Diagnostic Accuracy of Respiratory Distress Observation Scales as Surrogates of Dyspnea Self-report in Intensive Care Unit Patients

2015 ◽  
Vol 123 (4) ◽  
pp. 830-837 ◽  
Author(s):  
Romain Persichini ◽  
Frédérick Gay ◽  
Matthieu Schmidt ◽  
Julien Mayaux ◽  
Alexandre Demoule ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Intensive Care ◽  
Facial Expression ◽  
Respiratory Distress ◽  
Validation Cohort ◽  
Self Report ◽  
Neck Muscle ◽  
Icu Patients ◽  
Derivation Cohort

Abstract Background: Dyspnea, like pain, can cause major suffering in intensive care unit (ICU) patients. Its evaluation relies on self-report; hence, the risk of being overlooked when verbal communication is impaired. Observation scales incorporating respiratory and behavioral signs (respiratory distress observation scales [RDOS]) can provide surrogates of dyspnea self-report in similar clinical contexts (palliative care). Methods: The authors prospectively studied (single center, 16-bed ICU, large university hospital) 220 communicating ICU patients (derivation cohort, 120 patients; separate validation cohort, 100 patients). Dyspnea was assessed by dyspnea visual analog scale (D-VAS) and RDOS calculated from its eight components (heart rate, respiratory rate, nonpurposeful movements, neck muscle use during inspiration, abdominal paradox, end-expiratory grunting, nasal flaring, and facial expression of fear). An iterative principal component analysis and partial least square regression process aimed at identifying an optimized D-VAS correlate (intensive care RDOS [IC-RDOS]). Results: In the derivation cohort, RDOS significantly correlated with D-VAS (r = 0.43; 95% CI, 0.29 to 0.58). A five-item IC-RDOS (heart rate, neck muscle use during inspiration, abdominal paradox, facial expression of fear, and supplemental oxygen) significantly better correlated with D-VAS (r = 0.61; 95% CI, 0.50 to 0.72). The median area under the receiver operating curve of IC-RDOS to predict D-VAS was 0.83 (interquartile range, 0.81 to 0.84). An IC-RDOS of 2.4 predicted D-VAS of 4 or greater with equal sensitivity and specificity (72%); an IC-RDOS of 6.3 predicted D-VAS of 4 or greater with 100% specificity. Similar results were found in the validation cohort. Conclusions: Combinations of observable signs correlate with dyspnea in communicating ICU patients. Future studies in noncommunicating patients will be needed to determine the responsiveness to therapeutic interventions and clinical usefulness.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

scholarly journals Anxiety and depression symptoms in family members of icu patients

2015 ◽  
Vol 33 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Maria Kourti ◽  
Efstathia Christofilou ◽  
George Kallergis
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Demographic Data ◽  
Family Members ◽  
Depression Scale ◽  
Self Report ◽  
Anxiety And Depression ◽  
Depression Symptoms ◽  
Hospital Anxiety ◽  
Icu Patients

<p><strong>Objective:</strong> This study investigated symptoms of anxiety and depression in relatives of patients admitted in the Intensive Care Unit and determined whether these symptoms were associated to the seriousness of the patients’ condition.</p><p><strong>Metodology:</strong> A total of 102 patients’ relatives were surveyed<br />during the study. They were given a self-report questionnaire in order to assess demographic data, anxiety and depression symptoms. The symptoms of anxiety and depression were evaluated with the Hospital Anxiety and Depression Scale (hads). Patient’s condition was evaluated with a.p.a.ch.e ii Score.</p><p><strong>Results:</strong> More than 60% of patients’ relatives presented severe symptoms of anxiety and depression. No relation was found between symptoms of anxiety and depression of the relatives of patients and patients’ condition of health. On the<br />contrary, these feelings used to exist regardless of the seriousness of patient’s condition.</p><p><strong>Conclusions:</strong> The assessment of these patients is recommended in order serious problems of anxiety<br />and depression to be prevented. </p>

scholarly journals Renal dysfunction decreases specificity and overturns cutoff value of serum CC16 while prohibiting 7-day mortality prediction in diagnosing acute respiratory distress syndrome in intensive care unit.

2020 ◽  
Author(s):  
Jinle Lin ◽  
Wuyuan Tao ◽  
Jian Wei ◽  
Wu Jian ◽  
Wenwu Zhang ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Acute Respiratory Distress Syndrome ◽  
Intensive Care ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Renal Dysfunction ◽  
Predictive Value ◽  
Distress Syndrome ◽  
Icu Patients ◽  
Cutoff Value

