This article reports the complete screening-diagnosis-rehabilitation process for an Air Force non-commissioned officer diagnosed with narcolepsy（NP） comorbid with obstructive sleep apnea （OSA） and REM sleep behavior disorder（RBD）.The patient attended the hospital due to uncontrollable daytime sleep for 3 years and one episode of cataplexy，and a confirmed diagnosis was made based on polysomnography， multiple sleep latency test （MSLT）， and imaging examination， with the exclusion of organic lesions. As for treatment， since the patient showed no response to venlafaxine alone， the treatment regimen was switched to a comprehensive regimen of behavioral interventions， traditional Chinese medicine syndrome differentiation， and pitolisant. Based on this case report and the literature review， it is proposed that the armed forces should introduce a modern diagnostic and therapeutic mode integrating routine screening （Epworth Sleepiness Scale and MSLT）， multidisciplinary dynamic assessment， and individualized comprehensive care， in order to provide evidence-based guidance for the early identification， diagnosis， and treatment of sleep disorders in military personnel.
Narcolepsy ; Polysomnography

Objective: To evaluate the application value of a digital technology-based multiparameter vital signs monitoring system in perioperative comprehensive full-cycle surveillance. Methods: A comprehensive multidimensional vital signs monitoring system was developed through the integration of medical-grade wireless wearable devices, incorporating patch-type ambulatory electrocardiographic monitor, continuous glucose monitoring sensor, pulse oximeter, wireless digital thermometer, smart wristband, and bioelectrical impedance analyzer. This system facilitates continuous real-time acquisition of multiple physiological parameters including electrocardiogram, blood glucose, oxygen saturation, body temperature, physical activity, and body composition indices. The acquired data were systematically integrated and analyzed through a four-level digital architecture consisting of nurse mobile interfaces, bedside patient terminals, centralized ward monitoring displays, and hospital management information systems. One patient with gastric cancer complicated by diabetes mellitus was selected for full-cycle digital monitoring from preoperative evaluation to hospital discharge. The technical performance of the monitoring system was assessed in terms of data acquisition continuity and timeliness of abnormal event alerts. Results: The monitoring system effectively identified early postoperative abnormalities, such as decreased oxygen saturation and blood glucose fluctuations, providing timely guidance for clinical intervention. The built-in algorithm enabled visualization of perioperative stress levels through heart rate variability indices and continuous glucose monitoring data. The patient demonstrated good compliance with early postoperative mobilization, and the satisfaction score for monitoring management was 4 points based on the Likert 5-point scale. Conclusions: The multiparameter vital signs monitoring system enhanced the precision of perioperative management through continuous and dynamic physiological status assessment. Its modular design aligns with the principles of enhanced recovery after surgery, offering a novel technological solution for intelligent perioperative management.
Humans ; Stomach Neoplasms/physiopathology* ; Vital Signs ; Monitoring, Physiologic/instrumentation* ; Diabetes Mellitus ; Wearable Electronic Devices ; Perioperative Period

To ensure the quality of care for inpatients in ophthalmic hospitals, address the complex and variable conditions of postoperative patients, and conduct more comprehensive, accurate and real-time monitoring of patients, an intelligent monitoring system based on computer vision and artificial intelligence has been designed. This system is employed for real-time monitoring of patient health conditions and intelligent care, with primary applications in medical monitoring, rehabilitation therapy, and inpatient care. It comprises intelligent data acquisition devices, smart cameras, continuous physiological data analysis algorithms, AI algorithms, and software. Given the complex and variable conditions of postoperative patients in ophthalmic hospitals, a comprehensive, accurate, and real-time monitoring of patients is required. Therefore, it is necessary to explore a monitoring technology that imposes low physiological and psychological burdens. The intelligent monitoring system can continuously collect patients' physiological parameter indicators and transmit the monitoring data to doctors' workstations or nurse stations after analysis using intelligent algorithms, providing new tools for patient monitoring, disease assessment, risk warning, and more. Furthermore, through the application of computer vision and artificial intelligence technologies, the system can analyze facial expressions, body postures, and other data to identify patients' emotional states and bedridden postures, enabling the timely detection of abnormal situations and implementation corresponding measures. This helps improving the daily work of medical staff, enhance the nursing safety in single-patient rooms in wards, and potentially find applications in the care of critically ill patients and elderly patients, thereby improving nursing efficiency and quality.
Artificial Intelligence ; Humans ; Monitoring, Physiologic/methods* ; Algorithms

