BACKGROUND

Dengue is a global health concern, particularly in tropical regions such as the Philippines. In 2019,Cebu City reported the highest number of dengue cases in Central Visayas with 3,290 cases and 20 deaths, an 11.8% increase compared to 20181 . To help predict disease outcomes and provide timely management, a scoring system, the Daily Dengue Severity Score (DDSS)² was utilized.

To determine the clinicodemographic profile of dengue patients, determine the accuracy of the DDSS in assessing disease severity, and determine a cut off score that suggests severe dengue.

Patients 1 month to 18 years admitted for dengue at Perpetual Succour Hospital from January 2018 to December 2020 were included. Cases were classified as Dengue without Warning Signs, Dengue with Warning Signs, and Severe Dengue, and scored using the DDSS. Statistical analysis used were Geometric mean and Area Under the Receiver Operating Characteristic (AUROC) curves to analyze the discriminative performance of the DDSS among the different disease severity states.

Out of 327 cases, 34 were classified as Dengue without Warning Signs, 271 Dengue with Warning Signs, and 22 Severe Dengue. The highest mean DDSS was 17.7 ±14.0 at Day -4 among those with Severe Dengue, and the lowest mean DDSS was 1.1 ± 2.0 at Day +3 among those with Dengue without Warning Signs. A cut off point of 10 on Day -1 predicted subsequent Severe Dengue among patients with Dengue with Warning Signs. In 91.39% of cases, there was a significant relationship between the DDSS and dengue classification, and the higher the DDSS, the more severe the disease.

Majority of dengue patients were males, aged 8.1 to 9.2 years. DDSS showed 66.67% sensitivity, 92.86% specificity, a positive likelihood ratio of 9.3, and a cutoff of 10 is predictive of severe dengue among patients with dengue with warning signs.

This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis-dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.
Humans ; Artificial Intelligence ; Wearable Electronic Devices ; Monitoring, Physiologic/methods*

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.
Humans ; Artificial Intelligence ; Internet of Things ; Wearable Electronic Devices ; Monitoring, Physiologic/methods* ; Electrocardiography ; Internet

The aging population and the increasing prevalence of chronic diseases in the elderly have brought a significant economic burden to families and society. The non-invasive wearable sensing system can continuously and real-time monitor important physiological signs of the human body and evaluate health status. In addition, it can provide efficient and convenient information feedback, thereby reducing the health risks caused by chronic diseases in the elderly. A wearable system for detecting physiological and behavioral signals was developed in this study. We explored the design of flexible wearable sensing technology and its application in sensing systems. The wearable system included smart hats, smart clothes, smart gloves, and smart insoles, achieving long-term continuous monitoring of physiological and motion signals. The performance of the system was verified, and the new sensing system was compared with commercial equipment. The evaluation results demonstrated that the proposed system presented a comparable performance with the existing system. In summary, the proposed flexible sensor system provides an accurate, detachable, expandable, user-friendly and comfortable solution for physiological and motion signal monitoring. It is expected to be used in remote healthcare monitoring and provide personalized information monitoring, disease prediction, and diagnosis for doctors/patients.
Humans ; Aged ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Chronic Disease