Abstract Background: A contradictory tendency between occurrence of acute respiratory distress syndrome (ARDS) and serum club cell protein 16 (CC16) level, However, renal dysfunction (RD) separately raised serum CC16 in our current observation. The purpose of this study was to find the limitation caused by renal dysfunction in the diagnostic performance of CC16 on ARDS in intensive care unit (ICU) patients. Method: We measured serum CC16 in 479 ICU patients. Patients were divided into six subgroups: control, acute kidney injury (AKI), chronic kidney dysfunction (CKD), ARDS, ARDS+AKI, and ARDS+CKD. The cutoff value, sensitivity and specificity of serum CC16 were assessed by receiver operating characteristic curves. Result: Serum CC16 increased among the ARDS group when compared to the control group, which helps identify ARDS and predicts the outcome in patients with normal renal function. However, level of serum CC16 was similar among ARDS+AKI, ARDS+CKD, AIK and CKD groups. Consequently, when compare to AKI and CKD, specificity for diagnosing whether ARDS or ARDS with renal failure decreased from 86.62% to 2.82% or 81.70% to 2.12%. Consistently, a cutoff value of 11.57 ng/mL was overturned from previously at 32.77 ng/mL or 33.72 ng/mL. Moreover, its predictive value for mortality is prohibited before 7 day but works after 28 day. Conclusion: Renal dysfunction limits the specificity, cutoff point, and predictive value at 7-day mortality of CC16 in diagnosing ARDS among ICU patients.

scholarly journals Application of a 72 h National Early Warning Score and Incorporation with Sequential Organ Failure Assessment for Predicting Sepsis Outcomes and Risk Stratification in an Intensive Care Unit: A Derivation and Validation Cohort Study

2021 ◽  
Vol 11 (9) ◽  
pp. 910
Author(s):  
Chih-Yi Hsu ◽  
Yi-Hsuan Tsai ◽  
Chiung-Yu Lin ◽  
Ya-Chun Chang ◽  
Hung-Cheng Chen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Severe Sepsis ◽  
Intensive Care ◽  
Risk Stratification ◽  
Validation Cohort ◽  
Risk Group ◽  
Early Warning Score ◽  
Derivation Cohort ◽  
Failure Assessment ◽  
National Early Warning Score

We investigated the best timing for using the National Early Warning Score 2 (NEWS2) for predicting sepsis outcomes and whether combining the NEWS2 and the Sequential Organ Failure Assessment (SOFA) was applicable for mortality risk stratification in intensive care unit (ICU) patients with severe sepsis. All adult patients who met the Third International Consensus Definitions for Sepsis and Septic Shock criteria between August 2013 and January 2017 with complete clinical parameters and laboratory data were enrolled as a derivation cohort. The primary outcomes were the 7-, 14-, 21-, and 28-day mortalities. Furthermore, another group of patients under the same setting between January 2020 and March 2020 were also enrolled as a validation cohort. In the derivation cohort, we included 699 consecutive adult patients. The 72 h NEWS2 had good discrimination for predicting 7-, 14-, 21-, and 28-day mortalities (AUC: 0.780, 0.724, 0.700, and 0.667, respectively) and was not inferior to the SOFA (AUC: 0.740, 0.680, 0.684, and 0.677, respectively). With the new combined NESO tool, the hazard ratio was 1.854 (1.203–2.950) for the intermediate-risk group and 6.810 (3.927–11.811) for the high-risk group relative to the low-risk group. This finding was confirmed in the validation cohort using a separated survival curve for 28-day mortality. The 72 h NEWS2 alone was non-inferior to the admission SOFA or day 3 SOFA for predicting sepsis outcomes. The NESO tool was found to be useful for 7-, 14-, 21-, and 28-day mortality risk stratification in patients with severe sepsis.

scholarly journals Heart Rate and Oxygen Saturation Monitoring With a New Wearable Wireless Device in the Intensive Care Unit: Pilot Comparison Trial (Preprint)

2020 ◽  
Author(s):  
Srinivasan Murali ◽  
Francisco Rincon ◽  
Tiziano Cassina ◽  
Stephane Cook ◽  
Jean-Jacques Goy
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Intensive Care ◽  
Oxygen Saturation ◽  
Cardiac Monitoring ◽  
Data Loss ◽  
Clinical Value ◽  
Icu Patients ◽  
Conduction Disturbances ◽  
Wireless Biosensor

BACKGROUND Continuous cardiac monitoring with wireless sensors is an attractive option for early detection of arrhythmia and conduction disturbances and the prevention of adverse events leading to patient deterioration. We present a new sensor design (SmartCardia), a wearable wireless biosensor patch, for continuous cardiac and oxygen saturation (SpO<sub>2</sub>) monitoring. OBJECTIVE This study aimed to test the clinical value of a new wireless sensor device (SmartCardia) and its usefulness in monitoring the heart rate (HR) and SpO<sub>2</sub> of patients. METHODS We performed an observational study and monitored the HR and SpO<sub>2</sub> of patients admitted to the intensive care unit (ICU). We compared the device under test (SmartCardia) with the ICU-grade monitoring system (Dräger-Healthcare). We defined optimal correlation between the gold standard and the wireless system as &lt;10% difference for HR and &lt;4% difference for SpO<sub>2</sub>. Data loss and discrepancy between the two systems were critically analyzed. RESULTS A total of 58 ICU patients (42 men and 16 women), with a mean age of 71 years (SD 11), were included in this study. A total of 13.49 (SD 5.53) hours per patient were recorded. This represents a total recorded period of 782.3 hours. The mean difference between the HR detected by the SmartCardia patch and the ICU monitor was 5.87 (SD 16.01) beats per minute (bias=–5.66, SD 16.09). For SpO<sub>2</sub>, the average difference was 3.54% (SD 3.86; bias=2.9, SD 4.36) for interpretable values. SmartCardia’s patch measures SpO<sub>2</sub> only under low-to-no activity conditions and otherwise does not report a value. Data loss and noninterpretable values of SpO<sub>2</sub> represented 26% (SD 24) of total measurements. CONCLUSIONS The SmartCardia device demonstrated clinically acceptable accuracy for HR and SpO<sub>2</sub> monitoring in ICU patients.