Hemodynamic monitoring can reflect cardiac function and blood perfusion and is an indispensable monitoring method in clinical practice. Invasive hemodynamic monitoring methods represented by the thermodilution method are limited in their clinical application scope because they require vascular cannulation. Non-invasive hemodynamic monitoring has attracted extensive attention from medical companies and clinicians at home and abroad in recent years due to its advantages such as safety, non-invasiveness, continuous monitoring, simple operation, and low cost. This paper designs a non-invasive hemodynamic monitoring system based on the impedance cardiography, including hardware, algorithm, software design, and performance parameter evaluation. Among them, the hardware part mainly includes a differential high-frequency constant current source stimulation circuit, impedance cardiogram signal acquisition, and ECG signal acquisition circuit. Signal processing includes wave filtering, impedance cardiogram signal calibration, and ECG signal and impedance cardiogram signal feature point recognition. According to the collected impedance cardiogram and ECG signals, hemodynamic parameters such as heart rate (HR), stroke volume (SV), cardiac output (CO), stroke index (SI), cardiac index (CI), and cardiac contractility index (ICON) are calculated based on the Nyboer thoracic cylinder model. After testing, the key technical indicators of the system hardware are better than that of the relevant medical device standards. The system was used to collect impedance cardiogram and ECG signal data from 40 volunteers. The calculated HR, SV, and CO, three important hemodynamic indicators, were compared with the ICONCore non-invasive cardiac output monitor of OSYPKA Medical in Germany. Their Pearson correlation coefficients were 0.992 ( P<0.001), 0.948 ( P<0.001), and 0.933 ( P<0.001), respectively, verifying that the designed system has high accuracy and reliability.
Cardiography, Impedance/methods* ; Humans ; Hemodynamic Monitoring/methods* ; Equipment Design ; Signal Processing, Computer-Assisted ; Hemodynamics ; Algorithms ; Monitoring, Physiologic/methods* ; Electrocardiography

End-tidal carbon dioxide monitoring is an important means of evaluating human lung function and is widely used in fields such as clinical emergency treatment and cardiopulmonary resuscitation. This paper develops a microstream end-tidal carbon dioxide monitoring system. It adopts an integrated gas circuit design to further reduce the size of the equipment. The system uses the method of calculating the root mean square (RMS) of differential pressure signals to regulate the gas circuit flow, enabling the system to stably operate at a flow state of 30 mL/min. In addition, by simultaneously detecting multiple environmental parameters such as temperature and pressure, the system realizes system state monitoring and gas parameter compensation. The test results show that various indicators of the system meet the requirements of relevant standards, laying a good foundation for subsequent engineering applications.
Carbon Dioxide/analysis* ; Equipment Design ; Monitoring, Physiologic/methods* ; Humans

Cardiovascular disease (CVD) is the leading cause of death worldwide. As the key pathological basis of CVD, arteriosclerosis holds great significance for early screening. However, existing clinical and homecare detection devices have many shortcomings; for instance, the commonly used non-invasive indicator PWV (pulse wave velocity) is easily interfered by blood pressure.This study developed a homecare arteriosclerosis monitoring system, which integrates the measurement functions of cardio-ankle vascular index (CAVI) and ankle-brachial index (ABI). The hardware design of the system includes an integrated structure of flexible silver ion electrodes and clip-type cuffs, a contact heart sound sensor, and a stepped deflation blood pressure measurement module. Meanwhile, a high-precision analog-to-digital conversion module and the STM32F405 main control chip are used to realize the synchronous acquisition of multiple signals.In terms of software, the underlying driver program was designed through MDK (Keil5), and a user interface was built on the Visual Studio platform to achieve functions such as data acquisition, display, and storage. At the algorithm level, the system adopted algorithms like the Pan-Tompkins algorithm to identify key feature points of physiological signals, and then calculate CAVI and ABI.System test results show that the ECG input noise of the system is less than 20 μV, the common-mode rejection ratio is 95 dB, and the blood pressure measurement error does not exceed 2 mmHg, which meets the design goals. Clinical data analysis indicates that CAVI is highly positively correlated with pulse wave velocity (PWV) ( r=0.85, P<0.001), but CAVI is less affected by blood pressure fluctuations. In addition, with the increase of risk factors (such as hypertension, hyperlipidemia, coronary heart disease, etc.) and age, arteriosclerosis indicators (CAVI, PWV, ABI) all show an upward trend.In conclusion, the homecare arteriosclerosis monitoring system proposed in this study not only overcomes the problems of traditional devices that rely on professional operation and are susceptible to blood pressure interference, but also provides a reliable tool for arteriosclerosis screening in home scenarios, and has important reference value for clinical diagnosis.
Humans ; Cardio Ankle Vascular Index ; Home Care Services ; Atherosclerosis/diagnosis* ; Ankle Brachial Index ; Algorithms ; Pulse Wave Analysis ; Arteriosclerosis/diagnosis* ; Monitoring, Physiologic/instrumentation*

