Decision Curve Analysis Research Articles

Predicting major adverse cardiac events using radiomics nomogram of pericoronary adipose tissue based on CCTA: A multi-center study.

The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE). To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE. We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE was delineated as cardiovascular mortality, newly diagnosed myocardial infarction, hospitalization (or re-hospitalization) for heart failure, and coronary target vessel revascularization occurring more than 30days post-CCTA examination. All enrolled patients underwent CCTA scanning. Radiomic features were meticulously extracted from the optimal diastolic phase axial slices of CCTA images. Feature reduction was achieved through a composite feature selection algorithm, laying the groundwork for the radiomic signature model. Both univariate and multivariate analyses were employed to assess clinical variables. A multifaceted logistic regression analysis facilitated the crafting of a clinical-radiological-radiomic combined model (or nomogram). Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were delineated, with the area under the ROC curve (AUCs) computed to gauge the predictive prowess of the clinical model, radiomic model, and the synthesized ensemble. A total of 12 radiomic features closely associated with MACE were identified to establish the radiomic model. Multivariate logistic regression results demonstrated that smoking, age, hypertension, and dyslipidemia were significantly correlated with MACE. In the integrated nomogram, which amalgamated clinical, imaging, and radiomic parameters, the diagnostic performance was as follows: 0.970 AUC, 0.949 accuracy (ACC), 0.833 sensitivity (SEN), 0.981 specificity (SPE), 0.926 positive predictive value (PPV), and 0.955 negative predictive value (NPV). The calibration curve indicated a commendable concordance of the nomogram, and the decision curve analysis underscored its superior clinical utility. The integration of radiomic signatures from PCAT based on CCTA, clinical indices, and imaging parameters into a nomogram stands as a promising instrument for prognosticating MACE events.

Construction of OSA-related hypertension prediction model based on nomogram

Objective:This study aimed to construct a risk prediction model for obstructive sleep apnea（OSA） related hypertension based on the nomogram, and to explore the independent risk factors for OSA-related hypertension, so as to provide reference for clinical treatment decision-making. Methods:The clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 were collected retrospectively and randomly divided into training sets and validation sets. A total of 1 493 OSA patients with 27 variables were included. The least absolute shrinkage and selection operator（Lasso） logistic regression model was used to select potentially relevant features and establish a nomogram for OSA-related hypertension.The performance and clinical benefits of this nomogram were verified in terms of discrimination, calibration ability and clinical net benefit. Results:Multivariate logistic regression showed that body mass index（BMI）, family history of hypertension, lowest oxygen saturation（LSaO2）, age and cumulative percentage of total sleep time with oxygen saturation below 90% were independent risk factors for OSA-related hypertension. Lasso logistic regression identified BMI, family history of hypertension, LSaO2 and age as predictive factors for inclusion in the nomogram. The nomogram provided a favorable discrimination, with a C-indexes of 0.835（95% confidence interval[CI ]0.806-0.863） 0.865（95%CI 0.829-0.900） for the training and validation cohort, respectively, and well calibrated. The clinical decision curve analysis displayed that the nomogram was clinically useful. Conclusion:Compared with cumulative percentage of total sleep time with blood oxygen saturation below 90%, LSaO2 may have a greater impact on the incidence of OSA-related hypertension, and the effects of different times and degrees of hypoxia on OSA-related hypertension should be further explored in the future. Apnea hypopnea index involvement is weak in predicting OSA-related hypertension, and the blood oxygen index may be a better predictor variable. Furthermore, we established a risk prediction model for OSA-related hypertension patients using nomogram, and demonstrated that this prediction model was helpful to identify high-risk OSA-related hypertension patients. This model can provide early and individualized diagnosis and treatment plans, protect patients from the serious.

Risk Factors for Multidrug Resistance in Patients Infected with Carbapenem-Resistant Klebsiella pneumoniae: A Nomogram.

