Evaluation of keratoconus progression

2018 ◽  
Vol 103 (4) ◽  
pp. 551-557 ◽  
Author(s):  
Mehdi Shajari ◽  
Gernot Steinwender ◽  
Kim Herrmann ◽  
Kate Barbara Kubiak ◽  
Ivana Pavlovic ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Youden Index ◽  
Back Surface ◽  
Corneal Surface ◽  
Maximum Elevation ◽  
Index Index ◽  
Variance Index

AimTo define variables for the evaluation of keratoconus progression and to determine cut-off values.MethodsIn this retrospective cohort study (2010–2016), 265 eyes of 165 patients diagnosed with keratoconus underwent two Scheimpflug measurements (Pentacam) that took place 1 year apart ±3 months. Variables used for keratoconus detection were evaluated for progression and a correlation analysis was performed. By logistic regression analysis, a keratoconus progression index (KPI) was defined. Receiver-operating characteristic curve (ROC) analysis was performed and Youden Index calculated to determine cut-off values.ResultsVariables used for keratoconus detection showed a weak correlation with each other (eg, correlation r=0.245 between RPImin and Kmax, p<0.001). Therefore, we used parameters that took several variables into consideration (eg, D-index, index of surface variance, index for height asymmetry, KPI). KPI was defined by logistic regression and consisted of a Pachymin coefficient of −0.78 (p=0.001), a maximum elevation of back surface coefficient of 0.27 and coefficient of corneal curvature at the zone 3 mm away from the thinnest point on the posterior corneal surface of −12.44 (both p<0.001). The two variables with the highest Youden Index in the ROC analysis were D-index and KPI: D-index had a cut-off of 0.4175 (70.6% sensitivity) and Youden Index of 0.606. Cut-off for KPI was −0.78196 (84.7% sensitivity) and a Youden Index of 0.747; both 90% specificity.ConclusionsKeratoconus progression should be defined by evaluating parameters that consider several corneal changes; we suggest D-index and KPI to detect progression.

scholarly journals The severity and risk factors for mortality in immunocompromised adult patients hospitalized with influenza-related pneumonia

2021 ◽  
Author(s):  
Liang Chen ◽  
Xiudi Han ◽  
YanLi Li ◽  
Chunxiao Zhang ◽  
Xiqian Xing
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Neuraminidase Inhibitor ◽  
Teaching Hospitals ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression

Abstract Objective To explore disease severity and risk factors for 30-day mortality of adult immunocompromised (IC) patients hospitalized with influenza-related pneumonia (Flu-p).Method A total of 122 IC and 1,191 immunocompetent patients hospitalized with Flu-p from January 2012 to December 2018 were recruited retrospectively from five teaching hospitals in China. Results After controlling for confounders, multivariate logistic regression analysis showed that immunosuppression was associated with increased risks for invasive ventilation [odds ratio: (OR) 2.475, 95% confidence interval (CI): 1.511-4.053, p < 0.001], admittance to the intensive care unit (OR: 3.247, 95% CI: 2.064-5.106, p < 0.001), and 30-day mortality (OR: 3.206, 95% CI: 1.926-5.335, p < 0.001) in patients with Flu-p. Another multivariate logistic regression model revealed that baseline lymphocyte counts (OR: 0.993, 95% CI: 0.990-0.996, p < 0.001), coinfection (OR: 5.450, 95% C:I 1.638-18.167, p = 0.006), early neuraminidase inhibitor therapy (OR 0.401, 95% CI 0.127-0.878, p = 0.001), and systemic corticosteroid use at admission (OR: 6.414, 95% C:I 1.348-30.512, p = 0.020) were independently related to 30-day mortality in IC patients with Flu-p. Based on receiver operating characteristic curve (ROC) analysis, the optimal cutoff for lymphocyte counts was 0.6×109/L [area under the ROC (AUROC) = 0.824, 95% CI: 0.744 - 0.887], sensitivity: 97.8%, specificity: 73.7%].Conclusions IC conditions are associated with more severe outcomes in patients with Flu-p. The predictors for mortality that we identified may be valuable for the management of Flu-p among IC patients.

