scholarly journals Development and Validation of a Nomogram to Predict Deteriorating Trajectory in Patients with COVID-19 Infection: A Population-Based Prospective Study

2020 ◽  
Author(s):  
Wei Chen ◽  
Menglin Zhu ◽  
Jian Li ◽  
Cuiping Pan ◽  
Demian Zhao ◽  
...  
Keyword(s):  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Population Based ◽  
Clinical Prediction ◽  
Single Center ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Clinical Prediction Model ◽  
Validation Set ◽  
Development And Validation

Abstract Background Most of the patients with COVID-19 infection are mild to moderate initially. However, there is no effective prediction for the patients to develop into severe or extremely severe. This study aims to develop an effective clinical prediction model.Methods A single-center, retrospective, observational study conducted. A nomogram was conducted based on the results of multivariate logistic regression analysis. Results A total of 483 patients diagnosed mild to moderate were included, among these patients 62 developed severe or extremely critical illness. Seven variables including hyperlipidemia, vomiting, diarrhea, lymphocyte, imaging and mentality were associated with deteriorating trajectory. The ROC curve showed that model was robust, for which the area under the curve of the training set and the validation set are 0.873 and 0.813.Conclusions For patients with mild to moderate COVID-19 infection, nomogram score can effectively predict the possibility of patients developing into severe or extremely critical.

scholarly journals A Nomogram Based on Clinical Characteristics for Predicting Multiple Organ Dysfunction Syndrome(MODS) Following Multiple Trauma Patients

2020 ◽  
Author(s):  
ZhenJun Miao ◽  
Faxing Wei ◽  
Feng Zhou
Keyword(s):  
Logistic Regression ◽  
Multiple Trauma ◽  
Clinical Characteristics ◽  
Area Under The Curve ◽  
Trauma Patients ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Lemeshow Test ◽  
Validation Set ◽  
Multiple Trauma Patients

Abstract BackgroundMultiple organ dysfunction syndrome (MODS) is the one of common complications,and the leading cause of late mortality in multiple trauma patients.The present study aims to develop and validate a nomogram based on clinical characteristics in order to identify the patients with multiple trauma who were at risk of developing MODS.MethodsAn retrospective cohort study was performed with data from January 2011 to December 2019,totally 770 patients with multiple trauma were enrolled in our study.They were randomly categorized into training set (n=514) and validation set (n=256).The univariate and multivariate logistic regression analyses were used to screen the predictors for multiple trauma patients who were at risk of developing MODS from training set data.Then we established a nomogram based on these above predictors.The discriminative capacity was assessed by receiver operating characteristic (ROC) curve area under the curve (AUC), and the predictive precision was depicted by calibration plot.The Hosmer-Lemeshow test was used to evaluate the the model’s goodness of fit.ResultsOur study showed that age,ISS,hemorrhagic shock,heart rate,blood glucose,D-dimer and APTT were independent risk factors for MODS in patients with multiple trauma by multivariate logistic regression analysis.A nomogram was established on basis of these above risk factors.The area under the curve (AUC) was 0.868 (95% confidence interval [CI]:0.829-0.908) in the training set and 0.884 (95% confidence interval [CI]:0.833-0.935) in the validation set.The Hosmer-Lemeshow test has a p value of 0.227 in training set and 0.554 in validation set respectively,which confirm the model’s goodness of fit.Calibration plot showed that the predicted and actual incidence of MODS probability were fitted well on both internal and external validations.ConclusionsThe present nomogram had a well predictive precision and discrimination capacity,which can facilitate improved screening and early identification of multiple trauma patients who were at high risk of developing MODS.

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 Comorbidity index for predicting mortality at 6 months after reperfusion therapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyo Suk Nam ◽  
Young Dae Kim ◽  
Joonsang Yoo ◽  
Hyungjong Park ◽  
Byung Moon Kim ◽  
...  
Keyword(s):  
Prospective Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Reperfusion Therapy ◽  
Good Prediction ◽  
Multivariate Logistic Regression ◽  
Stroke Patients ◽  
Number Of Patients ◽  
Comorbidity Index ◽  
Using Data

