG stapling platforms rely on a staple gap setting feature (i.e., E-Beam or I-Beam) to set a precise distance between the staple anvil and the staple deployment surfaces of the jaws. This enables the staples to be formed to the precisely desired heights dictated by the device. During this phase, the jaws of the device are rapidly moved to the target closure distance to ensure proper staple formation. The rate at which this occurs depends largely on the initial gap between the jaws and the amount of closure required to achieve the target gap. Systems that apply greater amounts of initial compression and therefore initially drive the system closer to the final target gap would appear to apply slower rates of compression during this firing phase. Rates of final compression during firing can also be influenced by the firing speed of the stapler. The SIG system, for instance, fires staples at three different predetermined rates depending on real-time feedback [12]. There is considerable variability in tissue compression rates during stapling on account of variations between stapler platforms, variations in tissue characteristics and variations that result from changes in firing speed. Given the rate effects are likely amplified due to viscoelastic behavior, the resultant tissue and device loads can vary dramatically from one staple firing to the next. The extent and rate of compression, as well as the stabilization of tissue viscoelastic effects via sustained compression prior to staple firing, strongly impact the outcomes of stapling procedures, i.e., a biomechanically stable staple line with properly formed staples that reduce the risk of staple line leaks (SLLs) [13,14]. Complications due to SLLs are a leading cause of morbidity and mortality in gastric surgery patients [15].

Stapling systems have historically relied on the surgeon experience to optimize the stapling experience with the surgeon relying on their training, experience, and the tactile feedback from the device to control the rate of compression applied to tissue during jaw closure and firing maneuvers. The transition toward fully powered staplers and robotic surgery eliminates this tactile feedback component from some of these steps. Given the impact of tissue compression on the performance of the staple line, a thorough understanding of tissue biomechanics and viscoelastic response is vital to the design of next-generation stapling instruments.

The stomach is a large and diverse organ that undergoes large deformations during normal function [1,16]. The stomach is comprised of four layers from the outside in: i.e., the serosa, muscular, submucosa, and mucosa [15]. The biomechanics of the stomach are largely governed by the muscular and submucosal layers [16]. The contraction of the muscular layer results in the motility of the stomach while the submucosal layer constrains stomach deformations at large internal pressures [16]. Interactions between the stomach layers further increase the complexity of the mechanical response of the stomach. From esophagus to duodenum, the stomach may be divided into three regions: the fundus, corpus, and antrum (Fig. 2) with all three regions involved in VSG procedures [1,8,16]. These regions exhibit variations in their thicknesses and layer-wise compositions resulting from their individual functions and as such exhibit variations in their mechanical properties as well [16]. Due to the complexity of the structure, significantly greater thickness, and regional thickness variations, stapling of the stomach wall for gastric procedures is relatively more challenging than other sites along the gastrointestinal tract.

Fig. 2
Porcine stomach anatomy: (a) exterior view of the stomach and (b) interior view of the stomach opened along the greater curvature. Samples for mechanical testing are highlighted in green (right square) and orange (left square). Note, the green highlighted samples were tested at the slow indentation rate while the orange highlighted samples were tested at the fast indentation rate.
Fig. 2
Porcine stomach anatomy: (a) exterior view of the stomach and (b) interior view of the stomach opened along the greater curvature. Samples for mechanical testing are highlighted in green (right square) and orange (left square). Note, the green highlighted samples were tested at the slow indentation rate while the orange highlighted samples were tested at the fast indentation rate.
Close modal

While the mechanical properties of the stomach under tensile loads have been characterized extensively [1619], there is surprisingly limited or no data on the biomechanical response of the stomach wall to compressive loading, as seen during tissue stapling. Existing biaxial and uni-axial tensile tests performed on the stomach wall highlight its anisotropic (orientation-dependent) and heterogeneous (location-dependent) mechanical properties. Bauer et al. [16], through histological analysis of the stomach wall, highlighted the large intra-organ regional variations in thickness, layer composition/thicknesses, and fiber orientations. However, this prior work focuses only on tensile loading and the related biomechanical properties of the stomach wall. While this is relevant to the understanding of stomach functional mechanics (i.e., filling and peristalsis), tensile properties do little to inform the effects of compressive strains applied to the tissue during stapling procedures at different strain rates. Addressing this data and knowledge gap is the central theme of this research work which focuses on developing a deeper understanding of the nonlinear elastic and viscoelastic mechanical properties of the stomach wall during compressive loading.

