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

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

ABSTRACTIntroductionRisk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of pre-hospital SARS-CoV-2 testing, is lacking.MethodsMultivariate regression with bootstrapping was used to identify independent mortality predictors in a derivation cohort of COVID-19 patients. 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 5-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 pre-admission 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 Prognostic value of D-dimer for adverse outcomes in patients with infective endocarditis: an observational study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ying-Wen Lin ◽  
Mei Jiang ◽  
Xue-biao Wei ◽  
Jie-leng Huang ◽  
Zedazhong Su ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Infective Endocarditis ◽  
Adverse Events ◽  
Prognostic Value ◽  
Multivariable Analysis ◽  
Adverse Outcomes ◽  
Hospital Death ◽  
Large Sample Size ◽  
Cox Regression Analysis ◽  
D Dimer

Abstract Background Increased D-dimer levels have been shown to correlate with adverse outcomes in various clinical conditions. However, few studies with a large sample size have been performed thus far to evaluate the prognostic value of D-dimer in patients with infective endocarditis (IE). Methods 613 patients with IE were included in the study and categorized into two groups according to the cut-off of D-dimer determined by receiver operating characteristic (ROC) curve analysis for in-hospital death: > 3.5 mg/L (n = 89) and ≤ 3.5 mg/L (n = 524). Multivariable regression analysis was used to determine the association of D-dimer with in-hospital adverse events and six-month death. Results In-hospital death (22.5% vs. 7.3%), embolism (33.7% vs 18.2%), and stroke (29.2% vs 15.8%) were significantly higher in patients with D-dimer > 3.5 mg/L than in those with D-dimer ≤ 3.5 mg/L. Multivariable analysis showed that D-dimer was an independent risk factor for in-hospital adverse events (odds ratio = 1.11, 95% CI 1.03–1.19, P = 0.005). In addition, the Kaplan–Meier curve showed that the cumulative 6-month mortality was significantly higher in patients with D-dimer > 3.5 mg/L than in those with D-dimer ≤ 3.5 mg/L (log-rank test = 39.19, P < 0.0001). Multivariable Cox regression analysis showed that D-dimer remained a significant predictor for six-month death (HR 1.11, 95% CI 1.05–1.18, P < 0.001). Conclusions D-dimer is a reliable prognostic biomarker that independently associated with in-hospital adverse events and six-month mortality in patients with IE.

New Acceptance Sampling Plans Based on Percentiles for Exponentiated Fréchet Distribution

2016 ◽  
Vol 31 (1) ◽  
Author(s):  
Gadde Srinivasa Rao ◽  
Kanaparthi Rosaiah ◽  
Mothukuri Sridhar Babu ◽  
Devireddy Charanaudaya Sivakumar
Keyword(s):  
Operating Characteristic ◽  
Real Data ◽  
Acceptance Sampling ◽  
Producer’S Risk ◽  
Data Set ◽  
Sampling Plans ◽  
Fréchet Distribution ◽  
Acceptance Sampling Plans ◽  
Characteristic Values ◽  
Frechet Distribution

AbstractIn this article, acceptance sampling plans are developed for the exponentiated Fréchet distribution based on percentiles when the life test is truncated at a pre-specified time. The minimum sample size necessary to ensure the specified life percentile is obtained under a given customer's risk and producer's risk simultaneously. The operating characteristic values of the sampling plans are presented. One example with real data set is also given as an illustration.

