scholarly journals The Optimal Cutoff Value of D-dimer Levels to Predict in Hospital Mortality in Severe Cases of Coronavirus Disease 2019

2021 ◽  
Vol 9 (B) ◽  
pp. 1561-1564
Author(s):  
Ngakan Ketut Wira Suastika ◽  
Ketut Suega
Keyword(s):  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Optimal Cutoff ◽  
Cutoff Value ◽  
Operating Characteristic Curve ◽  
D Dimer

Introduction: Coronavirus disease 2019 (Covid-19) can cause coagulation parameters abnormalities such as an increase of D-dimer levels especially in severe cases. The purpose of this study is to determine the differences of D-dimer levels in severe cases of Covid-19 who survived and non-survived and determine the optimal cut-off value of D-dimer levels to predict in-hospital mortality. Method: Data were obtained from confirmed Covid-19 patients who were treated from June to September 2020. The Mann-Whitney U test was used to determine differences of D-dimer levels in surviving and non-surviving patients. The optimal cut-off value and area under the curve (AUC) of the D-dimer level in predicting mortality were obtained by the receiver operating characteristic curve (ROC) method. Results: A total of 80 patients were recruited in this study. Levels of D-dimer were significantly higher in non-surviving patients (median 3.346 mg/ml; minimum – maximum: 0.939 – 50.000 mg/ml) compared to surviving patients (median 1.201 mg/ml; minimum – maximum: 0.302 – 29.425 mg/ml), p = 0.012. D-dimer levels higher than 1.500 mg/ml are the optimal cut-off value for predicting mortality in severe cases of Covid-19 with a sensitivity of 80.0%; specificity of 64.3%; and area under the curve of 0.754 (95% CI 0.586 - 0.921; p = 0.010). Conclusions: D-dimer levels can be used as a predictor of mortality in severe cases of Covid-19.

Baseline NIH Stroke Scale is an inferior predictor of functional outcome in the era of acute stroke intervention

2018 ◽  
Vol 13 (8) ◽  
pp. 806-810 ◽  
Author(s):  
Michael E Reznik ◽  
Shadi Yaghi ◽  
Mahesh V Jayaraman ◽  
Ryan A McTaggart ◽  
Morgan Hemendinger ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Functional Outcome ◽  
Operating Characteristic ◽  
Mechanical Thrombectomy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Curve Analysis ◽  
Operating Characteristic Curve

Background and aims Baseline National Institutes of Health Stroke Scale (NIHSS) scores have frequently been used for prognostication after ischemic stroke. With the increasing utilization of acute stroke interventions, we aimed to determine whether baseline NIHSS scores are still able to reliably predict post-stroke functional outcome. Methods We retrospectively analyzed prospectively collected data from a high-volume tertiary-care center. We tested strength of association between NIHSS scores at baseline and 24 h with discharge NIHSS using Spearman correlation, and diagnostic accuracy of NIHSS scores in predicting favorable outcome at three months (defined as modified Rankin Scale 0–2) using receiver operating characteristic curve analysis with area under the curve. Results There were 1183 patients in our cohort, with median baseline NIHSS 8 (IQR 3–17), 24-h NIHSS 4 (IQR 1–11), and discharge NIHSS 2 (IQR 1–8). Correlation with discharge NIHSS was r = 0.60 for baseline NIHSS and r = 0.88 for 24-h NIHSS. Of all patients with follow-up data, 425/1037 (41%) had favorable functional outcome at three months. Receiver operating characteristic curve analysis for predicting favorable outcome showed area under the curve 0.698 (95% CI 0.664–0.732) for baseline NIHSS, 0.800 (95% CI 0.772–0.827) for 24-h NIHSS, and 0.819 (95% CI 0.793–0.845) for discharge NIHSS; 24 h and discharge NIHSS maintained robust predictive accuracy for patients receiving mechanical thrombectomy (AUC 0.846, 95% CI 0.798–0.895; AUC 0.873, 95% CI 0.832–0.914, respectively), while accuracy for baseline NIHSS decreased (AUC 0.635, 95% CI 0.566–0.704). Conclusion Baseline NIHSS scores are inferior to 24 h and discharge scores in predicting post-stroke functional outcomes, especially in patients receiving mechanical thrombectomy.

