scholarly journals A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
A. Jalali ◽  
P. Ghorbanian ◽  
A. Ghaffari ◽  
C. Nataraj
Keyword(s):  
Intensive Care ◽  
Healthy Subjects ◽  
Predictive Accuracy ◽  
Estimation Error ◽  
Autonomic Response ◽  
Membership Degree ◽  
Cumulative Sum ◽  
Scoring Method ◽  
Poincaré Plot ◽  
Hemodynamic Variables

Identification of patients requiring intensive care is a critical issue in clinical treatment. The objective of this study is to develop a novel methodology using hemodynamic features for distinguishing such patients requiring intensive care from a group of healthy subjects. In this study, based on the hemodynamic features, subjects are divided into three groups: healthy, risky and patient. For each of the healthy and patient subjects, the evaluated features are based on the analysis of existing differences between hemodynamic variables: Blood Pressure and Heart Rate. Further, four criteria from the hemodynamic variables are introduced: circle criterion, estimation error criterion, Poincare plot deviation, and autonomic response delay criterion. For each of these criteria, three fuzzy membership functions are defined to distinguish patients from healthy subjects. Furthermore, based on the evaluated criteria, a scoring method is developed. In this scoring method membership degree of each subject is evaluated for the three classifying groups. Then, for each subject, the cumulative sum of membership degree of all four criteria is calculated. Finally, a given subject is classified with the group which has the largest cumulative sum. In summary, the scoring method results in 86% sensitivity, 94.8% positive predictive accuracy and 82.2% total accuracy.

scholarly journals Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients

2008 ◽  
Vol 15 (7) ◽  
pp. 1089-1094 ◽  
Author(s):  
R. A. Lukaszewski ◽  
A. M. Yates ◽  
M. C. Jackson ◽  
K. Swingler ◽  
J. M. Scherer ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care ◽  
Clinical Diagnosis ◽  
Predictive Accuracy ◽  
High Sensitivity ◽  
Icu Patients ◽  
The Neural Network ◽  
Sensitivity And Selectivity ◽  
Ccl2 Mrna ◽  
Factor Α

ABSTRACT Postoperative or posttraumatic sepsis remains one of the leading causes of morbidity and mortality in hospital populations, especially in populations in intensive care units (ICUs). Central to the successful control of sepsis-associated infections is the ability to rapidly diagnose and treat disease. The ability to identify sepsis patients before they show any symptoms would have major benefits for the health care of ICU patients. For this study, 92 ICU patients who had undergone procedures that increased the risk of developing sepsis were recruited upon admission. Blood samples were taken daily until either a clinical diagnosis of sepsis was made or until the patient was discharged from the ICU. In addition to standard clinical and laboratory parameter testing, the levels of expression of interleukin-1β (IL-1β), IL-6, IL-8, and IL-10, tumor necrosis factor-α, FasL, and CCL2 mRNA were also measured by real-time reverse transcriptase PCR. The results of the analysis of the data using a nonlinear technique (neural network analysis) demonstrated discernible differences prior to the onset of overt sepsis. Neural networks using cytokine and chemokine data were able to correctly predict patient outcomes in an average of 83.09% of patient cases between 4 and 1 days before clinical diagnosis with high sensitivity and selectivity (91.43% and 80.20%, respectively). The neural network also had a predictive accuracy of 94.55% when data from 22 healthy volunteers was analyzed in conjunction with the ICU patient data. Our observations from this pilot study indicate that it may be possible to predict the onset of sepsis in a mixed patient population by using a panel of just seven biomarkers.

scholarly journals Using machine learning tools to predict outcomes for emergency department intensive care unit patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiangrong Zhai ◽  
Zi Lin ◽  
Hongxia Ge ◽  
Yang Liang ◽  
Nan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Learning Tools ◽  
Critical Patients

AbstractThe number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

scholarly journals Comparative Evaluation of the Autonomic Response to Cognitive and Sensory Stimulations through Wearable Sensors

