CN113823409A - Evaluation method and system for risk of hypotensive event in dialysis - Google Patents

Evaluation method and system for risk of hypotensive event in dialysis Download PDF

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CN113823409A
CN113823409A CN202111112325.5A CN202111112325A CN113823409A CN 113823409 A CN113823409 A CN 113823409A CN 202111112325 A CN202111112325 A CN 202111112325A CN 113823409 A CN113823409 A CN 113823409A
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高光勇
魏志雄
童锦
周俊全
李绍林
朱平
李斌
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Abstract

The invention discloses a method and a system for evaluating the risk of a hypotensive event in dialysis. The first derivative curve of the ideal RBV curve is determined. And collecting RBV data in the dialysis process at preset interval duration, filtering the collected data each time by using a Kalman filter, calculating the descent rate of the RBV data, and filtering the descent rate by using the Kalman filter. And calculating a safety line and a forbidden line at the current moment through the first-order derivative curve, and determining the risk coefficient according to the comparison of the reduction rate and the sizes of the safety line and the forbidden line. The invention can filter the interference in the collected RBV data and improve the accuracy of evaluating the risk of the hypotensive event in the dialysis process of the patient by using the RBV. At the same time, the dynamic threshold value is used for calculating the risk of hypotension events, so that the dynamic threshold value can better adapt to the tolerance change of the patient to the unbalance degree in different time periods. The filtering algorithm is simple, the processing speed is high, and real-time processing of RBV data can be realized.

Description

Evaluation method and system for risk of hypotensive event in dialysis
Technical Field
The invention relates to the technical field of medical management, in particular to a method and a system for evaluating the risk of a hypotensive event in dialysis.
Background
Intradialytic hypotension is the most common complication in dialysis treatment of chronic renal patients, and one of the main reasons for this is that an imbalance between the ultrafiltration rate during dialysis and the reinfusion rate in the patient results in a rapid decrease in the blood volume in the core region of the patient, beyond the patient's tolerance. By analyzing the relative volume of blood (RBV) changes during treatment of a patient, an imbalance in the patient may be inferred, thereby assessing the patient's risk of developing a hypotensive event. The risk value can be applied to biofeedback closed-loop control in the dialysis process, can intervene in the imbalance condition in the body of a patient in advance, and reduces the probability of hypotension events of the patient. Specifically, whether the patient has a hypotensive event or the risk of the occurrence is generally determined by whether the absolute value, the amount of change, the rate of change (first derivative), or the second derivative of the RBV exceeds one or more thresholds.
CN110151153A discloses a prior art solution for determining whether a patient is experiencing a hypotensive event by acquiring RBV data of the patient every 5 minutes, performing a threshold determination on the second derivative of the RBV, SDRBV, and optionally in combination with the blood pressure data. In addition, another fuzzy logic technique is used in which at least two fuzzy blocks are used to receive hemodynamic parameters (e.g., relative blood volume and blood pressure), and the output of each fuzzy block is weighted to ultimately output at least one variable for use in assessing the risk of a hypotensive event in a patient.
CN104346521A discloses another prior art, which employs an apparatus and method for predicting hemodynamic parameters of a patient under treatment after learning individual dialysis parameters of the patient using a learning algorithm or a neural network.
The prior art has the following defects:
noise of the RBV can greatly interfere with the derivative change of the RBV, thereby reducing the accuracy of the hypotension event judgment.
2. Patient tolerance to the degree of imbalance during treatment is not considered.
3. Non-real time, the best intervention opportunity may be missed.
4. The judgment result is a logical value, which is not favorable for subsequent accurate feedback control.
Disclosure of Invention
The invention aims to: in view of all or some of the above problems, a method and a system for assessing risk of an intradialytic hypotensive event are provided to improve the accuracy of assessing risk of an intradialytic hypotensive event in a patient using an RBV.