scholarly journals Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3546
Author(s):  
Katarzyna Sylwia Dobruch-Sobczak ◽  
Hanna Piotrzkowska-Wróblewska ◽  
Piotr Karwat ◽  
Ziemowit Klimonda ◽  
Ewa Markiewicz-Grodzicka ◽  
...  
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Quantitative Measure ◽  
Malignant Cell ◽  
Breast Cancer Patients ◽  
The Third ◽  
Leibler Divergence

The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

Predictors of thermal increase in magnetic resonance–guided focused ultrasound treatment for essential tremor: histogram analysis of skull density ratio values for 1024 elements

2021 ◽  
pp. 1-6
Author(s):  
Ken Iijima ◽  
Hajime Yokota ◽  
Toshio Yamaguchi ◽  
Masayuki Nakano ◽  
Takahiro Ouchi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Operating Characteristic ◽  
Focused Ultrasound ◽  
Characteristic Curve ◽  
Density Ratio ◽  
Predictive Ability ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Operating Characteristic Curve

OBJECTIVE Sufficient thermal increase capable of generating thermocoagulation is indispensable for an effective clinical outcome in patients undergoing magnetic resonance–guided focused ultrasound (MRgFUS). The skull density ratio (SDR) is one of the most dominant predictors of thermal increase prior to treatment. However, users currently rely only on the average SDR value (SDRmean) as a screening criterion, although some patients with low SDRmean values can achieve sufficient thermal increase. The present study aimed to examine the numerical distribution of SDR values across 1024 elements to identify more precise predictors of thermal increase during MRgFUS. METHODS The authors retrospectively analyzed the correlations between the skull parameters and the maximum temperature achieved during unilateral ventral intermediate nucleus thalamotomy with MRgFUS in a cohort of 55 patients. In addition, the numerical distribution of SDR values was quantified across 1024 elements by using the skewness, kurtosis, entropy, and uniformity of the SDR histogram. Next, the authors evaluated the correlation between the aforementioned indices and a peak temperature > 55°C by using univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis was performed to compare the predictive ability of the indices. The diagnostic performance of significant factors was also assessed. RESULTS The SDR skewness (SDRskewness) was identified as a significant predictor of thermal increase in the univariate and multivariate logistic regression analyses (p < 0.001, p = 0.013). Moreover, the receiver operating characteristic curve analysis indicated that the SDRskewness exhibited a better predictive ability than the SDRmean, with area under the curve values of 0.847 and 0.784, respectively. CONCLUSIONS The SDRskewness is a more accurate predictor of thermal increase than the conventional SDRmean. The authors suggest setting the SDRskewness cutoff value to 0.68. SDRskewness may allow for the inclusion of treatable patients with essential tremor who would have been screened out based on the SDRmean exclusion criterion.

scholarly journals Machine Learning-based in-hospital Mortality Prediction Models for Patients With Acute Coronary Syndrome

2020 ◽  
Author(s):  
Jun Ke ◽  
Yiwei Chen ◽  
Xiaoping Wang ◽  
Zhiyong Wu ◽  
qiongyao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Hospital Mortality ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Hdl Cholesterol ◽  
Coronary Syndrome

