A method and system for perioperative thermal management

Abstract

The application discloses a kind of perioperative body temperature management method and system, it is related to medical health data processing technical field, including decision unit, calculation unit, communication unit, body temperature acquisition unit, artificial input unit, temperature control heating unit etc..Based on multiple data, an efficient, fast response mathematical model is established, the intelligent decision adjustment of body temperature is realized, and the burden of medical staff during operation is reduced;Reduce the risk of postoperative complications, improve the effect of anesthesia and recovery quality, reduce postoperative chills and delayed recovery, improve the comfort and satisfaction of patients;Through effective body temperature and health information management, the rehabilitation of patients after operation can be promoted, thereby shortening the hospitalization time and saving medical resources;Through intelligent, individualized body temperature prediction and intervention technology, higher quality perioperative management is realized, which helps to promote the development of precision medicine.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical health data processing, in particular to a perioperative body temperature management method and system. BACKGROUND

[0002] Perioperative body temperature management is an important part of modern anesthesia and surgical nursing, aiming to maintain the core body temperature of patients in the preoperative, intraoperative and postoperative stages within the normal range (generally 36.0℃ to 37.5℃), in order to reduce the incidence of hypothermia-related complications. Studies have shown that the incidence of unplanned intraoperative hypothermia is as high as 20%-70%, mainly due to factors such as anesthesia-induced inhibition of body temperature regulation center function, intraoperative exposure, cold fluid infusion and low environmental temperature.

[0003] Unplanned hypothermia refers to the phenomenon that the core body temperature of a patient during the perioperative period drops below 36.0℃, which has serious consequences, as follows: (1) coagulopathy and increased intraoperative blood loss: hypothermia can inhibit platelet function and thrombin activity, prolong coagulation time, and increase intraoperative and postoperative bleeding volume, posing a threat to surgical safety; (2) increased postoperative infection rate: decreased body temperature can inhibit immune cell activity, reduce local tissue blood flow, delay wound healing, and significantly increase the risk of surgical site infection; (3) increased risk of cardiovascular complications: unplanned hypothermia can cause vasoconstriction, blood pressure fluctuations, arrhythmias (such as atrial fibrillation), and increased myocardial oxygen consumption, especially in elderly or cardiovascular patients; (4) delayed recovery and impaired metabolism of anesthetics: hypothermia can slow down the metabolism and clearance of anesthetics, sedatives and muscle relaxants by the liver and kidneys, delaying patient recovery time and prolonging the postoperative recovery period; (5) increased shivering and discomfort: postoperative shivering is a common reaction to hypothermia, which can increase oxygen consumption, myocardial burden and patient discomfort, and even lead to the risk of rebleeding; (6) prolonged hospital stay and increased medical costs: increased complications and prolonged recovery time can lead to increased postoperative hospital stay, further increasing the burden on hospital resources and economic costs.

[0004] In the prior art, there is a lack of a comprehensive decision-making body temperature management system that links multiple data, which cannot realize real-time collection and utilization of body temperature information and related health information. SUMMARY

[0005] The purpose of the present application is to provide a perioperative body temperature management method and system based on real-time collection and linkage of multiple data, which calculates body temperature through a body temperature prediction matrix operator and calculates the risk of hypothermia through a hypothermia risk probability model, realizes automatic temperature control decision-making and postoperative data summary and filing.

[0006] To solve the above technical problems, the technical solution adopted by the present application is as follows: a perioperative body temperature management method, comprising the following steps:

[0007] Step S1: According to the patient's hospital number, a unique online operation information data table is established to record the patient's name, operation name, gender, age, height, weight, operation site partition, anesthesia type, operating room environment temperature, operation time, intraoperative blood loss, intraoperative blood transfusion volume, intraoperative infusion volume, intraoperative urine volume and body temperature data, wherein the patient's body temperature, operation time, intraoperative blood loss, intraoperative blood transfusion volume, intraoperative infusion volume and intraoperative urine volume data are recorded every 5-10 minutes;

[0008] Step S2: At the time of operation start t , the operation information data table in the time interval t -1, t ] is parameterized to obtain a parameter information data table, the body temperature prediction matrix operator is derived according to the parameter information data table in the time interval t -1, t ], and the total predicted change amount Δ t of the patient's body temperature in the time interval t , t +1] is calculated according to the body temperature prediction matrix operator in the time interval t , T , wherein t =0,1,… n ; t The unit of Δ T is ℃; when t =0, the time interval (-1,0] represents the time period from 1 hour before the operation to the operation start;