The technical scheme adopted by the invention is as follows:
a method of assessing the risk of a hypotensive event in dialysis, comprising:
collecting RBV data in the dialysis process at preset interval time, and executing the following processes for each collected RBV data:
filtering the RBV data by using a pre-constructed Kalman filter; calculating a rate of decline of observed RBV data, filtering the calculated rate of decline using the Kalman filter;
and evaluating the filtered descending rate based on an evaluation threshold set for the current moment.
Further, the evaluation threshold set for the current time includes a first threshold and a second threshold higher than the first threshold.
Further, the filtered descent rate is evaluated based on an evaluation threshold set for the current time, and a risk coefficient of the filtered descent rate is calculated based on the first threshold and the second threshold.
Further, the evaluating the filtered decreasing rate based on the evaluation threshold set for the current time includes:
and when the filtered falling rate is between the first threshold and the second threshold, evaluating the filtered falling rate by adopting a linear interpolation method or a monotone increasing curve.
Further, the first threshold is a value of a first derivative of a set ideal RBV curve at a current time, the second threshold is N times the first threshold, and N is a constant greater than 1.
Further, the value FDRBV of the first derivative of the ideal RBV curve at the i-th time instantiThe calculation method comprises the following steps:
FDRBVi＝n×EDRBV×ai，
Figure BDA0003274283010000031
in the formula, DRBV is the expected RBV reduction, T is the total treatment time, n is the magnification factor, and a is the attenuation parameter.
Further, the system state prediction equation of the kalman filter is as follows:
Figure BDA0003274283010000032
the update equation is:
Figure BDA0003274283010000033
in the formula, XkFor the system state at the present moment, Xk-1Is the system state at the previous moment, ZkFor the observed system state at the current time, A is the state transition matrix, H is the observation matrix, Q, R represents the noise covariance matrix of the system prediction model and sensor, respectively, PkIs XkOf the covariance matrix, Pk-1Is Xk-1I is an identity matrix, KkThe Kalman gain matrix is an intermediate calculation quantity, and is an estimation value with the expression of 'A', and is a prediction value with the expression of 'minus'.
Further, the method for calculating the state transition matrix a includes:
constructing a calculation model of the RBV at the time t;
discretizing the computational model to a representation;
calculating the change rate of the RBV;
and representing the system state by using a discretized calculation model and the change rate, and obtaining a state transition matrix by analogy with a system state standard representation mode.
Further, the method for calculating the observation matrix H includes:
calculating the observed rate of decline of the RBV data;
and representing the observed system state by using a discretized calculation model and the descent rate, and comparing the discretized calculation model with the standard representation mode of the observed system state to obtain an observation matrix.
The invention provides an evaluation system for risks of a low blood pressure event in dialysis, which comprises a data acquisition part, a data evaluation part and a data output part, wherein a Kalman filter is pre-constructed in the data evaluation part, and evaluation thresholds corresponding to all moments are set;
the data acquisition part acquires RBV data in the dialysis process according to the configured acquisition cycle;
the data evaluation unit filters the RBV data using the kalman filter, calculates a rate of decrease of the observed RBV data, and filters the calculated rate of decrease using the kalman filter; evaluating the filtered descent rate based on an evaluation threshold at the current moment;
the data output unit outputs the evaluation result of the data evaluation unit.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the method, the relative blood volume data are preprocessed by adopting Kalman filtering, and the influence of interference signals is filtered by utilizing a simple algorithm, so that the actual relative blood volume change condition of the patient can be reflected better, and the accuracy of evaluating the low blood pressure event risk in the dialysis process of the patient by using RBV is improved. Moreover, the Kalman filtering algorithm is simple, the processing speed is high, real-time processing of RBV data can be realized, and the response time of correction measures is shortened.
2. The invention uses the dynamic threshold value to calculate the risk of hypotension events, so that the method can better adapt to the tolerance change of patients to the unbalance degree in different time periods.
