Tissue stress from laparoscopic grasper use and bowel injury in humans: establishing intraoperative force boundaries

Complex mechanical response of tissue to compression

It is challenging to accurately quantify and model biological soft tissues’ multifaceted and complex behavior in response to the compressive force exerted by laparoscopic graspers. The mechanical response of tissue is based on two factors: (a) the inherent mechanical properties of that tissue and (b) the environmental loading characteristics it is subjected to. The small and large bowel are composed of multiple tissue layers. Within each layer, different fibers are distributed according to specific spatial orientations, which creates a strongly anisotropic configuration where measured properties varies along its different axes.11

Grasper jaw geometry and stress on tissues

Laparoscopic graspers have jaws traditionally made from stainless steel due to its durability and ease of sterilization. The main disadvantage of using metal is that metal is a much stiffer material than delicate gastrointestinal tissues and as such, compressing tissue with metal graspers can cause damage at the cellular level (such as mechanical destruction of the cell membrane or nucleus) or tissue level (such as rupture of muscle fibers or ischemia from the destruction of blood vessels).12 Graspers also come in a variety of jaw geometries and teeth profiles such as straight or flared, fenestrated with waves or solid and single or dual action, which contribute both to their function and damage potential. For example, when a grasper’s jaw is serrated, increasing the size of the teeth will help prevent slippage but also causes more damage to tissue.13 Cheng and Hannaford investigated this relationship and created both a two-dimensional and three-dimensional finite element analysis study of calculated von Mises stress distributions under compression loads in a grasper and liver tissue model.14 They found that in the two-dimensional plane strain model, that 80% of the stress in the area directly beneath the grasper was over 300 kPa, which is over the damage limit they elucidated in their previous work of 240 kPa for liver tissue.15

Is porcine tissue an accurate surrogate for human tissues?

Previous studies exploring grasper jaw and tissue interactions have mostly centered on porcine tissue studies. This is due to the vast logistical and ethical challenges involved in human tissue experimentation and the previous assumption that porcine tissues are close enough to human tissues to be a surrogate model. Christensen et al’s work with porcine and human bowel tissues casts doubt on this assumption; however, as they found that human tissues were stronger, stiffer and less compliant than porcine tissue. Porcine tissue was able to stretch almost twice as much as human bowel tissue (with an elastic modulus of 1.83 MPa and 5.18, respectively), while human bowel tissue had a higher ultimate average strength (0.58 MPa compared with 0.87 for human tissues).16 Heijnsdijk et al also found that the inter-individual variability in perforation forces is quite large and that bowel strength could differ by a factor of two between patients.17

Establishing safe tissue force boundaries in humans with a histopathological analysis

This study aims to investigate the relationship between grasper jaw forces and human small and large bowel (colon) tissues. These tissue types were specifically chosen for two reasons: first, they are the most clinically relevant in relation to repeated grasp injury and second, bowel is one of the most delicate tissues in the human body. van der Voort et al4 found that the overall incidence of laparoscopy-induced bowel injury was 0.36%. The small intestine was most frequently injured (55.8%), followed by the large intestine (38.6%).3

To the authors’ knowledge, this will be the first study to investigate the upper limit of force by laparoscopic graspers in human tissues with a histological analysis of cellular damage. A histological model was chosen to objectively and quantifiably understand how the intestinal tissue structure is microscopically affected as a result of mechanical loading. These data will be important as we move into an age of “smart surgical tools” that can quantify tool-tissue force interactions.

Study design and experimental protocol

We previously created a prototype device, the Simple Crush Apparatus for Tissue to produce discrete grasp forces on human gastrointestinal tissue to test feasibility and workflow for human tissue experimentation.18 This current paper aims to build on the work of Chandler et al19 in defining an upper force limit for human small and large bowel tissue with a more sophisticated and accurate device than our previous work. To this end, a new custom device called the Precision Crush Apparatus for Tissue (PrecisionCAT) was created (figure 1A). Patients were consented by their operating surgeon, using a standard surgical consent form, which at our institution, includes a provision for using excess surgical tissues (not needed for surgical pathology) for research purposes. Patients were given an information form as well, that included a contact number if they chose to withdraw their sample from the study at any time postoperation. All methods were performed in accordance with the relevant guidelines and regulations of our institution and ethics office.