The nonlinear viscoelastic response of soft tissues depend on their composition, ultrastructure, and the resulting interactions between various components during deformation [20,21]. Tissues consist of cells, extracellular matrix (ECM), and extracellular fluid [22]. The major components of the ECM include collagen, proteoglycans (PGs), elastin, and cell-binding glycoproteins [23]. The organization of the collagen network drives the nonlinear mechanical properties of tissue [22]. This organization is maintained by the proteoglycan (PG) matrix which consist of a core protein to which a glycosaminoglycan (GAG) is bound [24]. Under compressive loads, the hydrostatic stress applied to the GAGs results in water being displaced from the region of high hydrostatic pressures [25]. When the compressive loads are released, the displaced water is reabsorbed back into the tissue resulting in hydration and swelling. As tissues largely consist of water, this hydrostatic compressive loading induced diffusion of water within the ECM manifests as viscoelastic mechanical behavior with respect to the macroscopic response of the tissue [22].

This study quantifies the viscoelasticity of the stomach wall in the context of the loading seen during surgical stapling procedures. During instrument-mediated tissue compression, the intrinsic viscoelasticity of the solid matrix (collagen-PG network) and the flow of fluid away from the region of compression are the likely underlying mechanisms that govern the nonlinear response of the tissue as well as its stress relaxation and creep behavior. In this study, the loading profile during stomach wall stapling was simulated through uni-axial indentation, followed by stress relaxation and reverse creep (tissue recovery) protocols. The loading profiles selected are based on compressive strains seen during typical stapling procedures, where the stomach wall undergoes large compressive strains. Current stapling devices apply this compression at a range of compression rates and with differing hold/relaxation periods. This study explores the effect of the indentation rate on the viscoelastic mechanical response (stress relaxation, and strain recovery), and water content of the stomach wall in a porcine model to drive the development of the next generation of tissue stapling devices.

2 Materials and Methods

2.1 Sample Preparation.

The porcine stomachs used in this study were sourced from Animal Technologies, Inc. (Tyler, TX). All stomachs were harvested from female pigs, between the ages of six to nine months, and weighing between 165 and 200 lbs. at the time of euthanasia to limit variations in the overall size and regional sizes of the stomachs. No breed control was placed on the source animals due to limitations imposed by the harvesting facility. Stomachs were washed at the harvesting facility prior to overnight shipment on ice to the testing laboratory where they were stored at 4 °C until testing. All testing on each stomach was completed within 36 h of stomach harvest.

Indentation studies were performed on 20 mm square samples excised from the corpus region of the stomach using a custom square punch. Sampling was limited to the corpus region to minimize variability arising from the region-dependent variations in stomach properties. Samples were oriented with one edge approximately parallel to the lesser curvature of the stomach (Fig. 2(b)). Two intact stomach wall samples, symmetrically located, were obtained, one from each side of the stomach. Based on similarity in the layer-wise composition at the symmetric locations, equivalency was assumed in the mechanical behavior at anatomic sites on either side of the plane of symmetry along the lesser curvature [16]. Samples obtained from the left side of the stomach were assigned to the fast indentation group, while the samples from the right side were assigned to the slow indentation group (Fig. 2(b)). The edge of the sample closest to the lesser curvature was marked with green dye to ensure consistent sample orientation during mechanical testing and subsequent histology. Samples were immersed in phosphate-buffered saline (PBS) for 5 min to achieve equilibrium water content prior to testing. Twenty-eight samples from a total of fourteen stomachs were collected for mechanical testing. Samples from four stomachs were used to analyze water content postindentation.

