scholarly journals Evaluation of PORT/PSI and SOFA scores in predicting in-hospital mortality of patients with COVID-19

2021 ◽  
Vol 1 ◽  
pp. e1196
Author(s):  
José M. Alanís-Naranjo ◽  
Salvador Hernández-Sandoval ◽  
Víctor M. Anguiano-Álvarez ◽  
Eduardo F. Hammeken-Larrondo ◽  
Gabriela Olguín-Contreras ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Sofa Score ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Prognostic Scores ◽  
Limited Information ◽  
Good Tool ◽  
Predictive Values ◽  
Study Population ◽  
Sensitivity Specificity

Introduction: There is limited information analyzing the utility of different prognostic scores in predicting in-hospital mortality among patients with COVID-19. This study aimed to evaluate the performance of PORT/PSI and SOFA scores in predicting the in-hospital mortality of patients with COVID-19. Material and methods: This was an observational, analytical, and retrospective study that included consecutive patients hospitalized for COVID-19 from April 1, 2020, to May 31, 2020. The study population was characterized, and ROC analysis was performed and used to calculate the area under the curve of PORT/PSI and SOFA scores as well as the sensitivity, specificity, and predictive values. Results: A total of 151 patients were included, with a median age of 52 years (IQR 45-64); 69.5% were men, with a median BMI of 29.3 kg/m2 (IQR 25.5-34.7). Of the total, 102 patients died during hospitalization (67.5%). The areas under the ROC curves for predicting in-hospital mortality were 0.74 (95% CI 0.67-0.81) for the SOFA score and 0.85 (95% CI 0.78-0.90) for the PORT/PSI score. When compared, the PORT/PSI score predicted mortality significantly better than the SOFA score (p: 0.01). Conclusions: The PORT/PSI score is a good tool to predict in-hospital mortality in patients with COVID-19.

Diagnostic accuracy and validity of the O-RADS MRI score based on a simplified MRI protocol: a single tertiary center retrospective study

2021 ◽  
pp. 028418512110604
Author(s):  
Serdar Aslan ◽  
Sebnem Alanya Tosun
Keyword(s):  
Area Under The Curve ◽  
Imaging Method ◽  
Predictive Values ◽  
Experienced Radiologist ◽  
Tertiary Center ◽  
Mri Protocol ◽  
Study Population ◽  
Magnetic Resonance Imaging Mri ◽  
Sensitivity Specificity ◽  
Mri Score

Background Adnexal masses (AM) are a common gynecological problem. It is important to use a reliable imaging method in the differentiation of benign and malignant AMs. Purpose To assess the accuracy and validity of the O-RADS magnetic resonance imaging (MRI) score for characterizing AM using a simplified MRI protocol. Material and Methods The study population comprised 332 women who underwent MRI due to the detection of indeterminate AM on ultrasonography between January 2018 and June 2020. An experienced radiologist calculated the O-RADS MRI score into five categories, using an MRI protocol with a simplified dynamic study. Sensitivity, specificity, positive and negative predictive values, and area under the curve (AUC) were calculated (cutoff for malignancy, score ≥ 4). The reference standard was histopathologic diagnosis or imaging findings during >24 months of follow-up. Results Of 237 AMs, 28 (11.9%) were malignant. The malignancy rates of AMs with scores of 1, 2, 3, 4, and 5 were 0% (0/12), 0% (0/111), 1.2% (1/77), 50% (10/20), and 100% (17/17), respectively. The O-RADS MRI score showed 96.3% sensitivity, 95.2% specificity, and 95.3% accuracy in malignancy prediction. The AUC for the differentiation of benign and malignant masses were 0.983. False positivity rate was high in cases with an O-RADS MRI score of 4 (50%). Conclusion The O-RADS MRI score, based on a simplified MRI protocol, has high accuracy and validity in distinguishing benign from malignant sonographically indeterminate AMs. Its use in clinical practice can classify the malignancy risks of masses and prevent unnecessary surgery in benign lesions.