[0009] Step S3: Based on the patient's body temperature t at the time of T , the total predicted change amount Δ t of the patient's body temperature in the time interval t , T and the normal distribution model, the hypothermia risk probability of the patient in the next hour is predicted, and the condition of hypothermia is that the body temperature is less than 35.5℃;

[0010] Step S4: Using the temperature control heating blanket to maintain or increase the patient's body temperature, based on the calculation result of the hypothermia risk probability, selecting the constant temperature control strategy or the variable temperature control strategy to control the real-time temperature of the temperature control heating blanket;

[0011] Step S5: During the patient's recovery in the anesthesia recovery room after the operation, the patient's body temperature data is recorded every 5-10 minutes, and after the patient wakes up and enters the hospital ward, the patient's body temperature data recorded during the operation and anesthesia recovery period is summarized and filed as health management information, which is used to provide reference for postoperative rehabilitation treatment and prevent postoperative complications caused by perioperative hypothermia.

[0012] Further, the body temperature prediction matrix operator comprises a weight matrix W, the expression of which is

[0013] ,

[0014] In the formula, is a first metabolic base weight, and the value range is 0.01 to 0.02; is a second metabolic base weight, and the value range is 0.001 to 0.002; is a hemodynamic weight, and the value range is 0.001 to 0.003; is a net liquid cooling effect weight, and the value range is -0.001 to -0.0006; is a time exposure weight, and the value range is -0.08 to -0.07; is an environmental heat exchange weight, and the value range is 0.04 to 0.07.

[0015] Further, the body temperature prediction matrix operator further comprises a coefficient matrix C, the expression of which is

[0016] ,

[0017] In the formula, is a gender coefficient, and the value range is 1.05 to 1.15 when the patient is female, and the value range is 0.95 to 1 when the patient is male; is an age coefficient, and the value range is 0.92 to 0.96 when the patient is less than 30 years old, the value range is 0.97 to 1.05 when the patient is between 30 years old and 65 years old, and the value range is 1.08 to 1.2 when the patient is more than 65 years old; is an anesthesia coefficient, and the value range is 1.2 to 1.3 when the anesthesia type is general anesthesia, the value range is 1.05 to 1.15 when the anesthesia type is intraspinal anesthesia, and the value range is 1 to 1.01 when the anesthesia type is local anesthesia; is a site coefficient, and the value range is 1.27 to 1.35 when the surgical site partition belongs to the abdominal cavity, the value range is 1.15 to 1.24 when the surgical site partition belongs to the chest cavity, the value range is 1.02 to 1.06 when the surgical site partition belongs to the pelvic cavity, the value is 1 when the surgical site partition belongs to the lower or upper limbs, and the value range is 0.95 to 0.99 when the surgical site partition belongs to the head; is the correction coefficient of urine volume, when the intraoperative urine volume is less than 200ml, the value range is 0.8 to 0.85, when the intraoperative urine volume is between 200ml and 800ml, the value range is 0.95 to 1.05, when the intraoperative urine volume is greater than 800ml but not more than 1500ml, the value range is 1.1 to 1.24, when the intraoperative urine volume is greater than 1500ml, the value range is 1.28 to 1.35.

[0018] Further, the body temperature prediction matrix operator also includes a data element matrix D, the expression of the data element matrix D is

[0019] ,

[0020] In the formula, is the reference BMI index, the value range is 30 to 35; is the actual BMI index, which is calculated by the actual height and weight of the patient; is the age of the patient; is the reference age, the value range is 45 to 50; is the intraoperative blood loss; is the intraoperative blood transfusion volume; is the intraoperative infusion volume; is the net liquid cooling coefficient, the value range is 0.7 to 0.8; is the intraoperative urine volume; is the time exposure coefficient, the value range is -0.03 to -0.015; is the reference temperature, the value range is 20 to 22; is the operating room environment temperature.

[0021] Further, Δ T The calculation formula of Δ

[0022] ;

[0023] When Δ t =0, because the operation has not started at this moment, the values of the operation duration, the intraoperative blood loss, the intraoperative blood transfusion volume, the intraoperative infusion volume and the intraoperative urine volume are all 0; when Δ t ≠0, the values of the intraoperative blood loss, the intraoperative blood transfusion volume, the intraoperative infusion volume and the intraoperative urine volume take the cumulative values in the time interval (Δ t -1, t ].