2.2 Indentation Testing.

Displacement-controlled indentation testing was conducted on the serosal surface of the stomach samples using a 5 mm diameter cylindrical indenter on an MTS Insight 1 mechanical test frame with a 500 N load cell that measured the applied compressive loads (Fig. 3). Testing was conducted in a fluid bath with PBS maintained at 37 °C. The mucosal surface of the stomach samples was lightly bonded to the base platen using a weak water-soluble adhesive (UHU® Stic; UHU GmbH and Co. KG, Bühl, Germany) to prevent samples from floating during testing. Staplers such as the ECH system can accommodate tissues up to about 4 mm thick [26], close to the double wall thickness of human stomach tissue [27], and compress these tissues to about 1 mm during the initial tissue compression phase (internal Ethicon data). We therefore chose 75% as a high-end estimate of the compressive strain applied during stapling. The test frame was programed to execute the indentation testing cycle shown in Fig. 4(a). The indentation testing cycle, approximating the loading regime of the Ethicon ECH stapler, was comprised of three stages: the ramp loading phase, the stress-relaxation phase, and the recovery phase (Fig. 4). During the ramp loading phase, samples were compressed to 75% of their original thickness at “slow” (7.5%/s) or “fast” (37.5%/s) indentation rates. Following ramp loading at either of these rates, the samples were held at the 75% compression level for 15 s (matching the stapling protocol used by the Ethicon ECH stapler2) while the compressive load on the tissue was tracked. Lastly, the indenter was rapidly retracted 4 mm at 8 mm/s, and the position of the indented tissue surface was tracked over 14 min to measure tissue recovery.

Fig. 3
Test frame configuration and test sample orientation. (a) Test frame setup consisting of the indenter, displacement (LVDT) sensor, with the sensor core threaded to an indenter cap. (b) Square intact porcine sample loaded on the test frame. Note the green dye used to orient samples consistently across tests. (Color version online.)
Fig. 3
Test frame configuration and test sample orientation. (a) Test frame setup consisting of the indenter, displacement (LVDT) sensor, with the sensor core threaded to an indenter cap. (b) Square intact porcine sample loaded on the test frame. Note the green dye used to orient samples consistently across tests. (Color version online.)
Close modal
Fig. 4
Indentation loading cycles for mechanical testing: (a)input signals used to indent stomach samples at the slow (7.5%/s) and fast (37.5%/s) indentation rates. (b) Sample response for a slow indented sample with resultant output curves for each corresponding phase of the cycle.
Fig. 4
Indentation loading cycles for mechanical testing: (a)input signals used to indent stomach samples at the slow (7.5%/s) and fast (37.5%/s) indentation rates. (b) Sample response for a slow indented sample with resultant output curves for each corresponding phase of the cycle.
Close modal

To apply compressive loads and also track the recovery of the tissue surface, a novel indenter (Fig. 5) was designed and fabricated incorporating a high-resolution miniature Linear Variable Differential Transformer (LVDT) displacement sensor (Parker LORD Micro-Strain™ M-LVDT 9, Cary, NC; 15 μm resolution). The LVDT body was bonded to a three-dimensional printed housing and a Delrin cap was attached to the LVDT core. During indentation (Figs. 5(a) and 5(b)) the cap prevented load transfer to the fragile sensor. During indenter retraction and the recovery phase (Figs. 5(c) and 5(d)), the cap and LVDT core remained in contact with the tissue. The motion of the tissue surface thus produced relative motion between the LVDT core and body, allowing measurement of the tissue recovery. The LVDT core and cap have negligible mass, allowing for close approximation of a zero-stress state during the recovery phase. The low mass also enabled low contact force measurement of the initial thickness of the sample using the LVDT by measuring the positions of the base platen and the tissue upper surface.