3. The final evaluation result of the method and the system is the normalized risk coefficient obtained by evaluating the descent rate, the judgment on the risk level is more precise, and the subsequent parameter determination of feedback control or correction measures is convenient.
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The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of one embodiment of a method of evaluating.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Example one
As shown in fig. 1, the method for assessing the risk of hypotensive events in dialysis comprises the following steps:
first step, determining a first derivative of ideal RBV curve (FDRBV) curve for dialysis
The expected RBV reduction (DRBV) for this treatment was first determined, along with the total length of treatment T, which was 4 hours with a default DRBV setting of 24%. Of course, both data may be determined based on patient reality. The mean RBV reduction rate (EDRBV) throughout the treatment is then:
Figure BDA0003274283010000051
the initial FDRBV was taken as 5 times the EDRBV, and the FDRBV at the i-th moment in the treatment was designated as FDRBViThen FDRBViCalculated using the formula:
FDRBVi＝5×EDRBV×0.985i
second step, establishing Kalman filter equation and determining filter parameters
Kalman filtering is an algorithm for performing optimal estimation on a system state through system input and output observation data, and the observation data comprises the influence of noise and interference in a system, so the optimal estimation can also be regarded as a filtering process. The kalman filter (hereinafter referred to as a filter) works according to two equations: a system state transition equation and a system observation equation.
With XkRepresenting the state of the system at the current time, the state transition equation of the system can be expressed as:
Xk＝AXk-1+Buk+ωk
wherein A is a state transition matrix; xk-1Representing the system state at the last moment; b is a control matrix; u. ofkRepresenting a control vector; omegak～N(0，Qk) Representing process excitation noise, is 0 in mean and Q in variancekWhite gaussian noise.
At the same time, with ZkRepresents the system state at the current time observed using the measurement system, which may be expressed as:
Zk＝HXk+vk
in the formula, H is called an observation matrix; v. ofk～N(0，Rk) Mean 0 and variance R, representing the observed noisekWhite gaussian noise.
The RBV of the patient during the dialysis treatment was constantly changing (decreasing) due to ultrafiltration, recorded as RBV, and the rate of decrease was recorded as m. Then rbv at time t may be expressed as:
rbvt＝rbvt-Δt+mt-Δt×Δt
in the formula, Δ t represents a change time period, rbvt-ΔtRbv, m representing time t-delta tt-ΔtRepresenting the rate of change of t-at.
Discretizing rbv for the above equation is represented as:
rbvk＝rbvk-1+mk-1
analysis of the collected rbv data shows that the rate of decline m is decreasing during treatment without unexpected interference (stable treatment of the patient), and the empirical formula is:
mk＝a×mk-1
the prediction equation for the system can be summarized as:
Figure BDA0003274283010000061
uniformly expressing the state of the system as XkNamely:
Figure BDA0003274283010000062
then there are:
Figure BDA0003274283010000071
namely:
Figure BDA0003274283010000072
B＝0
the value of a is 0.985 in this example, as in the first step of calculating FDRBViParameters in the formula.
For rbv in the directly observed treatment, it was designated rbv'. The rate of decrease m 'defined as rbv' is:
Figure BDA0003274283010000073
where C is a constant and linear fit (C) represents the slope of a linear fit to the nearest C rbv' data. The value of C determines the estimation of the short-term or long-term variation of rbv ', in this embodiment, if the short-term variation needs to be estimated, C takes a smaller value, the specific value depends on the acquisition period of rbv' data, and C takes 5 in the case of a period of 1 min.
The observed system state is uniformly denoted as Zk：
Figure BDA0003274283010000074
Since the observed parameters rbv ', m' are the same amount as the system states rbv, m, no translation is required, so ZkAnd can be represented as:
Figure BDA0003274283010000075
namely:
Figure BDA0003274283010000076
the filtering requires the following 5 equations:
system state prediction equation:
Figure BDA0003274283010000077
Figure BDA0003274283010000078
in the formula, the expression with "^" is an estimated value, and the expression with "-" is a predicted value (the same below).