Our aim was to determine the risk factors for multidrug resistance in patients with carbapenem-resistant Klebsiella pneumoniae (CRKP). The information of 196 patients with Klebsiella pneumoniae infection was collected. The patients were subsequently assigned to the carbapenem-resistant, multidrug-resistant, and non-multidrug-resistant groups. The risk factors for multidrug resistance in CRKP patients were assessed via least absolute shrinkage and selection operator and logistic regression analyses. Moreover, a nomogram was constructed dependent on the identified risk factors, and calibration and decision curves were plotted to detect its accuracy. Length of stay (LOS) [odds ratio (OR) and 95% confidence interval (CI): 4.558 (1.157-17.961), P = 0.030], intensive care unit (ICU) stay within 30 days [OR and 95% CI: 12.643 (3.780-42.293), P < 0.001], Glasgow Coma Scale (GCS) score [OR and 95% CI: 13.569 (2.738-67.236), P = 0.001], fungal infection [OR and 95% CI: 6.398 (1.969-20.785), P = 0.002], and cardiovascular disease (CVD) [OR and 95% CI: 3.871 (1.293-11.592), P = 0.016] were identified as risk factors for multidrug resistance in CRKP patients. The concordance index (C-index) of the constructed nomogram was 0.950 (95% CI: 0.945-0.955). Moreover, decision curve analysis elucidated the nomogram utilization across a wide range of probability thresholds, ranging from 1% to 100%. Finally, internal validation using random data validated the robustness of the predictive model, yielding a C-index of 0.937. The LOS, ICU stay within 30 days, GCS score, fungal infection, and CVD were recognized as risk factors for multidrug resistance in CRKP patients. The constructed nomogram could accurately predict multidrug-resistant CRKP infections in patients.

Development and verification of prognostic nomogram for extraskeletal Ewing's sarcoma based on the SEER database

BackgroundExtraskeletal Ewing's sarcoma (EES) is a rare tumor, and there is currently no predictive model for overall survival of EES patients. This study sought to use data from the Surveillance, Epidemiology, and End Results (SEER) database to develop a clinical predictive model that could be used to assess the prognosis of EES patients. MethodsWe selected and downloaded prognostic data on 356 patients diagnosed with extraskeletal Ewing’s sarcoma based on screening criteria,These patients were distributed between 2004 and 2015. 356 patients were randomly divided into a training cohort (70%) and an internal validation cohort (30%). After univariate or multifactor Cox regression analysis, the relevant variables were screened and a nomogram was constructed. The consistency index (C-index), calibration chart and receiver operating characteristic (ROC) curve were used to evaluate the established nomogram. The clinical utility of the model was verified by clinical decision curve.Study conducted and outcomes reported according to STROBE statement. ResultsAfter multifactor regression analysis, we identified five factors that were significantly associated with EES prognosis, and subsequently established a nomogram. Verification data showed that the C-index of this nomogram was 0.829 (95% CI 0.774 -- 0.884). the AUCs of the nomogram for predicting the 3- and 5-year OS were 0.91 and 0.863. the calibration curves and Decision curve analysis showed that nomogram could predict the prognosis of EES patients. ConclusionStage, age, tumour size, chemotherapy, and surgery may be independent prognostic factors for EES. our study produced a survival nomogram that can be used to predict the prognosis of patients with EES and validated its performance, which may help clinicians evaluate the condition of patients with EES and choose personalized treatment.

Role of m6A Methylation Regulators in the Diagnosis and Subtype Classification of COPD Based on the GEO Database.

N6-methyladenosine (m6A) is a prevalent mRNA modifier, yet its role in chronic obstructive pulmonary disease (COPD) remains unexplored. We sourced expression levels of m6A methylation regulators from the GSE76925 dataset. These regulators' differential expression (DEMs) predicted COPD risk via random forest and support vector machine models. Additionally, a nomogram model using DEMs estimated COPD prevalence. We employed consistent cluster analysis of m6A methylation regulators to categorise COPD samples into distinct subtypes. Analyses of immune cell infiltration in these subtypes and differential gene expression (DEGs) across m6A methylation subtypes were conducted. A cell model validated several m6A regulators and their associated pathways. Fifteen m6A methylation regulators showed differential expression and were used in random forest and support vector machine models. Eleven were selected for a nomogram model, which decision curve analysis suggested could benefit patients. Consensus cluster analysis divided the COPD samples into two subtypes: Cluster A and Cluster B. Cluster B was associated with neutrophil and eosinophil-dominated immunity, while Cluster A was linked with monocyte-dominated immunity. Validation of some research findings was achieved through cell experiments. m6A methylation regulators appear instrumental in diagnosing and classifying subtypes of COPD.

