Identifying sensors-based parameters associated with fall risk in community-dwelling older adults: an investigation and interpretation of discriminatory parameters.

BACKGROUND: Falls pose a severe threat to the health of older adults worldwide. Determining gait and kinematic parameters that are related to an increased risk of falls is essential for developing effective intervention and fall prevention strategies. This study aimed to investigate the discriminatory parameter, which lay an important basis for developing effective clinical screening tools for identifying high-fall-risk older adults. METHODS: Forty-one individuals aged 65 years and above living in the community participated in this study. The older adults were classified as high-fall-risk and low-fall-risk individuals based on their BBS scores. The participants wore an inertial measurement unit (IMU) while conducting the Timed Up and Go (TUG) test. Simultaneously, a depth camera acquired images of the participants' movements during the experiment. After segmenting the data according to subtasks, 142 parameters were extracted from the sensor-based data. A t-test or Mann-Whitney U test was performed on the parameters for distinguishing older adults at high risk of falling. The logistic regression was used to further quantify the role of different parameters in identifying high-fall-risk individuals. Furthermore, we conducted an ablation experiment to explore the complementary information offered by the two sensors. RESULTS: Fifteen participants were defined as high-fall-risk individuals, while twenty-six were defined as low-fall-risk individuals. 17 parameters were tested for significance with p-values less than 0.05. Some of these parameters, such as the usage of walking assistance, maximum angular velocity around the yaw axis during turn-to-sit, and step length, exhibit the greatest discriminatory abilities in identifying high-fall-risk individuals. Additionally, combining features from both devices for fall risk assessment resulted in a higher AUC of 0.882 compared to using each device separately. CONCLUSIONS: Utilizing different types of sensors can offer more comprehensive information. Interpreting parameters to physiology provides deeper insights into the identification of high-fall-risk individuals. High-fall-risk individuals typically exhibited a cautious gait, such as larger step width and shorter step length during walking. Besides, we identified some abnormal gait patterns of high-fall-risk individuals compared to low-fall-risk individuals, such as less knee flexion and a tendency to tilt the pelvis forward during turning.

Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study.

Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users' multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.

A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

Clinical prediction rule of termination of resuscitation for out-of-hospital cardiac arrest patient with pre-hospital defibrillation given.

OBJECTIVE: To derive a clinical prediction rule of termination of resuscitation (TOR) for out-of-hospital cardiac arrest (OHCA) with pre-hospital defibrillation given. METHOD: This was a retrospective multicenter cohort study performed in three emergency departments (EDs) of three regional hospitals from 1/1/2012 to 31/12/2018. Patients of OHCA aged ≥18 years old were included. Those with post-mortem changes, return of spontaneous circulation and receiving no resuscitation in EDs were excluded. A TOR rule was derived by logistic regression analysis based on demographics and end-tidal carbon dioxide level of included subjects with pre-hospital defibrillation given. RESULTS: There were 447 included patients had received pre-hospital defibrillation, in which 148 had return of spontaneous circulation (ROSC), with 22 survived to discharge (STD). Independent predictors for death on or before ED arrival (no ROSC) included EMS call to ED time >20 min and ETCO2 level <20 mmHg from first three sets. A 2-criteria rule predicting death on or before ED arrival by fulfilling both of the independent predictors had a specificity of 0.95 (95% CI 0.90-0.98) and positive predictive value (PPV) of 0.95 (95% CI 0.90-0.98), if 2nd set of ETCO2 was used. The positive likelihood ratio was 10.04 (95% CI 4.83-20.89). CONCLUSION: The 2-criteria TOR rule for OHCA patients with pre-hospital defibrillation had a high specificity and PPV for predicting death on or before ED arrival.

Influencing factors of Barthel index scores among the community-dwelling elderly in Hong Kong: a random intercept model.