[0024] Further, it is assumed that Δ T obeys normal distribution, that is,

[0025] ,

[0026] then in the time interval (Δ t ,t +1] the formula for calculating the predicted body temperature of the patient less than 35.5°C is

[0027] ,

[0028] wherein the mean is calculated by the formula

[0029] ;

[0030] wherein is the reference mean without considering the influence of the coefficient matrix C; is the anesthesia-site interaction mean considering the interaction under the anesthesia type and the surgical site partition; is the age-liquid interaction mean considering the interaction under the patient's age, intraoperative infusion volume and intraoperative urine volume; is the BMI-environment interaction mean considering the interaction under the patient's actual BMI index and the operating room environment temperature; is the anesthesia-site interaction coefficient, the value range is 0.13 to 0.18; is an intermediate variable when calculating ; is the anesthesia type center parameter, the value range is 1.4 to 1.6; is the anesthesia effect dispersion, the value range is 0.7 to 0.9; is the surgical site center parameter, the value range is 1.9 to 2.1; is the site effect dispersion, the value range is 1 to 1.4; is the age-liquid interaction coefficient, the value range is 0.002 to 0.005; is the BMI-environment interaction coefficient, the value range is -0.08 to -0.03; the formula for calculating the variance

[0031] ;

[0032] wherein is the anesthesia variance coefficient, the value range is 0.06 to 0.09; is the age variance coefficient, the value range is 0.02 to 0.05.

[0033] Further, when the value of does not exceed 50%, a constant temperature control strategy is adopted, and the temperature setting of the temperature control heating blanket is a constant temperature , the formula for calculating the constant temperature

[0034] ; ​

[0035] wherein, m is the weight of the patient in kg; H is the specific heat capacity of the human body in J / kg·°C; h is the heat exchange coefficient in W / m²·°C; A is the contact area between the patient and the temperature-controlled heating blanket in m²; the constant 3600 means 3600 seconds; when the value of is greater than 50%, a variable temperature control strategy is adopted, and the temperature of the temperature-controlled heating blanket is set as a temperature , The calculation formula of is

[0036] ;

[0037] wherein, K is a proportional adjustment factor, and the value range is 0.1 to 1; is a target temperature, and the target temperature is the body temperature that the patient wants to reach and maintain; is the real-time body temperature of the patient.

[0038] The application further provides a management system based on the perioperative body temperature management method, which comprises a decision unit, a calculation unit, a communication unit, a body temperature acquisition unit, a manual input unit and a temperature-controlled heating unit. The decision unit is used for sending decision instructions, receiving and processing data and storing data. The calculation unit and the decision unit interact with each other. The communication unit and the calculation unit interact with each other. The body temperature acquisition unit and the communication unit interact with each other. The temperature-controlled heating unit and the decision unit interact with each other. The manual input unit is used for manually inputting the data collected by medical staff into the decision unit. The body temperature acquisition unit transmits the body temperature data collected at regular time intervals to the calculation unit through the communication unit. The decision unit transmits the data input by the manual input unit to the calculation unit. The calculation unit calculates according to all the received data and transmits the result to the decision unit. The decision unit makes a temperature control decision scheme according to the calculation result input by the calculation unit. The decision unit controls the temperature of the temperature-controlled heating unit in real time according to the temperature control decision scheme. The temperature-controlled heating unit adopts a temperature-controlled heating blanket.

[0039] Further, the communication unit comprises a communication host, a display screen, a card reader, a communication interface, a power interface, a power key, a function key and an indicator light. The communication interface is arranged on the side surface of the communication host and is used for wired communication with the calculation unit. The power interface is arranged on the side surface of the communication host and is used for plugging a power line. The display screen is arranged on the upper surface of the communication host. One power key and multiple function keys are installed on the upper surface of the communication host. An indicator light is arranged beside each power key and function key. The card reader is fixedly installed on the communication host.

[0040] Further, the body temperature acquisition unit comprises a main controller, a sticky, a signal line, a sponge probe, a film, a key; the main controller is used for realizing identification and pairing with the card reader by approaching or contacting; a ring of sticky is fixedly installed on the side of the main controller; a layer of film is attached on the sticky; the sticky is used for sticking to the cheek skin of the patient after tearing off the film; the key is arranged on the side of the main controller; the sponge probe is connected with the main controller through the signal line.

[0041] The present application has the following advantages compared with the prior art: (1) a high-efficiency, fast-response mathematical model is established based on multi-element data, realizing intelligent decision adjustment of body temperature and reducing the burden of medical staff during operation; (2) reducing the risk of postoperative complications, improving the effect of anesthesia and recovery quality, reducing postoperative chills and delayed recovery, and improving the comfort and satisfaction of patients; (3) through effective body temperature and health information management, the rehabilitation of patients after operation can be promoted, thereby shortening the hospitalization time and saving medical resources; (4) through intelligent and individualized body temperature prediction and intervention technology, higher quality of perioperative period management is realized, which is helpful to promote the development of precision medicine. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The present application is a perioperative body temperature management method flow chart.