Identification of independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4: the construction of a nomogram.

Hypoalbuminemia is a common complication in patients with chronic kidney disease (CKD) and is associated with various adverse clinical events. Currently, there are few studies focused on identifying risk factors and constructing models for hypoalbuminemia in patients with CKD stages 3 and 4. This study aims to identify independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4 and construct a nomogram for predicting the risk of hypoalbuminemia in these patients. A total of 237 patients with CKD stages 3 and 4 treated at Anning First People's Hospital from January to December 2023 were included. Univariate and bidirectional stepwise multivariate logistic regression analyses were used to identify independent risk factors for hypoalbuminemia in these patients. Based on the results of logistic regression analyses, a nomogram was constructed. The model performance was assessed using area under the receiver operator characteristic curve, calibration curves, and decision curve analysis. Hemoglobin, red blood cells, serum sodium, and serum calcium were identified as independent risk factors for hypoalbuminemia in these patients. The contributions of each independent risk factor to hypoalbuminemia were visualized in a nomogram. The area under the receiver operator characteristic curve of the model was 0.819, indicating good discrimination. The calibration curves showed good agreement between predicted and observed outcomes. The decision curve analysis also verified that the model had the good clinical utility. Hemoglobin, red blood cells, serum sodium, and serum calcium were identified as independent risk factors of hypoalbuminemia in patients with CKD stages 3 and 4. The nomogram exhibits good discrimination, calibration, and clinical utility, offering a reliable tool for the early prediction and identification of hypoalbuminemia in these patients.

The value of ultrasonographic factors in predicting cesarean following induction.

This study aimed to develop and validate a prediction model of cesarean following induction of labor (IOL). A nomogram for the prediction of cesarean following IOL for singleton, cephalic term deliveries was created by comparing combinations of ultrasonographic and nonultrasonographic factors in a retrospective manner using patient data collected from a Chinese hospital between July, 2017 and December, 2023. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUROC) and a calibration curve. Subsequently, decision curve analysis (DCA) was conducted to pinpoint the optimal probability threshold for the predictive model to exhibit practical significance for clinical decision-making. A total of 738 women were included. The inclusion of ultrasound factors yielded a higher AUC when combined with nonultrasonographic factors. Of the three ultrasonographic factors analyzed, the most predictive factor for cesarean following IOL was fetal head circumference. After generating a nomogram with eight validated factors, including maternal age, gestational age, height, prior caesarean delivery, previous vaginal delivery, modified Bishop score, body mass index at delivery, and fetal head circumference by ultrasound, the trained and validated AUC values were 0.826 (95% confidence interval 0.786-0.867) and 0.883 (95% confidence interval 0.839-0.926), respectively. Decision curve analysis indicated that the model provided net benefits of between 0% and 80% of the probability threshold, indicating the benefits of using the model to make decisions concerning patients who fall within the identified range of the probability threshold. Our nomogram based on obstetric factors and fetal head circumference as obtained by ultrasound could be used to help counsel women who are considering IOL. The model demonstrates favorable net benefits within a probability threshold range of 0 to 80%.

Predicting major adverse cardiovascular events in angina patients using radiomic features of pericoronary adipose tissue based on CCTA.

