CN111419177A - Method and system for evaluating intraocular pressure fluctuation index during daytime - Google Patents
Method and system for evaluating intraocular pressure fluctuation index during daytime Download PDFInfo
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- CN111419177A CN111419177A CN202010286924.8A CN202010286924A CN111419177A CN 111419177 A CN111419177 A CN 111419177A CN 202010286924 A CN202010286924 A CN 202010286924A CN 111419177 A CN111419177 A CN 111419177A
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/16—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for measuring intraocular pressure, e.g. tonometers
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/0016—Operational features thereof
- A61B3/0025—Operational features thereof characterised by electronic signal processing, e.g. eye models
Abstract
The invention provides a method and a system for evaluating a diurnal intraocular pressure fluctuation index, which aim to describe fluctuation and instability of circadian rhythm intraocular pressure, mean amplitude of intraocular pressure fluctuation (MAPE) is obtained by calculating intraocular pressure values at different time points in the whole 24-hour intraocular pressure monitoring process, the MAPE can be used for describing the fluctuation value of the diurnal intraocular pressure, the blank of effective parameters of the current intraocular pressure fluctuation is filled, and meanwhile, the capability of evaluating the 24-hour intraocular pressure fluctuation is compared with other intraocular pressure parameters, so that the correlation between the new parameter MAPE and the severity of glaucoma is higher than that of other current intraocular pressure parameters, the MAPE can more effectively embody the intraocular pressure fluctuation, and the method and the system have important value on research on pathogenesis of glaucoma and disease management of patients.
Description
Technical Field
The invention relates to the field of medical diagnosis, in particular to a method and a system for evaluating intraocular pressure fluctuation indexes during the day.
Background
Glaucoma is the leading irreversible blinding eye disease in the world, and elevated intraocular pressure (IOP) is the most important interventable risk factor in the development of glaucoma disease. However, the intraocular pressure is not constant but dynamically changes according to the body's biorhythm, posture, sleep and other factors. Currently, the effect of intraocular pressure on the optic nerve can be influenced by two aspects: 1) the duration and extent of chronic persistent ocular hypertension, also known as "long-term fluctuations"; 2) fluctuations in intraocular pressure within 24 hours are also referred to as "circadian fluctuations" or "short-term fluctuations". The former is evaluated primarily by a single tonometry at each visit, the most widely used tonometry in the long-term management of glaucoma treatment. However, such measurements do not fully and accurately describe the individual's pattern of intraocular pressure fluctuations. The latter is then currently evaluated primarily by means of 24-hour tonometry. Studies have shown that ocular pressure fluctuations may be correlated with the risk of glaucoma progression.
However, although 24-hour intraocular pressure monitoring and its relationship to glaucomatous optic neuropathy is urgently under investigation, it is important that it is not yet clear which type of intraocular pressure parameter (mean, peak, or fluctuation) has a greater impact on glaucoma progression. Different study designs, statistical analyses, study populations and definitions of ocular pressure fluctuations may lead to differences in the conclusions of the above studies, with the defining and measuring of ocular pressure fluctuation values being the most confusing at present.
Clinical measurements assessed by 24-hour IOP monitoring included various time-point IOP values and parameters such as mean, peak, trough and maximum difference in 24-hour IOP values, which provide a general description of intraocular pressure changes. Typically, only the peak and maximum IOP differences are used clinically to describe the 24 hour fluctuations in intraocular pressure generally, which is clearly rough and inaccurate. In addition, the lack of accurate and more complete characterization of diurnal ocular pressure fluctuations not only makes evaluation difficult, but also limits further exploration of the 24-hour ocular pressure effect on optic nerve function.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating an index of intraocular pressure fluctuation during the daytime, which aim to fill the blank of effective parameters of the current intraocular pressure fluctuation by adopting a fluctuation value for describing the intraocular pressure during the day and night and evaluate the relationship between the intraocular pressure fluctuation and the severity of glaucoma.