Abstract TP325: Lack of Early Improvement Predicts Poor Clinical Outcome Following Acute Intracerebral Hemorrhage

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Vignan Yogendrakumar ◽  
Eric E Smith ◽  
Andrew M Demchuk ◽  
RIchard I Aviv ◽  
David Rodriguez-Luna ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Predictive Values ◽  
Early Improvement ◽  
Poor Outcome ◽  
Using Data ◽  
Definition Of ◽  
Sensitivity Specificity ◽  
The Cost

Background: Early Neurological Worsening (ENW) is common after ICH, and predicts poor outcome. However, there is limited data as to what degree of ENW best relates to outcome. We used two ICH cohorts to refine and validate a definition of ENW that best predicted 90-day outcomes. Methods: We generated receiver operating characteristic (ROC) curves for the association between 24-hour NIHSS change and ICH outcomes using data from the VISTA collaboration. Primary outcome was poor outcome at 90 days (mRS 4-6); secondary outcomes were other mRS cutpoints (mRS 2-6, 3-6, 5-6, 6). We tested the commonly used NIHSS≥4 definition and in addition employed Youden’s J Index to select optimal cutpoints and calculated sensitivity, specificity, and predictive values. Independent predictors of poor outcome were determined via multivariable logistic regression. Definitions were validated in the prospectively collected PREDICT-ICH cohort. Results: Using 552 patients from the VISTA cohort, ROC curves of 24hr NIHSS change had an area under the curve of 0.75. NIHSS change of ≥0 at 24hrs was seen in 46.4%. Youden’s method showed an optimum cutoff at -0.5. Based on this, ENW defined as >0 (Sens 43%, Spec 91%, PPV 83%, aOR 7.13 [CI:4.05-12.55]), ≥0 (Sens 65%, Spec 73%, PPV 70%, aOR 5.05 [CI:3.25-7.85]), or ≥-1 (Sens 78%, Spec 59%, PPV 65%, aOR 6.04 [CI:3.75-9.71]) all accurately predicted poor outcome. PPV increased with higher NIHSS cutoffs, but at the cost of lower sensitivities. Regression confirmed that all definitions independently predicted outcome at all mRS cutpoints. ENW definitions reproduced well in the validation cohort of 275 patients. Conclusion: All NIHSS cut-offs for ENW predict clinical outcome, regardless of outcome definition. In particular, lack of clinical improvement at 24 hours (i.e. NIHSS is the same or higher) robustly predicted poor outcome, but may not be sufficiently reliable to determine clinical management.

scholarly journals El dilema del vigilante. Valoración de pruebas diagnósticas.

2018 ◽  
Vol 10 (8) ◽  
pp. 4
Author(s):  
Manuel Molina
Keyword(s):  
Diagnostic Tests ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Likelihood Ratios ◽  
Predictive Values ◽  
Significant Parameter ◽  
Quantitative Results ◽  
Sensitivity Specificity

Existen una serie de parámetros que caracterizan las pruebas diagnósticas, como son la sensibilidad, la especificidad, los valores predictivos y los cocientes de probabilidad. Solo estos últimos nos permiten el cálculo de la probabilidad del paciente de presentar la enfermedad, con independencia de la prevalencia en la población. Para caracterizar las pruebas con resultado cuantitativo se utilizan las curvas ROC, cuyo parámetro más significativo es el área bajo la curva. ABSTRACT There are a series of parameters that characterize the diagnostic tests, such as sensitivity, specificity, predictive values and likelihood ratios. Only the latter allow us to calculate the patient's probability of presenting the disease, regardless of the prevalence in the population. To characterize the tests with quantitative results, the ROC curves are used, whose most significant parameter is the area under the curve.