[0043] Figure 2 The present application is a perioperative body temperature management system module diagram.

[0044] Figure 3 The present application is a communication unit hardware structure diagram.

[0045] Figure 4 The present application is a body temperature acquisition unit hardware structure diagram.

[0046] In the figure: 1 - decision unit; 2 - calculation unit; 3 - communication unit; 4 - body temperature acquisition unit; 5 - artificial input unit; 6 - temperature control heating unit; 301 - communication host; 302 - display screen; 303 - card reader; 304 - communication interface; 305 - power interface; 306 - power key; 307 - function key; 308 - indicator light; 401 - main controller; 402 - sticky; 403 - signal line; 404 - sponge probe; 405 - film; 406 - key. DETAILED DESCRIPTION

[0047] The technical solutions of the present application are further illustrated below in combination with the drawings and through specific embodiments, wherein the drawings are only used for illustrative purposes, represent only schematic diagrams, and cannot be understood as limitations on the present application; in order to better illustrate the embodiments of the present application, some components in the drawings are omitted, enlarged or reduced, and do not represent the actual product size; it is understandable for those skilled in the art that some well-known structures and their descriptions in the drawings can be omitted.

[0048] As shown in Figure 1 , the perioperative body temperature management method provided by the present application comprises the following steps:

[0049] Step S1: According to the patient's hospital number, a unique online operation information data table is established to record the patient's name, operation name, gender, age, height, weight, operation site partition, anesthesia type, operating room environment temperature, operation duration, intraoperative blood loss, intraoperative blood transfusion volume, intraoperative infusion volume, intraoperative urine volume and body temperature data, wherein the patient's body temperature, operation duration, intraoperative blood loss, intraoperative blood transfusion volume, intraoperative infusion volume and intraoperative urine volume data are recorded every 10 minutes; as shown in Table 1, part of the core operation information table of case 1 to case 8, corresponding to embodiments 1 to 8, is given.

[0050] Table 1 Part of the core operation information of 8 groups of cases in embodiments 1 to 8 within 1 hour after the start of operation

[0051]

[0052] Step S2: At the time t after the start of operation, t -1, t ] within the operation information data table is parameterized to obtain a parameter information data table, a body temperature prediction matrix operator is derived according to the parameter information data table within the time interval t -1, t ], and the total predicted change amount Δ t of the patient's body temperature within the time interval t , t +1] is calculated according to the body temperature prediction matrix operator within the time interval t , T wherein t =0,1,… n ; t The unit of Δ T is ℃; when t =0, the time interval (-1,0] represents the time period from 1 hour before the start of operation to the start of operation; in the 8 embodiments of the present application, t are all equal to 1.

[0053] The body temperature prediction matrix operator includes a weight matrix W, a coefficient matrix C, and a data element matrix D. In the eight embodiments of the present invention, the expression for the weight matrix W is as follows:

[0054] ,

[0055] The expression for the coefficient matrix C is as follows:

[0056] ,

[0057] Table 2 shows the parameter values ​​of the coefficient matrix C corresponding to Examples 1 to 8. In all 8 examples, the anesthesia type is general anesthesia.

[0058] Table 2. Parameter values ​​of coefficient matrix C for the eight groups of cases from Examples 1 to 8.

[0059]

[0060] The expression for the data element matrix D is:

[0061] ,

[0062] Table 3 shows the parameter values ​​for the data element matrix D corresponding to Examples 1 to 8;

[0063] Table 3. Parameter values ​​of the data element matrix D for the eight groups of cases from Examples 1 to 8.

[0064]

[0065] Based on the above information, by Δ T The calculation formula can be used to obtain the total expected change in patient body temperature Δ within the time interval (1,2]. T Δ T The calculation results are shown in Table 4.

[0066] Table 4. Δ values ​​for all 8 groups of cases from Examples 1 to 8 T Calculation results (unit: °C)

[0067]

[0068] Step S3: Based on t The patient's body temperature at all times T Time interval ( t , t The total expected change in patient body temperature within +1] Δ T The normal distribution model is used to predict the probability of hypothermia in a patient within the next hour, where hypothermia is defined as a body temperature below 35.5°C; let Δ... T It follows a normal distribution, that is...