Fig. 5
Illustration of the tissue recovery measurement protocol as performed by the displacement (LVDT) sensor. (a) The indenter and LVDT sensor configuration prior to start of the indentation cycle. (b) The sample is indented. The indenter cap transfers compressive loads to the fixture protecting the sensitive LVDT sensor. (c) The indenter is retracted (at the end of the relaxation phase) and the LVDT core is extended. (d) The indenter cap and LVDT core track the tissue recovery.
Fig. 5
Illustration of the tissue recovery measurement protocol as performed by the displacement (LVDT) sensor. (a) The indenter and LVDT sensor configuration prior to start of the indentation cycle. (b) The sample is indented. The indenter cap transfers compressive loads to the fixture protecting the sensitive LVDT sensor. (c) The indenter is retracted (at the end of the relaxation phase) and the LVDT core is extended. (d) The indenter cap and LVDT core track the tissue recovery.
Close modal

Compressive load and indenter displacement were sampled at 100 Hz via the test frame data acquisition system. Indenter displacement was used to calculate the applied strain during the loading and stress relaxation phases. The LVDT was connected to a LORD Micro-Strain DEMOD-DC™ signal conditioner with the output voltage signal sampled using a National Instruments (NI) USB-6002 data acquisition unit at 100 Hz. LVDT displacements were used to calculate the tissue strain during the recovery phase. The test frame was configured to output a voltage signal at the start of the test which was used to synchronize the two data acquisition platforms.

2.3 Indentation Data Analysis.

Data from the twenty-eight indentation tests was postprocessed using matlab (Release 2021b). Models were fit separately to each test phase (loading, stress relaxation, and strain recovery) from each sample using nonlinear least squares regression. Failures in the LVDT data acquisition unit necessitated the removal of strain recovery data from three stomachs (six samples) from the data analysis.

In the loading phase, a phenomenological exponential growth equation was used to fit the compressive stress (σ)–strain (δ) data [28]
(1)
where k and α represent the exponential growth constant and rate, respectively. In the stress relaxation phase, the compressive stress was normalized against the stress at the start of the relaxation phase, and a two-term generalized Maxwell model (using a Prony Series expansion of the relaxation modulus) was used to fit the normalized compressive stress (σ)–relaxation time (t) data [28]
(2)
where G1, G2 represent the reduced relaxation moduli and τ1, τ2 represent the associated characteristic relaxation times for the decay function over two-time scales, i.e., instantaneous (<1 s) and over the longer time scale of the experiment (i.e., several seconds). The initial slope of the relaxation function, i.e., the relaxation rate, can be computed via the time derivative evaluated at t = 0 as
(3)
The relaxation rate at later time points (including at 15 s) can be evaluated from
(4)
Similarly, in the strain recovery phase, a two-term exponential decay function was fit to the compressive strain (δ)–recovery time (t) data [28]
(5)

where S1, S2 represent the normalized recovery decay coefficients and τ1, τ2 represent the recovery time constants of each exponential term.

Each of the fitted model parameters for each test phase was compared for indentation rate dependence via a Wilcoxon signed-rank test, on account of the non-normality of the dataset. This paired samples test was used under the assumption that samples extracted from symmetric locations on the stomach about the lesser curvature exhibit symmetric layer-wise composition and thus, similar mechanical behaviors. Additional Wilcoxon signed-rank tests were performed examining the effect of indentation rate on the compressive stress at five discrete compressive strains: 0.15, 0.30, 0.45, 0.60, and 0.75. A final set of these tests were performed to examine the effect of indentation rate on the normalized relaxation load at the end of the relaxation phase, and the final residual compressive strain after 14 min of recovery.

2.4 Water Content Measurement.

Water content measurements were used to analyze the effect of indentation on the water content in the indentation zone. Correlations between recovery levels and water content could provide insight into the effect of indentation on the GAGs and PGs and help explain the mechanisms during relaxation and recovery.

Samples from four stomachs, i.e., four slow-indented samples, and four fast-indented samples were processed for water content measurements. From each square sample, a 6 mm biopsy punch was used to extract two cored samples, one from the indented region, and the other from an adjacent unindented region (Fig. 6). The cylindrical samples were weighed on a US Solid (Cleveland, OH) USS-DBS5 Analytic Balance after collection and then dried under vacuum at 37 °C. Samples were weighed every 24 h until weight measurements showed a less than 1% change between consecutive measurements. Water content was calculated as the change in weight in the dry state as a percentage of the original weight. Wilcoxon signed-rank tests were used to determine whether there was a statistically significant difference in water content between slow and fast indented samples, and between indented and unindented samples.

