Method and system for evaluating delirium risk of ICU patient

Abstract

According to the evaluation method and evaluation system for the delirium risk of the ICU patient, the sleep structure change before the delirium of the ICU patient occurs is determined through CPC monitoring, and the CPC parameters capable of predicting the delirium significance of the ICU patient in the early stage are screened out, so that intervention measures can be taken as early as possible to reduce the delirium occurrence and harm, and the risk of the delirium risk of the ICU patient is improved. The recovery of the whole illness state of the patient and the improvement of prognosis are facilitated. The method is simple in data acquisition, is not easily interfered by medical equipment, is high in accuracy, and has a good clinical popularization and application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of delirium prediction, and in particular to an evaluation method and an evaluation system for the delirium risk of ICU patients. Background Art

[0002] ICU patients are affected by many factors such as their own diseases, psychological states, stress states, medical operations, and external environments. Their normal sleep cycles and circadian rhythms are disrupted, and sleep disorders caused by sleep deprivation are common. Sometimes, although they have enough sleep time, their sleep time is often fragmented, and their sleep structure is different from normal, mainly with an increase in N1 and N2 stages, and a decrease in N3 and rapid eye movement. Sleep deprivation, as a strong stressor, aggravates sympathetic fatigue and can promote the occurrence of delirium. It is one of the independent risk factors for delirium in ICU patients, and the two affect each other.

[0003] Delirium is a common acute clinical syndrome in the ICU, characterized by acute disorders of consciousness, orientation, attention, and thinking, which cannot be explained by pre-existing neurocognitive disorders. The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) developed by the American Psychiatric Association divides delirium into three subtypes, namely excited, inhibited, and mixed. Excited delirium is characterized by agitation, irritability, and attempts to remove various tubes; inhibited delirium is characterized by emotional indifference, reduced speech, and drowsiness; mixed delirium is a typical feature of both. Among them, the inhibited type accounts for 43.5%. This type of patients rarely show excitement and are often neglected and missed in clinical diagnosis. Delirium can lead to prolonged mechanical ventilation and hospitalization time of ICU patients, increased mortality, long-term cognitive dysfunction and mental abnormalities, increased medical expenses, and seriously affect the prognosis of patients. Therefore, in clinical practice, taking relevant measures for early detection, early diagnosis, and early treatment can effectively shorten the duration of delirium in ICU patients, reduce the harm of delirium, and thus improve the quality of life of such patients and reduce mortality.

[0004] At present, international organizations recommend the Confusion Assessment Method for the ICU (CAM-ICU) or the Intensive Care Delirium Screening Checklist (ICDSC) to diagnose delirium, but it is mainly based on the subjective assessment of medical staff, and there is also a delay in clinical application. At present, there is a lack of objective monitoring indicators for delirium in clinical practice, so that 75% of delirium cannot be identified, especially for the early prediction and discrimination of delirium. Therefore, finding objective monitoring indicators related to delirium is a clinical problem that needs to be solved urgently. Summary of the invention

[0005] The present invention aims to provide an evaluation method and system for the risk of delirium in ICU patients, which can clarify the sleep structure changes of ICU patients before the occurrence of delirium through CPC (cardiopulmonary coupling) monitoring, and screen out CPC parameters that are meaningful for early prediction of delirium in ICU patients, thereby facilitating early intervention measures to reduce the occurrence and harm of delirium, and promoting the overall recovery of patients and improving prognosis.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] The present invention provides a method for assessing the risk of delirium in ICU patients, comprising:

[0008] 1) CPC analysis technology was used to monitor the sleep structure of the enrolled ICU patients. The CAM-ICU scoring scale was used to evaluate whether the ICU patients had delirium every day until delirium occurred or they were transferred out of the ICU. The patients were divided into a delirium group and a non-delirium group according to whether delirium occurred. The differences in CPC monitoring parameters between the two groups were compared, and the CPC parameters with statistical significance were screened out as objective indicators for early prediction of delirium.

[0009] 2) The clinical data of ICU patients and the CPC parameters obtained through screening were collected and used as predictors of delirium. Multivariate logistic regression analysis was used to screen out the risk factors for delirium, and the receiver operating characteristic (ROC) curve was drawn to evaluate the risk of delirium in ICU patients.

[0010] Preferably, in step 1), a dynamic ECG collector is worn on the patient's precordial area to automatically collect signal data including ECG, respiration and body movement, and then a sleep spectrum diagram is generated by the algorithm set by the CPC sleep quality assessment system to quantitatively measure the patient's sleep quality; the monitoring time is from 21:00 on the day of admission to the ICU to 7:00 in the morning of the next day.