In order to achieve the above object, an aspect of the present invention is a method for evaluating an indicator of intraocular pressure fluctuation during the day, comprising:
acquiring intraocular pressure detection data of a subject within 24 hours;
setting time intervals for sampling intraocular pressure data, and calculating fluctuation values of intraocular pressure (IOP) in each time interval;
defining Standard Deviation (SD) of intraocular pressure fluctuation values, judging whether each intraocular pressure fluctuation value is larger than intraocular pressure Standard Deviation (SD), and if the intraocular pressure fluctuation value is larger than the SD, determining the intraocular pressure fluctuation value as an effective intraocular pressure fluctuation value;
calculating the average value of each effective intraocular pressure fluctuation value to obtain the average value (MAPE) of the effective intraocular pressure fluctuation;
a linear regression model was established to evaluate the mean effective fluctuation in intraocular pressure (MAPE) in relation to visual field impairment and to evaluate the diagnostic performance of the mean effective fluctuation in intraocular pressure (MAPE) for glaucoma (POAG) patients.
Further, the method calculates the intraocular pressure fluctuation value within 2 hours by taking 2 hours as a time interval, and the Standard Deviation (SD) of the intraocular pressure is 1.8 mmHg.
Further, the intraocular pressure measurement data further comprises age, gender, cup to disk ratio, vision, central corneal thickness, ocular axis, mean intraocular pressure, peak intraocular pressure, trough intraocular pressure, standard deviation intraocular pressure (SD), and area under intraocular pressure curve (AUC _ IOP).
Further, the linear regression model is a partial least squares regression (P L S) model, and the establishing and analyzing process of the model includes:
establishing a healthy control group and a glaucoma (POAG) patient group, both of which comprise the intraocular pressure measurement data;
inputting the intraocular pressure detection data into a partial least squares regression (P L S) model, and acquiring a P value and an ROC value of an effective intraocular pressure fluctuation average value (MAPE) and other intraocular pressure detection data so as to evaluate the maximum difference and the diagnosis efficiency of each intraocular pressure detection data in diagnosing glaucoma;
and selecting intraocular pressure detection data with the maximum difference and diagnosis efficiency as an optimization parameter according to the evaluation result of the model.
Further, obtaining a VIP value of the optimized intraocular pressure detection data, and evaluating the importance of the intraocular pressure detection data for diagnosing glaucoma according to the VIP value.
In another aspect, the present invention provides a system for evaluating an indicator of intraocular pressure fluctuation during the day, comprising:
the data acquisition module is used for acquiring intraocular pressure detection data of a subject within 24 hours;
a MAPE calculation module for calculating a mean value of effective fluctuation in intraocular pressure (MAPE), the calculation comprising:
setting time intervals for sampling intraocular pressure data, and calculating fluctuation values of intraocular pressure (IOP) in each time interval;
defining Standard Deviation (SD) of intraocular pressure fluctuation values, judging whether each intraocular pressure fluctuation value is larger than intraocular pressure Standard Deviation (SD), and if the intraocular pressure fluctuation value is larger than the SD, determining the intraocular pressure fluctuation value as an effective intraocular pressure fluctuation value;
calculating the average value of each effective intraocular pressure fluctuation value to obtain the average value (MAPE) of the effective intraocular pressure fluctuation;
an evaluation module to establish a linear regression model to evaluate a mean effective fluctuation in intraocular pressure (MAPE) in relation to an impairment of visual field, to evaluate a diagnostic performance of the mean effective fluctuation in intraocular pressure (MAPE) for a patient with glaucoma (POAG).
Further, the MAPE calculating module calculates the intraocular pressure fluctuation value within 2 hours with a time interval of 2 hours, and the Standard Deviation (SD) of the intraocular pressure is 1.8 mmHg.
Further, the intraocular pressure measurement data further comprises age, gender, cup to disk ratio, vision, central corneal thickness, ocular axis, mean intraocular pressure, peak intraocular pressure, trough intraocular pressure, standard deviation intraocular pressure (SD), and area under intraocular pressure curve (AUC _ IOP).
Further, the evaluation module establishes a partial least squares regression (P L S) model, and the establishment and analysis process of the model comprises:
establishing a healthy control group and a glaucoma (POAG) patient group, both of which comprise the intraocular pressure measurement data;
inputting the intraocular pressure detection data into a partial least squares regression (P L S) model, and acquiring a P value and an ROC value of an effective intraocular pressure fluctuation average value (MAPE) and other intraocular pressure detection data so as to evaluate the maximum difference and the diagnosis efficiency of each intraocular pressure detection data in diagnosing glaucoma;
and selecting intraocular pressure detection data with the maximum difference and diagnosis efficiency as an optimization parameter according to the evaluation result of the model.