Fig. 6
Square sample post indentation cycle and prior to water content analysis. Note the indented region (leftmost and rightmost red circles) and an adjacent unindented region (middle green circle) were cored and used for dry weight measurements.
Fig. 6
Square sample post indentation cycle and prior to water content analysis. Note the indented region (leftmost and rightmost red circles) and an adjacent unindented region (middle green circle) were cored and used for dry weight measurements.
Close modal

3 Results

3.1 Indentation Testing

3.1.1 Loading Phase.

The data in the loading phase demonstrated significant overlap between the two indentation rates (Figs. 7(a) and 7(b)). Wilcoxon signed-rank tests at the 5 compressive strains indicated no statistically significant difference in compressive stress between the two groups below 0.6 strain (p > 0.05, N = 14). The compressive stress at 0.75 strain was higher (p < 0.05, N = 14) in the fast indented group (1.81 ± 0.43 MPa) compared to the slow indented group (1.59 ± 0.41 MPa). Wilcoxon signed-rank tests on the fitted model parameters indicated that indentation rate had no significant impact (p > 0.05, N = 14) on k and α (Fig. 8; Table 1). Model fits displayed average Root Mean Squared Error (RMSE) values below 0.025 MPa in both groups.

Fig. 7
Response curves from each phase of the indentation cycle, highlighting transformed data, and the spread of curve fit models. (a) and (b) Indenter stress response for the loading phase. (c) and (d) Stress-relaxation behavior as recorded during the relaxation phase. (e) and (f) Recovery of the samples over the course of 14 min as tracked by the LVDT sensor.
Fig. 7
Response curves from each phase of the indentation cycle, highlighting transformed data, and the spread of curve fit models. (a) and (b) Indenter stress response for the loading phase. (c) and (d) Stress-relaxation behavior as recorded during the relaxation phase. (e) and (f) Recovery of the samples over the course of 14 min as tracked by the LVDT sensor.
Close modal
Fig. 8
Spread of parameters obtained from curve fitting response curves from each corresponding phase highlighted using boxplots. Whiskers illustrate the range of the nonoutlier data. Outliers (crosses) are defined as data points more than 1.5× interquartile range away from the box. * - statistically significant difference between the two groups (p < 0.05).
Fig. 8
Spread of parameters obtained from curve fitting response curves from each corresponding phase highlighted using boxplots. Whiskers illustrate the range of the nonoutlier data. Outliers (crosses) are defined as data points more than 1.5× interquartile range away from the box. * - statistically significant difference between the two groups (p < 0.05).
Close modal
Table 1

Summary table of fitted model parameter means and standard deviations

SlowFast
PhaseModel ParameterMean ± Std. Dev.Mean ± Std. Dev.
Loading (N = 14)k (MPa)0.0281 ± 0.03560.0350 ± 0.0284
α8.98 ± 1.098.79 ± 1.18
Relaxation (N = 14)G1*0.378 ± 0.03120.518 ± 0.0385
G2*0.430 ± 0.01270.352 ± 0.0203
τ1 (s)*0.392 ± 0.03570.209 ± 0.0271
τ2 (s)*4.26 ± 0.2673.16 ± 0.263
Recovery (N = 11)S1*0.128 ± 0.02850.175 ± 0.0347
S20.0867 ± 0.01740.0944 ± 0.00904
τ1 (s)5.21 ± 2.056.35 ± 1.46
τ2 (s)137 ± 27.0140 ± 16.9
SlowFast
PhaseModel ParameterMean ± Std. Dev.Mean ± Std. Dev.
Loading (N = 14)k (MPa)0.0281 ± 0.03560.0350 ± 0.0284
α8.98 ± 1.098.79 ± 1.18
Relaxation (N = 14)G1*0.378 ± 0.03120.518 ± 0.0385
G2*0.430 ± 0.01270.352 ± 0.0203
τ1 (s)*0.392 ± 0.03570.209 ± 0.0271
τ2 (s)*4.26 ± 0.2673.16 ± 0.263
Recovery (N = 11)S1*0.128 ± 0.02850.175 ± 0.0347
S20.0867 ± 0.01740.0944 ± 0.00904
τ1 (s)5.21 ± 2.056.35 ± 1.46
τ2 (s)137 ± 27.0140 ± 16.9

* Statistically significant difference between the two groups (p < 0.05).