A simple method to estimate the time-dependent receiver operating characteristic curve and the area under the curve with right censored data

2016 ◽  
Vol 27 (8) ◽  
pp. 2264-2278 ◽  
Author(s):  
Liang Li ◽  
Tom Greene ◽  
Bo Hu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Disease Onset ◽  
Area Under The Curve ◽  
Time Dependent ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The time-dependent receiver operating characteristic curve is often used to study the diagnostic accuracy of a single continuous biomarker, measured at baseline, on the onset of a disease condition when the disease onset may occur at different times during the follow-up and hence may be right censored. Due to right censoring, the true disease onset status prior to the pre-specified time horizon may be unknown for some patients, which causes difficulty in calculating the time-dependent sensitivity and specificity. We propose to estimate the time-dependent sensitivity and specificity by weighting the censored data by the conditional probability of disease onset prior to the time horizon given the biomarker, the observed time to event, and the censoring indicator, with the weights calculated nonparametrically through a kernel regression on time to event. With this nonparametric weighting adjustment, we derive a novel, closed-form formula to calculate the area under the time-dependent receiver operating characteristic curve. We demonstrate through numerical study and theoretical arguments that the proposed method is insensitive to misspecification of the kernel bandwidth, produces unbiased and efficient estimators of time-dependent sensitivity and specificity, the area under the curve, and other estimands from the receiver operating characteristic curve, and outperforms several other published methods currently implemented in R packages.

scholarly journals COMPARISION OF BINARY DIAGNOSTIC PREDICTORS USING ENTROPY

2018 ◽  
Vol 6 (1) ◽  
pp. 440-447
Author(s):  
Kathare Alfred ◽  
Otieno Argwings ◽  
Kimeli Victor
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Gold Standard ◽  
Large Scale ◽  
Operating Characteristic ◽  
Standard Procedure ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The use of gold standard procedures in screening may be costly, risky or even unethical. It is, therefore, not admissible for large scale application. In this case, a more acceptable diagnostic predictor is applied to a sample of subjects alongside a gold standard procedure. The performance of the predictor is then evaluated using Receiver Operating Characteristic curve. The area under the curve, then, provides a summative measure of the performance of the predictor. The Receiver Operating Characteristic curve is a trade-off between sensitivity and specificity which in most cases are of different clinical significance. Also, the area under the curve is criticized for lack of coherent interpretation. In this study, we proposed the use of entropy as a summary index measure of uncertainty to compare diagnostic predictors. Noting that a diseased subject who is truly identified with the disease at a lower cut-off will also be identified at a higher cut-off, we substituted time variable in survival analysis for cut-offs in a binary predictor. We then derived the entropy of the functions of diagnostic predictors. Application of the procedure to real data showed that entropy was a strong measure for quantifying the amount of uncertainty engulfed in a set of cut-offs of binary diagnostic predictor.

scholarly journals Prediction of Advisability of Returning Home Using the Home Care Score

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Akiyoshi Matsugi ◽  
Keisuke Tani ◽  
Yoshiki Tamaru ◽  
Nami Yoshioka ◽  
Akira Yamashita ◽  
...  
Keyword(s):  
Home Care ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Functional Independence Measure ◽  
Functional Independence ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Purpose. The aim of this study was to assess whether the home care score (HCS), which was developed by the Ministry of Health and Welfare in Japan in 1992, is useful for the prediction of advisability of home care.Methods. Subjects living at home and in assisted-living facilities were analyzed. Binominal logistic regression analyses, using age, sex, the functional independence measure score, and the HCS, along with receiver operating characteristic curve analyses, were conducted.Findings/Conclusions. Only HCS was selected for the regression equation. Receiver operating characteristic curve analysis revealed that the area under the curve (0.9), sensitivity (0.82), specificity (0.83), and positive predictive value (0.84) for HCS were higher than those for the functional independence measure, indicating that the HCS is a powerful predictor for advisability of home care.Clinical Relevance. Comprehensive measurements of the condition of provided care and the activities of daily living of the subjects, which are included in the HCS, are required for the prediction of advisability of home care.

scholarly journals Can machine learning improve mortality prediction following cardiac surgery?

2020 ◽  
Vol 58 (6) ◽  
pp. 1130-1136
Author(s):  
Umberto Benedetto ◽  
Shubhra Sinha ◽  
Matt Lyon ◽  
Arnaldo Dimagli ◽  
Tom R Gaunt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Random Forest ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract OBJECTIVES Interest in the clinical usefulness of machine learning for risk prediction has bloomed recently. Cardiac surgery patients are at high risk of complications and therefore presurgical risk assessment is of crucial relevance. We aimed to compare the performance of machine learning algorithms over traditional logistic regression (LR) model to predict in-hospital mortality following cardiac surgery. METHODS A single-centre data set of prospectively collected information from patients undergoing adult cardiac surgery from 1996 to 2017 was split into 70% training set and 30% testing set. Prediction models were developed using neural network, random forest, naive Bayes and retrained LR based on features included in the EuroSCORE. Discrimination was assessed using area under the receiver operating characteristic curve, and calibration analysis was undertaken using the calibration belt method. Model calibration drift was assessed by comparing Goodness of fit χ2 statistics observed in 2 equal bins from the testing sample ordered by procedure date. RESULTS A total of 28 761 cardiac procedures were performed during the study period. The in-hospital mortality rate was 2.7%. Retrained LR [area under the receiver operating characteristic curve 0.80; 95% confidence interval (CI) 0.77–0.83] and random forest model (0.80; 95% CI 0.76–0.83) showed the best discrimination. All models showed significant miscalibration. Retrained LR proved to have the weakest calibration drift. CONCLUSIONS Our findings do not support the hypothesis that machine learning methods provide advantage over LR model in predicting operative mortality after cardiac surgery.