Updating an equation:
Figure BDA0003274283010000081
Figure BDA0003274283010000082
Figure BDA0003274283010000083
in the formula, PkIs XkOf the covariance matrix, Pk-1Is Xk-1Q, R respectively representing the noise covariance matrix of the system prediction model and the sensor, which needs to be determined according to the actual situation, KkThe Kalman gain matrix is an intermediate calculation quantity, and I is an identity matrix. In this embodiment, the preferred values are:
Figure BDA0003274283010000084
Figure BDA0003274283010000085
thirdly, collecting RBV data and filtering
The RBV data is collected at predetermined time intervals (e.g., every minute).
Using the collected RBV data as a first input of a filter; and obtaining a slope (linear fit (C)) according to a correlation algorithm in the second step, taking the slope (linear fit (C)) as a second input of the filter, filtering the RBV data and the slope thereof, removing noise, and outputting the filtered RBV and the slope. Of course, only the slope may be filtered.
Fourthly, generating the low blood pressure risk assessment coefficient
Calculating the FDRBV of the current time according to the method in the first stepiIt is taken as a safety line of RBV slope and amplified to some extent (e.g., n × FDRBV)iAnd n is a positive integer greater than 1) as a forbidden line of the RBV slope. And carrying out risk assessment on the collected RBV data based on the set safety line and the set forbidden line. Specifically, risk assessment is carried out according to the size relation between the slope of the filtered RVB and the safety line and the forbidden line.
1. If the slope of the filtered RVB exceeds FDRBVi(i.e., the safety line), it is considered that a hypotensive event is (or will be) most likely to have occurred, and the risk factor is recorded as 1.
2. If the slope of the filtered RBV is less than the FDRBViThen the risk of hypotension events is deemed to be absent and the risk factor is recorded as 0.
3. If the slope of the filtered RBV is at FDRBViAnd n x FDRBViAnd calculating the risk coefficient by adopting a linear interpolation method or other monotone increasing curves.
And fifthly, judging whether the treatment process is finished, if so, finishing the evaluation, otherwise, skipping to the third step, and continuing to evaluate the RBV data collected next time.
Example two
The embodiment discloses an evaluation system for risk of a hypotensive event in dialysis, which comprises a data acquisition part, a data evaluation part and a data output part. The data evaluation part is pre-constructed with a Kalman filter and is set with evaluation thresholds corresponding to each moment.
The evaluation threshold is set based on a first derivative curve (FDRBV) of an ideal RBV curve. The expected RBV reduction (DRBV) for this treatment was first determined, along with the total length of treatment T, which was 4 hours with a default DRBV setting of 24%. Of course, both data may be determined based on patient reality. The mean RBV reduction rate (EDRBV) throughout the treatment is then:
Figure BDA0003274283010000091
taking n times EDRBV as initial FDRBV, FDRBV at the i-th time in the treatment is expressed as FDRBViThen FDRBViCalculated using the formula:
FDRBVi＝n×EDRBV×ai；
in the formula, n is a magnification factor, a is an attenuation parameter, and a is less than 1; in this example n is 5 and a is 0.985.
The evaluation threshold includes a safety line and a prohibition line, and is FDRBV at the current timeiAs a safety line for the RBV slope and amplified to some extent (e.g., N × FDRBV)iN is a constant greater than 1) as a forbidden line for the RBV slope.
The pre-constructed Kalman filter is concretely as follows:
the kalman filter (hereinafter referred to as a filter) works according to two equations: a system state transition equation and a system observation equation.
With XkRepresenting the state of the system, the state transition equation for the system can be expressed as:
Xk＝AXk-1+Buk+ωk
wherein A is a state transition matrix; xk-1Representing the system state at the last moment; b is a control matrix; u. ofkRepresenting a control vector; omegak～N(0，Qk) Representing process excitation noise, is 0 in mean and Q in variancekWhite gaussian noise.