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Each patient had six 1 cm×1 cm tissue samples cut from their usable tissue. Each sample was loaded onto the grasper plate of the PrecisionCAT serosa side up on a small cellulose-fiber sheet to avoid tissue slippage, and one at a time, a discrete force was exerted on it ranging from 0 to 11.8 N (0–600 kPa). Specifically forces of 2 N, 3.9 N, 5.9 N, 7.8 N, 9.8 N and 11.8 N were used, or using our precision pin plate with known surface area of 19.6 mm², calculated pressures of 0 kPa, 100 kPa, 200 kPa, 300 kPa, 400 kPa, 500 kPa and 600 kPa. Each simulated grasp was for a duration of 10 s. This is consistent with our previous protocol which used precision weights and the same diameter of pin plate to generate equivalent force and pressure. These metrics were chosen to be consistent with the mean grasp force and 95% of grasp time elucidated by the Blue DRAGON system for laparoscopic tasks (8.52 N±2.77 N and 8.86 s±7.06 s), Zhou et al’s tribology studies (0–16 N with significant tissue damage achieved past 13 N) and Chandler et al’s protocol (0–300 kPa grasps with 10 s duration).4 20 21 The order in which tissues were compressed with each amount of force was randomized so that the pathologists did not know the tissue’s loading condition. After each tissue sample was compressed, it was cut in half and processed for histology. On average, from the time the sample was removed from the patient to the end of the full experimental protocol, the research team took only 20 min, to maximally preserve tissue integrity. When analyzing the slides, the two pathologists were again blinded to the amount of force each tissue was subjected to and slides were analyzed out of order to prevent bias.

Experimental test equipment

A test system (figure 1A, Build of Materials in online supplemental appendix) was developed based on Chandler et al’s work, to apply mechanically controlled “grasps” to tissue samples that are characteristic of those sustained intraoperatively. Key requirements were similar to Chandler’s: the test system should apply compressive grasps where the loading rate, peak stress and hold time are directly controlled and the grasper plate position precisely known. The ensuing system, named the PrecisionCAT, uses a linear actuator with 0.1 µm resolution (LCA50-025-72-1F-3, SMAC Moving Coil Actuators, California, USA) to drive together two “grasp plates” (representing the grasper jaws) and thus compress a sample of target tissue. The actuator was controlled via the SMAC LAC-1 servo motor controller (SMAC Moving Coil Actuators, California, USA) and used force data from the load cell to regulate velocity and position. The load cell used was a high precision compression-link load cell (LCM-703-25, Omega Engineering, Connecticut, USA) coupled with a precision differential instrumentation amplifier (DMD-465, Omega Engineering, Connecticut, USA) to amplify and convert the load cell’s voltage to a larger voltage for digitization. Position and load data was synchronously recorded at 31 Hz. The grasp plates were rapid prototyped at 75 µm resolution using a Somos WaterShed XC 11122 (ZRapid SL600, Jiangsu, China). The plates’ geometry is not based on a specific grasper jaw with fenestrations, but instead, comprise a smooth bottom test platform and the top, of a pin plate with an indentation tip, with the pin plate being driven closer to the test platform plate by the linear actuator (figure 1B). The cylindrical pin was fabricated with a known diameter of 5 mm and surface area of 19.6 mm² to allow for pressure to be calculated from force for a standardized measurement.