3.1.2 Relaxation Phase.

Relaxation phase data indicated that the two indentation rates produced distinct relaxation responses (Figs. 7(c) and 7(d)). At the beginning of the relaxation phase, fast indented samples exhibited higher relaxation rates (initial negative slope; Eq. (3) and Figs. 7(c) and 7(d)) compared to slow indented samples. At the end of the relaxation phase, slow indented samples retained a greater fraction of the initial stress compared to fast indented samples (20% ± 2.8% versus 12% ± 2.4%; p < 0.05, N = 14), i.e., stress relaxation of 80% and 88% for the slow and fast indentation rates, respectively. Indentation rate was observed to have a statistically significant impact (p < 0.05, N = 14) on all model parameters (G1, G2, τ1, and τ2) for the relaxation phase model (Fig. 8; Table 1). Interestingly G1 values were higher for the fast indented samples, while G2 values were higher for the slow indented samples. Model fits displayed average RMSE values below 0.9% in both groups.

3.1.3 Recovery Phase.

While the recovery phase data indicated significant overlap between the two test groups, the residual compressive strain at the end of the recovery phase was seen to be higher in the slow indented group compared to the fast indented group (Fig. 7(e)) (55% ± 4.6% versus 50% ± 4.2%; p < 0.05, N = 11). The model fits for the recovery phase (Fig. 8; Table 1) showed that the indentation rate only had a significant impact on the S1 parameter (p < 0.05, N = 11). The other three parameters (S2, τ1, and τ2) were independent of the loading rate. Model fits displayed average RMSE values below 0.3% in both groups.

3.2 Water Content Measurements.

Water content measurements (Fig. 9) indicated that unindented regions had significantly greater water content compared to indented regions (78% ± 1.9% versus 75% ± 1.8%; p < 0.05, N = 8) suggesting indentation related fluid movement within the tissue away from the indentation zone. However, no significant difference in water content was observed between slow and fast indented samples (75% ± 2.6% versus 76% ± 0.9%; p > 0.05, N = 4).

Fig. 9
Water content measurements. (a) Comparison of the water content of the indented regions for the two indentation rates (N = 4). (b) Comparison of water content of indented and unindented regions (N = 8). * - Statistically significant difference between the two groups (p < 0.05).
Fig. 9
Water content measurements. (a) Comparison of the water content of the indented regions for the two indentation rates (N = 4). (b) Comparison of water content of indented and unindented regions (N = 8). * - Statistically significant difference between the two groups (p < 0.05).
Close modal

4 Discussion

This study examined the transmural compressive viscoelastic properties of the porcine stomach wall with a focus on their behavior during surgical stapling. The data demonstrated the effect of two distinct indentation rates on the viscoelastic behavior of the intact stomach wall.

In soft biological tissues composed of collagen fibers/fiber bundles in a hydrated proteoglycan (PG) network there are likely two viscoelastic mechanisms at work under compressive (confined, unconfined and indentation) loading. The first mechanism arises from the time dependent flow of interstitial fluid through the porous solid phase in response to hydrostatic compressive stresses, and the second mechanism is related to the intrinsic viscoelasticity of the solid phase itself. These mechanisms have been demonstrated in a variety of soft biological tissues, e.g., cartilage [29,30], skin [31,32], and brain [33]. High loading rates are also known to produce collagen fiber damage and dissociation/depletion of the PGs, leading to changes in the creep and relaxation behavior [34]. It is likely that some or all of these mechanisms are active during the ramp and relaxation phase in the experiments described in this study, i.e., (1) the transient diffusion of unbound and bound water from the PG matrix from regions of elevated hydrostatic pressure (within the indentation zone) to lower pressure regions (outside the indentation zone), (2) the intrinsic viscoelastic behavior of the solid phase of the tissue (collagen and other structural proteins), and (3) collagen fiber damage and dissociation/depletion of the PGs.