At the same time, with ZkRepresents the current state of the system as observed using the measurement system, which can be expressed as:
Zk＝HXk+vk
in the formula, H is called an observation matrix; v. ofk～N(0，Rk) Mean 0 and variance R, representing the observed noisekWhite gaussian noise.
The RBV of the patient during the dialysis treatment was constantly changing (decreasing) due to ultrafiltration, recorded as RBV, and the rate of decrease was recorded as m. Then rbv at time t may be expressed as:
rbvt＝rbvt-Δt+mt-Δt×Δt
in the formula, Δ t represents a change time period, rbvt-ΔtRbv, m representing time t-delta tt-ΔtRepresenting the rate of change of t-at.
Discretizing rbv for the above equation is represented as:
rbvk＝rbvk-1+mk-1
analysis of the collected rbv data shows that the rate of decline m is decreasing during treatment without unexpected interference (stable treatment of the patient), and the empirical formula is:
mk＝a×mk-1
the prediction equation for the system can be summarized as:
Figure BDA0003274283010000101
uniformly expressing the state of the system as XkNamely:
Figure BDA0003274283010000111
then there are:
Figure BDA0003274283010000112
namely:
Figure BDA0003274283010000113
B＝0
the value of a is 0.985 in the scheme, and FDRBV is calculated in the same step as the first stepiParameters in the formula.
For rbv in the directly observed treatment, it was designated rbv'. The rate of decrease m 'defined as rbv' is:
Figure BDA0003274283010000114
where C is a constant and linear fit (C) represents the slope of a linear fit to the nearest C rbv' data. The value of C determines the estimation of the short-term or long-term variation of rbv ', in this embodiment, if the short-term variation needs to be estimated, C takes a smaller value, the specific value depends on the acquisition period of rbv' data, and C takes 5 in the case of a period of 1 min.
The observed system state is uniformly denoted as Zk：
Figure BDA0003274283010000115
Since the observed parameters rbv ', m' are the same amount as the system states rbv, m, no translation is required, so ZkAnd can be represented as:
Figure BDA0003274283010000116
namely:
Figure BDA0003274283010000117
the filtering requires the following 5 equations:
system state prediction equation:
Figure BDA0003274283010000121
Figure BDA0003274283010000122
wherein, the expression with "^" is an estimated value (the same below); with "-" is meant predictive value (same below).
Updating an equation:
Figure BDA0003274283010000123
Figure BDA0003274283010000124
Figure BDA0003274283010000125
wherein Q, R represents the noise covariance matrix of the system prediction model and sensor, respectively, and needs to be determined according to actual conditions, KkThe Kalman gain matrix is an intermediate calculation quantity, and I is an identity matrix. In this embodiment, the preferred values are:
Figure BDA0003274283010000126
Figure BDA0003274283010000127
the data acquisition section acquires RBV data once at a predetermined time interval (e.g., every minute), and the RBV data acquired each time is transferred to the data evaluation section.
The data evaluation part takes the received RBV data as a first input of the filter; and obtaining a slope (linear fit (C)) according to a correlation algorithm in the second step, taking the slope (linear fit (C)) as a second input of the filter, filtering the RBV data and the slope thereof, removing noise, and outputting the filtered RBV and the slope. Of course, only the slope may be filtered.
And (3) carrying out risk assessment on the filtered slope according to the size relation between the filtered slope and the safety line and the forbidden line:
1. if the slope of the filtered RVB exceeds FDRBVi(i.e., the safety line), it is considered that a hypotensive event is (or will be) most likely to have occurred, and the risk factor is recorded as 1.
2. If the slope of the filtered RBV is less than the FDRBViThen the risk of hypotension events is deemed to be absent and the risk factor is recorded as 0.
3. If the slope of the filtered RBV is at FDRBViAnd n x FDRBViAnd calculating the risk coefficient by adopting a linear interpolation method or other monotone increasing curves.