The geometry of the indentation tip and its size relative to the test material and test platform plate were important design considerations. A smooth, flat indentation tip with no fenestrations was used rather than actual grasper jaw geometry because a uniform circular flat tip ensures a constant and predictable contact profile between the material and the tip. This simplifies analysis as it avoids the difficulty of mapping applied pressure generated from a grasper jaw with fenestrations and a hinge mechanism. It also isolates strain effects on the tissue from the confounding effects of grasper geometry. Pressure would vary along the length of the jaw and depend on the mechanical advantage at the grasper linkage mechanism and would include areas of local stress at the peaks of the fenestrations. A large sample-to-tip dimension ratio also fulfills the half-space assumption used during analytical derivation for material testing.22

The full assembly schematics, .STL files and build of materials is available to use for free, via our Harvard Dataverse repository.23 Existing Python code was adapted and expanded to create custom software to control the actuator motion and hold its position once a predefined load threshold from the load cell was reached.24 This code is available to use freely on our Github repository.25

Biospecimen tissue preparation and analysis

Out of necessity, tissues included in this study were a convenience sample and were based on what operations were scheduled. All surgeons in the Division of General Surgery at St. Michael’s Hospital were enrolled in the study and when they had a scheduled operation that included a surgical pathology sample (with wide enough margins of normal tissue), our research team would be present in the operating room and acquire the sample as soon as it was removed from the body (figure 1C). Samples were kept in a fresh state to preserve cellular integrity and mechanical properties. Once removed, samples were immediately taken to an adjacent histology suite where the two pathologists, CS and CR would assess the tissue so that sections for testing were taken from normal tissue not needed for pathological analysis. Six 1 cm×1 cm tissue squares were cut from each sample and each loaded onto the PrecisionCAT serosa side up for experimentation. Blue tissue marking dye (#1003-5 Blue, Davidson Marking System, Minnesota, USA) was used to coat the indentation tip of the grasp plate before the experiment was performed so that when the tip made contact with the tissue, the area of compression would be evident when preparing and viewing slides. Once the experimental protocol was complete, the tested section was cut in half across the area of compression, fixed in formalin for normal histology processing and cut en-face to allow a full cross-sectional view across the area of compression. Tissues were fixed in 10% buffered formalin, processed and embedded in paraffin. Sections with a thickness of 4 µm were cut and tissues were mounted to glass slides. Sections were stained with H&E for typical visualization of cell morphology and structure. Once slide preparation was complete, they were scanned at 20× using a brightfield digital pathology scanner. Slides were analyzed using digital slide viewing software (Aperio ImageScope, Leica Biosystems, Wetzlar, Germany). Serosal thickness was specifically targeted for quantification because it is serosal disruption that is hypothesized to be the basis of adhesion formation.26

Tissue trauma score and serosa thickness calculations

Due to the experimental protocol being ex-vivo, normal markers of cellular injury such as increased granulocyte recruitment (neutrophils and eosinophils) or apoptosis could not be used. Instead, areas of tissue injury were identified by quantifiable denudation of the normal layers of the intestine or from increased thinning and elongation of the cell with dense hyperchromatic nuclei. Two metrics of damage were quantified: an intestinal layer thickness calculation where the serosa (outermost) layer was measured in the area of compression (C) and compared with a local control region that was not compressed as a per cent deformation and a histological scoring scale for tissue trauma (figure 2). The histological scoring scale was created by the two pathologists in this study as we were unable to find a suitable pathologist-validated scale endorsed in the literature.18 The criteria for the scale is outlined in table 1 and representative images shown in figure 2.

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Histological images were taken at 400× and all serosa layer thickness measurements were performed at the center of the experimental area of compression. Five measurements were taken of the thickness of the serosa layer in the area of compression and then averaged for a total value. Five measurements were also taken in an adjacent non-compressed area and averaged to serve as a local control. Per cent deformation (rather than an absolute delta in microns) was calculated as the per cent difference between the thicknesses of the compressed area to its local control of uncompressed tissue.

Statistical analysis

Statistical analysis and modeling was performed using NumPy, pandas and Scikit-learn using the Python programming language.27–29 All graphs were created with Matplotlib.30 A series of one-tailed t-tests were performed to compare sample serosal thickness measurements at compressed versus local control area in the histological slides. The tests were to investigate significant decrease in thickness due to compression of the tissue. To investigate the relationship between serosal thickness change and the pathologist’s trauma score ratings, a correlation study was also completed.