Further, the evaluation module obtains a VIP value of the optimized intraocular pressure detection data, and evaluates the importance of diagnosing glaucoma of the intraocular pressure detection data according to the VIP value.
In order to better describe fluctuation and instability of circadian rhythm intraocular pressure, a new parameter, namely mean intraocular pressure fluctuation amplitude (MAPE), is obtained by calculating intraocular pressure values at different time points in the whole 24-hour intraocular pressure monitoring process, the MAPE can be used for describing fluctuation values of the circadian rhythm intraocular pressure, the blank of the effective parameter of the current intraocular pressure fluctuation is filled, and meanwhile, the capability of evaluating the 24-hour intraocular pressure fluctuation is compared with other intraocular pressure parameters, so that the correlation between the new parameter MAPE and the severity degree of glaucoma is higher than that of other current intraocular pressure parameters, and the MAPE can more effectively embody intraocular pressure fluctuation and has important value on research on pathogenesis of glaucoma and disease management of patients.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for evaluating an indicator of intraocular pressure fluctuations during the day according to the present invention;
fig. 2 is a schematic flow chart of a method for calculating an average intraocular pressure fluctuation value according to the present invention.
FIG. 3 is a schematic flow chart of a method for estimating intraocular pressure fluctuation values according to the present invention;
FIG. 4 is a block diagram of an indicator system for evaluating diurnal ocular pressure fluctuations in accordance with the present invention;
FIG. 5 is a graph showing diurnal fluctuations in intraocular pressure in a typical POAG patient in accordance with one embodiment of the present invention;
FIG. 6 is a graphical representation of projected scores of predictors in a P L S partial least squares regression, in accordance with an embodiment of the present invention;
FIG. 7 is a graphical illustration of the diagnostic efficiency of evaluating the mean amplitude of intraocular pressure excursion in accordance with one embodiment of the present invention;
FIG. 8 is a table of clinical profiles of healthy individuals and POAG patients according to one embodiment of the present invention;
figure 9 is a table of coefficients of MAPE effect on glaucomatous visual field defects, in accordance with one embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
The method and the system for evaluating the daytime intraocular pressure fluctuation index can be arranged in any electronic equipment and used for calculating and analyzing the daytime intraocular pressure fluctuation index of a subject and storing data in the calculation and analysis to a storage device. The electronic devices include, but are not limited to, wearable devices, head-mounted devices, medical health platforms, personal computers, server computers, hand-held or laptop devices, mobile devices (such as mobile phones, Personal Digital Assistants (PDAs), media players, and the like), multiprocessor systems, consumer electronics, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The electronic device is preferably a server for performing daytime intraocular pressure fluctuation index calculation, and the server performs daytime intraocular pressure fluctuation index calculation and analysis based on 24-hour intraocular pressure monitoring data.
Fig. 1 is a schematic flow chart of a method for evaluating an index of daytime intraocular pressure fluctuation according to the present invention, and as shown in fig. 1, the method for evaluating an index of daytime intraocular pressure fluctuation according to the present invention includes the following steps:
and S1, acquiring intraocular pressure detection data of the subject within 24 hours.
S2, calculating the mean value of effective fluctuation of intraocular pressure (MAPE).
S3, establishing a linear regression model to evaluate the relation between the mean value of effective fluctuation of intraocular pressure (MAPE) and visual field damage, and evaluating the diagnostic performance of the mean value of effective fluctuation of intraocular pressure (MAPE) on glaucoma (POAG) patients.
The following describes in detail the specific flow of each step of the method for evaluating an index of intraocular pressure fluctuation during the day with reference to the accompanying drawings.
In step S1, in the step of obtaining intraocular pressure test data within 24 hours for subjects, in one particular embodiment, intraocular pressure tests were performed by recruiting healthy volunteers from the eye, nose and throat Hospital, university of Compound denier and untreated POAG patients all subjects received a comprehensive ocular test including best corrected vision, slit lamp biomicroscopy, ophthalmoscope, Central Corneal Thickness (CCT) and axial length (A L) (IO L Master, Carl Zeiss Inc..) glaucoma patients required VF testing (Humphrey automated visual field test or Octopus automated visual field test).