This study aims to evaluate whether radiomic features of pericoronary adipose tissue (PCAT) derived from coronary computed tomography angiography (CCTA) can better predict major adverse cardiovascular events (MACE) in patients with angina pectoris. A single-center retrospective study included 239 patients with angina pectoris who underwent coronary CT examinations. Participants were divided into MACE (n = 46) and non-MACE (n = 193) groups based on the occurrence of MACE during follow-up, and further allocated into a training cohort (n = 167) and a validation cohort (n = 72) at a 7:3 ratio. Automatic segmentation of PCAT surrounding the proximal segments of the left anterior descending artery (LAD), left circumflex coronary artery (LCX), and right coronary artery (RCA) was performed for all patients. Radiomic features of the coronary arteries were extracted, screened, and integrated while quantifying the fat attenuation index (FAI) for the three vessels. Univariate and multivariate logistic regression analyses were utilized to select clinical predictors of adverse cardiovascular events. Subsequently, machine learning techniques were employed to construct models based on FAI, clinical features, and radiomic characteristics. The predictive performance of each model was assessed and compared using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis for clinical utility. The radiomics model demonstrated superior performance in predicting MACE in patients with angina pectoris within both the training and validation cohorts, yielding areas under the curve (AUC) of 0.83 and 0.71, respectively, which significantly outperformed the FAI model (AUC = 0.71, 0.54) and the clinical model (AUC = 0.81, 0.67), with statistically significant differences in AUC (p < 0.05). Calibration curves for all three predictive models exhibited good fit (all p > 0.05). Decision curve analysis indicated that the radiomics model provided higher clinical benefit than the traditional clinical and FAI models. The CCTA-based PCAT radiomics model is an effective tool for predicting MACE in patients with angina pectoris, assisting clinicians in optimizing risk stratification for individual patients. The CCTA-based radiomics model significantly surpasses traditional FAI and clinical models in predicting major adverse cardiovascular events in patients with angina pectoris.

Construction and Validation of a Prognostic Prediction Model for Gastric Mucinous Adenocarcinoma Based on the SEER Database

This study focused on the development and validation of a prognostic model to predict overall survival (OS) in patients with gastric mucinous adenocarcinoma (GMA), utilizing data from the SEER database. A cohort of 537 GMA patients was analyzed to identify factors affecting survival, using both univariate and multivariate Cox regression analyses on demographic and clinicopathological variables. The analysis led to the creation of a nomogram model. Findings revealed that TNM staging and the administration of radiotherapy or chemotherapy were significant factors impacting survival outcomes. Specifically, patients at stages T4, N3, and M1 faced a notably higher mortality risk, while those who received radiotherapy and chemotherapy experienced a significant reduction in mortality risk. The model's performance, as indicated by C-indices of 0.73 in the training set and 0.66 in the validation set, suggests robust predictive capability. The area under the curve (AUC) analysis confirmed high accuracy in predicting 1-year, 3-year, and 5-year survival rates, and calibration curves displayed strong agreement between predicted and actual outcomes. Decision curve analysis (DCA) demonstrated that the model exhibited significant clinical utility across various thresholds. Overall, this study presents a reliable prognostic tool for individualized risk stratification and clinical treatment in GMA patients. Future work should focus on enhancing the model by incorporating molecular biological data to increase its predictive accuracy and applicability.

Radiomics nomogram combined with clinical factors for predicting pathological complete response in resectable esophageal squamous cell carcinoma.

Predicting the efficacy of neoadjuvant immunochemotherapy (NICT) for esophageal squamous cell carcinoma (ESSC) prior to surgery can minimize unnecessary surgical interventions and facilitate personalized treatment strategies. Our goal is to develop and validate an image-based radiomic model using preoperative computed tomography (CT) scans and clinical data to predict pathological complete response (pCR) in resectable ESSC following neoadjuvant immunotherapy. We retrospectively collected data from patients diagnosed with ESCC at the First Affiliated Hospital of Soochow University between January 2018 and May 2023, who received preoperative neoadjuvant immunochemotherapy. Eligible patients were randomly divided into training and validation sets. Radiomic features extracted from preprocessed CT images were used to develop a radiomic model, incorporating Radiomic score (Rad-score) and clinical factors through multivariate logistic regression analysis. The model's performance was assessed for calibration, discrimination, and clinical utility in an independent validation cohort. We enrolled a total of 105 eligible participants who were randomly divided into two groups: a training set (N=74) and a validation set (N=31). After data dimension reduction and feature selection, we identified 11 radiomic features, which collectively formed the Rad-score. Rad-score had an area under the curve (AUC) of 0.83 (95% CI 0.72-0.93) in the training set and 0.78 (95% CI 0.60-0.95) in the validation set. Multivariate analysis revealed that radiological response and Neutrophil-Lymphocyte Ratio (NLR) were independent predictors of pCR, with p-values of 0.0026 and 0.0414, respectively. We developed and validated a nomogram combining Rad-score and clinical features, achieving AUCs of 0.90 (95% CI 0.82-0.98) in the training set and 0.85 (95% CI 0.70-0.99) in the validation set. The Delong test confirmed the nomogram's superiority over pure radiomic and clinical models. Decision curve analysis (DCA) and integrated discrimination improvement (IDI) assessment supported the clinical value and superiority of the combined model. The nomogram, which integrates Rad-score and clinical features, offers a precise and reliable method for predicting pCR status in ESCC patients who have undergone neoadjuvant immunochemotherapy. This tool aids in tailoring treatment strategies to individual patients.