[0011] Preferably, in step 1), the CPC monitoring parameters include: total sleep time, deep sleep time, light sleep time, rapid eye movement sleep time, awakening time, deep sleep latency, sleep efficiency and sleep apnea-hypopnea index.

[0012] Preferably, in step 1), when comparing CPC monitoring parameters of two groups, if they conform to normal distribution, paired sample t test is used, and if they are not normally distributed, non-parametric test analysis is used.

[0013] Preferably, in step 2), the clinical data of the ICU patient include: gender, age, body mass index, smoking and drinking history, disease name, history of underlying diseases, whether mechanical ventilation is used, whether sedatives have been used, Acute Physiology and Chronic Health Evaluation II (APACHE II), etc.

[0014] The present invention also provides an evaluation system for the risk of delirium in ICU patients, comprising:

[0015] CPC monitoring module is used to perform CPC monitoring on the sleep structure of enrolled ICU patients. After grouping, CPC monitoring parameter differences between the two groups are compared, and statistically significant parameters are screened out as objective indicators for early prediction of delirium.

[0016] The CAM-ICU assessment module is used to assess the occurrence of delirium in ICU patients using the CAM-ICU scoring scale every day until delirium occurs or patients are transferred out of the ICU. Patients are divided into a delirium group and a non-delirium group according to whether delirium occurs.

[0017] The delirium risk assessment module is used to use the collected clinical data of ICU patients and the CPC parameters obtained through screening as predictors of delirium, screen out the risk factors for delirium using multivariate logistic regression analysis, and draw the receiver operating characteristic (ROC) curve to assess the delirium risk of ICU patients.

[0018] Preferably, the CPC monitoring module includes a dynamic ECG collector and a CPC sleep quality assessment system. The ECG signal collector is worn on the precordial area of ​​the subject to automatically collect signal data including ECG, respiration, and body movement. Then, a sleep spectrum diagram is generated through the algorithm set by the CPC sleep quality assessment system to quantitatively measure the sleep structure of the subject. The monitoring time is from 21:00 on the day of admission to the ICU to 7:00 in the morning of the next day.

[0019] Preferably, the CPC monitoring parameters include: total sleep time, deep sleep time, light sleep time, rapid eye movement sleep time, awakening time, deep sleep latency, sleep efficiency and sleep apnea-hypopnea index.

[0020] Preferably, when comparing CPC monitoring parameters of two groups, a paired sample t-test is used if they conform to normal distribution, and a non-parametric test is used for non-normal distribution.

[0021] Preferably, the clinical data of the ICU patients include: gender, age, body mass index, smoking and drinking history, disease name, history of underlying diseases, whether mechanical ventilation is used, whether sedatives have been used, Acute Physiology and Chronic Health Evaluation II (APACHE II), etc.

[0022] The beneficial effects of the present invention are:

[0023] Delirium can lead to prolonged mechanical ventilation time and hospital stay of ICU patients, increased mortality, and greatly increased medical expenses. This application uses CPC analysis technology to monitor the changes in sleep structure of ICU patients to predict the occurrence of delirium early, and can take corresponding intervention measures as early as possible to improve sleep structure to avoid or reduce the occurrence and development of delirium, thereby reducing disability and mortality, allowing patients to better return to their families and society, and having certain social benefits. At the same time, it shortens the number of days of mechanical ventilation and hospital stay of such patients as much as possible, and reduces per capita medical expenses. The data acquisition of the present invention is simple, not easily disturbed by medical equipment, has high accuracy, and has good prospects for clinical promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the structure of the evaluation system of the present invention.

[0025] Figure 2 The ROC curve of deep sleep time for predicting delirium (A) and the ROC curve of deep sleep latency for predicting delirium (B). DETAILED DESCRIPTION

[0026] In order to explain the present invention more clearly, the present invention is further described in detail below in conjunction with embodiments and with reference to the accompanying drawings. It should be understood by those skilled in the art that the content described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0027] Example

[0028] 1. Collect clinical data of subjects and complete relevant examinations and tests

[0029] A total of 135 patients hospitalized in the ICU of our hospital were enrolled.

[0030] Inclusion criteria: (1) age ≥ 18 years; (2) signed informed consent for CPC monitoring. Exclusion criteria: (1) patients with a previous diagnosis of mental illness, sleep disorders, language and emotional expression and communication disorders; (2) patients with hemodynamic instability; (3) patients with terminal tumors; (4) patients after pacemaker implantation; (5) patients with arrhythmia; (6) comatose and sedated patients; (7) pregnant women.