[0069] ,

[0070] then in the time interval (t, t+1], the calculation formula of the predicted body temperature of the patient less than 35.5℃ is t , t

[0071] ,

[0072] wherein the calculation formula of the mean value is

[0073] ;

[0074] wherein is the reference mean value without considering the influence of the coefficient matrix C; is the anesthesia-site interaction mean value considering the interaction under the anesthesia type and the surgical site partition; is the age-liquid interaction mean value considering the interaction under the patient's age, intraoperative infusion volume and intraoperative urine volume; is the BMI-environment interaction mean value considering the interaction under the actual BMI index of the patient and the operating room environment temperature; in Embodiments 1 to 8, is the anesthesia-site interaction coefficient, and the values are all 0.15; is an intermediate variable when calculating ; is the anesthesia type center parameter, and the values are all 1.5; is the anesthesia effect dispersion, and the values are all 0.8; is the surgical site center parameter, and the values are all 2.0; is the site effect dispersion, and the values are all 1.2; is the age-liquid interaction coefficient, and the values are all 0.004; is the BMI-environment interaction coefficient, and the values are all -0.05; the calculation formula of the variance is

[0075] ;

[0076] in Embodiments 1 to 8, is the anesthesia variance coefficient, and the values are all 0.08; is the age variance coefficient, and the values are all 0.03; based on the above information, the calculation results of of the 8 groups of cases in Embodiments 1 to 8 are shown in Table 5;

[0077] Table 5 Calculation results of of the 8 groups of cases in Embodiments 1 to 8 ​

[0078]

[0079] As shown in Table 5, only case 7... The value is greater than 50%.

[0080] Step S4: Use a temperature-controlled heating blanket to maintain or raise the patient's body temperature. Based on the calculation of the probability of hypothermia risk, select a constant temperature control strategy or a variable temperature control strategy to control the real-time temperature of the heating blanket; when When the value does not exceed 50%, a constant temperature control strategy is adopted, and the temperature of the temperature-controlled heating blanket is set to a constant temperature. , The calculation formula is:

[0081] ;

[0082] In the formula, m For the patient's weight, see Table 1 for the weight values ​​of cases 1 to 8; H The specific heat capacity of the human body was 3470 J / kg·°C in Examples 1 to 8. h The heat exchange coefficient is 42 W / m²·°C in Examples 1 to 8. A The contact area between the patient and the temperature-controlled heating blanket was 0.8 m² in Examples 1 to 8; the constant 3600 means 3600 seconds; the body temperature and temperature data of the temperature-controlled heating blanket for a total of 6 groups of cases other than Examples 2 and 7 are shown in Table 6. The data were recorded starting 1 hour after the start of the operation and every 10 minutes until 2 hours after the start of the operation.

[0083] Table 6. Body temperature (unit: °C) and temperature data of temperature-controlled heating blankets for a total of 6 groups of cases excluding Examples 2 and 7.

[0084]

[0085] when When the value is greater than 50%, that is, for Examples 2 and 7, a variable temperature control strategy is adopted, and the temperature of the temperature-controlled heating blanket is set to a temperature that varies with time. , The calculation formula is:

[0086] ;

[0087] In the formula, K The value of the scaling factor is 0.6 in both Example 2 and Example 7; The target temperature is 36°C in both Examples 2 and 7. The target temperature is the body temperature that the patient wants to reach and maintain. The real-time body temperature of the patient; the body temperature and temperature control heating blanket temperature data of example 2 are shown in table 7, and the body temperature and temperature control heating blanket temperature data of example 7 are shown in table 8.

[0088] Table 7 Body temperature and temperature control heating blanket temperature data of example 2 (unit: ℃)

[0089]

[0090] Table 8 Body temperature and temperature control heating blanket temperature data of example 7 (unit: ℃)

[0091]

[0092] Step S5: During the recovery period of the patient in the anesthesia recovery room after the operation, the body temperature data of the patient is recorded every 10 minutes, and after the patient wakes up and enters the hospital ward, the body temperature data recorded during the operation and the anesthesia recovery period of the patient is summarized and filed as health management information, which is used to provide a reference basis for postoperative rehabilitation treatment and prevent postoperative complications caused by perioperative hypothermia.

[0093] The application also provides a management system based on the foregoing perioperative temperature management method, as shown in Figure 2 The decision unit 1 is used for sending decision instructions to the outside, receiving and processing data, and storing data; the computing unit 2 and the decision unit 1 interact with each other; the communication unit 3 and the computing unit 2 interact with each other; the body temperature acquisition unit 4 and the communication unit 3 interact with each other; the temperature control heating unit 6 and the decision unit 1 interact with each other; the artificial input unit 5 is used for artificially inputting the data collected by medical staff into the decision unit 1; the body temperature acquisition unit 4 transmits the body temperature data collected at a fixed time to the computing unit 2 through the communication unit 3, the decision unit 1 transmits the data input by the artificial input unit 5 to the computing unit 2, the computing unit 2 calculates according to all the received data and transmits the result to the decision unit 1, the decision unit 1 makes a temperature control decision scheme according to the calculation result input by the computing unit 2, and the decision unit 1 controls the temperature of the temperature control heating unit 6 in real time according to the temperature control decision scheme; the temperature control heating unit 6 adopts a temperature control heating blanket.

