Subjects
Six normal subjects were invited to participate in this exploratory study in the anorectal physiology lab in the Department of Surgery at Prince of Wales Hospital in Hong Kong. Recruitment was made by local advertisement or by contacting subjects who had previously been participating in our anorectal experiments as normal subjects. The inclusion criterion was asymptomatic normal persons aged over 18 years who gave informed consent. No upper age limit was imposed. The exclusion criteria were persons with history of chronic constipation or fecal incontinence (FI), abdominal pain, prior abdominal, pelvic and anal surgery, medication and diseases that may affect bowel function and defecation such as cancer, diabetes and infectious diseases. Data were obtained on age, health status, symptoms, diseases, and previous treatments. FI Severity Index (FISI) scores and FI QOL scores were obtained (Rockwood 1999, 2000 and 2004). The subjects had ARM-BET done at the same day if recordings were not available from other studies within the past three months. The subjects were recruited from June to December 2021.
Prior to functional testing, the subjects were asked to empty their rectum if they were able to. Enema was not used to make the test as natural as possible. Digital rectal examination was performed prior to insertion of Fecobionics to assess anal tone and verify that the lower rectum was empty. Experiments using Fecobionics and ARM-BET were done in randomized order if done on the same day using a predefined scheme with at least 20 minutes between the tests. The London protocol for ARM was followed. All subjects had the tests completed. FISI scores < 5 was considered normal (Rockwood 1999) and constipation scores below 8 were considered normal (Agachan 1996). The study was carried out according to The Code of Ethics of World Medical Association (Declaration of Helsinki), and the informed consent was obtained, and that the IRB was approved (protocol no. 2017.122, Joint CUHK-NT East Cluster Clinical Research Ethics Committee). All experiments were performed in accordance with relevant guidelines and regulations.
Fecobionics
The basic design of Fecobionics has been described (Fig. 1) (Gregersen et al. 2018; Sun et al. 2019). Fecobionics was 10-mm-OD, 10-cm-long and made of Silicone rubber (PS6600, Yipin Mould Material Ltd, China). It contained pressure sensors and electronic circuit boards. Miniature pressure sensors (MS5837-30BA, TE connectivity, USA) were embedded in the silicone rubber core at the front, inside the bag, and at the rear of the core. The front and rear sensors pointed in the direction of the defecatory trajectory. Two 9-axis Motion Processor Units (MPUs) (MPU9250, InvenSense, USA) for orientation, angle, and bending measurements were placed towards the front and rear of the probe.
A 30µm-thick and 8cm-long polyester-urethane bag spanned most of the core length. The spherically shaped bag contained up to 80mL without being stretched and had a maximum diameter of 5 cm. The bag was connected through a thin tube extending from the front of Fecobionics to a syringe containing saline.
With the architecture, silicone hardness shore (A5) and the bag, Fecobionics obtained shape and consistency that corresponds approximately to type 4 (range 3–4) on the Bristol stool form scale (Heaton et al. 1992). The range from types 3–4 is found in + 60% of normal subjects (Heaton et al. 1992). Wires were threaded inside a thin tube extending from the front to a PCs USB port for power supply and real-time data transmission and display of data. Further processing was done in Matlab.
Procedures
The settings were made as private as possible using a curtain to shield the patient. Fecobionics was manually inserted into the rectum. After checking probe placement by asking the subjects to squeeze the anal sphincter and coughing, the bag was filled with liquid until the subjects felt urge to defecate. The urge volume was noted. The subjects were asked to defer defecation for 4hrs. They were allowed to sit on a chair or lay on the hospital bed. Changes in posture were recorded. They were asked to rest and refrain from contracting the abdominal muscle unnecessarily. After deferring defecation for 4 hours, the subjects were asked to evacuate Fecobionics as they normally do at home and without excessive straining. The investigators left the room, and the subjects defecated the device in privacy.
The Fecobionics devices were inspected for leaks and damage or malfunction. Any safety issue and adverse effects were characterized and reported as unanticipated adverse device effects. No adverse effects were recorded. The subjects were instructed to contact a specific member of the research team if they experienced any problem after leaving the clinic.
ARM-BET was conducted with a standard HRM single-use 8ch anorectal catheter (G-90150, MMS, Enschede, Netherlands). It was inserted with the subjects lying in left lateral position with bended hip and knees. The bag was placed in the rectum and pressure was measured at 0.5cm distance in the anal canal. Resting anal pressure, maximum anal squeeze pressure, the recto-anal inhibitory reflex (RAIR), urge volume, maximum tolerable volume, and expulsion duration for the 50mL balloon were evaluated. BET was done on the commode chair and the investigators left the room during BET defecation.