[0031] Routine data registration was performed on the clinical data of ICU patients included in this study: gender, age, body mass index, smoking and drinking history, disease name, history of underlying diseases, whether mechanical ventilation was used, whether sedatives were used, Acute Physiology and Chronic Health Evaluation II (APACHE II), etc. All patients underwent some routine examinations and tests, including electrocardiogram, chest X-ray, B-ultrasound, etc., and blood tests included blood routine, liver and kidney function, blood sugar, blood lipids, thyroid function, myocardial injury markers, etc. 2. Complete CPC monitoring of ICU patients, delirium assessment and grouping of ICU patients

[0032] CPC monitoring of sleep structure was performed on ICU patients. ICU patients underwent CPC monitoring on the day of admission to the ICU, and the monitoring time was from 21:00 to 7:00 the next morning. The CPC monitoring system included a dynamic ECG collector (AECG-600D, Fengsheng Yongkang Software Technology Co., Ltd., China) and a CPC sleep quality assessment system (V2.0). The ECG signal collector was worn on the patient's precordial area to automatically collect ECG, respiration, body movement and other signal data, and then a sleep spectrum was generated by the algorithm set by the CPC sleep quality assessment system (V2.0) to quantitatively measure the patient's sleep quality. CPC monitoring parameters were recorded: total sleep time, deep sleep time, light sleep time, rapid eye movement sleep time, awakening time, deep sleep latency, sleep efficiency, and sleep apnea-hypopnea index. ICU patients were evaluated for delirium every day after admission using the CAM-ICU scoring scale (see Table 1) until delirium occurred or they were transferred out of the ICU. Those who developed delirium at any evaluation time during ICU stay were included in the delirium group, and those who did not develop delirium were included in the non-delirium group.

[0033] Table 1. Confusion Assessment Sheet for ICU Patients (CAM-ICU)

[0034]

[0035]

[0036] 3. Statistical Analysis

[0037] SPSS 21.0 statistical software [SPSS Inc. Chicago, IL, USA.] was used to perform statistical analysis on the data collected in this study. The data were expressed as percentages, and the categorical data were expressed as proportions. The CPC monitoring parameters were compared using paired sample t-test if they were in accordance with normal distribution, and nonparametric test analysis was used for non-normal distribution. Multivariate logistic regression analysis was used to screen out the risk factors for delirium (including CPC parameters and relevant clinical data parameters collected), and ROC curves were drawn to analyze and clarify the early predictive value of CPC parameters for delirium in ICU patients. P < 0.05 indicated that the difference was statistically significant.

[0038] IV. Experimental Results

[0039] According to the above method, a pilot experiment was conducted, and 135 ICU patients were included, of which 44 patients developed delirium, with a delirium incidence of 32.59%. The clinical data of all enrolled patients are shown in Table 2. The age, proportion of sedative use, and APACHE II score of the delirium group were significantly higher / greater than those of the non-delirium group (all P < 0.05, Table 2). The total sleep time, deep sleep time, and sleep efficiency of the delirium group were significantly lower than those of the control group, while the light sleep time, rapid eye movement sleep time, awakening time, deep sleep latency, and sleep apnea hypopnea index of the delirium group were significantly higher than those of the control group (all P < 0.05; Table 3). The variables with P<0.05 in the univariate analysis were introduced into the Logistic regression analysis as independent variables. As shown in Table 4, Logistic regression analysis showed that advanced age (OR, 1.075; 95% CI, 1.027-1.125; P<0.01) and sleep apnea hypopnea index (OR, 1.033; 95% CI1.002-1.065; P<0.05) were risk factors for delirium, while deep sleep time (OR, 0.254; 95% CI-0.121-0.533; P<0.001) was a protective factor for delirium. ROC analysis showed that the optimal critical value of deep sleep time to predict delirium was 0.65h, with a sensitivity of 84.1%, a specificity of 81.3%, and an AUC of 0.888 (95% confidence interval: 0.824-0.953, P<0.001; Figure 2 A). The optimal critical value of deep sleep latency for predicting delirium was 171.5 minutes, with a sensitivity of 69.2%, a specificity of 80.5%, and an AUC of 0.704 (95% confidence interval: 0.522–0.885, P = 0.019; Figure 2 B).

[0040] Table 2. Clinical characteristics of the delirium group and the non-delirium group

[0041]

[0042] * P < 0.05; ** P<0.001.

[0043] Table 3. Comparison of CPC parameters between groups

[0044]

[0045]

[0046] * P < 0.05; ** P<0.001.