[0094] As shown in Figure 3 As shown in

[0095] As Figure 4 shown, in the body temperature acquisition unit 4, the main control unit 401 is used for identifying and pairing with the card reader 303 through close or contact; a ring-shaped adhesive 402 is fixedly installed on the side of the main control unit 401; a layer of film 405 is attached on the adhesive 402; the adhesive 402 is used for being bonded with the cheek skin of the patient after the film 405 is torn off; a key 406 is arranged on the side of the main control unit 401; and the sponge probe 404 is connected with the main control unit 401 through a signal line 403.

[0096] The working principle of the present application is that the computer integrated with the calculation unit 2 and the decision unit 1 is adopted in the embodiment; as Figure 3 shown, before the patient enters the operating room, the computer is turned on, the communication interface 304 is connected with the computer through a data line, the power supply interface 305 is connected with the power supply, then the power key 306 is long-pressed to start the communication host 301, then the function key 307 is pressed to set the data acquisition mode and activate the card reader 303; as Figure 4 shown, after the patient enters the operating room, the key 406 on the main control unit 401 is turned on, the main control unit 401 is held and touched with the card reader 303 to complete the wireless connection between the main control unit 401 and the communication host 301; then the sponge probe 404 is made of shape memory sponge material, the sponge probe 404 is twisted by the medical staff, the twisted end of the sponge probe 404 is inserted into the ear canal of the patient, and the sponge probe 404 is kept for 5-10 seconds; after the shape of the sponge probe 404 rebounds and the ear canal is filled, the film 405 is manually torn off, the main control unit 401 is bonded on the skin of the cheek of the patient through the adhesive 402, and the deployment of the body temperature acquisition unit 4 is completed.

[0097] The body temperature acquisition unit 4 is a temperature control heating blanket with heating and cooling functions, the temperature control heating blanket needs to be laid on the operating bed before the patient starts the operation and connected with the computer; the manual input unit 5 is a mouse and a keyboard connected with the computer, during the operation, a special nurse is responsible for manually recording the intraoperative blood loss, intraoperative blood transfusion, intraoperative infusion and intraoperative urine volume data every 5-10 minutes, and inputting the data into the computer through the mouse and the keyboard; the computer realizes the automatic control of the temperature of the temperature control heating blanket according to the aforementioned perioperative temperature management method.

[0098] In particular, in the online operation information data table established according to the patient hospitalization number, the patient's name, operation name, gender, age, height, weight, operation site partition, anesthesia type data are text data, and the operation room environment temperature, operation duration, intraoperative blood loss, intraoperative blood transfusion volume, intraoperative infusion volume, intraoperative urine volume and body temperature data are numerical data; at a time t after the operation starts, the parameterization processing of the operation information data table in a time interval (t-1, t] obtains a parameter information data table, which refers to the conversion of the text data in the operation information data table except the patient's name and the original numerical data into corresponding numerical data according to the calculation rule of Δ T .

