scholarly journals Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score

2020 ◽  
Author(s):  
Stephen R Knight ◽  
Antonia Ho ◽  
Riinu Pius ◽  
Iain Buchan ◽  
Gail Carson ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Value ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
Clinical Decision ◽  
Validation Dataset ◽  
Peripheral Oxygen Saturation ◽  
Oxygen Saturation Level ◽  
Derivation Cohort ◽  
Risk Stratification Score

Objectives To develop and validate a pragmatic risk score to predict mortality for patients admitted to hospital with covid-19. Design Prospective observational cohort study: ISARIC WHO CCP-UK study (ISARIC Coronavirus Clinical Characterisation Consortium [4C]). Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited between 21 May and 29 June 2020. Setting 260 hospitals across England, Scotland, and Wales. Participants Adult patients (≥18 years) admitted to hospital with covid-19 admitted at least four weeks before final data extraction. Main outcome measures In-hospital mortality. Results There were 34 692 patients included in the derivation dataset (mortality rate 31.7%) and 22 454 in the validation dataset (mortality 31.5%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea, and C-reactive protein (score range 0-21 points). The 4C risk stratification score demonstrated high discrimination for mortality (derivation cohort: AUROC 0.79; 95% CI 0.78 - 0.79; validation cohort 0.78, 0.77-0.79) with excellent calibration (slope = 1.0). Patients with a score ≥15 (n = 2310, 17.4%) had a 67% mortality (i.e., positive predictive value 67%) compared with 1.0% mortality for those with a score ≤3 (n = 918, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (AUROC range 0.60-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). Conclusions We have developed and validated an easy-to-use risk stratification score based on commonly available parameters at hospital presentation. This outperformed existing scores, demonstrated utility to directly inform clinical decision making, and can be used to stratify inpatients with covid-19 into different management groups. The 4C Mortality Score may help clinicians identify patients with covid-19 at high risk of dying during current and subsequent waves of the pandemic. Study registration ISRCTN66726260

P1858Meta-analysis of performance of risks scores for predicting outcomes after percutaneous Mitraclip implantation

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
T K M Wang ◽  
M T M Wang
Keyword(s):  
Confidence Interval ◽  
Risk Stratification ◽  
Clinical Decision Making ◽  
Operative Mortality ◽  
Data Extraction ◽  
Meta Analysis ◽  
Critical Role ◽  
Clinical Decision ◽  
Risk Scores ◽  
Sts Score

Abstract Background The Mitraclip is the most established percutaneous mitral valve intervention indicated for severe mitral regurgitation at high or prohibitive surgical risk. Risk stratification plays a critical role in selecting the appropriate treatment modality in high risk valve disease patients but have been rarely studied in this setting. We compared the performance of risk scores at predicting mortality after Mitraclip in this meta-analysis. Methods MEDLINE, Embase and Cochrane databases from 1 January 1980 to 31 December 2018 were searched. Two authors reviewed studies which reported c-statistics of risk models to predict mortality after Mitraclip for inclusion, followed by data extraction and pooled analyses. Results Amongst 494 articles searched, 47 full-text articles were evaluated, and 4 studies totalling 879 Mitraclip cases were included for analyses. Operative mortality was reported at 3.3–7.4% in three studies, and 1-year mortality 11.2%-13.5% in two studies. C-statistics (95% confidence interval) for operative mortality were EuroSCORE 0.66 (0.57–0.75), EuroSCORE II 0.72 (0.60–0.85) and STS Score 0.64 (0.56–0.73). Respective Peto's odds ratios (95% confidence interval) to assess calibration were EuroSCORE 0.21 (0.14–0.31), EuroSCORE II 0.43 (0.24–0.76) and STS Score 0.36 (0.21–0.61). C-statistics (95% confidence interval) for 1-year mortality were EuroSCORE II 0.64 (0.57–0.70) and STS Score (0.58–0.64). Conclusion All scores over-estimated operative mortality, and EuroSCORE II had the best moderate discrimination while the other two scores were only modestly prognostic. Development of Mitraclip-specific scores may improve accuracy of risk stratification for this procedure to guide clinical decision-making.

scholarly journals A multicentre development and validation study of a novel lower gastrointestinal bleeding score—The Birmingham Score

2019 ◽  
Vol 35 (2) ◽  
pp. 285-293 ◽  
Author(s):  
Samuel C. L. Smith ◽  
Alina Bazarova ◽  
Efe Ejenavi ◽  
Maria Qurashi ◽  
Uday N. Shivaji ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Retrospective Review ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Lower Gastrointestinal Bleeding ◽  
Adverse Outcomes ◽  
Derivation Cohort ◽  
Risk Stratification Tool ◽  
Risk Stratification Score