[0047] Table 4. Logistic regression analysis of risk factors for delirium

[0048]

[0049] * P < 0.05; ** P<0.001.

[0050] Obviously, the above embodiments of the present invention are merely examples to more clearly illustrate the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A method for assessing the risk of delirium in ICU patients, comprising: 1) CPC analysis technology was used to monitor the sleep structure of the enrolled ICU patients. The CAM-ICU scoring scale was used to evaluate whether the ICU patients had delirium every day until delirium occurred or they were transferred out of the ICU. The patients were divided into a delirium group and a non-delirium group according to whether delirium occurred. The differences in CPC monitoring parameters between the two groups were compared, and the CPC parameters with statistical significance were screened out as objective indicators for early prediction of delirium. 2) The clinical data of ICU patients were collected and the CPC parameters obtained through screening were used as predictors of delirium. Multivariate logistic regression analysis was used to screen out the risk factors for delirium, and the receiver operating characteristic curve was drawn to evaluate the risk of delirium in ICU patients.

2. The method for assessing the risk of delirium in ICU patients according to claim 1, characterized in that: In step 1), the Holter collector is worn on the patient's precordial area to automatically collect signal data including ECG, respiration and body movement. Then, a sleep spectrum diagram is generated through the algorithm set by the CPC sleep quality assessment system to quantitatively measure the patient's sleep quality. The monitoring time is from 21:00 on the day of admission to the ICU to 7:00 in the morning of the next day.

3. The method for assessing the risk of delirium in ICU patients according to claim 1, characterized in that: In step 1), the CPC monitoring parameters include: total sleep time, deep sleep time, light sleep time, rapid eye movement sleep time, awakening time, deep sleep latency, sleep efficiency and sleep apnea hypopnea index.

4. The method for assessing the risk of delirium in ICU patients according to claim 1, characterized in that: In step 1), when comparing CPC monitoring parameters of the two groups, paired sample t test was used if they conformed to normal distribution, and nonparametric test analysis was used if they were not normally distributed.

5. The method for assessing the risk of delirium in ICU patients according to claim 1, characterized in that: In step 2), the clinical data of the ICU patients include: gender, age, body mass index, smoking and drinking history, disease name, history of underlying diseases, whether mechanical ventilation is used, whether sedatives have been used, and acute physiology and chronic health score II.

6. A system for assessing the risk of delirium in ICU patients, comprising: CPC monitoring module is used to perform CPC monitoring on the sleep structure of enrolled ICU patients. After grouping, CPC monitoring parameter differences between the two groups are compared, and statistically significant CPC parameters are screened out as objective indicators for early prediction of delirium. The CAM-ICU assessment module is used to assess the occurrence of delirium in ICU patients using the CAM-ICU scoring scale every day until delirium occurs or patients are transferred out of the ICU. Patients are divided into a delirium group and a non-delirium group according to whether delirium occurs. The delirium risk assessment module is used to use the collected clinical data of ICU patients and the CPC parameters obtained through screening as predictors of delirium, screen out the risk factors for delirium using multivariate logistic regression analysis, and draw the receiver operating characteristic curve to assess the delirium risk of ICU patients.

7. The system for evaluating the risk of delirium in ICU patients according to claim 6, characterized in that: The CPC monitoring module includes a dynamic ECG collector and a CPC sleep quality assessment system. The dynamic ECG collector is worn on the patient's precordial area to automatically collect signal data including ECG, respiration, and body movement. A sleep spectrum diagram is then generated through the algorithm set by the CPC sleep quality assessment system to quantitatively measure the subject's sleep quality. The monitoring time is from 21:00 on the day of admission to the ICU to 7:00 a.m. the next day.

8. The system for evaluating the risk of delirium in ICU patients according to claim 6, characterized in that: The CPC monitoring parameters include: total sleep time, deep sleep time, light sleep time, rapid eye movement sleep time, awakening time, deep sleep latency, sleep efficiency and sleep apnea-hypopnea index.

9. The system for evaluating the risk of delirium in ICU patients according to claim 6, characterized in that: When comparing CPC monitoring parameters between the two groups, paired sample t test was used if they conformed to normal distribution, and nonparametric test analysis was used if they were not normally distributed.

10. The system for evaluating the risk of delirium in ICU patients according to claim 6, characterized in that: The clinical data of the ICU patients included: gender, age, body mass index, smoking and drinking history, disease name, history of underlying diseases, whether mechanical ventilation was used, whether sedatives were used, and acute physiology and chronic health score II.

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