BACKGROUND: Barthel Index (BI) is one of the most widely utilized tools for assessing functional independence in activities of daily living. Most existing BI studies used populations with specific diseases (e.g., Alzheimer's and stroke) to test prognostic factors of BI scores; however, the generalization of these findings was limited when the target populations varied. OBJECTIVES: The aim of the present study was to utilize electronic health records (EHRs) and data mining techniques to develop a generic procedure for identifying prognostic factors that influence BI score changes among community-dwelling elderly. METHODS: Longitudinal data were collected from 113 older adults (81 females; mean age = 84 years, SD = 6.9 years) in Hong Kong elderly care centers. Visualization technologies were used to align annual BI scores with individual EHRs chronologically. Linear mixed-effects (LME) regression was conducted to model longitudinal BI scores based on socio-demographics, disease conditions, and features extracted from EHRs. RESULTS: The visualization presented a decline in BI scores changed by time and health history events. The LME model yielded a conditional R2 of 84%, a marginal R2 of 75%, and a Cohen's f2 of 0.68 in the design of random intercepts for individual heterogeneity. Changes in BI scores were significantly influenced by a set of socio-demographics (i.e., sex, education, living arrangement, and hobbies), disease conditions (i.e., dementia and diabetes mellitus), and EHRs features (i.e., event counts in allergies, diagnoses, accidents, wounds, hospital admissions, injections, etc.). CONCLUSIONS: The proposed visualization approach and the LME model estimation can help to trace older adults' BI score changes and identify the influencing factors. The constructed long-term surveillance system provides reference data in clinical practice and help healthcare providers manage the time, cost, data and human resources in community-dwelling settings.

Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation.

BACKGROUND: Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. OBJECTIVE: The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. METHODS: In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. RESULTS: The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. CONCLUSIONS: The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community's burden of continuous health monitoring.

Assessing elderly's functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests.

BACKGROUND: Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly's functional balance based on Short Form Berg Balance Scale (SFBBS) score. METHODS: Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. RESULTS: Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. CONCLUSIONS: The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals.

Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients' health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of 0.12±10.83 mmHg, 0.13±5.90 mmHg, and 0.08±6.47 mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension.

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study.

BACKGROUND: Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. OBJECTIVE: Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance. METHODS: The system was designed to capture and record older adults' health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users' attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system. RESULTS: A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants' daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78). CONCLUSIONS: The proposed health monitoring system provides an example design for monitoring older adults' health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system.

Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks.

Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep learning approaches, which are fast and accurate at the same time, are proposed to inspect the key components of railway track including rail, bolt, and clip. The inspection results show that the enhanced model, the second version of you only look once (YOLOv2), presents the best component detection performance with 93% mean average precision (mAP) at 35 image per second (IPS), whereas the feature pyramid network (FPN) based model provides a smaller mAP and much longer inference time. Besides, the detection performances of more deep learning approaches are evaluated under varying input sizes, where larger input size usually improves the detection accuracy but results in a longer inference time. Overall, the YOLO series models could achieve faster speed under the same detection accuracy.

Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals.

BACKGROUND: The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease diagnosis and widely utilized for monitoring personalized health conditions. METHOD: In this study, we present a novel approach to forecasting one-day-forward wellness conditions for community-dwelling elderly by analyzing single lead short ECG signals acquired from a station-based monitoring device. More specifically, exponentially weighted moving-average (EWMA) method is employed to eliminate the high-frequency noise from original signals at first. Then, Fisher-Yates normalization approach is used to adjust the self-evaluated wellness score distribution since the scores among different individuals are skewed. Finally, both deep learning-based and traditional machine learning-based methods are utilized for building wellness forecasting models. RESULTS: The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and F value are 93.21% and 91.98% respectively. The deep learning-based methods, with the merit of non-hand-crafted engineering, have superior wellness forecasting performance towards the competitive traditional machine learning-based methods. CONCLUSION: The developed approach in this paper is effective in wellness forecasting for community-dwelling elderly, which can provide insights in terms of implementing a cost-effective approach to informing healthcare provider about health conditions of elderly in advance and taking timely interventions to reduce the risk of malignant events.

Preliminary Agreement on Tracking Sleep Between a Wrist-Worn Device Fitbit Alta and Consensus Sleep Diary.