Abstract BackgroundThe purpose of this study is to identify the risk factors of in-hospital mortality in patients with acute coronary syndrome (ACS) and to evaluate the performance of traditional regression and machine learning prediction models.MethodsThe data of ACS patients who entered the emergency department of Fujian Provincial Hospital from January 1, 2017 to March 31, 2020 for chest pain were retrospectively collected. The study used univariate and multivariate logistic regression analysis to identify risk factors for in-hospital mortality of ACS patients. The traditional regression and machine learning algorithms were used to develop predictive models, and the sensitivity, specificity, and receiver operating characteristic curve were used to evaluate the performance of each model.ResultsA total of 7810 ACS patients were included in the study, and the in-hospital mortality rate was 1.75%. Multivariate logistic regression analysis found that age and levels of D-dimer, cardiac troponin I, N-terminal pro-B-type natriuretic peptide (NT-proBNP), lactate dehydrogenase (LDH), high-density lipoprotein (HDL) cholesterol, and calcium channel blockers were independent predictors of in-hospital mortality. The study found that the area under the receiver operating characteristic curve of the models developed by logistic regression, gradient boosting decision tree (GBDT), random forest, and support vector machine (SVM) for predicting the risk of in-hospital mortality were 0.963, 0.960, 0.963, and 0.959, respectively. Feature importance evaluation found that NT-proBNP, LDH, and HDL cholesterol were top three variables that contribute the most to the prediction performance of the GBDT model and random forest model.ConclusionsThe predictive model developed using logistic regression, GBDT, random forest, and SVM algorithms can be used to predict the risk of in-hospital death of ACS patients. Based on our findings, we recommend that clinicians focus on monitoring the changes of NT-proBNP, LDH, and HDL cholesterol, as this may improve the clinical outcomes of ACS patients.

Preoperative skeletal muscle density is associated with postoperative mortality in patients with cardiovascular disease

2019 ◽  
Vol 30 (4) ◽  
pp. 515-522 ◽  
Author(s):  
Masashi Yamashita ◽  
Kentaro Kamiya ◽  
Atsuhiko Matsunaga ◽  
Tadashi Kitamura ◽  
Nobuaki Hamazaki ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Skeletal Muscle ◽  
Receiver Operating Characteristic Curve ◽  
Prognostic Model ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Postoperative Mortality ◽  
Youden Index ◽  
Muscle Density ◽  
Operating Characteristic Curve

Abstract OBJECTIVES Although skeletal muscle density (SMD) is useful for predicting mortality, the cut-off in an acute clinical setting is unclear, especially in patients with cardiovascular disease (CVD). This study was performed to determine the preoperative SMD cut-off using the psoas muscle and to investigate the effect on postoperative outcomes, including sarcopaenia, in CVD patients. METHODS Preoperative psoas SMD was measured by abdominal computed tomography in CVD patients. Postoperative sarcopaenia was defined according to the criteria of the Asia Working Group for Sarcopaenia. The Youden index was used to test the predictive accuracy of survival models. The prognostic capability was evaluated using multivariable survival and receiver operating characteristic curve analyses. RESULTS Continuous data were available for 1068 patients (mean age 65.5 years; 63.6% male). A total of 105 (9.8%) deaths occurred during the 1.99-year median follow-up period (interquartile range 0.71–4.15). The psoas SMD cut-off estimated by the Youden index was 45 Hounsfield units with high sensitivity and moderate specificity for all-cause mortality and was consistent in various stratified analyses. After adjusting for the existing prognostic model, EuroSCORE II, preoperative and postoperative physical status, psoas SMD cut-off was predicted for mortality (hazard ratio 2.42, 95% confidence interval 1.32–4.45). The psoas SMD cut-off was also significantly associated with postoperative sarcopaenia and provided additional prognostic information to EuroSCORE II on receiver operating characteristic curve analysis (area under the curve 0.627 vs 0.678, P = 0.011). CONCLUSIONS Reduced psoas SMD was associated with postoperative mortality and added information prognostic for mortality to the existing prognostic model in CVD patients.

scholarly journals Accuracy and adequacy of waist circumference cut-off points currently recommended in Brazilian adults

2013 ◽  
Vol 17 (4) ◽  
pp. 861-869 ◽  
Author(s):  
Carolina Avila Vianna ◽  
Rogério da Silva Linhares ◽  
Renata Moraes Bielemann ◽  
Eduardo Coelho Machado ◽  
David Alejandro González-Chica ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Waist Circumference ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Youden Index ◽  
Southern Brazil ◽  
Receiver Operating