Abstract Purpose Lower gastrointestinal bleeding (LGIB) is common and risk stratification scores can guide clinical decision-making. There is no robust risk stratification tool specific for LGIB, with existing tools not routinely adopted. We aimed to develop and validate a risk stratification tool for LGIB. Methods Retrospective review of LGIB admissions to three centres between 2010 and 2018 formed the derivation cohort. Using regressional analysis within a machine learning technique, risk factors for adverse outcomes were identified, forming a simple risk stratification score—The Birmingham Score. Retrospective review of an additional centre, not included in the derivation cohort, was performed to validate the score. Results Data from 469 patients were included in the derivation cohort and 180 in the validation cohort. Admission haemoglobin OR 1.07(95% CI 1.06–1.08) and male gender OR 2.29(95% CI 1.40–3.77) predicted adverse outcomes in the derivation cohort AUC 0.86(95% CI 0.82–0.90) which outperformed the Blatchford 0.81(95% CI 0.77–0.85), Rockall 0.60(95% CI 0.55–0.65) and AIM65 0.55(0.50–0.60) scores and in the validation cohort AUC 0.80(95% CI 0.73–0.87) which outperformed the Blatchford 0.77(95% CI 0.70–0.85), Rockall 0.67(95% CI 0.59–0.75) and AIM 65 scores 0.61(95% CI 0.53–0.69). The Birmingham Score also performs well at predicting adverse outcomes from diverticular bleeding AUC 0.87 (95% CI 0.75–0.98). A score of 7 predicts a 94% probability of adverse outcome. Conclusion The Birmingham Score represents a simple risk stratification score that can be used promptly on patients admitted with LGIB.

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.

Abstract W P266: A Simple Score for Prediction of Outcome after Cerebral Venous Thrombosis

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Miguel A Barboza ◽  
Erwin Chiquete ◽  
Antonio Arauz ◽  
Jonathan Colín ◽  
Alejandro Quiroz-Compean ◽  
...  
Keyword(s):  
Decision Making ◽  
Venous Thrombosis ◽  
Predictive Value ◽  
Cerebral Venous Thrombosis ◽  
Goodness Of Fit ◽  
Clinical Decision Making ◽  
Case Fatality ◽  
Clinical Decision ◽  
Classification Systems ◽  
Cox Proportional Hazards Model

Background and purpose: Cerebral venous thrombosis (CVT) not always implies a good prognosis. There is a need for robust and simple classification systems of severity after CVT that help in clinical decision-making. Methods: We studied 467 patients (81.6% women, median age: 29 years, interquartile range: 22-38 years) with CVT who were hospitalized from 1980 to 2014 in two third-level referral hospitals. Bivariate analyses were performed to select variables associated with 30-day mortality to integrate a further multivariate analysis. The resultant model was evaluated with the Hosmer-Lemeshow test for goodness of fit, and on Cox proportional hazards model for reliability of the effect size. After the scale was configured, security and validity were tested for 30-day mortality and modified Rankin scale (mRS) >2. The prognostic performance was compared with that of the CVT risk score (CVT-RS, 0-6 points) as the reference system. Results: The 30-day case fatality rate was 8.7%. The CVT grading scale (CVT-GS, 0-9 points) was integrated by stupor/coma (4 points), parenchymal lesion >6 cm (2 points), mixed (superficial and deep systems) CVT (1 point), meningeal syndrome (1 point) and seizures (1 point). CVT-GS was categorized into mild (0-3 points, 1.1% mortality), moderate (4-6 points, 19.6% mortality) and severe (7-9 points, 61.4% mortality). For 30-day mortality prediction, as compared with CVT-RS (cut-off 4 points), CVT-GS (cut-off 5 points) was globally better in sensitivity (85% vs 37%), specificity (90% vs 95%), positive predictive value (44% vs 40%), negative predictive value (98% vs 94%), and accuracy (94% vs 80%). For 30-day mRS >2 the performance of CVT-GS over CVT-RS was comparably improved. Conclusion: The CVT-GS is a simple and reliable score for predicting outcome that may help in clinical decision-making and that could be used to stratify patients recruited into clinical trials.

scholarly journals Manipulative and Manual Therapies in the Management of Patients with Prior Lumbar Surgery: A Systematic Review

2020 ◽  
Author(s):  
Clinton J Daniels ◽  
Zachary A. Cupler ◽  
Jordan A Gliedt ◽  
Sheryl Walters ◽  
Alec L Schielke ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Clinical Decision Making ◽  
Case Reports ◽  
Data Extraction ◽  
Case Series ◽  
Clinical Decision ◽  
Manual Therapies ◽  
Lumbar Surgery ◽  
Pilot Studies ◽  
Surgical Interventions