OBJECTIVE: To investigate the clinical value of analgesia and sedation under bispectral index (BIS) monitoring combined with hydraulic coupled intracranial pressure (ICP) monitoring in severe craniocerebral injury (sTBI). METHODS: (1) A prospective self-controlled parallel control study was conducted. A total of 32 patients with sTBI after craniotomy admitted to the intensive care unit (ICU) of the First People's Hospital of Huzhou from December 2020 to July 2021 were selected as the research objects. ICP was monitored by Codman monitoring system and hydraulically coupled monitoring system, and the difference and correlation between them were compared. (2) A prospective randomized controlled study was conducted. A total of 108 sTBI patients admitted to the ICU of the First People's Hospital of Huzhou from August 2021 to August 2022 were selected patients were divided into 3 groups according to the random number table method. All patients were given routine treatment after brain surgery. On this basis, the ICP values of the patients in group A (35 cases) were monitored by Codman monitoring system, the ICP values of the patients in group B (40 cases) were monitored by hydraulic coupling monitoring system, and the ICP values of the patients in group C (33 cases) were monitored combined with hydraulic coupling monitoring system, and the analgesia and sedation were guided by BIS. The ICP after treatment, cerebrospinal fluid drainage time, ICP monitoring time, ICU stay time, complications and Glasgow outcome score (GOS) at 6 months after surgery were compared among the 3 groups. In addition, patients in group B and group C were further grouped according to the waveforms. If P₁ = P₂ wave or P₂ and P₃ wave were low, they were classified as compensatory group. If the round wave or P₂ > P₁ wave was defined as decompensated group, the GOS scores of the two groups at 6 months after operation were compared. RESULTS: (1) There was no significant difference in ICP values measured by Codman monitoring system and hydraulic coupling monitoring system in the same patient (mmHg: 11.94±1.76 vs. 11.88±1.90, t = 0.150, P = 0.882; 1 mmHg≈0.133 kPa). Blan-altman analysis showed that the 95% consistency limit (95%LoA) of ICP values measured by the two methods was -4.55 to 4.68 mmHg, and all points fell within 95%LoA, indicating that the two methods had a good correlation. (2) There were no significant differences in cerebrospinal fluid drainage time, ICP monitoring time, ICU stay time, and incidence of complications such as intracranial infection, intracranial rebleeding, traumatic hydrocephalus, cerebrospinal fluid leakage, and accidental extubation among the 3 groups of sTBI patients (P > 0.05 or P > 0.017). The ICP value of group C after treatment was significantly lower than that of group A and group B (mmHg: 20.94±2.37 vs. 25.86±3.15, 26.40±3.09, all P < 0.05), the incidence of pulmonary infection (9.1% vs. 45.7%, 42.5%), seizure (3.0% vs. 31.4%, 30.0%), reoperation (3.0% vs. 31.4%, 40.0%), and poor prognosis 6 months after operation (33.3% vs. 65.7%, 65.0%) were significantly lower than those in group A and group B (all P < 0.017). According to the hydraulic coupling waveform, GOS scores of 35 patients in the compensated group were significantly higher than those of 38 patients in the decompensated group 6 months after operation (4.03±1.18 vs. 2.39±1.50, t = 5.153, P < 0.001). CONCLUSIONS The hydraulic coupled intracranial pressure monitoring system has good accuracy and consistency in measuring ICP value, and it can better display ICP waveform changes than the traditional ICP monitoring method, and has better prediction value for prognosis evaluation, which can replace Codman monitoring to accurately guide clinical work. In addition, analgesia and sedation under BIS monitoring combined with hydraulic coupled ICP monitoring can effectively reduce ICP, reduce the incidence of complications, and improve the prognosis, which has high clinical application value.
Humans ; Intracranial Pressure ; Prospective Studies ; Monitoring, Physiologic/methods* ; Craniocerebral Trauma ; Analgesia ; Cerebrospinal Fluid Leak

Blood glucose monitoring has become the weakest point in the overall management of diabetes in China. Long-term monitoring of blood glucose levels in diabetic patients has become an important means of controlling the development of diabetes and its complications, so that technological innovations in blood glucose testing methods have far-reaching implications for accurate blood glucose testing. This article discusses the basic principles of minimally invasive and non-invasive blood glucose testing assays, including urine glucose assays, tear assays, methods of extravasation of tissue fluid, and optical detection methods, etc., focuses on the advantages of minimally invasive and non-invasive blood glucose testing methods and the latest relevant results, and summarizes the current problems of various testing methods and prospects for future development trends.
Humans ; Blood Glucose ; Blood Glucose Self-Monitoring/methods* ; Diabetes Mellitus/diagnosis* ; Monitoring, Physiologic/methods* ; Tears