AbstractObjectiveTo evaluate the adequacy and accuracy of cut-off values currently recommended by the WHO for assessment of cardiovascular risk in southern Brazil.DesignPopulation-based study aimed at determining the predictive ability of waist circumference for cardiovascular risk based on the use of previous medical diagnosis for hypertension, diabetes mellitus and/or dyslipidaemia. Descriptive analysis was used for the adequacy of current cut-off values of waist circumference, receiver operating characteristic curves were constructed and the most accurate criteria according to the Youden index and points of optimal sensitivity and specificity were identified.SettingPelotas, southern Brazil.SubjectsIndividuals (n2112) aged ≥20 years living in the city were selected by multistage sampling, since these individuals did not report the presence of previous myocardial infarction, angina pectoris or stroke.ResultsThe cut-off values currently recommended by WHO were more appropriate in men than women, with overestimation of cardiovascular risk in women. The area under the receiver operating characteristic curve showed moderate predictive ability of waist circumference in men (0·74, 95 % CI 0·71, 0·76) and women (0·75, 95 % CI 0·73, 0·77). The method of optimal sensitivity and specificity showed better performance in assessing the accuracy, identifying the values of 95 cm in men and 87 cm in women as the best cut-off values of waist circumference to assess cardiovascular risk.ConclusionsThe cut-off values currently recommended for waist circumference are not suitable for women. Longitudinal studies should be conducted to evaluate the consistency of the findings.

scholarly journals Regional function-structure relationships in lungs of an elastase murine model of emphysema

2012 ◽  
Vol 112 (1) ◽  
pp. 135-148 ◽  
Author(s):  
Masaru Ishii ◽  
Kiarash Emami ◽  
Yi Xin ◽  
Amy Barulic ◽  
Charles J. Kotzer ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Murine Model ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Healthy Male ◽  
Diseased Animal ◽  
Future Possibility ◽  
Probability Maps ◽  
Imaging Results ◽  
Apparent Diffusion

Changes in lung function and structure were studied using hyperpolarized 3He MRI in an elastase-induced murine model of emphysema. The combined analysis of the apparent diffusion coefficient (ADC) and fractional ventilation ( R) were used to distinguish emphysematous changes and also to develop a model for classifying sections of the lung into diseased and normal. Twelve healthy male BALB/c mice (26 ± 2 g) were randomized into healthy and elastase-induced mice and studied ∼8–11 wk after model induction. ADC and R were measured at a submillimeter planar resolution. Chord length ( L x) data were analyzed from histology samples from the corresponding imaged slices. Logistic regression was applied to estimate the probability that an imaged pixel came from a diseased animal, and bootstrap methods (1,000 samples) were used to compare the regression results for the morphological and imaging results. Multivariate ANOVA (MANOVA) was used to analyze transformed ADC (ADCBC), and R ( RBC) data and also to control for the experiment-wide error rate. MANOVA and ANOVA showed that elastase induced a statistically measureable change in the average transformed L x and ADCBC but not in the average RBC. Marginal mean analysis demonstrated that ADCBC was on average 0.19 [95% confidence interval (CI): 0.16, 0.22] higher in the emphysema group, whereas RBC was on average 0.05 (95% CI: 0.04, 0.06) lower. Logistic regression supported the hypothesis that ADCBC and RBC, together, were better at differentiating normal from diseased tissue than either measurement alone. The odds ratios for ADCBC and RBC were 7.73 (95% CI: 5.23, 11.42) and 9.14 × 10−5 (95% CI: 3.33 × 10−5, 25.06 × 10−5), respectively. Using a 50% probability cutoff, this model classified 70.6% of pixels correctly. The sensitivity and specificity of this model at the 50% cutoff were 74.9% and 65.2%, respectively. The area under the receiver operating characteristic curve was 0.76 (95% CI: 0.74, 0.78). The regression model presented can be used to map MRI data to disease probability maps. These probability maps present a future possibility of using both measurements in a more clinically feasible method of diagnosing this disease.

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