Claims

1. A perioperative body temperature management system comprising a decision unit (1), a calculation unit (2), a communication unit (3), a body temperature acquisition unit (4), a manual input unit (5), a temperature-controlled heating unit (6), and a perioperative body temperature management method using the same, characterized in that, The method comprises the following steps: Step S1: establishing a unique online operation information data table according to a patient's hospitalization number, recording the patient's name, operation name, gender, age, height, weight, operation site partition, anesthesia type, operation room environment temperature, operation duration, intraoperative bleeding volume, intraoperative blood transfusion volume, intraoperative infusion volume, intraoperative urine volume and body temperature data, wherein the patient's body temperature, operation duration, intraoperative bleeding volume, intraoperative blood transfusion volume, intraoperative infusion volume and intraoperative urine volume data are recorded every 5-10 minutes; Step S2: At a time point after the start of the surgery t , the surgery information data table in the time interval t -1, t ] is parameterized to obtain a parameter information data table, a body temperature prediction matrix operator is derived from the parameter information data table in the time interval t -1, t ], and a total predicted change amount Δ t of the patient's body temperature in the time interval t is calculated from the body temperature prediction matrix operator in the time interval t , t +1]. T , wherein t =0, 1, …, n ; t The unit of Δ T is ℃; when t =0, the time interval (-1, 0] represents a time period from 1 hour before the start of the surgery to the start of the surgery. Step S3: Based on t The patient's body temperature at all times T Time interval ( t , t The total expected change in patient body temperature within +1] Δ T The normal distribution model was used to predict the probability of hypothermia in patients within the next hour, where hypothermia is defined as a body temperature of less than 35.5°C. Step S4: using a temperature control heating blanket to maintain or increase the patient's body temperature, and selecting a constant temperature control strategy or a variable temperature control strategy to control the real-time temperature of the temperature control heating blanket based on the calculation result of the hypothermia risk probability; Step S5: recording the patient's body temperature data every 5-10 minutes during the patient's recovery in the anesthesia recovery room, and after the patient wakes up and enters the hospital ward, the body temperature data recorded during the operation and the anesthesia recovery period of the patient are collected and filed as health management information, which is used to provide a reference basis for postoperative rehabilitation treatment and prevent postoperative complications caused by perioperative hypothermia; The decision unit (1) is used for sending decision instructions, receiving and processing data and storing data; the calculation unit (2) and the decision unit (1) interact with data; the communication unit (3) and the calculation unit (2) interact with data; the body temperature collection unit (4) and the communication unit (3) interact with data; the temperature control heating unit (6) and the decision unit (1) interact with data; the artificial input unit (5) is used for artificially inputting the data collected by medical staff into the decision unit (1); the body temperature collection unit (4) transmits the body temperature data collected at regular intervals to the calculation unit (2) through the communication unit (3), the decision unit (1) transmits the data input by the artificial input unit (5) to the calculation unit (2), the calculation unit (2) calculates according to all the received data and transmits the result to the decision unit (1), the decision unit (1) makes a temperature control decision scheme according to the calculation result input by the calculation unit (2), and the decision unit (1) controls the temperature of the temperature control heating unit (6) in real time according to the temperature control decision scheme; the temperature control heating unit (6) adopts a temperature control heating blanket.

2. A perioperative thermal management system as claimed in claim 1, characterized in that: The body temperature prediction matrix operator comprises a weight matrix W, and the expression of the weight matrix W is ， wherein, is a first metabolic basis weight having a value ranging from 0.01 to 0.02; is a second metabolic basis weight having a value ranging from 0.001 to 0.002; is a hemodynamic weight having a value ranging from 0.001 to 0.003; is a net liquid cooling effect weight having a value ranging from -0.001 to -0.0006; is a time exposure weight having a value ranging from -0.08 to -0.07; is an environmental heat exchange weight having a value ranging from 0.04 to 0.

07.

3. A perioperative thermal management system as claimed in claim 2, characterized in that: The body temperature prediction matrix operator further comprises a coefficient matrix C, and the expression of the coefficient matrix C is ， In the formula, is a gender coefficient, which is in the range of 1.05 to 1.15 when the patient is female and in the range of 0.95 to 1 when the patient is male; is an age coefficient, which is in the range of 0.92 to 0.96 when the patient is less than 30 years old, in the range of 0.97 to 1.05 when the patient is between 30 and 65 years old, and in the range of 1.08 to 1.2 when the patient is more than 65 years old; is an anesthesia coefficient, which is in the range of 1.2 to 1.3 when the anesthesia type is general anesthesia, in the range of 1.05 to 1.15 when the anesthesia type is intraspinal anesthesia, and in the range of 1 to 1.01 when the anesthesia type is local anesthesia; is a site coefficient, which is in the range of 1.27 to 1.35 when the surgical site is in the abdominal cavity, in the range of 1.15 to 1.24 when the surgical site is in the chest cavity, in the range of 1.02 to 1.06 when the surgical site is in the pelvic cavity, is 1 when the surgical site is in the lower or upper limbs, and is in the range of 0.95 to 0.99 when the surgical site is in the head; is a urine volume correction coefficient, which is in the range of 0.8 to 0.85 when the intraoperative urine volume is less than 200 ml, in the range of 0.95 to 1.05 when the intraoperative urine volume is between 200 ml and 800 ml, in the range of 1.1 to 1.24 when the intraoperative urine volume is more than 800 ml but not more than 1500 ml, and in the range of 1.28 to 1.35 when the intraoperative urine volume is more than 1500 ml.