Background: Sleep is related to various kinds of health outcomes. Many studies traditionally collect data on sleep using questionnaires or sleep diaries. An increasing popular alternative is a wrist-worn device. The accuracy of these devices is uncertain, and assessment of this accuracy is important.Introduction: The purpose of this study is to compare consensus sleep diary (CSD) and an actigraphy-based wrist-worn device (Fitbit Alta™ [Fitbit, San Francisco, CA]) measurements of total sleep time (TST), sleep onset latency, wake time after sleep onset, number of awakenings, and sleep efficiency.Materials and Methods: Ten healthy young adults (50% female, 100% Asian) in the age range between 20 to 24 years old wore a Fitbit Alta around their nondominated hand during seven consecutive nights. The participants also filled in a CSD every day.Results: On average, the wrist-worn device (Fitbit Alta) recorded a TST per night of 437.15 min, which is 5.46 min shorter on average than the CSD recorded (442.61 min). Bland-Altman plots indicate that there is large variance in the sleep recorded between Fitbit™ (Fitbit, San Francisco, CA) and CSD. For example, Fitbit recorded 2.15 more awakenings per night than CSD, which is equal to 13.09 min on average longer wake time after sleep onset.Conclusion: Fitbit and CSD show significant differences in recording sleep. We find that for most sleep metrics, the level of disagreement is small enough for the devices to be interchangeably used except for recording wakes during the night.

Simultaneously characterizing the comparative economics of routine female adolescent nonavalent human papillomavirus (HPV) vaccination and assortativity of sexual mixing in Hong Kong Chinese: a modeling analysis.

BACKGROUND: Although routine vaccination of females before sexual debut against human papillomavirus (HPV) has been found to be cost-effective around the world, its cost-benefit has rarely been examined. We evaluate both the cost-effectiveness and cost-benefit of routine female adolescent nonavalent HPV vaccination in Hong Kong to guide its policy, and by extension that of mainland China, on HPV vaccination. One major obstacle is the lack of data on assortativity of sexual mixing. Such difficulty could be overcome by inferring sexual mixing parameters from HPV epidemiologic data. METHODS: We use an age-structured transmission model coupled with stochastic individual-based simulations to estimate the health and economic impact of routine nonavalent HPV vaccination for girls at age 12 on cervical cancer burden and consider vaccine uptake at 25%, 50%, and 75% with at least 20 years of vaccine protection. Bayesian inference was employed to parameterize the model using local data on HPV prevalence and cervical cancer incidence. We use the human capital approach in the cost-benefit analysis (CBA) and GDP per capita as the indicative willingness-to-pay threshold in the cost-effectiveness analysis (CEA). Finally, we estimate the threshold vaccine cost (TVC), which is the maximum cost for fully vaccinating one girl at which routine female adolescent nonavalent HPV vaccination is cost-beneficial or cost-effective. RESULTS: As vaccine uptake increased, TVC decreased (i.e., economically more stringent) in the CBA but increased in the CEA. When vaccine uptake was 75% and the vaccine provided only 20 years of protection, the TVC was US$444 ($373-506) and $689 ($646-734) in the CBA and CEA, respectively, increasing by approximately 2-4% if vaccine protection was assumed lifelong. TVC is likely to be far higher when non-cervical diseases are included. The inferred sexual mixing parameters suggest that sexual mixing in Hong Kong is highly assortative by both age and sexual activity level. CONCLUSIONS: Routine HPV vaccination of 12-year-old females is highly likely to be cost-beneficial and cost-effective in Hong Kong. Inference of sexual mixing parameters from epidemiologic data of prevalent sexually transmitted diseases (i.e., HPV, chlamydia, etc.) is a potentially fruitful but largely untapped methodology for understanding sexual behaviors in the population.

Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach.