Abstract BackgroundThe purpose was to identify, summarize, and rate scholarly literature that describes manipulative and manual therapy following lumbar surgery.MethodsThe review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and was registered with PROSPERO. PubMed, Cochrane Database of Systematic Reviews, ICL, CINAHL, and PEDro were searched through July 2019. Articles were screened independently by at least two reviewers for inclusion. Articles included described the practice, utilization, and/or clinical decision making to post surgical intervention with manipulative and/or manual therapies. Data extraction consisted of principal findings, pain and function/disability, patient satisfaction, opioid/medication consumption, and adverse events. Scottish Intercollegiate Guidelines Network critical appraisal checklists were utilized to assess study quality.ResultsLiterature search yielded 1916 articles, 348 duplicates were removed, 109 full-text articles were screened and 50 citations met inclusion criteria. There were 37 case reports/case series, 3 randomized controlled trials, 3 pilot studies, 5 systematic/scoping/narrative reviews, and 2 commentaries. ConclusionThe findings of this review may help inform practitioners who utilize manipulative and/or manual therapies regarding levels of evidence for patients with prior lumbar surgery. Following lumbar surgery, the evidence indicated inpatient neural mobilization does not improve outcomes. There is inconclusive evidence to recommend for or against most manual therapies after most surgical interventions.Trial registrationProspectively registered with PROSPERO (#CRD42020137314). Registered 24 January 2020.

scholarly journals Early Identification of Patients with Acute Gastrointestinal Bleeding using Electronic Health Record Phenotyping

2020 ◽  
Author(s):  
Dennis Shung ◽  
Cynthia Tsay ◽  
Loren Laine ◽  
Prem Thomas ◽  
Caitlin Partridge ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Real Time ◽  
Decision Rule ◽  
Language Processing ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Internal Validation ◽  
Electronic Health

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], P<0.001) and external validation (PPV=85% [84%-87%] vs. 69% [67%-71%], P<0.001). The NLP algorithm (external validation PPV=80% [79-82%]) was not superior to the structured-datafields decision rule.ConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

scholarly journals CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Tiansong Xie ◽  
Xuanyi Wang ◽  
Zehua Zhang ◽  
Zhengrong Zhou
Keyword(s):  
Predictive Value ◽  
Cross Validation ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Cystic Neoplasm ◽  
Cystic Neoplasms ◽  
Serous Cystadenomas ◽  
Radiological Model ◽  
Fold Cross Validation

ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

Likelihood Ratio, Skull Roentgenograms, and Clinical Decision-Making

PEDIATRICS ◽  
1983 ◽  
Vol 71 (4) ◽  
pp. 673-674
Author(s):  
JOHN C. LEONIDAS ◽  
ANNA BINKIEWICZ ◽  
R. MICHAEL SCOTT ◽  
STEPHEN G. PAUKER
Keyword(s):  
Decision Making ◽  
Likelihood Ratio ◽  
Predictive Value ◽  
Clinical Finding ◽  
Posterior Probability ◽  
Clinical Decision Making ◽  
Skull Fracture ◽  
Clinical Decision ◽  
Negative Finding ◽  
The Individual

In Reply.— We appreciate the thoughtful comments of Leventhal and Lembo and concur with their conclusion that the clinician needs to know "the probability of skull fracture in a patient with head trauma." Unfortunately, their proposed "clinical likelihood ratio" (CR) will not further that end because it compares the predictive value (or, more precisely, the posterior probability) of a skull fracture after a positive clinical finding to the posterior probability after a negative finding. After the patient has been examined, the patient does not have both findings; thus, the CR cannot apply to the individual patient.

scholarly journals A novel severity score to predict inpatient mortality in COVID-19 patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
David J. Altschul ◽  
Santiago R. Unda ◽  
Joshua Benton ◽  
Rafael de la Garza Ramos ◽  
Phillip Cezayirli ◽  
...  
Keyword(s):  
Severity Score ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Validation Cohort ◽  
Clinical Decision ◽  
International Normalized Ratio ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Derivation Cohort ◽  
Mortality Incidence

Abstract COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814–0.851) and an AUC of 0.798 (95% CI 0.789–0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0–3), moderate (4–6) and high (7–10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

scholarly journals P071: Artificial intelligence in emergency medicine: A scoping review

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S90-S90
Author(s):  
A. Kirubarajan ◽  
A. Taher ◽  
S. Khan ◽  
S. Masood
Keyword(s):  
Artificial Intelligence ◽  
Emergency Medicine ◽  
Language Processing ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
A Priori ◽  
Clinical Decision ◽  
Hospital Environment ◽  
Clinical Implementation

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.

Sign in / Sign up

Export Citation Format

Share Document