scholarly journals Serum Ratio of Free Triiodothyronine to Thyroid-Stimulating Hormone: A Novel Index for Distinguishing Graves’ Disease From Autoimmune Thyroiditis

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhiyong Wu ◽  
Yu Zhu ◽  
Min Zhang ◽  
Chen Wang ◽  
Lingli Zhou ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Medical Records ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Thyroid Stimulating Hormone ◽  
Free Triiodothyronine ◽  
Optimal Cutoff ◽  
Operating Characteristic Curve ◽  
Receiver Operating

ObjectiveGraves’ disease (GD) and autoimmune thyroiditis (AIT) are two major causes of thyrotoxicosis that require correct diagnosis to plan appropriate treatment. The objectives of this study were to evaluate the usefulness of thyroid-related parameters for distinguishing GD from AIT and identify a novel index for differential diagnosis of thyrotoxicosis.DesignThis retrospective study was performed using electronic medical records in Peking University People’s Hospital (Beijing, China).MethodsIn total, 650 patients with GD and 155 patients with AIT from December 2015 to October 2019 were included in cohort 1. Furthermore, 133 patients with GD and 14 patients with AIT from December 2019 to August 2020 were included in cohort 2 for validation of the novel index identified in cohort 1. All patients were of Chinese ethnicity and were newly diagnosed with either GD or AIT. Thyroid-related clinical information was collected before intervention by reviewing the patients’ electronic medical records. Receiver operating characteristic curve analysis was used to identify the optimal cutoff for distinguishing GD from AIT.ResultsIn cohort 1, thyroid-stimulating hormone (TSH) receptor antibody was identified as the best indicator for distinguishing GD from AIT. The area under the receiver operating characteristic curve was 0.99(95% confidence interval: 0.98–0.99, p<0.0001)and the optimal cutoff was 0.84 IU/l (98% sensitivity and 99% specificity). The free triiodothyronine (FT3)/TSH ratio (FT3/TSH) was the second –best for distinguishing GD from AIT, the area under the receiver operating characteristic curve of FT3/TSH was 0.86 (95% confidence interval: 0.84–0.88, p<0.0001); its optimal cutoff was 1.99 pmol/mIU (79% sensitivity and 80% specificity). Its effectiveness was confirmed in cohort 2 (81% sensitivity and 100% specificity).ConclusionsThe FT3/TSH ratio is a new useful index for differential diagnosis of thyrotoxicosis, especially when combined with TRAb.

scholarly journals Comparative Analysis of Anterior Segment Parameters in Normal and Keratoconus Eyes Generated by Scheimpflug Tomography

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Faik Orucoglu ◽  
Ebru Toker
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Anterior Segment ◽  
Area Under The Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Purpose. To assess and compare the anterior and posterior corneal surface parameters, keratoconus indices, thickness profile data, and data from enhanced elevation maps of keratoconic and normal corneas with the Pentacam Scheimpflug corneal tomography and to determine the sensitivity and specificity of these parameters in discriminating keratoconus from normal eyes.Methods. The study included 656 keratoconus eyes and 515 healthy eyes with a mean age of30.95±9.25and32.90±14.78years, respectively. Forty parameters obtained from the Pentacam tomography were assessed by the receiver operating characteristic curve analysis for their efficiency.Results. Receiver operating characteristic curve analyses showed excellent predictive accuracy (area under the curve, ranging from 0.914 to 0.972) for 21 of the 40 parameters evaluated. Among all parameters indices of vertical asymmetry, keratoconus index, front elevation at thinnest location, back elevation at thinnest location, Ambrósio Relational Thickness (ARTmax), deviation of average pachymetric progression, deviation of ARTmax, and total deviation showed excellent (>90%) sensitivity and specificity in addition to excellent area under the receiver operating characteristic curve (AUROC).Conclusions. Parameters derived from the topometric and Belin-Ambrósio enhanced ectasia display maps very effectively discriminate keratoconus from normal corneas with excellent sensitivity and specificity.

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