All subjects were monitored for 24 hours intraocular pressure, participants were asked to maintain normal biological activity and rhythm, and they were placed in a special area of the hospital the day before the 24 hour recording. Tonometery was performed by the same experienced operator using a non-contact tonometer (NIDEK, japan) every 2 hours from 8:00AM to 6:00AM on the next day. Lights were turned off to promote sleep from 10:00PM to 6:00 AM. During this period, the participants woke up every 2 hours and immediately measure in an upright position. At each time point, 3 measurements of eye pressure were taken and the average was used for analysis without CCT correction.
In step S2, in the step of calculating the mean value of effective fluctuation in intraocular pressure (MAPE). The principle of mean value of effective fluctuations in intraocular pressure (MAPE) is to determine that fluctuations in intraocular pressure in each subject exceed a certain limit or "effective fluctuation" by identifying large fluctuations in intraocular pressure and ignoring trivial fluctuations. Therefore, we first defined a fluctuation value exceeding the Standard Deviation (SD) of the diurnal intraocular pressure of each subject as an effective fluctuation. Then, the arithmetic mean of the intraocular pressure fluctuation values (effective fluctuations) satisfying this criterion is calculated, i.e., MAPE.
The specific steps are as shown in fig. 2, and include S201, setting time intervals for sampling intraocular pressure data, and calculating fluctuation values of intraocular pressure (IOP) in each time interval; and S202, defining Standard Deviation (SD) of intraocular pressure fluctuation values, judging whether each intraocular pressure fluctuation value is larger than the intraocular pressure Standard Deviation (SD), and if the intraocular pressure fluctuation value is larger than the SD, determining the intraocular pressure fluctuation value as an effective intraocular pressure fluctuation value. And S203, calculating the average value of the effective intraocular pressure fluctuation values to obtain the average value (MAPE) of the effective intraocular pressure fluctuation.
For example, as shown in fig. 5, fig. 5 is a diagram illustrating diurnal fluctuations in intraocular pressure in a typical POAG patient in accordance with an embodiment of the present invention. The intraocular pressure Standard Deviation (SD) value was set to 1.8 mmHg. By calculating the difference between each two adjacent intraocular pressure values (2 hour time interval), we can obtain a series of fluctuation values. As shown in fig. 1, the first fluctuation is 1.6mmHg from 17.4mmHg to 15.8mmHg, less than the SD value (1.8mmHg), and by definition, the first fluctuation is not an effective fluctuation. Thus, only the fourth fluctuation (3.5 mmhg, from 16.1 mmhg, to 19.6 mmhg), the sixth fluctuation (5.3 mmhg, from 20.7 mmhg, to 15.4 mmhg), the tenth fluctuation (3.9 mmhg, from 18.4 mmhg, to 14.5 mmhg), the eleventh fluctuation (3.5 mmhg, from 18.4 mmhg-18 mmhg) was considered to be an effective intraocular pressure fluctuation at 24 hours intraocular pressure. The patient MAPE therefore has a value of (3.5+5.3+3.9+ 3.5)/4.05 mmHg.
And step S3, establishing a partial least squares regression model to evaluate the relation between the mean value of effective fluctuation of intraocular pressure (MAPE) and visual field damage, and evaluating the diagnostic performance of the mean value of effective fluctuation of intraocular pressure (MAPE) on glaucoma (POAG) patients.
Fig. 3 is a schematic flowchart of a method for estimating intraocular pressure fluctuation according to the present invention, and as shown in fig. 3, step S3 further includes:
step S301, a healthy control group and a glaucoma control group are established to compare the difference between untreated Primary Open Angle Glaucoma (POAG) patients and healthy volunteers. Specifically, in one example, 79 healthy controls and 164 glaucoma patients participated in a circadian rhythm fluctuation study. The mean age (48.3 + -15.18 vs.49.0 + -14.01) and gender composition of the subjects were not statistically different between the healthy control group and the glaucoma group.