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography

INTRODUCTION: Arrhythmias, especially atrial fibrillation (AF) and ventricular arrhythmias, are independent risk factors of mortality in patients with ischaemic heart disease (IHD). While there is a growing body of evidence that suggests an association between obstructive sleep apnoea (OSA) and cardiac arrhythmias, evidence on this relationship in patients with IHD has been scant and inconsistent. We hypothesised that in patients with IHD, severe OSA is associated with an increased risk of nocturnal arrhythmias. METHODS: We studied 103 consecutive patients with IHD who underwent an overnight polysomnography. Exposed subjects were defined as patients who had an apnoea-hypopnoea index (AHI) ≥30/h (severe OSA), and nonexposed subjects were defined as patients who had an AHI <30/h (nonsevere OSA). All electrocardiograms (ECGs) were interpreted by the Somte ECG analysis software and confirmed by a physician blinded to the presence or absence of exposure. Arrhythmias were categorised as supraventricular and ventricular. Arrhythmia subtypes (ventricular, atrial and conduction delay) were analysed as dichotomous outcomes using multiple logistic regression models. RESULTS: Atrial fibrillation and AF/flutter (odds ratio 13.5, 95% confidence interval 1.66-109.83; P = 0.003) were found to be more common in the severe OSA group than in the nonsevere OSA group. This association remained significant after adjustment for potential confounders. There was no significant difference in the prevalence of ventricular and conduction delay arrhythmias between the two groups. CONCLUSION In patients with IHD, there was a significant association between severe OSA and nocturnal AF/flutter. This underscores the need to evaluate for OSA in patients with IHD, as it may have important implications on clinical outcomes.
Humans ; Sleep Apnea, Obstructive/diagnosis* ; Atrial Fibrillation/diagnosis* ; Male ; Female ; Middle Aged ; Polysomnography ; Electrocardiography ; Myocardial Ischemia/complications* ; Aged ; Risk Factors ; Logistic Models

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

Objective:The variability of the apnea-hypopnea index（AHI） measured in the first and second halves of the night is significant in patients with obstructive sleep apnea hypopnea syndrome（OSAHS）. This variation may be related to fluid redistribution caused by the supine position during sleep. Methods:Eighty-nine adult subjects were enrolled. Circumferences（neck, chest, waist, and calf） were measured before sleep onset and upon awakening. Polysomnography（PSG） was performed, and the night was divided into two halves based on the midpoint of total sleep time to calculate AHI for each half. The correlation between changes in AHI and changes in circumferences was analyzed. Results:Twenty simple snorers and sixty-nine OSAHS patients were included, with a median AHI of 22.6（11.8, 47.3） events/hour. Compared to pre-sleep measurements, there was no significant change in neck circumference upon awakening in the control group（P=0.073）, while reductions were observed in the other three measurements（P=0.006, P=0.038, P<0.001）. In the OSAHS group, neck circumference increased（P<0.001）, and reductions were noted in the other three measurements（P<0.001 for all）, with the most significant change observed in calf circumference 40.0（37.1, 42.0） cm to 38.0（35.8, 40.5） cm. Compared to the first half of the night, total AHI, supine AHI, and NREM AHI significantly decreased in the second half（P=0.010, P=0.031, P=0.001）, while no significant changes were observed in lateral AHI and REM AHI（P=0.988, P=0.530）. Further analysis revealed a significant relationship between increased chest circumference and decreases in NREM AHI, supine AHI, and supine NREM AHI（P=0.036, P=0.072, P=0.034）, as well as between decreased lateral position AHI and increased waist circumference（P=0.048）. Additionally, this study found a negative correlation between changes in calf circumference and changes in AHI（R=-0.24, P=0.048）, while neck circumference changes positively correlated with changes in AHI（R=0.26, P=0.03）. Conclusion:In OSAHS patients during the second half of sleep compared to before sleeping, chest circumference, waist circumference, and calf circumference decrease while neck circumference increases; total AHI, supine position AHI, and NREM period AHI decrease; increases in chest circumference are associated with decreases in NREM period AHI, supine position AHI, supine position NREM period AHI. There is nocturnal variability in AHI among OSAHS patients that may be associated with fluid shifts during sleep.
Humans ; Sleep Apnea, Obstructive/physiopathology* ; Male ; Female ; Polysomnography ; Fluid Shifts/physiology* ; Adult ; Middle Aged ; Neck ; Severity of Illness Index ; Sleep/physiology* ; Snoring/physiopathology*