The data output part is connected with the data evaluation part and outputs the evaluation result (namely the risk coefficient) of the RBV data acquired by the data acquisition part each time by the data evaluation part. The data output by the data output part can be used as a parameter of feedback control or a correction measure.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed.

Claims (10)

1. A method for assessing the risk of a hypotensive event in dialysis, comprising:
collecting RBV data in the dialysis process at preset interval time, and executing the following processes for each collected RBV data:
filtering the RBV data by using a pre-constructed Kalman filter; calculating a rate of decline of observed RBV data, filtering the calculated rate of decline using the Kalman filter;
and evaluating the filtered descending rate based on an evaluation threshold set for the current moment.
2. The method of claim 1, wherein the evaluation threshold set for the current time comprises a first threshold and a second threshold higher than the first threshold.
3. The method of claim 2, wherein the filtered rate of decline is evaluated based on an evaluation threshold set for a current time, and wherein a risk factor for the filtered rate of decline is calculated based on the first threshold and the second threshold.
4. The method of claim 3, wherein the evaluating the filtered rate of decline based on an evaluation threshold set for a current time comprises:
and when the filtered falling rate is between the first threshold and the second threshold, evaluating the filtered falling rate by adopting a linear interpolation method or a monotone increasing curve.
5. The method of any of claims 2-4, wherein the first threshold is a value of a first derivative of a given ideal RBV curve at a current time, the second threshold is N times the first threshold, and N is a constant greater than 1.
6. The method of claim 5, wherein the first derivative of the ideal RBV curve FDRBV is the value of FDRBV at time iiThe calculation method comprises the following steps:
FDRBVi＝n×EDRBV×ai，
Figure FDA0003274282000000011
wherein n is the magnification, DRBV is the expected RBV reduction, T is the total treatment time, and a is the attenuation parameter.
7. The method of assessing the risk of a intradialytic hypotensive event of claim 1, wherein the system state prediction equation of the kalman filter is:
Figure FDA0003274282000000021
the update equation is:
Figure FDA0003274282000000022
in the formula, XkFor the system state at the present moment, Xk-1Is the system state at the previous moment, ZkFor the observed system state at the current time, A is the state transition matrix, H is the observation matrix, Q, R represents the noise covariance matrix of the system prediction model and sensor, respectively, PkIs XkOf the covariance matrix, Pk-1Is Xk-1I is an identity matrix, KkThe Kalman gain matrix is an intermediate calculation quantity, and is an estimation value with the expression of 'A', and is a prediction value with the expression of 'minus'.
8. The method of assessing the risk of a intradialytic hypotensive event of claim 7, wherein said method of computing the state transition matrix a comprises:
constructing a calculation model of the RBV at the time t;
discretizing the computational model to a representation;
calculating the change rate of the RBV;
and representing the system state by using the discretized calculation model and the change rate, and obtaining a state transition matrix by using the system state standard representation mode category.
9. The method of assessing the risk of a intradialytic hypotensive event of claim 8, wherein the method of calculating the observation matrix H comprises:
calculating the observed rate of decline of the RBV data;
and representing the observed system state by using a discretized calculation model and the descent rate, and comparing the discretized calculation model with the standard representation mode of the observed system state to obtain an observation matrix.
10. The evaluation system for the risk of the intradialytic hypotensive event is characterized by comprising a data acquisition part, a data evaluation part and a data output part, wherein a Kalman filter is pre-constructed in the data evaluation part, and evaluation thresholds corresponding to all moments are set;
the data acquisition part acquires RBV data in the dialysis process according to the configured acquisition cycle;
the data evaluation unit filters the RBV data using the kalman filter, calculates a rate of decrease of the observed RBV data, and filters the calculated rate of decrease using the kalman filter; evaluating the filtered descent rate based on an evaluation threshold at the current moment;
the data output unit outputs the evaluation result of the data evaluation unit.
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