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CN-121982961-A - Improvement method and system for umbilical vein catheterization module of neonate umbilical model

CN121982961ACN 121982961 ACN121982961 ACN 121982961ACN-121982961-A

Abstract

The invention belongs to the technical field of medical teaching information, and particularly relates to an improvement method and system of a neonate umbilical vein catheterization module. The technology focuses on clinical training pain points of umbilical vein catheterization in Neonatal Intensive Care (NICU), and a closed loop training system of high-fidelity simulation, full-range monitoring, precise guidance and scientific assessment is constructed through the integrated design of bionic material preparation, multi-parameter sensing, intelligent algorithm regulation and control, quantitative assessment feedback. The umbilical vein catheterization training solution is suitable for scenes such as medical institutions, neonates in hospitals, nursing training institutions and the like, provides practical umbilical vein catheterization training solution with clinical reality and training high efficiency for medical staff, and can remarkably improve training quality and clinical operation conversion effects.

Inventors

  • HUANG SHUXIA

Assignees

  • 首都医科大学附属北京朝阳医院

Dates

Publication Date
20260505
Application Date
20251230

Claims (10)

  1. 1. An improvement method of a novel material umbilical vein catheterization module of an umbilical model is characterized by comprising the following steps: s1, preparing a gradient diameter bionic umbilical vein, namely preparing a bionic umbilical vein with a tube cavity diameter linearly gradually changed from a near end to a far end by adopting a gradient extrusion and bionic coating process, wherein the tube wall hardness of the bionic umbilical vein linearly gradually changed from a Shore 30D to a Shore 35D, and wrapping a foaming silica gel buffer layer on the outer wall of the bionic umbilical vein; S2, arranging a distributed multi-parameter sensing network, namely arranging a group of sensing units at intervals of 1cm along the axial direction of a bionic umbilical vein, wherein each group comprises a pressure sensor, an angle sensor and an acceleration sensor, and the sensing units are connected with a data acquisition module through a flexible circuit board; s3, acquiring the tube setting parameters in real time, wherein when medical staff performs tube setting operation, the sensing network synchronously acquires 12 key parameters such as angle, propulsion acceleration, tube wall contact pressure and the like, and transmits the 12 key parameters to the intelligent terminal after filtering treatment; s4, performing real-time regulation and control on the operation accuracy, namely calculating an Acc value by an accuracy regulation and control algorithm of the intelligent terminal based on the acquisition parameters, triggering early warning when the Acc is less than 0.75, and prompting operation adjustment; s5, dynamically simulating interactive resistance, namely calculating real-time resistance through a catheter-blood vessel interactive force model, adjusting a built-in air valve to change the pressure of a blood vessel wall, and feeding back real operation resistance; S6, after training is finished, calculating total scores according to 5 dimensions such as precision coefficients, resistance control stability and the like by the system to generate an evaluation report; and S7, personalized guidance and training, namely generating a special training scheme by the terminal based on the evaluation report, and improving the operation capability by targeted training by medical staff.
  2. 2. The method according to claim 1, wherein the preparation process of the graded diameter bionic umbilical vein in the step S1 is characterized in that a main material obtained by blending PDMS and TPE according to a mass ratio of 7:3 is adopted, 5% of micron-sized calcium carbonate particles are added, the mixture is extruded and molded through a conical graded extrusion die (3 mm of an inlet/2 mm of an outlet) and a sectional temperature control (180 ℃ to 200 ℃) process, a 0.5mm thick foamed silica gel buffer layer is sprayed on the outer wall, and the peeling strength is more than or equal to 1.5N/cm.
  3. 3. The method of claim 1, wherein the parameters of the distributed sensing network in the step S2 are that the measuring range of the pressure sensor is 0-5N, the precision is 0.001N, the measuring range of the angle sensor is 0-360 degrees, the precision is 0.1 degrees, the measuring range of the acceleration sensor is +/-10 g, the sampling frequency is 100Hz,10 groups of sensing units are distributed circumferentially at 120 degrees, and the total thickness is less than or equal to 0.2mm.
  4. 4. The method according to claim 1, wherein the formula of the accuracy control algorithm of the pipe setting operation in step S4 is: wherein Triggering early warning.
  5. 5. The method according to claim 1, wherein the catheter-vessel interaction force simulation model in step S5 is formulated as: wherein mu is taken as a value And the resistance dynamic feedback is realized through the built-in air valve adjustment P 0 .
  6. 6. The method of claim 1, wherein the multi-dimensional evaluation system in step S6 comprises 5 dimensions, namely, precision coefficient (20 minutes), resistance control stability (20 minutes), angle maintenance consistency (20 minutes), depth control accuracy (20 minutes), operation completion time (20 minutes), total score equal to or greater than 90 minutes is excellent, and <60 minutes is failed.
  7. 7. The method according to claim 1, wherein the personalized training scheme in step S7 comprises "gradient resistance training" with low precision coefficient and "angle calibration training" with poor angle control, and the scheme comprises information such as scene parameters, training times, assessment standards and the like.
  8. 8. The method of any one of claims 1-7, wherein scene adaptation is supported, and wherein the premature infant scene parameters are The scene parameters of the term infants are as follows 。
  9. 9. The novel material umbilical vein catheterization module improvement system for realizing the method of any one of claims 1-8 is characterized by comprising a gradual change diameter bionic umbilical vein model, a distributed multi-parameter sensing network, a data acquisition processing module, an intelligent algorithm engine and an intelligent feedback and training guidance terminal, wherein the gradual change diameter bionic umbilical vein model is made of a PDMS and TPE blending material and has a diameter and hardness gradient, the distributed sensing network comprises 10 groups of sensing units, the intelligent algorithm engine integrates a precision regulation algorithm and an interaction force simulation model, and the modules are communicated through a flexible circuit board or Bluetooth 5.2 protocol, and the data transmission delay is less than or equal to 10ms.
  10. 10. The system of claim 9, wherein the intelligent feedback and training guidance terminal is a 10.1 inch touch screen supporting real-time parameter display, acousto-optic pre-warning, evaluation report generation, personalized training scheme pushing, data management and remote guidance functions, and can derive Excel format training data.