Data analysis
Pressures were recorded directly by the pressure sensors with atmospheric pressure as zero. Data from the MPUs were more complex and required processing to compute the bend angle of the device. The Madgwick algorithm is an orientation filter applicable to MPUs consisting of tri-axial gyroscopes, accelerometers and magnetometers (Madgwick et al. 2011). An estimated orientation of the sensor frame relative the earth frame, \({q}_{est,t}\), is obtained through the weighted fusion of the orientation calculation, \({q}_{\omega ,t}\) and \({q}_{\nabla ,t}\) with a simple complementary filter:
$${q}_{est,t}={\alpha }_{1}{q}_{\nabla ,t}+\left(1-{\alpha }_{1}\right){q}_{\omega ,t}, 0\le {\alpha }_{1}\le 1$$
1
where \({\alpha }_{1}\) and \((1-{\alpha }_{1})\) is the weights, ranging between 0 and 1, applied to each orientation calculation. In this study, it was used to estimate the accurate orientation of each MPUs, \({q}_{est,t}^{front}\) and \({q}_{est,t}^{rear}\). The raw ϕ, pitch \({\theta }\) and yaw \({\psi }\) is calculated as:
$$\left[\begin{array}{c}\varphi \\ \theta \\ \psi \end{array}\right]=\left[\begin{array}{c}atan2(2\left({q}_{0}{q}_{1}+{q}_{2}{q}_{3}\right), 1-2\left({q}_{1}^{2}+{q}_{2}^{2}\right))\\ \text{a}\text{s}\text{i}\text{n}\left(2\left({q}_{0}{q}_{2}-{q}_{3}{q}_{1}\right)\right)\\ atan2\left(2\right({q}_{0}{q}_{3}+{q}_{1}{q}_{2}, 1-2\left({q}_{2}^{2}+{q}_{3}^{2}\right))\end{array}\right]$$
2
where \({q}_{0}, {q}_{1}, {q}_{2}, {q}_{3}\) is the quaternions of \({q}_{est,t}^{front}\) and \({q}_{est,t}^{rear}\). From multiple experiments, we found that the bend angle was more related to the pitch angle, i.e., it was defined as:
$$bend=\{\begin{array}{c}180^\circ -\left|\left|{{\theta }}_{\text{r}\text{e}\text{a}\text{r}}\right|-\left|{{\theta }}_{\text{f}\text{r}\text{o}\text{n}\text{t}}\right|\right|, \left|\left|{{\theta }}_{\text{r}\text{e}\text{a}\text{r}}\right|-\left|{{\theta }}_{\text{f}\text{r}\text{o}\text{n}\text{t}}\right|\right|, <{90}^{^\circ }\\ \left|\left|{{\theta }}_{\text{r}\text{e}\text{a}\text{r}}\right|-\left|{{\theta }}_{\text{f}\text{r}\text{o}\text{n}\text{t}}\right|\right|, \left|\left|{{\theta }}_{\text{r}\text{e}\text{a}\text{r}}\right|-\left|{{\theta }}_{\text{f}\text{r}\text{o}\text{n}\text{t}}\right|\right|, \ge 90^\circ \end{array}$$
3
180° means that the probe is straight.
The entire recordings were viewed after the studies to get a visual impression of the collected data. For further analysis, we picked six 5-minute time periods, when we had stable signals in the beginning, middle (after 2hrs), and at the end of the 4hrs deferment period, i.e., two periods from each part of the study.
Fourier analysis converts a signal from its original domain to a representation in the frequency domain and vice versa. The discrete Fourier transform (DFT) is obtained by decomposing a sequence of values into components of different frequencies (Heideman et al. 1984). This operation is useful in many fields, but computing it directly is often too slow to be practical. A Fast Fourier Transform (FFT) rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors (Van Loan 1992). In this study, FFT was used for frequency domain analysis on the pressure and bend angle recordings. In order to filter out high-frequency clutter, all data were low-pass filtered. The frequency peaks were defined as the highest point of each phase of amplitude rise with more than 30% deviation from the preceding baseline. The frequency and amplitude of the first two major peaks were analyzed. Furthermore, the distribution characteristics of the frequency components of subtracted recordings, e.g. the front pressure subtracted from the rear pressure, was used to evaluate their coordination.
Statistics
Data were expressed as mean ± SEM unless otherwise stated. One-way ANOVA was used for statistical testing of parameters during the time course of the 4hrs deferment period. Least Significance Difference (LSD) was used in post hoc test to compare each parameter (IBM SPP Statistics 22, IBM Corp.). The results were considered significant when p < 0.05.