Spontaneous Retro Peritoneal Bleeding with COVID 19: Management Modalities and Mortality Predictors

2021 ◽  
Vol 14 (1) ◽  
pp. 57-65
Author(s):  
Mostafa Abdelrahman ◽  
Mohammed Gouda ◽  
Mahmoud Rayan ◽  
Mohamed Fouly
Keyword(s):  
Mortality Predictors

scholarly journals Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2020-216425
Author(s):  
Felix Chua ◽  
Rama Vancheeswaran ◽  
Adrian Draper ◽  
Tejal Vaghela ◽  
Matthew Knight ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Fatal Outcome ◽  
Individual Characteristics ◽  
Adverse Outcomes ◽  
Hospital Death ◽  
Outpatient Monitoring ◽  
Characteristic Values ◽  
Large Random Sample ◽  
Mortality Predictors

IntroductionRisk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking.MethodsMultivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290).Results983 patients (median age 70, IQR 53–83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS (SpO2, Obesity, Age, Respiratory rate, Stroke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0–1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%).ConclusionThe SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.

scholarly journals Exploring sex-specific patterns of mortality predictors among patients undergoing cardiac resynchronization therapy: a machine learning approach

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Tokodi ◽  
A Behon ◽  
E.D Merkel ◽  
A Kovacs ◽  
Z Toser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sex Differences ◽  
Cardiac Resynchronization Therapy ◽  
Cardiac Resynchronization ◽  
Funding Source ◽  
Resynchronization Therapy ◽  
Test Set ◽  
All Cause Mortality ◽  
Mortality Predictors

Abstract Background The relative importance of variables explaining sex differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). Purpose We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in patients undergoing CRT implantation. We also aimed to assess the sex-specific differences and similarities in the predictors of mortality using ML approaches. Methods A retrospective registry of 2191 CRT patients (75% males) was used in the current analysis. ML models were implemented in 6 partially overlapping patient subsets (all patients, females or males with 1- or 3-year follow-up data available). Each cohort was randomly split into a training (80%) and a test set (20%). After hyperparameter tuning with 10-fold cross-validation in the training set, the best performing algorithm was also evaluated in the test set. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC) and the associated 95% confidence intervals. The most important predictors were identified using the permutation feature importances method. Results Conditional inference random forest exhibited the best performance with AUCs of 0.728 [0.645–0.802] and 0.732 [0.681–0.784] for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction and QRS morphology had higher predictive power in females, whereas hemoglobin was less important than in males. The importance of atrial fibrillation and age increased, whereas the relevance of serum creatinine decreased from 1- to 3-year follow-up in both sexes. Conclusions Using advanced ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in patients undergoing CRT implantation. The in-depth analysis of features has revealed marked sex differences in mortality predictors. These results support the use of ML-based approaches for the risk stratification of patients undergoing CRT implantation. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Research, Development and Innovation Office of Hungary

scholarly journals Census-based and personally reported income measures as long-term risk-adjusted mortality predictors

Public Health ◽  
2007 ◽  
Vol 121 (12) ◽  
pp. 898-901 ◽  
Author(s):  
A.J. Thomas ◽  
L.E. Eberly ◽  
J.D. Neaton ◽  
G. Davey Smith
Keyword(s):  
Mortality Predictors ◽  
Reported Income

scholarly journals Intelligent Management of Sepsis in the Intensive Care Unit

2012 ◽  
pp. 1-16
Author(s):  
Vicent J. Ribas ◽  
Juan Carlos Ruiz-Rodríguez ◽  
Alfredo Vellido
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Support Vector ◽  
Clinical Settings ◽  
Svm Model ◽  
Intelligent Management ◽  
Alternative Techniques ◽  
Research Challenge ◽  
Mortality Predictors

Sepsis is a transversal pathology and one of the main causes of death in the Intensive Care Unit (ICU). It has in fact become the tenth most common cause of death in western societies. Its mortality rates can reach up to 60% for Septic Shock, its most acute manifestation. For these reasons, the prediction of the mortality caused by Sepsis is an open and relevant medical research challenge. This problem requires prediction methods that are robust and accurate, but also readily interpretable. This is paramount if they are to be used in the demanding context of real-time decision making at the ICU. In this brief contribution, three different methods are presented. One is based on a variant of the well-known support vector machine (SVM) model and provides and automated ranking of relevance of the mortality predictors while the other two are based on logistic-regression and logistic regression over latent Factors. The reported results show that the methods presented outperform in terms of accuracy alternative techniques currently in use in clinical settings, while simultaneously assessing the relative impact of individual pathology indicators.

scholarly journals MORTALITY PREDICTORS AND SURVIVAL ANALYSIS IN ANCA-ASSOCIATED VASCULITIS: EXPERIENCE OF A BRAZILIAN MONOCENTRIC COHORT OF A RHEUMATOLOGY CENTER

2019 ◽  
Author(s):  
MARILIA AMBIEL DAGOSTIN ◽  
SERGIO LUIZ OLIVEIRA NUNES ◽  
SAMUEL KATSUYUKI SHINJO ◽  
ROSA MARIA RODRIGUES PEREIRA
Keyword(s):  
Survival Analysis ◽  
Anca Associated Vasculitis ◽  
Mortality Predictors

scholarly journals Clinical profile of mortality predictors in Leptospirosis:a prospective study in a tertiary care center

2017 ◽  
Vol 5 (9) ◽  
pp. 857-864
Author(s):  
Dr. Arun Divakar ◽  
◽  
Dr. M. Gopala Krishna Pillai ◽  
Dr. Elizabeth Thomas ◽  
◽  
...  
Keyword(s):  
Prospective Study ◽  
Care Center ◽  
Tertiary Care ◽  
Clinical Profile ◽  
Tertiary Care Center ◽  
Mortality Predictors
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