REcognizing DElirium in geriatric Emergency Medicine: The REDEEM Risk Stratification Score

2021 ◽  
Author(s):  
Lucas Oliveira J. e Silva ◽  
Jessica Stanich ◽  
Molly M. Jeffery ◽  
Aidan Mullan ◽  
Susan Bower ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Risk Stratification ◽  
Geriatric Emergency Medicine ◽  
Risk Stratification Score

Geriatric Emergency Medicine

2013 ◽  
Author(s):  
Joseph H. Kahn ◽  
Jonathan S. Olshaker
Keyword(s):  
Emergency Medicine ◽  
Geriatric Emergency Medicine

scholarly journals Risk stratification scores for hospitalization duration and disease progression in moderate and severe patients with COVID-19

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiaqi Huang ◽  
Yu Xu ◽  
Bin Wang ◽  
Ying Xiang ◽  
Na Wu ◽  
...  
Keyword(s):  
Platelet Count ◽  
Lactate Dehydrogenase ◽  
Risk Stratification ◽  
Disease Progression ◽  
Risk Score ◽  
Healthcare Providers ◽  
Maximum Body ◽  
Score Systems ◽  
Risk Categories ◽  
Risk Stratification Score

Abstract Background During outbreak of Coronavirus Disease 2019 (COVID-19), healthcare providers are facing critical clinical decisions based on the prognosis of patients. Decision support tools of risk stratification are needed to predict outcomes in patients with different clinical types of COVID-19. Methods This retrospective cohort study recruited 2425 patients with moderate or severe COVID-19. A logistic regression model was used to select and estimate the factors independently associated with outcomes. Simplified risk stratification score systems were constructed to predict outcomes in moderate and severe patients with COVID-19, and their performances were evaluated by discrimination and calibration. Results We constructed two risk stratification score systems, named as STPCAL (including significant factors in the prediction model: number of clinical symptoms, the maximum body temperature during hospitalization, platelet count, C-reactive protein, albumin and lactate dehydrogenase) and TRPNCLP (including maximum body temperature during hospitalization, history of respiratory diseases, platelet count, neutrophil-to-lymphocyte ratio, creatinine, lactate dehydrogenase, and prothrombin time), to predict hospitalization duration for moderate patients and disease progression for severe patients, respectively. According to STPCAL score, moderate patients were classified into three risk categories for a longer hospital duration: low (Score 0–1, median = 8 days, with less than 20.0% probabilities), intermediate (Score 2–6, median = 13 days, with 30.0–78.9% probabilities), high (Score 7–9, median = 19 days, with more than 86.5% probabilities). Severe patients were stratified into three risk categories for disease progression: low risk (Score 0–5, with less than 12.7% probabilities), intermediate risk (Score 6–11, with 18.6–69.1% probabilities), and high risk (Score 12–16, with more than 77.9% probabilities) by TRPNCLP score. The two risk scores performed well with good discrimination and calibration. Conclusions Two easy-to-use risk stratification score systems were built to predict the outcomes in COVID-19 patients with different clinical types. Identifying high risk patients with longer stay or poor prognosis could assist healthcare providers in triaging patients when allocating limited healthcare during COVID-19 outbreak.

Neonatal acute kidney injury risk stratification score: STARZ study

2021 ◽  
Author(s):  
Sanjay Wazir ◽  
Sidharth Kumar Sethi ◽  
Gopal Agarwal ◽  
Abhishek Tibrewal ◽  
Rohan Dhir ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Risk Stratification ◽  
Injury Risk ◽  
Kidney Injury ◽  
Risk Stratification Score

scholarly journals Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Verena Schöning ◽  
Evangelia Liakoni ◽  
Christine Baumgartner ◽  
Aristomenis K. Exadaktylos ◽  
Wolf E. Hautz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Clinical Outcomes ◽  
Predictive Value ◽  
Validation Cohort ◽  
Tertiary Hospital ◽  
Learning Models ◽  
Clinical Risk ◽  
Machine Learning Models ◽  
Risk Stratification Score

Abstract Background Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. Methods In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58). Conclusion With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.

Validation of the STARZ neonatal acute kidney injury risk stratification score

2022 ◽  
Author(s):  
Sidharth Kumar Sethi ◽  
Rupesh Raina ◽  
Abhyuday Rana ◽  
Gopal Agrawal ◽  
Abhishek Tibrewal ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Risk Stratification ◽  
Injury Risk ◽  
Kidney Injury ◽  
Risk Stratification Score

scholarly journals 111DEVELOPING A EUROPEAN GERIATRIC EMERGENCY MEDICINE CURRICULUM

2017 ◽  
Vol 46 (suppl_1) ◽  
pp. i28-i30
Author(s):  
S Turpin ◽  
S Conroy
Keyword(s):  
Emergency Medicine ◽  
Geriatric Emergency Medicine

scholarly journals The development of a European curriculum in Geriatric Emergency Medicine

2016 ◽  
Vol 7 (4) ◽  
pp. 315-321 ◽  
Author(s):  
S. Conroy ◽  
C.H. Nickel ◽  
A.B. Jónsdóttir ◽  
M. Fernandez ◽  
J. Banerjee ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Geriatric Emergency Medicine
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