BACKGROUND: Hand, foot, and mouth disease (HFMD) has been recognized as one of the leading infectious diseases among children in China, which causes hundreds of annual deaths since 2008. In China, the reports of monthly HFMD cases usually have a delay of 1-2 months due to the time needed for collecting and processing clinical information. This time lag is far from optimal for policymakers making decisions. To alleviate this information gap, this study uses a meta learning framework and combines publicly Internet-based information (Baidu search queries) for real-time estimation of HFMD cases. METHODS: We incorporate Baidu index into modeling to nowcast the monthly HFMD incidences in Guangxi, Zhejiang, Henan provinces and the whole China. We develop a meta learning framework to select appropriate predictive model based on the statistical and time series meta features. Our proposed approach is assessed for the HFMD cases within the time period from July 2015 to June 2016 using multiple evaluation metrics including root mean squared error (RMSE) and correlation coefficient (Corr). RESULTS: For the four areas: whole China, Guangxi, Zhejiang, and Henan, our approach is superior to the best competing models, reducing the RMSE by 37, 20, 20, and 30% respectively. Compared with all the alternative predictive methods, our estimates show the strongest correlation with the observations. CONCLUSIONS: In this study, the proposed meta learning method significantly improves the HFMD prediction accuracy, demonstrating that: (1) the Internet-based information offers the possibility for effective HFMD nowcasts; (2) the meta learning approach is capable of adapting to a wide variety of data, and enables selecting appropriate method for improving the nowcasting accuracy.

Impact of hypotension after return of spontaneous circulation on survival in patients of out-of-hospital cardiac arrest.

OBJECTIVE: To investigate the relationship between hypotension in the first 3h after return of spontaneous circulation (ROSC) in patients with out-of-hospital cardiac arrest. METHOD: This retrospective cohort study occurred at two regional hospitals and included adult OHCA patients who experienced ROSC from July 1, 2014 to December 31, 2015. Hemodynamic and inotrope administration data were retrieved for 3h after ROSC. We calculated the hypotensive exposure index (HEI) as the surrogate marker of the exposure of hypotension. The area under the ROC curve and multivariate logistic regression models were performed to analyze the effect of HEI on survival. Mean arterial pressure (MAP) was explored in the surviving and non-surviving patient groups using repeated measures MANCOVA, adjusted for the use of inotropes and down time. RESULTS: A total of 289 patients were included in the study, and 29 survived. The median 1-hour HEI and 3-hour HEI were significantly lower in the survival group (p<0.001). The area under the ROC curve for 3-hour HEI was 0.861. The repeated measures MANCOVA indicated that an interaction existed between post-ROSC time and downtime [F(5,197)=2.31, p=0.046]. No significant change in the MAP was observed in the 3h after ROSC, except in the group with a prolonged down time. According to the tests examining the effects of the use of inotropes on the survival outcomes of the different subjects, the MAP was significantly higher in the surviving group [F(1,201)=4.11; p=0.044; ηp2=0.020]. CONCLUSION: Among the patients who experienced ROSC after OHCA, post-ROSC hypotension was an independent predictor of survival.

Predictive model integrating dynamic parameters for massive blood transfusion in major trauma patients: The Dynamic MBT score.

BACKGROUND: Currently existing predictive models for massive blood transfusion in major trauma patients had limitations for sequential evaluation of patients and lack of dynamic parameters. OBJECTIVE: To establish a predictive model for predicting the need of massive blood transfusion major trauma patients, integrating dynamic parameters. DESIGN: Multi-center retrospective cohort study. SETTING: Four designated trauma centers in Hong Kong. METHODS: Trauma patients aged >12years were recruited from the trauma registries from 2005 to 2012. MBT was defined as delivery of ≥10units of packed red cells within 24h. Split sampling method was adopted for model building and validation. Multivariate logistic regression was adopted for model building, with weight assigned based on logarithmic of adjusted odds ratios. The performance of the dynamic MBT score (DMBT) was compared with the PWH score and the Trauma Associated Severe Hemorrhage (TASH) score in the validation data set. RESULTS: 4991 patients were included in the study. The DMBT was established with 8 parameters: systolic blood pressure, heart rate, hemoglobin, hemoglobin drop within the first 2h, INR, base deficit, unstable pelvic fracture and hemoperitoneum in radiological imaging. At cut-off score of 6 the DMBT achieved sensitivity of 78.2% and specificity of 89.2%. In the validation set, the AUCs of the DMBT, PWH score, and TASH score were 0.907, 0.844, and 0.867 respectively. CONCLUSIONS: The DMBT score allows both snapshot and sequential activation along the trauma care pathway and has better performance than the PWH score and TASH score.

EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings.

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions.

Presenting hydrothorax predicts failure of needle aspiration in primary spontaneous pneumothorax.