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4661 ◽  
Author(s):  
Alessandro Tonacci ◽  
Lucia Billeci ◽  
Elisa Burrai ◽  
Francesco Sansone ◽  
Raffaele Conte
Keyword(s):  
Heart Rate ◽  
Human Body ◽  
Healthy Subjects ◽  
Comparative Evaluation ◽  
Stress Test ◽  
Wearable Sensors ◽  
Cognitive Testing ◽  
Autonomic Response ◽  
Cognitive Stress ◽  
Similar Decrease

Psychological stress is known to activate the autonomic nervous system (ANS), thus representing a useful target to be monitored to understand the physiological, unconscious effect of stress on the human body. However, little is known about how differently the ANS responds to cognitive and sensory stimulations in healthy subjects. To this extent, we enrolled 23 subjects and administered a stress protocol consisting of the administration of sensory (olfactory) and cognitive (mathematical) stressors. Autonomic parameters were unobtrusively monitored through wearable sensors for capturing electrocardiogram and skin conductance signals. The results obtained demonstrated an increase of the heart rate during both stress protocols, with a similar decrease of the heart rate variability. Cognitive stress test appears to affect the autonomic parameters to a greater extent, confirming its effects on the human body. However, olfactory stimulation could be useful to study stress in specific experimental settings when the administration of complex cognitive testing is not feasible.

scholarly journals Impact of gender on the cardiac autonomic response to angiotensin II in healthy humans

2012 ◽  
Vol 112 (6) ◽  
pp. 1001-1007 ◽  
Author(s):  
M. C. Mann ◽  
D. V. Exner ◽  
B. R. Hemmelgarn ◽  
T. C. Turin ◽  
D. Y. Sola ◽  
...  
Keyword(s):  
Angiotensin Ii ◽  
Healthy Subjects ◽  
Spectral Power ◽  
Low Frequency ◽  
Premenopausal Women ◽  
Renin Angiotensin System ◽  
Autonomic Response ◽  
Vagal Activity ◽  
Ang Ii ◽  
Healthy Humans

Premenopausal women have a lower risk of cardiovascular disease (CVD) compared with men of a similar age. Furthermore, the regulation of factors that influence CVD appears to differ between the sexes, including control of the autonomic nervous system (ANS) and the renin-angiotensin system. We examined the cardiac ANS response to angiotensin II (Ang II) challenge in healthy subjects to determine whether differences in women and men exist. Thirty-six healthy subjects (21 women, 15 men, age 38 ± 2 years) were studied in a high-salt balance. Heart-rate variability (HRV) was calculated by spectral power analysis [low-frequency (LF) sympathetic modulation, high-frequency (HF) parasympathetic/vagal modulation, and LF:HF as a measure of overall ANS balance]. HRV was assessed at baseline and in response to graded Ang II infusions (3 ng·kg−1·min−1 × 30 min; 6 ng·kg−1·min−1 × 30 min). Cardiac ANS tone did not change significantly in women after each Ang II dose [3 ng·kg−1·min−1 mean change (Δ)LF:HF (mean ± SE) 0.5 ± 0.3, P = 0.8, vs. baseline; 6 ng·kg−1·min−1 ΔLF:HF (mean ± SE) 0.5 ± 0.4, P = 0.4, vs. baseline], whereas men exhibited an unfavorable shift in overall cardiac ANS activity in response to Ang II (ΔLF:HF 2.6 ± 0.2, P = 0.01, vs. baseline; P = 0.02 vs. female response). This imbalance in sympathovagal tone appeared to be largely driven by a withdrawal in cardioprotective vagal activity in response to Ang II challenge [ΔHF normalized units (nu), −5.8 ± 2.9, P = 0.01, vs. baseline; P = 0.006 vs. women] rather than an increase in sympathetic activity (ΔLF nu, −4.5 ± 5.7, P = 0.3, vs. baseline; P = 0.5 vs. women). Premenopausal women maintain cardiac ANS tone in response to Ang II challenge, whereas similarly aged men exhibit an unfavorable shift in cardiovagal activity. Understanding the role of gender in ANS modulation may help guide risk-reduction strategies in high-risk CVD populations.