A prediction nomogram for mortality in patients following wasp stings: a retrospective study

Numerous studies have shown that wasp stings can lead to serious, sometimes fatal, health outcomes. Predicting deaths associated with wasp stings remains challenging yet is of critical importance. This study was conducted to identify predictors and develop a visual model for predicting mortality following wasp stings. Clinical data from 486 patients were analyzed, dividing them into two groups: survival group (N = 435) and death group (N = 51). Various statistical methods were used to create a prognostic model, including one-way analysis, the least absolute shrinkage and selection operator (LASSO) regression, and binary logistic regression. The model’s accuracy was evaluated through ROC curves, calibration plots, and decision curve analysis (DCA). The study identified four key predictors of mortality: receiving more than 50 stings, having serum lactate dehydrogenase (LDH) levels of ≥ 2200 U/L, activated partial thromboplastin time (APTT) of ≥ 90 s, and the requirement for invasive mechanical ventilation within 24 h. These factors contributed to a model with an area under the ROC curve of 0.980 (95% CI: [0.968–0.992]), indicating high calibration and applicability. The decision curve analysis confirmed the model’s substantial net clinical benefit. Thus, the number of stings, serum LDH, APTT, and the need for early invasive mechanical ventilation are reliable, independent predictors of death among patients experiencing wasp stings. The developed predictive model exhibits high levels of accuracy, sensitivity, consistency, and practical use.

Development and validation of a tumor marker-based model for the prediction of lung cancer: an analysis of a multicenter retrospective study in Shanghai, China.

The incidence and mortality rates of cancer are the highest globally. Developing novel methodologies that precisely, safely, and economically differentiate between benign and malignant lung conditions holds immense clinical importance. This research seeks to construct a predictive model utilizing a combination of diverse biomarkers to effectively discriminate between benign and malignant lung diseases. This retrospective study included patients admitted to the two general hospitals in Shanghai from 2014 to 2015. This study was developed using five tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), cytokeratin fragment 21-1 (CA211), squamous cell carcinoma antigen (SCC), and neuron specific enolase (NSE). The entire sample was divided into two groups according to the hospital: 1033 cases were included in the development cohort and 300 cases in the validation cohort. Logistic regression analysis was used for univariate analysis to explore individual correlations between each selected clinical variable and lung cancer diagnostic outcome. Diagnostic prediction models were constructed and validated based on independent prognostic factors identified using multifactorial analysis. A nomogram was created using these tumor markers (age and sex were additionally included) and validated using the concordance index and calibration curves. Clinical prediction models were evaluated using decision curve analysis. Fully adjusted multivariate analysis showed that the risk of lung cancer was 2.38 times higher in men than in women. CEA positivity was associated with an 13.41-fold increased risk in lung cancer. The area under the curve (AUC) values for the development cohort and validation cohort models were 0.907 and 0.954, respectively. In the established nomogram, the AUC for the receiver operating characteristic curve was 0.907 (95% CI, 0.889-0.925). The validation model confirmed the strong discriminative power of the nomogram (AUC = 0.954). The described calibration curves demonstrated good fit predictions and observation probabilities. In addition, decision curve analysis concluded that the newly established nomogram has important implications for clinical decision making. Combined prediction models based on CEA, CA199, CA211, SCC, and NSE biomarkers could significantly the differentiation between benign and malignant lung diseases, thus facilitating better clinical decision making.

Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI

BackgroundFemoral head collapse is a critical pathological change and is regarded as turning point in disease progression in osteonecrosis of the femoral head (ONFH). In this study, we aim to build an automatic femoral head collapse prediction pipeline for ONFH based on magnetic resonance imaging (MRI) radiomics.MethodsIn the segmentation model development dataset, T1-weighted MRI of 222 hips from two hospitals were retrospectively collected and randomly split into training (n = 190) and test (n = 32) sets. In the prognosis prediction model development dataset, 206 hips were also retrospectively collected from two hospitals and divided into training set (n = 155) and external test set (n = 51) according to data source. A deep learning model for automatic lesion segmentation was trained with nnU-Net, from which three-dimensional regions of interest were segmented and a total of 107 radiomics features were extracted. After intra-class correlation coefficients screening, feature correlation coefficient screening and Least Absolute Shrinkage and Selection Operator regression feature selection, a machine learning model for ONFH prognosis prediction was trained with Logistic Regression (LR) and Light Gradient Boosting Machine (LightGBM) algorithm.ResultsThe segmentation model achieved an average dice similarity coefficient of 0.848 and an average 95% Hausdorff distance of 3.794 in the test set, compared to the manual segmentation results. After feature selection, nine radiomics features were included in the prognosis prediction model. External test showed that the LightGBM model exhibited acceptable predictive performance. The area under the curve (AUC) of the prediction model was 0.851 (95% CI: 0.7268–0.9752), with an accuracy of 0.765, sensitivity of 0.833, and specificity of 0.727. Decision curve analysis showed that the LightGBM model exhibited favorable clinical utility.ConclusionThis study presents an automated pipeline for predicting femoral head collapse in ONFH with acceptable performance. Further research is necessary to determine the clinical applicability of this radiomics-based approach and to assess its potential to assist in treatment decision-making for ONFH.

The Comparative Effectiveness of Potent P2Y12 Inhibitors Versus Clopidogrel in Patients with Acute Myocardial Infarction Undergoing PCI: National Registry Data.

Dual antiplatelet therapy (DAPT), which is essential in AMI management, combines aspirin with a P2Y12 receptor antagonist. This study compared the effectiveness of potent P2Y12 inhibitors versus clopidogrel in AMI patients treated with percutaneous coronary intervention (PCI). Methods: 65,986 AMI patients included in a nationwide prospective registry who underwent PCI and received DAPT were studied. In total, 9,014 patients received potent P2Y12 inhibitors, and 56,074 received clopidogrel. This study focused on mortality, recurrent myocardial infarction, stroke, repeat revascularization, and major adverse cardiovascular events (MACE) over seven years. The analysis utilized unadjusted models and inverse probability of treatment weighting (IPTW) to compare prognosis, and decision curve analyses were constructed to aid clinical decision making. Results: Potent P2Y12 inhibitors significantly reduced mortality risk (unadjusted hazard ratio (HR): 0.58; IPTW HR: 0.68) and MACE (unadjusted HR: 0.66; IPTW HR: 0.78). Diabetic patients showed greater benefits (HR:0.45). In patients at high bleeding risk, the mortality rate was 13% (HR: 0.87, p = 0.08). For patients aged 75-79, the HR for mortality was 0.82, whereas for those aged >80 years, it was 0.79, indicating significant mortality risk reduction. Similar trends were observed for MACE. Conclusion: This study demonstrated that potent P2Y12 inhibitors are more effective than clopidogrel in reducing mortality and MACE in patients with AMI and underscored their potential role in improving outcomes across diverse patient subgroups. The trend was consistent even during the COVID-19 pandemic. These findings highlight the need for personalized DAPT strategies, particularly for high-bleeding-risk patients, and challenge current guidelines favoring clopidogrel use in older patients.