OBJECTIVE: The objective was to evaluate if existence of hydrothorax in initial chest radiograph predicts treatment outcome in patients with primary spontaneous pneumothorax who received needle thoracostomy. METHODS: This is a retrospective cohort study carried out from January 2011 to August 2014 in 1 public hospital in Hong Kong. All consecutive adult patients aged 18years or above who attended the emergency department with the diagnosis of primary spontaneous pneumothorax with needle aspiration performed as primary treatment were included. Age, smoking status, size of pneumothorax, previous history of pneumothorax, aspirated gas volume and presence of hydropneumothorax in initial radiograph were included in the analysis. The outcome was success or failure of the needle aspiration. Logistic regression was used to identify the predicting factors of failure of needle aspiration. RESULT: There were a total of 127 patients included. Seventy-three patients (57.5%) were successfully treated with no recurrence upon discharge. Among 54 failure cases, 13 patients (10.2%) failed immediately after procedure as evident by chest radiograph and required second treatment. Forty-one patients (32.3%) failed upon subsequent chest radiographs. Multivariate logistic regression showed factors independently associated with the failure of needle aspiration, which included hydropneumothorax in the initial radiograph (odds ratio [OR]=4.47 [1.56i12.83], P=.005), previous history of pneumothorax (OR=3.92 [1.57-9.79], P=.003), and large size of pneumothorax defined as apex-to-cupola distance ≥5cm (OR=2.75 [1.21-6.26], P=.016). CONCLUSIONS: Hydropneumothorax, previous history of pneumothorax, and large size were independent predictors of failure of needle aspiration in treatment of primary spontaneous pneumothorax.

Role of copeptin in dual-cardiac marker strategy for patients with chest pain presented to ED.

OBJECTIVE: The objective of the study is to evaluate the role of copeptin in the diagnosis of acute coronary syndrome (ACS) and its role in dual-cardiac marker diagnostic strategy with troponin. DESIGN: A prospective cohort study was carried out from May 2012 to October 2012. SETTING: The study was conducted at the emergency department (ED) of a public hospital in a cluster of Hong Kong. METHODS: Patients aged at least 18 years presented with chest pain to ED who have intermediate or high likelihood of ACS were included. All patients had blood taken in the ED for copeptin and troponin I. The adjudicated diagnoses of ACS were made by 2 independent physicians based on the universal definition. Diagnostic characteristics were calculated. Receiver operating characteristic curves were created. Areas under the curves were compared for copeptin, troponin I, and dual-marker strategy with copeptin and troponin I. RESULTS: A total of 637 patients were recruited. Seventy-eight had been diagnosed to be ACS. The negative predictive value of copeptin for ACS was 0.881 (0.849-0.907) compared with troponin I, 0.937 (0.913-0.956). The areas under the receiver operating characteristic curves of copeptin, troponin I, and dual-marker strategy were 0.68, 0.859, and 0.880, respectively. CONCLUSIONS: Addition of copeptin to troponin does not have significant improvement of the diagnostic accuracy of ACS in patients presented with chest pain.

Optimal Composite Likelihood Estimation and Prediction for Distributed Gaussian Process Modeling.

Large-scale Gaussian process (GP) modeling is becoming increasingly important in machine learning. However, the standard modeling method of GPs, which uses the maximum likelihood method and the best linear unbiased predictor, is designed to run on a single computer, which often has limited computing power. Therefore, there is a growing demand for approximate alternatives, such as composite likelihood methods, that can take advantage of the power of multiple computers. However, these alternative methods in the literature offer limited options for practitioners because most methods focus more on computational efficiency rather than statistical efficiency. Limited accurate solutions to the parameter estimation and prediction for fast GP modeling are available in the literature for supercomputing practitioners. Therefore, this study develops an optimal composite likelihood (OCL) scheme for distributed GP modeling that can minimize information loss in parameter estimation and model prediction. The proposed predictor, called the best linear unbiased block predictor (BLUBP), has the minimum prediction variance given the partitioned data. Numerical examples illustrate that both the proposed composite likelihood estimation and prediction methods provide more accurate performance than their traditional counterparts under various cases, and an extremely close approximation to the standard modeling method is observed.