scholarly journals GRAPHICAL METHODS FOR NON-LINEAR ANALYSIS OF ELECTROCARDIOGRAPHIC DATA

2019 ◽  
Vol 7 ◽  
Author(s):  
Evgeniya Gospodinova
Keyword(s):  
Healthy Subject ◽  
Healthy Subjects ◽  
Study Groups ◽  
Recurrence Plot ◽  
Quantitative Information ◽  
Linear Analysis ◽  
Graphical Methods ◽  
Healthy Controls ◽  
Poincaré Plot ◽  
Non Linear

INTRODUCTION: One of the most widely used methods for studying the bioelectric activity of the heart is the electrocardiogram (ECG). An important diagnostic parameter that can be determined by the ECG is heart rate variability (HRV), which takes into account the difference between successive strokes of the heart. Changing HRV can be an indicator of a number of disease states, such as low HRV levels can show poor health that is not only associated with cardiovascular disease but also with other diseases such as internal, nervous, mental, and other disorders. OBJECTIVES:  The subject of this article is the study of 24-hour ECG signals by applying non-linear graphical methods for HRV analysis. The non-linear graphical methods aim at obtaining graphical and quantitative information on the cardiovascular status of the study groups to complement the information obtained from traditional linear methods of analysis. METHODS: For the non-linear analysis of HRV, graphical methods were used: Poincaré plot and Recurrence plot were used, which are suitable for the examination of electrocardiographic signals. Two groups of people were investigated: 20 healthy controls and 20 patients with arrhythmia. RESULTS: Based on the nonlinear analysis of RR time series, the graphs of a healthy subject and a patient with arrhythmia were constructed using the Poincare plot. The graph of the healthy subject has the shape of a comet, while the graph of the patient with arrhythmia has the shape of a fan. The quantitative characteristics of patients with arrhythmia significantly change compared to the healthy subjects. The SD1 (p <0.003) and SD2 (p <0.0001) values decreased in patients with arrhythmia compared to the healthy controls. This reduction leads to reduction of the areas of the ellipse in the patients with arryhthmia. The ratio of SD1/SD2 (p <0.05) is lower for the healthy controls. The graphs obtained by the Recurrence plot of the investigated signals differ in healthy subjects and in patients with arrhythmia. For a healthy subject, the graph has a diagonal line and fewer squares showing a higher HRV. The graph of a patient with arrhythmia contains more squares, indicating periodicity in the investigated signal. The Recurrence Quantification Analysis showed that the values of the investigated parameters DET% (p <0.0001), REC% (p <0.0001) and ENTR (p <0.001) in patients with arrhythmia are increased. CONCLUSIONS:  The importance of the graphical nonlinear methods used for the analysis of HRV consists in forming a parametric and graphical assessment of the patient's health status.

scholarly journals Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenbin Ma ◽  
Haoran Xu ◽  
Muyang Yan ◽  
Jie Huang ◽  
Wei Yan ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Time Domain ◽  
Healthy Subjects ◽  
Multiscale Entropy ◽  
Healthy Population ◽  
Poincaré Plot ◽  
Longitudinal Changes ◽  
Hypoxic Environment