Features of the course of coronavirus infection in patients with diabetes mellitus

2022 ◽  
pp. 18-21
Author(s):  
Vladimir Anatolievich Klimov ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Human Population ◽  
Fatal Outcome ◽  
Significant Risk ◽  
Adverse Outcomes ◽  
Concomitant Disease ◽  
Patients With Diabetes ◽  
Coronavirus Infection ◽  
Population Mortality

Diabetesmellitus, overweight and the age of a patient over 65 years old are identified by clinicians as themain factors that can complicate the course of the coronavirus infection and increase the likelihood of fatal outcome. Although in the general human population mortality from coronavirus fluctuateswithin 3–5 %, sometimes very significantly differing in individual countries, this level can reach 15–25 % among patientswith diabetes, especially for those receiving insulin therapy. Diabetes mellitus as a concomitant disease in COVID-19 is considered one of the most significant risk factors for the development of adverse outcomes due to a more severe course of infection in conditions of hyperglycemia and other aggravating factors.

Real-world observational study of assessment of CHA2DS2-VASc, C2HEST and HAVOC scores for atrial fibrillation among patients with rheumatological disorders: a nationwide analysis

2021 ◽  
pp. postgradmedj-2021-140754
Author(s):  
Wei Syun Hu ◽  
Cheng Li Lin
Keyword(s):  
Atrial Fibrillation ◽  
Retrospective Study ◽  
Predictive Models ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Scoring Systems ◽  
Gray Model ◽  
Discriminative Ability ◽  
Discriminatory Ability ◽  
Rheumatological Disease

PurposeThis is a nationwide-based retrospective study aiming to compare the three different scoring systems (CHA2DS2-VASc, C2HEST and HAVOC scores) in the prediction of atrial fibrillation (AF) in patients with rheumatological disease.MethodsWe used the Fine and Gray model to estimate the risk of AF (subhazard ratio and 95% CI). The predictive accuracy and discriminatory ability of the predictive model were evaluated by receiver operating characteristic (ROC) curve.ResultsAmong the three predictive models, the model using CHA2DS2-VASc score had the better discriminative ability with an ROC of 0.79. The model with C2HEST score had an ROC of 0.78. The discriminative ability of the HAVOC score was 0.77, estimated by ROC.ConclusionWe concluded the CHA2DS2-VASc score has better performance in predicting AF compared with C2HEST score or HAVOC score.

scholarly journals Predicting dengue importation into Europe, using machine learning and model-agnostic methods

2019 ◽  
Author(s):  
Donald Salami ◽  
Carla Alexandra Sousa ◽  
Maria do Rosário Oliveira Martins ◽  
César Capinha
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Transport Network ◽  
Air Transport ◽  
Health Concern ◽  
Centrality Measures ◽  
Network Centrality

ABSTRACTThe geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation.Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale.Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions.We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

scholarly journals Rhabdomyolysis is Associated with Hospital Mortality in Patients with COVID-19

2020 ◽  
Author(s):  
Yan Geng ◽  
Yong-sheng Du ◽  
Na Peng ◽  
Ting Yang ◽  
Shi-yu Zhang ◽  
...  
Keyword(s):  
Kidney Injury ◽  
Adverse Outcomes ◽  
Hospital Death ◽  
Proportional Hazard ◽  
Coagulation Activation ◽  
Virus Clearance ◽  
Cox Proportional Hazard ◽  
Independent Risk Factors ◽  
Cox Proportional Hazard Regression ◽  
Serum Myoglobin

Abstract Purpose: To evaluate the clinical features and outcomes of rhabdomyolysis (RM) in patients with COVID-19. Method: A single center retrospective cohort study of 1,014 consecutive hospitalized patients with confirmed COVID-19 at the Huoshenshan hospital in Wuhan, China, between February 17 and April 12, 2020. Results: The overall incidence of RM was 2.2%. Comparing with patients without RM, patients with RM tended to have a higher risk of deterioration, representing by higher ratio to be admitted to the intensive care unit (ICU) (90.9 % vs 5.3%, P<0.001), and to undergo mechanical ventilation (86.4 % vs 2.7% P<0.001). Compared with patients without RM, patients with RM had laboratory test abnormalities, including indicators of inflammation, coagulation activation and kidney injury. Patients with RM had a higher risk of hospital death (P < 0.001). Cox proportional hazard regression model confirmed that RM indicators, including peak creatine kinase (CK) >1000 IU/L (HR=6.46, 95% CI: 3.02-13.86), peak serum myoglobin (MYO) >1000 ng/mL (HR=9.85, 95% CI: 5.04-19.28) were independent risk factors for in-hospital death. Additionally, patients with COVID-19 that developed RM tended to have a delayed virus clearance.Conclusion: RM might be an important factor contributing to adverse outcomes of patients with COVID-19. Early detection and effective intervention of RM may help reduce deaths of patients with COVID-19.