scholarly journals Early Improvement of Acute Respiratory Distress Syndrome in Patients With COVID-19 in the Intensive Care Unit: Retrospective Analysis

10.2196/24843 ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. e24843
Author(s):  
Zhu Zhan ◽  
Xin Yang ◽  
Hu Du ◽  
Chuanlai Zhang ◽  
Yuyan Song ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Acute Respiratory Distress Syndrome ◽  
Intensive Care ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Icu Patients ◽  
Early Improvement ◽  
Icu Stay ◽  
Arterial Blood

Background Since the start of the COVID-19 pandemic, there have been over 2 million deaths globally. Acute respiratory distress syndrome (ARDS) may be the main cause of death. Objective This study aimed to describe the clinical features, outcomes, and ARDS characteristics of patients with COVID-19 admitted to the intensive care unit (ICU) in Chongqing, China. Methods The epidemiology of COVID-19 from January 21, 2020, to March 15, 2020, in Chongqing, China, was analyzed retrospectively, and 75 ICU patients from two hospitals were included in this study. On day 1, 56 patients with ARDS were selected for subgroup analysis, and a modified Poisson regression was performed to identify predictors for the early improvement of ARDS (eiARDS). Results Chongqing reported a 5.3% case fatality rate for the 75 ICU patients. The median age of these patients was 57 (IQR 25-75) years, and no bias was present in the sex ratio. A total of 93% (n=70) of patients developed ARDS during ICU stay, and more than half had moderate ARDS. However, most patients (n=41, 55%) underwent high-flow nasal cannula oxygen therapy, but not mechanical ventilation. Nearly one-third of patients with ARDS improved (arterial blood oxygen partial pressure/oxygen concentration >300 mm Hg) in 1 week, which was defined as eiARDS. Patients with eiARDS had a higher survival rate and a shorter length of ICU stay than those without eiARDS. Age (<55 years) was the only variable independently associated with eiARDS, with a risk ratio of 2.67 (95% CI 1.17-6.08). Conclusions A new subphenotype of ARDS—eiARDS—in patients with COVID-19 was identified. As clinical outcomes differ, the stratified management of patients based on eiARDS or age is highly recommended.

scholarly journals Heart Rate and Oxygen Saturation Monitoring With a New Wearable Wireless Device in the Intensive Care Unit: Pilot Comparison Trial

10.2196/18158 ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. e18158
Author(s):  
Srinivasan Murali ◽  
Francisco Rincon ◽  
Tiziano Cassina ◽  
Stephane Cook ◽  
Jean-Jacques Goy
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Intensive Care ◽  
Oxygen Saturation ◽  
Cardiac Monitoring ◽  
Data Loss ◽  
Clinical Value ◽  
Icu Patients ◽  
Conduction Disturbances ◽  
Wireless Biosensor

Background Continuous cardiac monitoring with wireless sensors is an attractive option for early detection of arrhythmia and conduction disturbances and the prevention of adverse events leading to patient deterioration. We present a new sensor design (SmartCardia), a wearable wireless biosensor patch, for continuous cardiac and oxygen saturation (SpO2) monitoring. Objective This study aimed to test the clinical value of a new wireless sensor device (SmartCardia) and its usefulness in monitoring the heart rate (HR) and SpO2 of patients. Methods We performed an observational study and monitored the HR and SpO2 of patients admitted to the intensive care unit (ICU). We compared the device under test (SmartCardia) with the ICU-grade monitoring system (Dräger-Healthcare). We defined optimal correlation between the gold standard and the wireless system as <10% difference for HR and <4% difference for SpO2. Data loss and discrepancy between the two systems were critically analyzed. Results A total of 58 ICU patients (42 men and 16 women), with a mean age of 71 years (SD 11), were included in this study. A total of 13.49 (SD 5.53) hours per patient were recorded. This represents a total recorded period of 782.3 hours. The mean difference between the HR detected by the SmartCardia patch and the ICU monitor was 5.87 (SD 16.01) beats per minute (bias=–5.66, SD 16.09). For SpO2, the average difference was 3.54% (SD 3.86; bias=2.9, SD 4.36) for interpretable values. SmartCardia’s patch measures SpO2 only under low-to-no activity conditions and otherwise does not report a value. Data loss and noninterpretable values of SpO2 represented 26% (SD 24) of total measurements. Conclusions The SmartCardia device demonstrated clinically acceptable accuracy for HR and SpO2 monitoring in ICU patients.

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