Background: The autonomic nervous system (ANS) is crucial for acclimatization. Investigating the responses of acute exposure to a hypoxic environment may provide some knowledge of the cardiopulmonary system’s adjustment mechanism.Objective: The present study investigates the longitudinal changes and recovery in heart rate variability (HRV) in a young healthy population when exposed to a simulated plateau environment.Methods: The study followed a strict experimental paradigm in which physiological signals were collected from 33 healthy college students (26 ± 2 years, 171 cm ± 7 cm, 64 ± 11 kg) using a medical-grade wearable device. The subjects were asked to sit in normoxic (approximately 101 kPa) and hypoxic (4,000 m above sea level, about 62 kPa) environments. The whole experimental process was divided into four stable resting measurement segments in chronological order to analyze the longitudinal changes of physical stress and recovery phases. Seventy-six time-domain, frequency-domain, and non-linear indicators characterizing rhythm variability were analyzed in the four groups.Results: Compared to normobaric normoxia, participants in hypobaric hypoxia had significantly lower HRV time-domain metrics, such as RMSSD, MeanNN, and MedianNN (p &lt; 0.01), substantially higher frequency domain metrics such as LF/HF ratio (p &lt; 0.05), significantly lower Poincaré plot parameters such as SD1/SD2 ratio and other Poincaré plot parameters are reduced considerably (p &lt; 0.01), and Refined Composite Multi-Scale Entropy (RCMSE) curves are reduced significantly (p &lt; 0.01).Conclusion: The present study shows that elevated heart rates, sympathetic activation, and reduced overall complexity were observed in healthy subjects exposed to a hypobaric and hypoxic environment. Moreover, the results indicated that Multiscale Entropy (MSE) analysis of RR interval series could characterize the degree of minor physiological changes. This novel index of HRV can better explain changes in the human ANS.

scholarly journals Prevalence of Delirium and Its Clinical Outcome in Adult Filipino Patients Admitted in The Intensive Care Unit

2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Abram P Tanuatmadja ◽  
Jacqueline R Vea
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Scoring Method ◽  
Icu Admission ◽  
Icu Stay ◽  
Manpower Requirement ◽  
Tertiary Teaching ◽  
Metro Manila ◽  
Tertiary Teaching Hospital ◽  
Dsm Iv

Delirium is common in the ICU setting and is associated with increased morbidity, manpower requirement, and costs. This study aims to investigate the prevalence of delirium and its outcome in terms of 14-days mortality and length of ICU stay in ICU patients. The study was done at a 150-bed tertiary teaching hospital, located in Quezon City, Metro Manila, February to September 2016. This is a prospective studyinvolving 136 adults. Screening for delirium was done within 24 hours of ICU admission using both CAM-ICU scoring method and DSM-IV-TR criteria for delirium. Delirium prevalence was found to be 5.15%. The average age was higher in the subjects positive for delirium (70.14 + 21.15 years versus 60.43 + 16.10 years, p=0.1286). At the time of ICU admission, 11.54% of sedated patients were positive for delirium compared to 3.64% of non-sedated patients, p=0.1513 ;OR 3.457. Delirium was associated with higher 14 days mortality (OR 16.8, p=0.0212). Subjects positive for delirium had 2.74 longer days average ICU stay compared to the other group, with p=0.026. We concluded delirium was associated with higher 14-days mortality and longer ICU stay. Keywords : delirium, prevalence, Intensive Care Unit

Monitoring Item Performance With CUSUM Statistics in Continuous Testing

2021 ◽  
pp. 107699862199456
Author(s):  
Yi-Hsuan Lee ◽  
Charles Lewis
Keyword(s):  
Group Size ◽  
Estimation Error ◽  
Ability Estimation ◽  
Cumulative Sum ◽  
Continuous Testing ◽  
Item Parameters ◽  
Analytical Work ◽  
Educational Assessments ◽  
Over Time ◽  
Item Preknowledge

In many educational assessments, items are reused in different administrations throughout the life of the assessments. Ideally, a reused item should perform relatively similarly over time. In reality, an item may become easier with exposure, especially when item preknowledge has occurred. This article presents a novel cumulative sum procedure for detecting item preknowledge in continuous testing where data for each reused item may be obtained from small and varying sample sizes across administrations. Its performance is evaluated with simulations and analytical work. The approach is effective in detecting item preknowledge quickly with group size at least 10 and is easy to implement with varying item parameters. In addition, it is robust to the ability estimation error introduced in the simulations.

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