scholarly journals Corneal Elevation Values in Normal Eyes, forme fruste Keratoconus and Keratoconus at Different Stages Measured by Scheimpflug Imaging

2014 ◽  
Vol 3 (1) ◽  
pp. 36-39 ◽  
Author(s):  
Luis Izquierdo ◽  
Maria A Henriquez ◽  
David Dañin
Keyword(s):  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Optimal Cutoff ◽  
Receiver Operating Characteristic Curves ◽  
Scheimpflug Imaging ◽  
Prospective Comparative Study ◽  
Best Fit ◽  
Best Fit Sphere ◽  
Forme Fruste Keratoconus ◽  
Progressive Stage

ABSTRACT Purpose To compare corneal elevation values in normal eyes, forme fruste keratoconus (FFKC) and different stages of keratoconus using Scheimpflug imaging. Materials and methods This prospective, comparative study included 267 eyes (107 normal eyes, 21 FFKC and 139 keratoconus). Keratoconic eyes were divided into four groups according to keratometry values. Maximum posterior elevation (PE) above the (best fit sphere (BFS) at the central 5 mm were measured using the Pentacam (Oculus Optikgeräte GmbH). Receiver operating characteristic curves were used to determine the test's overall predictive accuracy and to identify optimal cutoff points to discriminate between the groups. Results PE had the smallest values in normal eyes and increased in FFKC and each progressive stage of keratoconus. Mean PE was 9.98 ± 5.33 µm in normal eyes, 18.09 ± 9.23 µm in FFKC and 24.97 µm ± 15.89, 37.82 ± 18.64, 46.82 ± 21.41 and 66.07 ± 39.09, in keratoconus stage I, II, III and IV respectively. Conclusion Posterior elevation values increased according to the severity of keratoconus disease. PE can be used as indicator of keratoconus progression. How to cite this article Henriquez MA, Izquierdo L Jr, Dañin D. Corneal Elevation Values in Normal Eyes, forme fruste Keratoconus and Keratoconus at Different Stages Measured by Scheimpflug Imaging. Int J Kerat Ect Cor Dis 2014;3(1):36-39.

scholarly journals Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes

2012 ◽  
Vol 1 (3) ◽  
pp. 145-150
Author(s):  
Bernardo Lopes ◽  
Allan Luz ◽  
Bruno Fontes ◽  
Isaac C Ramos ◽  
Fernando Correia ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Linear Model ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Corneal Resistance Factor ◽  
Corneal Deformation ◽  
Eye Examination ◽  
The Individual