Fig. 8 is a table of clinical profiles of healthy individuals and POAG patients according to one embodiment of the present invention. As shown in fig. 8, glaucoma patients had higher mean intraocular pressure (13.43 ± 2.57vs.17.36 ± 4.43), greater vertical cup-to-disk ratio (0.52 ± 0.12vs.0.77 ± 0.45), and poorer vision (1.10 ± 0.24vs.0.76 ± 0.30) compared to healthy participants.
Fig. 8 also shows measurements including MAPE and other ocular pressure parameters, such as mean, peak, valley, maximum difference and standard deviation of intraocular pressure, SD. Wherein the area under diurnal IOP curve (AUC _ IOP) is an average value of the areas between IOP fluctuation curves by correlating the IOP value at each time point with the basal IOP (0mmHg) every 2 hours; standard deviation of intraocular pressure (SD), standard deviation obtained from all time points in a 24 hour recording in glaucoma patients; the maximum intraocular pressure difference is calculated by subtracting the intraocular pressure valley from the intraocular pressure peak value within 24 hours; peak IOP and trough IOP, as the highest and lowest values, respectively, in a plotted diurnal intraocular pressure curve.
And step S302, inputting the intraocular pressure detection data into a partial least squares regression (P L S) model, and acquiring a P value and an ROC value of an effective intraocular pressure fluctuation average value (MAPE) and other intraocular pressure detection data so as to evaluate the maximum difference and diagnosis efficiency of diagnosing glaucoma of each intraocular pressure detection data.
As shown in FIG. 8, MAPE (4.16. + -. 1.90vs. 2.45. + -. 0.89) and AUC _ IOP (379.40. + -. 96.42vs. 294.40. + -. 56.88) were significantly higher (p <0.001) than in healthy volunteers. Also, glaucoma patients have higher maximal difference and SD of IOP (p < 0.001).
Figure 7 is a graphical illustration of the diagnostic efficiency of evaluating the mean amplitude of intraocular pressure excursion in accordance with one embodiment of the present invention. As shown in FIG. 7, the diagnostic efficiency analysis found that the areas under the diagnostic curves for MAPE, AUC _ IOP, maximum difference, SD and mean were 0.822 (95% CI, 0.768-0.868), 0.788(0.731-0.838), 0.797(0.740-0.845), 0.817(0.763-0.864) and 0.792(0.735-0.841), respectively, and all p for the intraocular pressure diagnostic parameters described above were < 0.01. There was no statistical difference in diagnostic efficiency between the parameters (p > 0.05).
Step S303, selecting intraocular pressure detection data with the maximum difference and the diagnosis efficiency as optimization parameters, acquiring a VIP value of the optimized intraocular pressure detection data, determining a mode standard deviation (PSD) as an index of glaucoma visual field defect, namely glaucoma severity, and evaluating the importance of the intraocular pressure detection data for diagnosing glaucoma according to the VIP value.
FIG. 6 is a graph showing projected scores of predictor variables in P L S partial least squares regression according to an embodiment of the present invention, as shown in FIG. 6, a VIP score lower than 0.8 indicates less significance, the maximum difference, standard deviation of intraocular pressure (SD) not reaching 0.8, AUC _ IOP lower than MAPE, and MAPE is therefore the most important fluctuation parameter for studying diurnal intraocular pressure fluctuation in glaucoma patients.
FIG. 9 is a table of coefficients of influence of MAPE on glaucoma visual field defects according to one embodiment of the present invention, the Pattern Standard Deviation (PSD) was determined by calculation as an indicator of glaucoma visual field defects, i.e., the severity of glaucoma, and FIG. 9 shows the relationship between MAPE and PSD based on the analysis of the partial least squares regression model of P L S, the standard regression coefficient for MAPE was 0.533, which is the largest, while the standard regression coefficient for the other parameters did not exceed 0.2, so the mean value of effective fluctuation of intraocular pressure (MAPE) is the most effective indicator for assessing glaucoma visual field defects.
Referring to fig. 4, fig. 4 is a system frame diagram of a system for evaluating an index of intraocular pressure fluctuation during the day. The system for evaluating an index of diurnal intraocular pressure fluctuation according to the present embodiment includes:
the data acquisition module 1 is used for acquiring intraocular pressure detection data of a subject within 24 hours.