Data from the ramp loading phase of the indentation tests indicate that the response of the stomach is largely independent of the indentation rate over the range of compression rates examined in this study. Studies have shown that rate sensitivity in soft tissues (porcine adipose and dermal tissue) is insignificant or minimal at lower strain rates (0.01 - 100%/s) and begins to show effects only at very high strain rates (>100%/s) [35]. Studies in a variety of other soft tissues, e.g., tendons and ligaments [36,37], human gastrointestinal tract [17], also indicate that rate sensitivity within one decade of strain rate variation is minimal. Since the rates in our study only go up to a maximum of 37.5%/s, which is five times the slow rate (7.5%/s) (and within one decade of variation), the rate dependence for the ramp loading phase is likely to be insignificant. This is also reflected in the lack of statistical differences seen for the fitted material parameters (k and α) of the loading phase model for the two rates of loading.

The impact of indentation rate was most prominent in the relaxation phase data with the fast indented samples relaxing faster (Eqs. 3 and 4) and to lower levels of stress (at 15 s) compared to the relaxation in the slow indented samples. High values of G1 (which are associated with the short-term relaxation times τ1) indicate that fast indented samples relax more rapidly. In contrast, slow indented samples show higher G2 values (associate with longer time scales τ2), indicating slower and lesser relaxation over the longer time scale. This behavior is likely due to the longer indentation time in the slow indentation group which could produce simultaneous solid phase relaxation and fluid diffusion out of the indented zone during the indentation phase itself. In contrast during fast indentation, it is likely that the tissues do not relax as much during the fast-loading ramp phase (as seen in the greater stress at 75% compression), but instead display a greater relaxation rate during the subsequent hold phase, driven by the hydrostatic pressures and solid phase tissue stress developed under the indenter. Higher relaxation rates could also be induced as a result of mechanical damage to the collagen-PG network wherein rupture of collagen fibers/fibrils, or dissociation of fibers from the PG network during ramp loading [34] could potentially affect the intrinsic viscoelastic properties of the solid phase, and likely induce rapid reductions in stress during relaxation. In a recent study that examined the effects of slow and high rate cyclic compressive loading on articular cartilage [34], it was demonstrated that high rates of loading produced collagen damage and associated damage to the PG network. This could also be a potential mechanism that could explain the higher rates of relaxation in the fast indented samples. Interestingly, this rate dependence of stress relaxation has also been seen in colon tissue, subjected to multirate compression [38] wherein higher loading rates during the initial ramp phase induced higher stress relaxation rates during the hold phase of a compression experiment [38]. Future studies employing detailed micromolecular analysis of ECM components such as PGs along with quantitation of the changes to PG content [34] may provide concrete evidence toward further elucidating these mechanisms.

During the recovery phase, tissue thickness recovery (which represents a viscoelastic creep deformation occurring in the absence of external load), is driven, in part, by reabsorption of the interstitial tissue fluid expelled or redistributed during the preceding indentation and stress relaxation phases of the experiments. The extent of fluid reabsorption is likely to be affected by damage to the collagen fibers and/or damage/depletion of the PG matrix caused by the indentation cycle. These mechanisms could contribute to a variety of recovery mechanisms, i.e., more rapid recovery (collagen fiber damage) or slower recovery due to impaired ability to recapture water (PG damage/depletion) [39].

