| Literature DB >> 32195364 |
Siddharth R Krishnan1,2, Hany M Arafa2,3, Kyeongha Kwon2, Yujun Deng4,5, Chun-Ju Su2, Jonathan T Reeder2, Juliet Freudman2,3, Izabela Stankiewicz2,3, Hsuan-Ming Chen2, Robert Loza2, Marcus Mims6, Mitchell Mims7, KunHyuck Lee2,8, Zachary Abecassis9, Aaron Banks2, Diana Ostojich2, Manish Patel2,10, Heling Wang5,8,11, Kaan Börekçi2, Joshua Rosenow12, Matthew Tate12, Yonggang Huang2,5,8,11, Tord Alden12,13, Matthew B Potts12, Amit B Ayer12, John A Rogers1,2,3,8,12.
Abstract
Hydrocephalus is a common disorder caused by the buildup of cerebrospinal fluid (CSF) in the brain. Treatment typically involves the surgical implantation of a pressure-regulated silicone tube assembly, known as a shunt. Unfortunately, shunts have extremely high failure rates and diagnosing shunt malfunction is challenging due to a combination of vague symptoms and a lack of a convenient means to monitor flow. Here, we introduce a wireless, wearable device that enables precise measurements of CSF flow, continuously or intermittently, in hospitals, laboratories or even in home settings. The technology exploits measurements of thermal transport through near-surface layers of skin to assess flow, with a soft, flexible, and skin-conformal device that can be constructed using commercially available components. Systematic benchtop studies and numerical simulations highlight all of the key considerations. Measurements on 7 patients establish high levels of functionality, with data that reveal time dependent changes in flow associated with positional and inertial effects on the body. Taken together, the results suggest a significant advance in monitoring capabilities for patients with shunted hydrocephalus, with potential for practical use across a range of settings and circumstances, and additional utility for research purposes in studies of CSF hydrodynamics.Entities:
Keywords: Diagnostic markers; Sensors and biosensors
Year: 2020 PMID: 32195364 PMCID: PMC7060317 DOI: 10.1038/s41746-020-0239-1
Source DB: PubMed Journal: NPJ Digit Med ISSN: 2398-6352
Fig. 1Wireless sensors for continuous monitoring of CSF hydrodynamics through shunts.
a Exploded view illustration of the device, highlighting the flexible circuit board, electronic components, thermally insulating foam, elastomeric encapsulation and adhesive layers. b Optical images of devices illustrating their flexible construction and ability to mount on the skin over anatomical regions relevant to VP shunts. c Finite element analysis (FEA) of the distributions of strain across the fPCB during bending to a degree comparable to that required to mount on the neck region of a child (radius of curvature = 40 mm) and an adult (55 mm). d Schematic illustration of the electronic design for actuation, data acquisition and wireless transmission. e Optical image of smartphone application used for wireless readout and communication. Participants provided written informed consent to have their photos/images included as a part of this publication.
Fig. 2Thermal and mechanical characterization of the flow sensor.
a Optical micrograph of the circular thermal actuator and sensing elements on fPCB. b Infrared (IR) images of thermal actuation in the absence (left) and presence (right) of flow (Q = 0.07 ml/min) in benchtop shunt assembly. c Temperature distributions generated by 3D finite element analysis (FEA)(top-view), illustrating the effects of no flow (left) and flow (right) for a representative case, with color scheme corresponding to a normalized change in temperature. d Cutaway view of images generated by 3D FEA illustrating heat transfer through near-surface skin layers for Q = 0.07 ml/min. e Plot of α/αqss = (TDS − TUS)/ (TDS − TUS)qss as a function of time before and after a step-change in flow, from 0 ml/min to 0.3 ml/min, at t = 65 s, for the shunt phantom system with an equivalent skin thickness of 1.7 mm and fitted with an exponential form to yield a time constant of 31 s to reach 63.7% of a quasi-steady-state value f Plot of α as function of different physiologically relevant flow rates (0.03 < Q < 0.5 ml/min) and skin thickness, hskin = 0.7 mm (black), 1.7 mm (red) and 4.0 mm (blue), highlighting non-monotonic behavior and an inflection point at Q = 0.07 ml/min. g β = (TDS + TUS)/2 computed for the same flow rates and skin thicknesses as in f. Error bars in f and g correspond to standard deviations over 100 s for a single experiment.