A MAPE calculation module 2, the MAPE calculation module is used for calculating a mean value of effective fluctuation of intraocular pressure (MAPE), and the calculation process comprises:
setting time intervals for sampling intraocular pressure data, and calculating fluctuation values of intraocular pressure (IOP) in each time interval;
defining Standard Deviation (SD) of intraocular pressure fluctuation values, judging whether each intraocular pressure fluctuation value is larger than intraocular pressure Standard Deviation (SD), and if the intraocular pressure fluctuation value is larger than the SD, determining the intraocular pressure fluctuation value as an effective intraocular pressure fluctuation value;
and calculating the average value of each effective intraocular pressure fluctuation value to obtain the average value (MAPE) of the effective intraocular pressure fluctuation.
Specifically, based on the above-described method for evaluating an index of intraocular pressure fluctuation during the day, the intraocular pressure Standard Deviation (SD) value was set to 1.8 mmHg. A series of fluctuation values can be obtained by calculating the difference between every two adjacent intraocular pressure values (2 hour time interval), the fluctuation value larger than 1.8mmHg is selected as an effective fluctuation value, the average value of the effective fluctuation values is calculated, and the average value (MAPE) of the intraocular pressure effective fluctuation is obtained.
An evaluation module 3 for establishing a linear regression model to evaluate the correlation of mean effective fluctuation in intraocular pressure (MAPE) to visual field impairment, and for evaluating the diagnostic performance of the mean effective fluctuation in intraocular pressure (MAPE) for glaucoma (POAG) patients.
Specifically, based on the above method for evaluating an intraocular pressure fluctuation index during the day, the evaluation module establishes a partial least squares regression (P L S) model, and the establishment and analysis processes of the model include:
establishing a healthy control group and a glaucoma (POAG) patient group, both of which comprise the intraocular pressure measurement data;
inputting the intraocular pressure detection data into a partial least squares regression (P L S) model, and acquiring a P value and an ROC value of an effective intraocular pressure fluctuation average value (MAPE) and other intraocular pressure detection data so as to evaluate the maximum difference and the diagnosis efficiency of each intraocular pressure detection data in diagnosing glaucoma;
and selecting intraocular pressure detection data with the maximum difference and diagnosis efficiency as an optimization parameter according to the evaluation result of the model. The evaluation module obtains a VIP value of the optimized intraocular pressure detection data and evaluates the importance of the intraocular pressure detection data for diagnosing glaucoma according to the VIP value.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of evaluating an indicator of diurnal ocular pressure fluctuations, comprising:
acquiring intraocular pressure detection data of a subject within 24 hours;
setting time intervals for sampling intraocular pressure data, and calculating fluctuation values of intraocular pressure (IOP) in each time interval;
defining Standard Deviation (SD) of intraocular pressure fluctuation values, judging whether each intraocular pressure fluctuation value is larger than intraocular pressure Standard Deviation (SD), and if the intraocular pressure fluctuation value is larger than the SD, determining the intraocular pressure fluctuation value as an effective intraocular pressure fluctuation value;
calculating the average value of each effective intraocular pressure fluctuation value to obtain the average value (MAPE) of the effective intraocular pressure fluctuation;
a linear regression model was established to evaluate the mean effective fluctuation in intraocular pressure (MAPE) in relation to visual field impairment and to evaluate the diagnostic performance of the mean effective fluctuation in intraocular pressure (MAPE) for glaucoma (POAG) patients.
2. The method for evaluating an indicator of intraocular pressure fluctuation during the day according to claim 1, wherein the intraocular pressure fluctuation value within 2 hours is calculated with 2 hours as a time interval, and the Standard Deviation (SD) of intraocular pressure is 1.8 mmHg.
3. The method of claim 1, wherein said intraocular pressure measurement data further comprises age, gender, cup-to-disk ratio, vision, central corneal thickness, ocular axis, mean intraocular pressure, peak intraocular pressure, trough intraocular pressure, standard deviation intraocular pressure (SD), and area under the intraocular pressure curve (AUC _ IOP).