AbstractThe eligibility of reperfusion therapy has been expanded to increase the number of patients. However, it remains unclear the reperfusion therapy will be beneficial in stroke patients with various comorbidities. We developed a reperfusion comorbidity index for predicting 6-month mortality in patients with acute stroke receiving reperfusion therapy. The 19 comorbidities included in the Charlson comorbidity index were adopted and modified. We developed a statistical model and it was validated using data from a prospective cohort. Among 1026 patients in the retrospective nationwide reperfusion therapy registry, 845 (82.3%) had at least one comorbidity. As the number of comorbidities increased, the likelihood of mortality within 6 months also increased (p < 0.001). Six out of the 19 comorbidities were included for developing the reperfusion comorbidity index on the basis of the odds ratios in the multivariate logistic regression analysis. This index showed good prediction of 6-month mortality in the retrospective cohort (area under the curve [AUC], 0.747; 95% CI, 0.704–0.790) and in 333 patients in the prospective cohort (AUC, 0.784; 95% CI, 0.709–0.859). Consideration of comorbidities might be helpful for the prediction of the 6-month mortality in patients with acute ischemic stroke who receive reperfusion therapy.

scholarly journals Musculoskeletal Disorders, Pain Medication and In-Hospital Mortality among Patients with COVID-19 in South Korea: A Population-Based Cohort Study

2021 ◽  
Vol 18 (13) ◽  
pp. 6804
Author(s):  
Tak-Kyu Oh ◽  
In-Ae Song ◽  
Joon Lee ◽  
Woosik Eom ◽  
Young-Tae Jeon
Keyword(s):  
South Korea ◽  
Hospital Mortality ◽  
Musculoskeletal Disorders ◽  
Multivariate Logistic Regression Analysis ◽  
Population Based ◽  
Pain Medication ◽  
Multivariate Logistic Regression ◽  
Lower Back ◽  
Inflammatory Drugs ◽  
Strong Opioids

We aimed to investigate whether comorbid musculoskeletal disorders (MSD)s and pain medication use was associated with in-hospital mortality among patients with coronavirus disease 2019 (COVID-19). Adult patients (≥20 years old) with a positive COVID-19 diagnosis until 5 June 2020 were included in this study, based on the National Health Insurance COVID-19 database in South Korea. MSDs included osteoarthritis, neck pain, lower back pain, rheumatoid arthritis, and others, while pain medication included paracetamol, gabapentin, pregabalin, glucocorticoid, nonsteroidal anti-inflammatory drugs (NSAIDs), opioids (strong and weak opioids), and benzodiazepine. Primary endpoint was in-hospital mortality. A total of 7713 patients with COVID-19 were included, and in-hospital mortality was observed in 248 (3.2%) patients. In multivariate logistic regression analysis, no MSDs (p > 0.05) were significantly associated with in-hospital mortality. However, in-hospital mortality was 12.73 times higher in users of strong opioids (odds ratio: 12.73, 95% confidence interval: 2.44–16.64; p = 0.002), while use of paracetamol (p = 0.973), gabapentin or pregabalin (p = 0.424), glucocorticoid (p = 0.673), NSAIDs (p = 0.979), weak opioids (p = 0.876), and benzodiazepine (p = 0.324) was not associated with in-hospital mortality. In South Korea, underlying MSDs were not associated with increased in-hospital mortality among patients with COVID-19. However, use of strong opioids was significantly associated with increased in-hospital mortality among the patients.

scholarly journals Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Early Recurrence ◽  
Prediction Performance ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Combined Model ◽  
Test Set ◽  
Pathological Model ◽  
Radiomics Signature

AbstractTo investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840–0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637–0.828) and 0.844 (95% CI 0.751–0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.

scholarly journals A Radiomics nomogram for preoperative predicting recurrence of low grade gliomas based on multiparametric MRI

2021 ◽  
Author(s):  
Zhenhua Wang ◽  
Xinlan Xiao ◽  
Zhaotao Zhang ◽  
Keng He ◽  
Peipei Pang ◽  
...  
Keyword(s):  
Multivariate Logistic Regression Analysis ◽  
Low Grade ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Logistic Analysis ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery ◽  
Validation Set ◽  
The Individual ◽  
Good Agreement