Development and validation of prognostic nomogram in pediatric melanoma: a population-based study.

The aim of the study is to use the Surveillance, Epidemiology, and End Results (SEER) database to develop a useful clinical nomogram that uses prognosis prediction for pediatric melanoma patients. We obtained clinical information on pediatric melanoma patients from the SEER database between 2000 and 2018. Each patient was split into a training cohort or a validation cohort at random. Results between various subgroups were compared using Kaplan-Meier analyses. We created a nomogram to calculate the probability of survival for pediatric patients with melanoma. The performance of nomograms was assessed using calibration and discrimination. To assess the clinical use of this newly created model, decision curve analysis was also performed. In this study, a total of 890 eligible patients were chosen at random and allocated to 70% of training cohorts ( n = 623) and 30% of validation cohorts ( n = 267). After applying the chosen various components to create a nomogram, validated indexes showed that the nomogram had a strong capacity for discrimination. The training set's and validation set's C-index values were 0.817 and 0.832, respectively. The calibration plots demonstrated a strong correlation between the observation and the forecast. The model has a good clinical net benefit for pediatric melanoma patients, according to the clinical decision curve. In conclusion, we created an effective survival prediction model for pediatric melanoma. This nomogram is accurate and useful for clinical decision-making. Still, more external confirmation is required.

Development and validation of a renal involvement model for patients with hyperuricemia: A cross-sectional study.

To construct a prediction model for renal involvement in patients with hyperuricemia (HUA) based on logistic regression analysis, to achieve early risk stratification. In this cross-sectional study, we collected data from the National Health and Nutrition Examination Survey (NHANES), and constructed a predicted model for renal involvement in HUA patients. The discriminative ability of the model was assessed using the receiver operating characteristic (ROC) curve. Model accuracy was evaluated using the Hosmer-Lemeshow test and calibration curve, while clinical utility was assessed using decision curve analysis (DCA). Furthermore, internal and external validation cohorts were also applied to validate the model. A total of 1669 patients from NHANES between 2007 and 2010 were included in the final analysis for modeling and validation. Six predictive factors including age, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Cr, Uric Acid (UA), and sex were identified by binary logistic regression analysis for renal involvement in HUA patients and used to construct a nomogram with good consistency and accuracy. The AUC values for the predictive model, internal validation, and external validation were 0.881 (95% CI: 0.836-0.926), 0.908 (95% CI: 0.871-0.944), and 0.927 (95% CI: 0.897-0.957), respectively. The calibration curves demonstrated consistency between the nomogram and observed values. The DCA curves of the model and validation cohort indicated good clinical utility. This study developed a predictive model for renal involvement in hyperuricemia patients with strong predictive performance and validated by internal and external cohorts, aiding in the early detection of high-risk populations for renal involvement.

A novel computed tomography enterography radiomics combining intestinal and creeping fat features could predict surgery risk in patients with Crohn's disease.

The objective of this study is to segment creeping fat and intestinal wall on computed tomography enterography (CTE) and develop a radiomic model to predict 1-year surgery risk in patients with Crohn's disease. This retrospective study included 135 Crohn's disease patients who underwent CTE between January and December 2021 (training cohort) and 69 patients between January and June 2022 (test cohort). A total of 1874 radiomic features were extracted from the intestinal wall and creeping fat respectively on the venous phase CTE images, and radiomic models were constructed based on the selected features using the Boruta and extreme gradient boosting algorithms. The combined models were established by integrating clinical predictors and radiomic models. The receiver operating characteristic curve, calibration curve, and decision curve analyses were used to compare the predictive performance of models. In the training and test cohorts, the area under the curve (AUC) values of the creeping fat radiomic model for surgery risk stratification were 0.916 and 0.822, respectively, similar to the intestinal model with AUC values of 0.889 and 0.822. Moreover, the combined radiomic model was superior to the single models, showing good discrimination with the highest AUC values (training cohort: 0.963; test cohort: 0.882). Addition of clinical predictors to the radiomic models failed to significantly improve the diagnostic ability. The CTE-based creeping fat radiomic model provided additional information to the intestinal radiomic model, and their combined radiomic model enables accurate surgery risk prediction of Crohn's disease patients within 1 year of CTE.