4. The method for evaluating an indicator of diurnal intraocular pressure fluctuation according to claim 3, wherein the linear regression model is a partial least squares regression (P L S) model, and the model is constructed and analyzed by:
establishing a healthy control group and a glaucoma (POAG) patient group, both of which comprise the intraocular pressure measurement data;
inputting the intraocular pressure detection data into a partial least squares regression (P L S) model, and acquiring a P value and an ROC value of an effective intraocular pressure fluctuation average value (MAPE) and other intraocular pressure detection data so as to evaluate the maximum difference and the diagnosis efficiency of each intraocular pressure detection data in diagnosing glaucoma;
and selecting intraocular pressure detection data with the maximum difference and diagnosis efficiency as an optimization parameter according to the evaluation result of the model.
5. The method for evaluating an indicator of diurnal ocular pressure fluctuations of claim 4 wherein the VIP value of the optimized ocular pressure measurement data is obtained and the importance of diagnosing glaucoma is assessed based on the VIP value.
6. A system for evaluating an indicator of diurnal intraocular pressure fluctuations, comprising:
the data acquisition module is used for acquiring intraocular pressure detection data of a subject within 24 hours;
a MAPE calculation module for calculating a mean value of effective fluctuation in intraocular pressure (MAPE), the calculation comprising:
setting time intervals for sampling intraocular pressure data, and calculating fluctuation values of intraocular pressure (IOP) in each time interval;
defining Standard Deviation (SD) of intraocular pressure fluctuation values, judging whether each intraocular pressure fluctuation value is larger than intraocular pressure Standard Deviation (SD), and if the intraocular pressure fluctuation value is larger than the SD, determining the intraocular pressure fluctuation value as an effective intraocular pressure fluctuation value;
calculating the average value of each effective intraocular pressure fluctuation value to obtain the average value (MAPE) of the effective intraocular pressure fluctuation;
an intraocular pressure fluctuation evaluation module to establish a linear regression model to evaluate a mean effective fluctuation of intraocular pressure (MAPE) in relation to visual field impairment to evaluate a diagnostic performance of the mean effective fluctuation of intraocular pressure (MAPE) for glaucoma (POAG) patients.
7. The system for evaluating an indicator of intraocular pressure fluctuations during the day according to claim 6, wherein the MAPE calculation module calculates the value of intraocular pressure fluctuations over a 2 hour period, the standard deviation of intraocular pressure (SD) being 1.8 mmHg.
8. The system for evaluating an indicator of diurnal intraocular pressure fluctuations of claim 6 wherein the intraocular pressure measurement data further includes age, gender, cup to disk ratio, vision, central corneal thickness, ocular axis, mean intraocular pressure, peak intraocular pressure, trough intraocular pressure, standard deviation intraocular pressure (SD), and area under the intraocular pressure curve (AUC _ IOP).
9. The system for evaluating an indicator of intraocular pressure fluctuations during the day of claim 8, wherein the intraocular pressure fluctuation assessment module builds a partial least squares regression (P L S) model, the model building and analyzing comprising:
establishing a healthy control group and a glaucoma (POAG) patient group, both of which comprise the intraocular pressure measurement data;
inputting the intraocular pressure detection data into a partial least squares regression (P L S) model, and acquiring a P value and an ROC value of an effective intraocular pressure fluctuation average value (MAPE) and other intraocular pressure detection data so as to evaluate the maximum difference and the diagnosis efficiency of each intraocular pressure detection data in diagnosing glaucoma;
and selecting intraocular pressure detection data with the maximum difference and diagnosis efficiency as an optimization parameter according to the evaluation result of the model.
10. The system for evaluating an indicator of diurnal intraocular pressure fluctuations of claim 9 wherein the intraocular pressure fluctuation assessment module obtains a VIP value of the optimized intraocular pressure measurement data and assesses the importance of the intraocular pressure measurement data for diagnosing glaucoma based on the VIP value.
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| CN102831326A (en) * | 2012-09-06 | 2012-12-19 | 南方医科大学 | Mean amplitude of glucose excursions (MAGE) calculation method |
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| CN102831326A (en) * | 2012-09-06 | 2012-12-19 | 南方医科大学 | Mean amplitude of glucose excursions (MAGE) calculation method |
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