Fig. 3On-body ultrasound, IR and device measurements on a healthy outpatient (M, 21) with a VP shunt.
a Schematic illustration highlighting on-shunt, off-shunt and ultrasound measurement locations. b Ultrasound image of VP shunt at location 2 cm distal to clavicle (left), with optical micrograph of unimplanted shunt (Medtronic, Bactiseal) for scale (right). c Optical image of device mounted on skin over shunt. d IR thermograph of operational device, illustrating a maximum local temperature rise at the thermal actuator of 4.7 °C, with outline of shunt visible downstream of actuator. e Raw temperature recorded from temperature sensors at off-shunt measurement at base of pectoral muscle distal to clavicle. f Raw temperature measurement from four temperature sensors at on-shunt measurement site, showing difference between downstream and upstream temperature signals, α. g α computed for two separate trials on the same patient over the shunt and one trial at off-shunt location. Participants provided written informed consent to have their photos/images included as a part of this publication.
Fig. 4Short “spot-check” measurements on in-patients with confirmed flow.
a Optical image of device mounted on skin location overlying shunt, with smartphone for continuous data readout. b Mean of α computed for n = 5 patients with confirmed flow, or who are asymptomatic, over on-shunt and off-shunt locations. (**p < 0.01 for paired student-t test). Error bars correspond to standard deviations across five subjects. Participants provided written informed consent to have their photos/images included as a part of this publication.
Fig. 5Advanced measurements of continuous CSF hydrodynamics on healthy out-patients.
a α computed for a healthy outpatient (Male, 21) at three positions, sitting up at 90°(grey shaded area), supine at 180°(red shaded area) and sitting up at 90° (blue shaded area). b α averaged over a 100 s window for the three positions, across n = 3 patients at on-shunt locations. c Same as b. but measured at off-shunt locations. d α for a volunteer (F,16) leaning forwards (F) and backwards (B) at 45°, and sitting up at 90°, respectively. e α computed for continuous measurement over on-shunt location as volunteer (Male, 21) outpatient descended on elevator moving at 1.5 m/s across an elevation of 17 m. f Same as e but measured while same volunteer ascended on elevator, with baseline adjusted to same starting value of α. g α computed for continuous 1.5-h measurement on volunteer (Male, 21) during normal activities, showing intermittent flow. h Area-under curve, γ, computed for α(t) over different sampling windows for data in g as a measure of total flow output during fixed intervals. i γ computed over 5-min sampling window for same patient across three days, during morning (M) and afternoon (A) measurements showcasing variability in flow patterns across time of day. Error bars in b and c correspond to standard deviations across three subjects.
Fig. 6Flow rate measurements on a healthy out-patient (M, 21).
a FEA-simulated curves for α(Q) (top) and β(Q) (bottom) for hskin = 1.4 mm, corresponding to measured value on the out-patient, with an inflection point at Q = 0.07 ml/min corresponding to β = 1.3 K. b Simulated data (circles) and fit (line, exponential curve) for low-flow regime corresponding to 0.007 < Q < 0.07 ml/min. c Simulated data (circles) and fit (line, power-law curve) for high-flow regime corresponding to 0.07 < Q < 1 ml/min. d Representative continuous high-flow measurements on the out-patient with shaded regions representing uncertainty estimates (±15%). e Representative low-flow measurements on the out-patient with shaded regions representing uncertainty estimates (±15%).
Flow rates calculated for 12 spot check measurements on healthy out-patient (M,21) across 3-day period, with values of β for classification into high (β < 1.3 K green), low (β > 1.3 K, red) and transition (β ~ 1.3 K blue) flow regimes.
| Day | Time of Measurement | Classification | Flow rate (ml/min) | |
|---|---|---|---|---|
| 1 | 12:57 PM | 1.12 ± 0.02 | High Flow | 0.14 ± 0.03 |
| 1 | 1:17 PM | 1.74 ± 0.04 | Low Flow | 0.01 ± 0.002 |
| 1 | 1:46 PM | 1.48 ± 0.03 | Low Flow | <0.01 |
| 1 | 2:01 PM | 1.64 ± 0.02 | Low Flow | 0.02 ± 0.007 |
| 1 | 2:45 PM | 1.70 ± 0.02 | Low Flow | 0.05 ± 0.02 |
| 2 | 1:36 PM | 1.12 ± 0.01 | High Flow | 0.12 ± 0.04 |
| 2 | 3:20 PM | 1.00 ± 0.04 | High Flow | 0.20 ± 0.04 |
| 2 | 4:08 PM | 1.65 ± 0.01 | Low Flow | 0.01 ± 0.003 |
| 2 | 5:00 PM | 1.11 ± 0.04 | High Flow | 0.26 ± 0.05 |
| 3 | 10:57 AM | 1.40 ± 0.03 | Transition | 0.06 ± 0.01 |
| 3 | 11:30 AM | 1.72 ± 0.01 | Low Flow | 0.01 ± 0.002 |
| 3 | 1:30 PM | 1.37 ± 0.01 | Transition | 0.08 ± 0.01 |
All data are averaged over 100 s measurement windows, and uncertainty estimates represent standard deviations over 100 s combined with 15% fitting uncertainty.