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 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 The significance of National Early Warning Score for predicting prognosis and evaluating conditions of patients in resuscitation room

2018 ◽  
Vol 25 (6) ◽  
pp. 324-330 ◽  
Author(s):  
Wang Chang Yuan ◽  
Cao Tao ◽  
Zhu Dan Dan ◽  
Sun Chang Yi ◽  
Wang Jing ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Early Warning ◽  
Chronic Health Evaluation ◽  
Early Warning Score ◽  
Health Evaluation ◽  
Chronic Health ◽  
Resuscitation Room ◽  
Receiver Operating Characteristic Curves ◽  
National Early Warning Score

Background: For critical patients in resuscitation room, the early prediction of potential risk and rapid evaluation of disease progression would help physicians with timely treatment, leading to improved outcome. In this study, it focused on the application of National Early Warning Score on predicting prognosis and conditions of patients in resuscitation room. The National Early Warning Score was compared with the Modified Early Warning Score) and the Acute Physiology and Chronic Health Evaluation II. Objectives: To assess the significance of NEWS for predicting prognosis and evaluating conditions of patients in resuscitation rooms. Methods: A total of 621 consecutive cases from resuscitation room of Xuanwu Hospital, Capital Medical University were included during June 2015 to January 2016. All cases were prospectively evaluated with Modified Early Warning Score, National Early Warning Score, and Acute Physiology and Chronic Health Evaluation II and then followed up for 28 days. For the prognosis prediction, the cases were divided into death group and survival group. The Modified Early Warning Score, National Early Warning Score, and Acute Physiology and Chronic Health Evaluation II results of the two groups were compared. In addition, receiver operating characteristic curves were plotted. The areas under the receiver operating characteristic curves were calculated for assessing and predicting intensive care unit admission and 28-day mortality. Results: For the prognosis prediction, in death group, the National Early Warning Score (9.50 ± 3.08), Modified Early Warning Score (4.87 ± 2.49), and Acute Physiology and Chronic Health Evaluation II score (23.29 ± 5.31) were significantly higher than National Early Warning Score (5.29 ± 3.13), Modified Early Warning Score (3.02 ± 1.93), and Acute Physiology and Chronic Health Evaluation II score (13.22 ± 6.39) in survival group ( p < 0.01). For the disease progression evaluation, the areas under the receiver operating characteristic curves of National Early Warning Score, Modified Early Warning Score, and Acute Physiology and Chronic Health Evaluation II were 0.760, 0.729, and 0.817 ( p < 0.05), respectively, for predicting intensive care unit admission; they were 0.827, 0.723, and 0.883, respectively, for predicting 28-day mortality. The comparison of the three systems was significant ( p < 0.05). Conclusion: The performance of National Early Warning Score for predicting intensive care unit admission and 28-day mortality was inferior than Acute Physiology and Chronic Health Evaluation II but superior than Modified Early Warning Score. It was able to rapidly predict prognosis and evaluate disease progression of critical patients in resuscitation room.

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 and validation of risk stratification trees for incident slow gait speed in persons at high risk for knee osteoarthritis

2019 ◽  
Vol 78 (10) ◽  
pp. 1412-1419 ◽  
Author(s):  
Leena Sharma ◽  
Kent Kwoh ◽  
Jungwha (Julia) Lee ◽  
Jane Cauley ◽  
Rebecca Jackson ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Knee Osteoarthritis ◽  
Gait Speed ◽  
Validation Cohort ◽  
Risk Group ◽  
Risk Groups ◽  
Classification And Regression Tree ◽  
Predictive Values ◽  
Derivation Cohort

ObjectivesDisability prevention strategies are more achievable before osteoarthritis disease drives impairment. It is critical to identify high-risk groups, for strategy implementation and trial eligibility. An established measure, gait speed is associated with disability and mortality. We sought to develop and validate risk stratification trees for incident slow gait in persons at high risk for knee osteoarthritis, feasible in community and clinical settings.MethodsOsteoarthritis Initiative (derivation cohort) and Multicenter Osteoarthritis Study (validation cohort) participants at high risk for knee osteoarthritis were included. Outcome was incident slow gait over up to 10-year follow-up. Derivation cohort classification and regression tree analysis identified predictors from easily assessed variables and developed risk stratification models, then applied to the validation cohort. Logistic regression compared risk group predictive values; area under the receiver operating characteristic curves (AUCs) summarised discrimination ability.Results1870 (derivation) and 1279 (validation) persons were included. The most parsimonious tree identified three risk groups, from stratification based on age and WOMAC Function. A 7-risk-group tree also included education, strenuous sport/recreational activity, obesity and depressive symptoms; outcome occurred in 11%, varying 0%–29 % (derivation) and 2%–23 % (validation) depending on risk group. AUCs were comparable in the two cohorts (7-risk-group tree, 0.75, 95% CI 0.72 to 0.78 (derivation); 0.72, 95% CI 0.68 to 0.76 (validation)).ConclusionsIn persons at high risk for knee osteoarthritis, easily acquired data can be used to identify those at high risk of incident functional impairment. Outcome risk varied greatly depending on tree-based risk group membership. These trees can inform individual awareness of risk for impaired function and define eligibility for prevention trials.

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