Clinical Early Warning Scores: New Clinical Tools in Evolution

Doyle, D. John

doi:10.2174/2589645801812010026

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…We obtained demographics, clinical data, comorbidities, time of disease onset and length of stay. The National Early Warning Score (NEWS) obtained from the emergency department (ED) [ 9 ] was used to evaluate baseline illness severity based on vital signs (systolic blood pressure, oxygen saturation, respiratory rate, heart rate, temperature and level of consciousness).…”

Section: Methodsmentioning

confidence: 99%

Baricitinib improves respiratory function in patients treated with corticosteroids for SARS-CoV-2 pneumonia: an observational cohort study

et al. 2020

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Objectives The Janus kinase (JAK) inhibitor baricitinib may block viral entry into pneumocytes and prevent cytokine storm in patients with SARS-CoV-2 pneumonia. We aimed to assess whether baricitinib improved pulmonary function in patients treated with high-dose corticosteroids for moderate to severe SARS-CoV-2 pneumonia. Methods This observational study enrolled patients with moderate to severe SARS-CoV-2 pneumonia [arterial oxygen partial pressure (PaO2)/fraction of inspired oxygen (FiO2) <200 mmHg] who received lopinavir/ritonavir and HCQ plus either corticosteroids (CS group, n = 50) or corticosteroids and baricitinib (BCT-CS group, n = 62). The primary end point was the change in oxygen saturation as measured by pulse oximetry (SpO2)/FiO2 from hospitalization to discharge. Secondary end points included the proportion of patients requiring supplemental oxygen at discharge and 1 month later. Statistics were adjusted by the inverse propensity score weighting (IPSW). Results A greater improvement in SpO2/FiO2 from hospitalization to discharge was observed in the BCT-CS vs CS group (mean differences adjusted for IPSW, 49; 95% CI: 22, 77; P < 0.001). A higher proportion of patients required supplemental oxygen both at discharge (62.0% vs 25.8%; reduction of the risk by 82%, OR adjusted for IPSW, 0.18; 95% CI: 0.08, 0.43; P < 0.001) and 1 month later (28.0% vs 12.9%, reduction of the risk by 69%, OR adjusted for IPSW, 0.31; 95% CI: 0.11, 0.86; P = 0.024) in the CS vs BCT-CS group. Conclusions . In patients with moderate to severe SARS-CoV-2 pneumonia a combination of baricitinib with corticosteroids was associated with greater improvement in pulmonary function when compared with corticosteroids alone. Trial registration European Network of Centres for Pharmacoepidemiology and Pharmacovigilance, ENCEPP (EUPAS34966, http://www.encepp.eu/encepp/viewResource.htm? id = 34967)

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Section: Methodsmentioning

confidence: 99%

Baricitinib improves respiratory function in patients treated with corticosteroids for SARS-CoV-2 pneumonia: an observational cohort study

et al. 2020

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“…An important issue is that even the best early warning systems (NEWS2, MEWS and PEWS) depend on a particular clinical population (surgical, pediatric, trauma, pre-hospital etc.) [15], staff training, workload, and resources [16].…”

Section: A Early Warning Scorementioning

confidence: 99%

A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals

Kobylarz,

Santos,

Barletta

et al. 2020

Preprint

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Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. The challenging task of clinical deterioration identification in hospitals lies in the intense daily routines of healthcare practitioners, in the unconnected patient data stored in the Electronic Health Records (EHRs) and in the usage of low accuracy scores. Since hospital wards are given less attention compared to the Intensive Care Unit, ICU, we hypothesized that when a platform is connected to a stream of EHR, there would be a drastic improvement in dangerous situations awareness and could thus assist the healthcare team. With the application of machine learning, the system is capable to consider all patient's history and through the use of high-performing predictive models, an intelligent early warning system is enabled. In this work we used 121,089 medical encounters from six different hospitals and 7,540,389 data points, and we compared popular ward protocols with six different scalable machine learning methods (three are classic machine learning models, logistic and probabilistic-based models, and three gradient boosted models). The results showed an advantage in AUC (Area Under the Receiver Operating Characteristic Curve) of 25 percentage points in the best Machine Learning model result compared to the current state-of-the-art protocols. This is shown by the generalization of the algorithm with leave-one-group-out (AUC of 0.949) and the robustness through cross-validation (AUC of 0.961). We also perform experiments to compare several window sizes to justify the use of five patient timestamps. A sample dataset 1 , experiments, and code are available for replicability purposes.

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“…Four principal vital signs serve to establish an early warning score, namely the body temperature, blood pressure, heart rate (RR) and respiratory rate (RR). This score is the gold standard when it comes to quantifying the degree of illness of patients [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

A Spectral-Based Approach for BCG Signal Content Classification

Nasr

Jebara

Otis³

et al. 2021

Sensors

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This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%.

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Clinical Early Warning Scores: New Clinical Tools in Evolution

Cited by 14 publications

References 22 publications

Baricitinib improves respiratory function in patients treated with corticosteroids for SARS-CoV-2 pneumonia: an observational cohort study

Baricitinib improves respiratory function in patients treated with corticosteroids for SARS-CoV-2 pneumonia: an observational cohort study

A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals

A Spectral-Based Approach for BCG Signal Content Classification

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