Description

Improvement method and system for umbilical vein catheterization module of neonate umbilical model Technical Field The invention relates to the technical field of medical teaching models, in particular to an improvement method and an improvement system of a umbilical vein catheterization module of a neonate umbilical model. Background The umbilical vein catheterization is a key operation technology in neonatal intensive care and is used for venous nutrition supply, drug infusion, blood sample collection and hemodynamic monitoring of high-risk neonates such as premature infants, low-birth-weight infants and the like. The operation has extremely high requirements on accuracy, and serious complications such as umbilical vein rupture, thrombosis, infection and the like can be caused by incorrect control of the placement position of the catheter and incorrect force control, and even the life of a neonate is endangered. In clinical training, the simulation umbilical model is a core carrier for medical staff to master operation skills, but the prior art has two core bottlenecks, which severely restricts the conversion of training effects to clinical capabilities: Inadequate bionic degree leads to distorted operation hand feeling and poor clinical mobility The true neonatal umbilical vein has obvious physiological gradient characteristics that the diameter of a lumen is gradually changed linearly from 3mm near the proximal end (near the umbilical wheel) to 2mm far away from the distal end (near the umbilical vein sinus), the hardness of the tube wall is gradually changed from 30D to 35D along with the reduction of the diameter, and the foaming structure of the Wharton's jelly enables the tube placement resistance to show nonlinear change of ' increasing before decreasing and then stabilizing ', so that the friction coefficient is maintained between 0.3 and 0.4. The umbilical vein module of the existing training model is generally formed by integrally molding a single material (such as common silica gel), and cannot simulate the physiological gradient: 1. The bionic defect of the structure is that the diameter of a blood vessel of a traditional model is uniform (at most 2.5-3mm fixed value), and the traditional model has no gradual change characteristic, so that medical staff cannot train clinical key skills of adjusting the propelling force according to the diameter change. Clinical data of a certain child hospital show that the incidence rate of catheter discount reaches 22% when medical staff trained by using a traditional model perform clinical operation for the first time, and is far higher than the incidence rate of 5% of advanced doctors; 2. The mechanical characteristic deviation is that the Shore hardness of the traditional silica gel blood vessel is 35-45D, the hardness gradient is not changed, the tube placement resistance is constant at 0.8-1.0N, and the deviation from the clinical actual resistance fluctuation characteristic is 40%. The hand feeling distortion makes it difficult for medical staff to accurately control the force in clinical operation, and increases the risk of vascular wall injury; 3. the model lacks a foaming buffer structure of the Walker's rubber, and has no flexible blocking feeling when the catheter is pushed, so that the difference between the operation hand feeling and clinical practice is further aggravated. (II) the lack of an evaluation system leads to low training efficiency and slow capacity improvement The training evaluation mode of the existing model is very extensive, and operation success or failure can be judged only through whether a catheter passes through a preset outlet or not, so that the fine evaluation and guidance of the opposite-tube process can not be realized: 1. Parameter monitoring blank, namely, key operation parameters such as a tube setting angle, a pushing acceleration, tube wall contact pressure distribution and the like cannot be acquired, so that hidden improper operations such as ' angle deviation (> 18 degrees) ' too fast pushing (acceleration >0.3m/s 2) ' and the like cannot be identified, and the parameters are core indexes of clinical operation normalization; 2. the subjective assessment is serious, the training effect depends on visual observation and experience judgment of teachers with education, for example, judgment on whether a tube setting angle is proper or not, standard differences of different teachers can reach 15 degrees, and assessment results lack objectivity and consistency; 3. The guidance lacks pertinence, namely, weak links of medical staff cannot be positioned, so that training falls into the dilemma of repeated operation without accurate improvement. The statistical data of a medical college shows that the average time for a student trained by adopting a traditional model to reach the clinical operation qualification standard is 10 months, the accumulated training times are more than 150 times, and the training efficiency is