Evaluating the Predictive Value of Lactate in Patients With Cancer Having Septic Shock

2018 ◽  
Vol 35 (8) ◽  
pp. 789-796
Author(s):  
Lama H. Nazer ◽  
Dalia Rimawi ◽  
Feras I. Hawari
Keyword(s):  
Septic Shock ◽  
Hospital Mortality ◽  
Predictive Validity ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Serum Lactate ◽  
Predictive Values ◽  
Patients With Cancer ◽  
Predict Hospital Mortality

Purpose: Limited studies evaluated the predictive value of serum lactate (LA) in critically ill patients with cancer. The main objective of this study was to evaluate the predictive validity of LA single measurements as well as LA clearance in predicting mortality in patients with cancer having septic shock. The study also aimed to determine the LA measurement over the first 24 hours with the highest predictability for hospital mortality. Materials and Methods: A retrospective cohort study of adult patients with cancer having septic shock and LA measurements during the first 24 hours. Three receiver–operating characteristic (ROC) curves were constructed to evaluate the predictive validity for hospital mortality of LA at baseline, at 6 hours and at 24 hours after identifying septic shock. The ROC with the largest area under the curve was analyzed to determine LA level with the highest predictability for hospital mortality. In addition, the ability of LA normalization (LA <2 mmol/L at 6 hours and at 24 hours) and the degree of LA elimination (>10% and >20% at 24 hours) to predict hospital mortality were evaluated by determining the predictive values for each clearance end point. Results: The study included 401 patients. LA >2.5 mmol/L at 24 hours showed the largest area under the ROC curve to predict hospital mortality (ROC area: 0.648; 95% confidence interval: 0.585-0.711) with a sensitivity of 58.4% and specificity of 62.8%. The LA normalization, LA clearance >10%, and LA clearance >20% were also predictors of hospital mortality, with the highest sensitivity for LA normalization at 6 hours (74%) and LA normalization at 24 hours (73.4%). Conclusion: In patients with cancer having septic shock, LA >2.5 mmol/L at 24 hours of septic shock had the highest predictability for hospital mortality. The LA normalization and clearance were also predictors of hospital mortality. However, all LA end points were not strong predictors.

scholarly journals Establishment and Characterization of an Empirical Biomarker SS/PV-ROC Plot Using Results of the UBC® Rapid Test in Bladder Cancer

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 729 ◽  
Author(s):  
Peter Oehr ◽  
Thorsten Ecke
Keyword(s):  
Bladder Cancer ◽  
Roc Curve ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Rapid Test ◽  
Roc Curves ◽  
Threshold Value ◽  
Control Group ◽  
Predictive Values ◽  
Sensitivity Specificity

Background: This investigation included both a study of potential non-invasive diagnostic approaches for the bladder cancer biomarker UBC® Rapid Test and a study including comparative methods about sensitivity–specificity characteristic (SS-ROC) and predictive receiver operating characteristic (PV-ROC) curves that used bladder cancer as a useful example. Methods: The study included 289 urine samples from patients with tumors of the urinary bladder, patients with non-evidence of disease (NED) and healthy controls. The UBC® Rapid Test is a qualitative point of care assay. Using a photometric reader, quantitative data can also be obtained. Data for pairs of sensitivity/specificity as well as positive/negative predictive values were created by variation of threshold values for the whole patient cohort, as well as for the tumor-free control group. Based on these data, sensitivity–specificity and predictive value threshold distribution curves were constructed and transformed into SS-ROC and PV-ROC curves, which were included in a single SS/PV-ROC plot. Results: The curves revealed TPP-asymmetric improper curves which cross the diagonal from above. Evaluation of the PV-ROC curve showed that two or more distinct positive predictive values (PPV) can correspond to the same value of a negative predictive value (NPV) and vice versa, indicating a complexity in PV-ROC curves which did not exist in SS-ROC curves. In contrast to the SS-ROC curve, the PV-ROC curve had neither an area under the curve (AUC) nor a range from 0% to 100%. Sensitivity of the qualitative assay was 58.5% and specificity 88.2%, PPV was 75.6% and NPV 77.3%, at a threshold value of approximately 12.5 µg/L. Conclusions: The SS/PV-ROC plot is a new diagnostic approach which can be used for direct judgement of gain and loss of predictive values, sensitivity and specificity according to varied threshold value changes, enabling characterization, comparison and evaluation of qualitative and quantitative bioassays.

Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

2020 ◽  
Vol 163 (6) ◽  
pp. 1156-1165
Author(s):  
Juan Xiao ◽  
Qiang Xiao ◽  
Wei Cong ◽  
Ting Li ◽  
Shouluan Ding ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Study ◽  
Diagnostic Efficiency ◽  
Training Set ◽  
Multivariable Logistic Regression Model ◽  
Predictive Values ◽  
Validation Set ◽  
Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

scholarly journals Value and Diagnostic Efficacy of Fetal Morphology Assessment Using Ultrasound in A Poor-Resource Setting

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 109 ◽  
Author(s):  
Ukweh ◽  
Ugbem ◽  
Okeke ◽  
Ekpo
Keyword(s):  
Predictive Value ◽  
Performance Metrics ◽  
Area Under The Curve ◽  
Youden Index ◽  
Likelihood Ratios ◽  
Diagnostic Efficacy ◽  
Predictive Values ◽  
Resource Setting ◽  
Post Test ◽  
Sensitivity Specificity

Background: Ultrasound is operator-dependent, and its value and efficacy in fetal morphology assessment in a low-resource setting is poorly understood. We assessed the value and efficacy of fetal morphology ultrasound assessment in a Nigerian setting. Materials and Methods: We surveyed fetal morphology ultrasound performed across five facilities and followed-up each fetus to ascertain the outcome. Fetuses were surveyed in the second trimester (18th–22nd weeks) using the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) guideline. Clinical and surgical reports were used as references to assess the diagnostic efficacy of ultrasound in livebirths, and autopsy reports to confirm anomalies in terminated pregnancies, spontaneous abortions, intrauterine fetal deaths, and still births. We calculated sensitivity, specificity, positive and negative predictive values, Area under the curve (AUC), Youden index, likelihood ratios, and post-test probabilities. Results: In total, 6520 fetuses of women aged 15–46 years (mean = 31.7 years) were surveyed. The overall sensitivity, specificity, and AUC were 77.1 (95% CI: 68–84.6), 99.5 (95% CI: 99.3–99.7), and 88.3 (95% CI: 83.7–92.2), respectively. Other performance metrics were: positive predictive value, 72.4 (95% CI: 64.7–79.0), negative predictive value, 99.6 (95% CI: 99.5–99.7), and Youden index (77.1%). Abnormality prevalence was 1.67% (95% CI: 1.37–2.01), and the positive and negative likelihood ratios were 254 (95% CI: 107.7–221.4) and 0.23 (95% CI: 0.16–0.33), respectively. The post-test probability for positive test was 72% (95% CI: 65–79). Conclusion: Fetal morphology assessment is valuable in a poor economics setting, however, the variation in the diagnostic efficacy across facilities and the limitations associated with the detection of circulatory system anomalies need to be addressed.

scholarly journals Identification of the most specific markers to differentiate primary pulmonary carcinoma from metastatic gastrointestinal carcinoma to the lung

2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Bachar Alabdullah ◽  
Amir Hadji-Ashrafy
Keyword(s):  
Gastrointestinal Tract ◽  
Predictive Value ◽  
Regression Models ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Multivariate Models ◽  
Gastrointestinal Carcinoma ◽  
Lung Tumours ◽  
Napsin A ◽  
Sensitivity Specificity

Abstract Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

scholarly journals 006 Can seizure-related heart rate differentiate epileptic seizures from psychogenic non-epileptic seizures?