Our recovery phase data indicates that the fast indented samples have higher initial rates of recovery (i.e., higher initial negative slope—Figs. 7(e) and 7(f); and higher values of S1—Fig. 8/Table 1) and recover more of the applied deformation compared to slow indented samples. However, water content measurements showed no significant difference in water content between indented samples in the two groups, albeit with a low sample size (N = 4). Further, the difference in water content between indented and unindented samples was less than 6%. Therefore, the difference in the recovery response of these two groups is likely governed by multiple mechanisms, i.e., the intrinsic viscoelasticity of the solid phase of the tissue and/or due to faster water uptake in the fast indented samples as a result of damage to the collagen fibers accrued during the indentation phase. Collagen fibers provide the restraining tensile stresses to counterbalance the osmotic swelling pressures generated within the proteoglycan-PG network and damage to fiber network disrupts this equilibrium [4042]. Based on this, it is likely that fiber damage occurs at high ramp loading rates in our experiments thereby diminishing the ability of the collagen network to provide the restraining forces to the PG network during the recovery-reswelling phase, resulting in more rapid thickness recovery.

5 Limitations

The study, while generating interesting and relevant viscoelastic data on stomach wall tissue, has several limitations. Firstly, the test methodology focuses only on the biomechanics of the stomach tissue and lacks other detailed experiments to probe and quantify the microstructural morphology of the effects of loading and relaxation phases on the multiple layers of tissue (serosa, muscularis, submucosa, and mucosa) comprising the stomach wall. The current experimental protocol also evaluates the composite viscoelastic response of the stomach wall, but the approach as designed does not enable deeper investigations around the interplay between loading rate, microstructural deformation mechanisms, collagen fiber-PG network interactions, and the extent of their contribution to the viscoelastic behavior during relaxation and recovery. This calls for more rigorous future explorations to develop a deeper understanding of these mechanisms. Finally, it is evident that given the through thickness heterogeneity of the tissue (serosa, muscularis, submucosa and mucosa layers), it is important to understand the relative contributions of each layer to the overall viscoelastic response, in addition to the effects arising from the microstructural interactions between collagen, proteoglycans, and interstitial fluid. Characterization of each of the constituent layers of the stomach wall would be a very important next step in developing a more fundamental description of the transmural deformation mechanisms in the stomach wall, as well as other sites along the gastrointestinal tract during surgical stapling and related maneuvers.

6 Conclusion

Using a simple indentation system, this study provides for the first time, insights into the compressive viscoelastic behavior, and water content of the porcine stomach wall. This represents our first steps toward addressing a known gap in current knowledge on the transmural compressive viscoelastic properties of stomach wall tissues, particularly as they relate to the compression applied by surgical instruments. Efforts to model the behavior of the stomach during a stapling process are limited by available data and constitutive models of stomach tissue under compression. Through this study and future planned studies on layer-wise characterization of the stomach wall components, we aim to generate data that may be used toward the development of constitutive models to support the design and optimization of surgical stapling systems. The current study's data demonstrates that the viscoelastic behavior (stress relaxation and recovery) of stomach tissue is dependent on the initial compression rate applied to the tissue. These findings underscore the importance of developing a deeper understanding of tissue mechanics that could enable more precise control of compression profiles and help refine the designs for powered tissue staplers, with the eventual goal of achieving optimal staple line performance. The data presented in this study thus establishes a foundation for the design of the next generation of laparoscopic tissue staplers.

Acknowledgment

Panel A of Fig. 1 was created with Bio-Render.com.

Funding Data

  • Ethicon Endosurgery to the Department of Biomedical Engineering, Texas A&M University (Principal Investigator: Balakrishna Haridas).

Conflict of Interest

Dr. Jason Harris is an Employee of Ethicon Endosurgery.

Nomenclature

NHANES =

National Health and Nutrition Examination Survey

FDA =

Food and Drug Administration

GI =

gastrointestinal

SIG =

Signia stapling system

SLL =

staple line leak

ECM =

extra cellular matrix

PG =

proteoglycans

GAG =

glycosaminoglycan

PBS =

phosphate buffered saline

LVDT =

linear variable displacement transformer

H&E =

hematoxylin and eosin stain

VSG =

vertical sleeve gastrectomy

Footnotes

2

ECHELON FLEX Powered and Powered Plus Staplers In-Service | Ethicon, 2020, “ECHELON FLEX Powered and Powered Plus Staplers In-Service | Ethicon,” accessed Sep. 12, Accessed: Jan, 31, https://www.youtube.com/watch?v=d_rT8rs56EI

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