4. A perioperative thermal management system as claimed in claim 3, characterized in that: The body temperature prediction matrix operator further comprises a data element matrix D, and the expression of the data element matrix D is ， In the formula, is the reference BMI index, and the value range is 30 to 35; is the actual BMI index, which is calculated by the actual height and weight of the patient; is the age of the patient; is the reference age, and the value range is 45 to 50; is the intraoperative bleeding volume; is the intraoperative blood transfusion volume; is the intraoperative infusion volume; is the net liquid cooling coefficient, and the value range is 0.7 to 0.8; is the intraoperative urine volume; is the time exposure coefficient, and the value range is -0.03 to -0.015; is the reference temperature, and the value range is 20 to 22; is the operating room environment temperature.

5. A perioperative thermal management system as claimed in claim 4, characterized in that: Δ T The calculation formula is ； When t = 0, since the surgery has not started at this moment, the values of the surgery duration, the intraoperative blood loss, the intraoperative blood transfusion, the intraoperative infusion and the intraoperative urine output are all 0; when t ≠ 0, the values of the intraoperative blood loss, the intraoperative blood transfusion, the intraoperative infusion and the intraoperative urine output take the cumulative values in the time interval ( t -1, t ].

6. A perioperative thermal management system as claimed in claim 5, characterized in that: Let Δ T Subject to normal distribution, namely ， then the patient's predicted body temperature is less than 35.5°C within the time interval t , t +1] is given by ， wherein the mean value The formula for calculating the mean value is ； wherein, is the reference mean without considering the influence of the coefficient matrix C; is the anesthesia-site interaction mean considering the interaction between anesthesia type and surgical site partition; is the age-fluid interaction mean considering the interaction between patient's age, intraoperative fluid volume and intraoperative urine volume; is the BMI-environment interaction mean considering the interaction between patient's actual BMI index and operating room environment temperature; is the anesthesia-site interaction coefficient, ranging from 0.13 to 0.18; is the intermediate variable for calculating ; is the anesthesia type central parameter, ranging from 1.4 to 1.6; is the anesthesia effect dispersion, ranging from 0.7 to 0.9; is the surgical site central parameter, ranging from 1.9 to 2.1; is the site effect dispersion, ranging from 1 to 1.4; is the age-fluid interaction coefficient, ranging from 0.002 to 0.005; is the BMI-environment interaction coefficient, ranging from -0.08 to -0.03; and the calculation formula of the variance ； In the formula, is the anesthesia variance coefficient, and the value range is 0.06 to 0.09; is the age variance coefficient, and the value range is 0.02 to 0.

05.

7. A perioperative thermal management system as claimed in claim 6, characterized in that: When When the value of the temperature difference is not more than 50%, a constant temperature control strategy is adopted, and the temperature of the temperature control heating blanket is set to a constant temperature , The calculation formula is ； wherein m is the weight of the patient in kg; H is the specific heat capacity of the human body in J / kg °C; h is the heat exchange coefficient in W / m2 °C; A is the contact area of the patient with the temperature-controlled heating blanket in m2; the constant 3600 has the meaning of 3600 seconds; when is greater than 50%, a variable temperature control strategy is adopted and the temperature of the temperature-controlled heating blanket is set to a temperature , the calculation formula for ； In the formula, K is a proportional adjustment factor, and has a value ranging from 0.1 to 1; is a target temperature, which is a body temperature that the patient is intended to reach and maintain; is a real-time body temperature of the patient.

8. A perioperative thermal management system as claimed in claim 1, characterized by: The communication unit (3) comprises a communication host (301), a display screen (302), a card reader (303), a communication interface (304), a power interface (305), a power key (306), a function key (307), and an indicator light (308); the communication interface (304) is arranged on the side of the communication host (301) and is used for wired communication with the computing unit (2); the power interface (305) is arranged on the side of the communication host (301) and is used for plugging a power cord; the display screen (302) is arranged on the upper surface of the communication host (301); one power key (306) and multiple function keys (307) are installed on the upper surface of the communication host (301); one indicator light (308) is arranged beside each power key (306) and function key (307); and the card reader (303) is fixedly installed on the communication host (301).

9. A perioperative thermal management system as claimed in claim 8, characterized in that: The body temperature collection unit (4) comprises a main controller (401), adhesive tape (402), a signal line (403), a sponge probe (404), a film (405), and a key (406); the main controller (401) is used for identification and pairing with the card reader (303) by approaching or contacting; a ring-shaped adhesive tape (402) is fixedly installed on the side of the main controller (401); a layer of film (405) is attached to the adhesive tape (402); the adhesive tape (402) is used for sticking to the cheek skin of a patient after the film (405) is torn off; the key (406) is arranged on the side of the main controller (401); and the sponge probe (404) is connected with the main controller (401) through the signal line (403).

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