2019 ◽  
Vol 90 (e7) ◽  
pp. A2.3-A3
Author(s):  
Hue Mun Au Yong ◽  
Erica Minato ◽  
Eldho Paul ◽  
Udaya Seneviratne
Keyword(s):  
Heart Rate ◽  
Area Under The Curve ◽  
Epileptic Seizures ◽  
Roc Curve Analysis ◽  
Predictive Values ◽  
Minute Post ◽  
Poor Differentiation ◽  
Good Differentiation ◽  
Sensitivity Specificity ◽  
Video Eeg

IntroductionThis study aims to (i)evaluate the diagnostic sensitivity, specificity and predictive values of seizure-related heart rate (HR) in differentiating epileptic seizures(ES) from psychogenic non-epileptic seizures(PNES), (ii)define the most useful point of HR measurement: pre-ictal, ictal-onset, maximal-ictal or post-ictal, and (iii)define the HR cut-off points to differentiate ES from PNES.MethodsAll video EEG(VEEG) at Monash Health from May 2009 to November 2015 were retrospectively reviewed. Baseline(during wakefulness), one-minute pre-ictal, ictal-onset, maximal-ictal and one-minute post-ictal HR were measured for each ES and PNES event. Events less than ten seconds or with uninterpretable ECG due to artefacts were excluded. ROC curve analysis was performed to study the diagnostic accuracy reflected by area under the curve(AUC). The AUC was interpreted as follows; ≤0.5, differentiation of PNES from ES no better than chance; 0.80–0.89, good differentiation; and 0.9–1, excellent differentiation.ResultsVEEG of 341 ES and 265 PNES from 130 patients were analysed. The AUC for pre-ictal, ictal-onset, maximal-ictal and post-ictal HR were found to have poor differentiation between ES and PNES. Comparing PNES and bilateral tonic-clonic ES, AUC for absolute maximal-ictal HR was 0.84(CI 0.73–0.95) and for absolute post-ictal HR was 0.90(CI 0.81–1.00). Using Youden’s index, to diagnose tonic-clonic ES, the optimal cut-off point for absolute maximal-ictal HR was 114bpm (sensitivity 84%;specificity 82%;PPV 26.7%,NPV 98.5%) and for absolute post-ictal HR was 90bpm(sensitivity 91%;specificity 82%;PPV 30.3%;NPV 99.1%).ConclusionsThese findings suggest that seizure-related HR increase is useful in differentiating bilateral tonic-clonic ES from PNES. Based on the AUC, the best diagnostic measureme.

scholarly journals The Effectiveness of Neutrophil-Lymphocyte Ratio in Predicting in-Hospital Mortality in Non-ST-Elevation Myocardial Infarction

2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Begüm Şeyda Avci ◽  
Akkan Avci ◽  
Yurdaer Dönmez ◽  
Adem Kaya ◽  
Müge Gülen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Hospital Mortality ◽  
Strong Predictor ◽  
White Blood Cells ◽  
Area Under The Curve ◽  
Simple Method ◽  
Distribution Width ◽  
Predictive Values ◽  
Neutrophil Lymphocyte Ratio ◽  
Cheap Method

Background. Myocardial infarction is the most common cause of death all over the world. There are many studies in predicting mortality. The aim of this study was to determine the effectiveness of hematologic parameters measured at the moment of admission to the emergency room in predicting in-hospital mortality and to determine cutoff values of strongly predictive values. Methods. A total of 681 patients over 18 years of age, whose date could be obtained, were included in the study. From the hemogram parameters, white blood cells (WBC), red cell distribution width (RDW), mean platelet volume (MPV), and neutrophils-to-lymphocytes ratio (NLR) values were determined and recorded. CK-MB and high-sensitive troponin T values were recorded as cardiac markers. For statistical analysis, “SPSS for Windows version 21” package program was used. Results. 62.6% (n = 426) of the patients were male, and 37.4% (n = 255) of the patients were female. The NLR was found to be the strongest predictor (area under the curve (AUC), 0.783, SD = 0.052, 95% confidence interval (CI)). It was found that the WBC value came in the second place after NLR as a strong predictor of mortality (AUC, 0.702, SD = 0.075, 95% CI). Conclusion. According to the hemogram results which were acquired with a simple and cheap method, we found that WBC and especially NLR values obtained with a simple method can be used as powerful predictors.

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