The continuous glucose monitoring system (CGMS) has been clinically applied to monitor the dynamic change of the subcutaneous interstitial glucose concentration which is a function of the blood glucose level by glucose sensors. It can track blood glucose levels all day along, and thus provide comprehensive and reliable information about blood glucose dynamics. The clinical application of CGMS enables monitoring of blood glucose fluctuations and the discovery of hidden hyperglycemia and hypoglycemia that are difficult to be detected by traditional methods. As a CGMS needs to work subcutaneously for a long time, a series of factors such as biocompatibility, enzyme inactivation, oxygen deficiency, foreign body reaction, implant size, electrode flexibility, error correction, comfort, device toxicity, electrical safety, et al. should be considered beforehand. The study focused on the difficulties in the technology, and compared the products of Abbott, Medtronic and DexCom, then summarized their cutting-edge. Finally, this study expounded some key technologies in dynamic blood glucose monitoring and therefore can be utilized as a reference for the development of CGMS.
Blood Glucose ; Blood Glucose Self-Monitoring/methods* ; Humans ; Hyperglycemia ; Hypoglycemia ; Monitoring, Ambulatory/methods* ; Monitoring, Physiologic

OBJECTIVE: To explore the feasibility of remote monitoring of neonatal jaundice in newborns with ABO hemolytic disease. METHODS: Forty six neonates of gestational age >35 weeks with ABO hemolytic disease admitted to Women's Hospital, Zhejiang University School of Medicine from January 20th, 2020 to February 29th, 2020 were enrolled in the study (study group). The newborns were followed up at home after discharge, the transcutaneous bilirubin (TCB) levels were measured by parents using the provided device and the results were sent to the doctor by smart phone using the installed APP. Fifty six newborns with ABO hemolytic disease admitted in 2018 who received conventional outpatient follow-up after discharge served as the control group. The demographic characteristics, total serum bilirubin (TSB) level during hospitalization, number of outpatient visit and rate of re-admission due to rebound hyperbilirubinemia were compared between the two groups. RESULTS: There were no significant differences between the two groups in gestational age, birth weight, delivery mode, gender, length of the first hospitalization, TSB level before phototherapy and before discharge, and the managements during the first hospitalization (all CONCLUSIONS The remote follow-up for neonatal jaundice at home can effectively reduce the number of outpatient visits without increasing the risk of readmission and severe neonatal hyperbilirubinemia for newborns with ABO hemolytic disease.
Bilirubin ; Erythroblastosis, Fetal/diagnosis* ; Female ; Humans ; Hyperbilirubinemia, Neonatal/diagnosis* ; Infant, Newborn ; Jaundice, Neonatal/diagnosis* ; Monitoring, Physiologic/methods* ; Phototherapy

Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can't continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.
Algorithms ; Area Under Curve ; Blood Gas Analysis ; Humans ; Machine Learning ; Monitoring, Physiologic ; methods ; ROC Curve ; Respiratory Distress Syndrome, Adult ; diagnosis ; Sensitivity and Specificity

OBJECTIVES: To successfully introduce an Internet of Things (IoT) system in the hospital environment, this study aimed to identify issues that should be considered while implementing an IoT based on a user demand survey and practical experiences in implementing IoT environment monitoring systems. METHODS: In a field test, two types of IoT monitoring systems (on-premises and cloud) were used in Department of Laboratory Medicine and tested for approximately 10 months from June 16, 2016 to April 30, 2017. Information was collected regarding the issues that arose during the implementation process. RESULTS: A total of five issues were identified: sensing and measuring, transmission method, power supply, sensor module shape, and accessibility. CONCLUSIONS: It is expected that, with sufficient consideration of the various issues derived from this study, IoT monitoring systems can be applied to other areas, such as device interconnection, remote patient monitoring, and equipment/environmental monitoring.
Electric Power Supplies ; Environmental Monitoring ; Hospitals, General* ; Internet ; Methods ; Monitoring, Physiologic