Abstract Objective To develop a radiomics nomogram to predict the recurrence of Low grade glioma(LGG) after their first surgery; Methods A retrospective analysis of pathological, clinical and Magnetic resonance image(MRI) of LGG patients who underwent surgery and had a recurrence between 2017 and 2020 in our hospital was performed. After a rigorous selection,64 patients were eligible and enrolled in the study(22 cases were with recurrent gliomas),which was randomly assigned in a 7:3 ratio to either the training set and validation set; T1WI,T2WI fluid-attenuated-inversion-recovery(T2WI-FLAIR) and contrast-enhanced T1-weighted(T1CE) sequences, 396 radiomics features were extracted from each image sequence, minimum-redundancy maximum-relevancy(mRMR) alone or combining with univariate logistic analysis were used for features screening, the screened features were performed by multivariate logistic regression analysis and developed a predictive model both in training set and validation set; Receiver operating characteristic(ROC) curve, calibration curve, and decision curve analysis(DCA) were used to assess the performance of each model. Results The radiomics nomogram derived from three MRI sequence yielded an ideal performance than the individual ones, the AUC in the training set and validation set were 0.966 and 0.93 respectively, 95% confidence interval(95%CI) were 0.949-0.99 and 0.905-0.973 respectively; the calibration curves indicated good agreement between the predictive and the actual probability. The DCA demonstrated that a combination of three sequences had more favorable clinical predictive value than single sequence imaging. Conclusion Our multiparametric radiomics nomogram could be an efficient and accurate tool for predicting the recurrence of LGG after its first resection.

scholarly journals The role of red cell distribution width in the differential diagnosis of iron deficiency anemia and non-transfusion dependent thalassemia patients

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Pokpong Piriyakhuntorn ◽  
Adisak Tantiworawit ◽  
Thanawat Rattanathammethee ◽  
Chatree Chai-Adisaksopha ◽  
Ekarat Rattarittamrong ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Microcytic Anemia ◽  
Deficiency Anemia ◽  
Operating Characteristics ◽  
Training Set ◽  
Distribution Width ◽  
Validation Set ◽  
Sensitivity Specificity

This study aims to find the cut-off value and diagnostic accuracy of the use of RDW as initial investigation in enabling the differentiation between IDA and NTDT patients. Patients with microcytic anemia were enrolled in the training set and used to plot a receiving operating characteristics (ROC) curve to obtain the cut-off value of RDW. A second set of patients were included in the validation set and used to analyze the diagnostic accuracy. We recruited 94 IDA and 64 NTDT patients into the training set. The area under the curve of the ROC in the training set was 0.803. The best cut-off value of RDW in the diagnosis of NTDT was 21.0% with a sensitivity and specificity of 81.3% and 55.3% respectively. In the validation set, there were 34 IDA and 58 NTDT patients using the cut-off value of >21.0% to validate. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 70.6%, 83.1% and 72.7% respectively. We can therefore conclude that RDW >21.0% is useful in differentiating between IDA and NTDT patients with high diagnostic accuracy

scholarly journals Use of serum biomarkers in staging of canine hepatic fibrosis

2019 ◽  
Vol 31 (5) ◽  
pp. 665-673 ◽  
Author(s):  
Maud Menard ◽  
Alexis Lecoindre ◽  
Jean-Luc Cadoré ◽  
Michèle Chevallier ◽  
Aurélie Pagnon ◽  
...  
Keyword(s):  
Liver Biopsy ◽  
Hepatic Fibrosis ◽  
External Validation ◽  
Model Performance ◽  
Area Under The Curve ◽  
Gamma Glutamyl Transferase ◽  
Training Set ◽  
Internal Validation ◽  
Validation Set ◽  
Glutamyl Transferase

Accurate staging of hepatic fibrosis (HF) is important for treatment and prognosis of canine chronic hepatitis. HF scores are used in human medicine to indirectly stage and monitor HF, decreasing the need for liver biopsy. We developed a canine HF score to screen for moderate or greater HF. We included 96 dogs in our study, including 5 healthy dogs. A liver biopsy for histologic examination and a biochemistry profile were performed on all dogs. The dogs were randomly split into a training set of 58 dogs and a validation set of 38 dogs. A HF score that included alanine aminotransferase, alkaline phosphatase, total bilirubin, potassium, and gamma-glutamyl transferase was developed in the training set. Model performance was confirmed using the internal validation set, and was similar to the performance in the training set. The overall sensitivity and specificity for the study group were 80% and 70% respectively, with an area under the curve of 0.80 (0.71–0.90). This HF score could be used for indirect diagnosis of canine HF when biochemistry panels are performed on the Konelab 30i (Thermo Scientific), using reagents as in our study. External validation is required to determine if the score is sufficiently robust to utilize biochemical results measured in other laboratories with different instruments and methodologies.

Sign in / Sign up

Export Citation Format

Share Document