A logistic regression model was trained to predict significant serosal thickness change as a classification task. A second logistic regression model was trained to predict a positive tissue trauma score. The intention of these predictive models was evaluate how well input features that could be measured intraoperatively such as force, position of the grasper and time that the tissue was compressed and features that could be derived from those such as stress, strain and stiffness of the tissue, could be used to predict the likelihood of subsequent tissue trauma. This algorithm then could be incorporated into a “smart” laparoscopic tool that can, in real-time, alert a surgeon in the operating room about force use and subsequent pathological tissue response.

Demographics and experimental cases

Overall, 26 samples were procured but 6 tissue samples were unusable due to a variety of factors such as the pathologists being unavailable for immediate experimentation or the tissue was too inflamed. In total, we were able to complete the full experimental protocol on 20 patients’ tissues, with 10 colon samples (1:1 male to women, mean age 65.2) and 10 small bowel (7:3 male to female, mean age 54.3) samples for a total of 120 experimental cases and 120 control cases.

Tissue measurements

Serosa thickness deformation

A series of paired t-tests were conducted between the five serosal control measurements and the five compression site measurements at each loading condition to determine significance (table 2), where p values ≤0.05 are indicated by bold text and missing values indicate that the slide was unable to be analyzed due to slide artifacts.

Table 2

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T-test results between control and compression sites

All 20 patients experienced a significant difference (p<0.05) in serosal thickness postcompression at both 500 and 600 kPa for both tissue types. The majority of patients at 400 kPa (6/10 for small bowel and 5/6 for colon) also had significant differences precompression and post compression. This is similar to our pilot study where all patients had significant p values starting at 450 kPa.18 This is also higher than the minimum pressure of 150 kPa that Chandler et al’s group found for damage to porcine colon, but their measurements were for the mucosal and muscle layers of the colon and did not focus on serosal change as they had this layer of tissue stripped.19 There were no significant changes at 100 and 200 kPa (except for patient 20 for colon) and a mixed picture at 300 kPa with 50% (8/16) of patients experiencing a significant difference between control and compressed tissues (4/7 for small bowel and 4/9 for colon). These data were visually displayed in figure 3B,C. When all small bowel patient data were combined together and analyzed by loading condition, the trend was similar to individual results. Significance is achieved at 300 kPa and continues onwards from 400 to 600 kPa with significant p values. This is similar to grouped colon patient data, where significance is achieved at 400 kPa and continues onwards to 600 kPa with significant p values. All patient data were then grouped together (both small bowel and colon) and analyzed by loading condition and this too followed a comparable pattern where significance is achieved at 300 kPa and continues onwards from 400 to 600 kPa with p values of<0.001. Serosal thickness as a per cent deformation (to normalize measurements) was also calculated for each patient and plotted in figure 3A. Per cent deformation steadily increases as the experimental loading condition is increased and both tissue types follow similar patterns (figure 3B,C).

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Figure 3

Serosal thickness change and Trauma Score. (A) A line graph of the serosal thickness per cent deformation by tissue type at each loading condition. (B,C) The percentage of patients with significant p values for serosal deformation by tissue type. (D) A line graph comparing positive trauma scores (either a Trauma Score of 1 or 2 indicating evidence of damage is present) of small bowel to colon tissues at each loading condition. (E, F) Stacked bar graphs of Trauma Score ratings at each loading condition by tissue type.

Logistic regression model

Two logistic regression models were trained in order to predict target metrics: a positive Trauma Score and significant serosa thickness change. Logistic regression was selected because it is an easily interpretable model and could be implemented in an intraoperative environment in a manual or automatic approach. The models were trained on a single feature (target stress, table 3), which was selected as it was the most predictive (it was the feature with the highest correlation). This feature was expected to be the most important to predict tissue trauma because it is directly derived from the maximum force.

The features that both models included were derived from the force and displacement measurements obtained during each experimental condition. First, the force measurements were transformed to stress on the tissue based on the contact surface area of the indentation pin. The displacement measurements, measured with a resolution of 1 µm, were converted into strain of the tissue, taking into account the initial tissue thickness, as determined by the tool coming into contact with the tissue. Both of these measurements, in addition to time stamps, could be measured in a sensorized grasper tool.

From these data, further features were derived, which is shown in table 3. Additionally, patient age, gender and tissue type were included as features as well.