ABSTRACT Purpose To compare and assess the ability of pressure-derived parameters and corneal deformation waveform signal-derived parameters of the ocular response analyzer (ORA) measurement to distinguish between keratoconus and normal eyes, and to develop a combined parameter to optimize the diagnosis of keratoconus. Materials and methods One hundred and seventy-seven eyes (177 patients) with keratoconus (group KC) and 205 normal eyes (205 patients; group N) were included. One eye from each subject was randomly selected for analysis. Patients underwent a complete clinical eye examination, corneal topography (Humphrey ATLAS), tomography (Pentacam Oculus) and biomechanical evaluations (ORA Reichert). Differences in the distributions between the groups were assessed using the Mann- Whitney test. The receiver operating characteristic (ROC) curve was used to identify cutoff points that maximized sensitivity and specificity in discriminating keratoconus from normal corneas. Logistic regression was used to identify a combined linear model (Fisher 1.0). Results Significant differences in all studied parameters were detected (p < 0.05), except for W2. For the corneal resistance factor (CRF): Area under the ROC curve (AUROC) 89.1%, sensitivity 81.36%, specificity 84.88%. For the p1area: AUROC 91.5%, sensitivity 87.1%, specificity 81.95%. Of the individual parameters, the highest predictive accuracy was for the Fisher 1.0, which represents the combination of all parameters (AUROC 95.5%, sensitivity 88.14%, specificity 93.17%). Conclusion Waveform-derived ORA parameters displayed greater accuracy than pressure-derived parameters for identifying keratoconus. Corneal hysteresis (CH) and CRF, a diagnostic linear model that combines different parameters, provided the greatest accuracy for differentiating keratoconus from normal corneas. How to cite this article Luz A, Fontes B, Ramos IC, Lopes B, Correia F, Schor P, Ambrósio R. Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes. Int J Kerat Ect Cor Dis 2012;1(3):145-150.

A Nomogram to Predict the Probability of Relapse during Hospitalization after Minor Ischemic Stroke in Chinese Patients.

2020 ◽  
Author(s):  
Yu xianfeng ◽  
Yin wenwen ◽  
Huang chaojuan ◽  
Yuan xin ◽  
Xia yu ◽  
...  
Keyword(s):  
Heart Disease ◽  
Ischemic Stroke ◽  
Total Bilirubin ◽  
Individual Characteristics ◽  
Adverse Outcomes ◽  
Unfavorable Outcome ◽  
Chinese Patients ◽  
Serum Total Bilirubin ◽  
Risk Of Recurrence ◽  
Minor Ischemic Stroke

Abstract Background: Predicting the risk of recurrence during hospitalization in patients with minor ischemic stroke (MIS) is of great significance for clinical and treatment. Compared with early models and prognostic scores, nomogram is a better visualization tool for predicting clinical outcomes. It combines different factors to develop a graphical continuous scoring system, and accurately calculates the risk probability of adverse outcomes based on individual characteristics. Our goal is to develop and validate a nomogram for individualized prediction of hospitalization recurrence in patients with mild ischemic stroke in the Chinese population.Methods: Based on retrospective collection, a single center study was conducted in the first affiliated Hospital of Anhui Medical University from January 2014 to December 2019. The subjects were stroke patients with NIHSS≤5.In order to generate the nomogram, age, systolic blood pressure,previous heart disease, serum total bilirubin, ferritin and smoking were integrated into the model. The predictive accuracy of the nomogram model to predict the probability of unfavorable outcome was assessed by calculation of the area under the receiver operating characteristic curve (AUC–ROC). Calibration of the risk prediction model was assessed by the plot comparing the observed probability of unfavorable outcome against the predicted, and by using the Hosmer–Lemeshow test.Results: Age at admission (OR,0.946; 95% CI,-0.002 to 0.048), SBP (OR,0.012,95%CI,0.000 to 0.024), previous heart disease (OR,0.867,95%CI, 0.084 to 1.651), UA (OR,-0.003,95%CI,-0.006 to 0.001), serum total bilirubin (OR,-0.022,95%CI,-0.036 to -0.008), ferritin (OR,0.004,95%CI, 0.002 to 0.005), smoking (OR,0.494,95%CI,-0.115 to 1.103) are significant predictors of in-hospital recurrence in Chinese patients with minor ischemic stroke.The model shows good discrimination, the AUC-ROC value is 0.737 (95%CI:0.676-0.798), and has perfect calibration performance. Calibration was good (p=0.1457 for the Hosmer-Lemesshow test), which could predict the risk of recurrence of MIS patients during hospitalization.Conclusion: The nomogram developed and validated in this study can provide individualized, intuitive and accurate prediction of recurrence in Chinese patients with minor ischemic stroke during hospitalization.

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