Development and internal and external validation of a nomogram model for predicting the risk of chronic kidney disease progression in IgA nephropathy patients.

IgA nephropathy (IgAN) is the most common primary glomerular disease in chronic kidney disease (CKD), exhibiting significant heterogeneity in both clinical and pathological presentations. We aimed to explore the risk factors influencing short-term prognosis (≥90 days) and to construct a nomogram model for evaluating the risk of CKD progression in IgAN patients. Clinical and pathological data of patients diagnosed with IgAN through biopsy at two centers were retrospectively collected. Logistic regression was employed to analyze the training cohort dataset and identify the independent predictors to construct a nomogram model based on the final variables. The predictive model was validated both internally and externally, with its performance assessed using the area under the curve (AUC), calibration curves, and decision curve analysis. Out of the patients in the modeling group, 129 individuals (41.6%) did not achieve remission following 3 months of treatment, indicating a high risk of CKD progression. A multivariate logistic regression analysis demonstrated that body mass index, urinary protein excretion, and tubular atrophy/interstitial fibrosis were identified as independent predictors for risk stratification. A nomogram model was formulated utilizing the final variables. The AUCs for the training set, internal validation set, and external validation set were 0.746 (95% confidence intervals (CI) [0.691-0.8]), 0.764 (95% CI [0.68-0.85]), and 0.749 (95% CI [0.65-0.85]), respectively. The validation of the subgroup analysis also demonstrated a satisfactory AUC. This study developed and validated a practical nomogram that can individually predict short-term treatment outcomes (≥90 days) and the risk of CKD progression in IgAN patients. It provides reliable guidance for timely and personalized intervention and treatment strategies.

A nomogram incorporating linezolid and metabolite concentrations for predicting linezolid induced thrombocytopenia in patients with renal impairment

A nomogram to estimate the risk of linezolid-induced thrombocytopenia in patients with renal impairment is not available. The aim of the study is to develop a nomogram for predicting linezolid-induced thrombocytopenia in patients with renal impairment and to investigate the incremental value of PNU-142300 concentration beyond clinical factors and linezolid trough concentration (Cmin) for risk prediction. Logistic regression was used to identify independent risk factors for linezolid-induced thrombocytopenia in patients with renal impairment and nomograms were established. The performance of the nomograms was assessed in terms of area under the receiver operating characteristic curve (AUROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI) , decision curve analysis (DCA) and calibration. Internal validation and external validation of the nomograms were also performed. Four nomograms were created: nomogram A including total bilirubin, creatinine clearance and concomitant mannitol use; nomogram B containing linezolid Cmin additionally; nomogram C containing total bilirubin, concomitant mannitol use, linezolid Cmin, and PNU142300 concentration; nomogram D including total bilirubin, concomitant mannitol use, and PNU142300 concentration. Nomogram C improved the prediction performance than nomogram A (AUROC 0.881 vs. 0.749; NRI 0.290; IDI 0.226) and nomogram B (AUROC 0.881 vs. 0.812; NRI 0.152; IDI 0.130) in the training cohort. DCA analysis showed that nomogram C yielded a greater net benefit. Compared with nomogram A and nomogram B, nomogram C also showed superior discriminatory efficacy, good calibration and clinical usefulness in the external validation cohort. The nomogram containing PNU-142300 concentration and linezolid Cmin had better predictive capability than that containing linezolid Cmin for predicting linezolid-induced thrombocytopenia in patients with renal impairment.

Automatic tumor segmentation and lymph node metastasis prediction in papillary thyroid carcinoma using ultrasound keyframes.

Accurate preoperative prediction of cervical lymph node metastasis (LNM) for papillary thyroid carcinoma (PTC) patients is essential for disease staging and individualized treatment planning, which can improve prognosis and facilitate better management. To establish a fully automated deep learning-enabled model (FADLM) for automated tumor segmentation and cervical LNM prediction in PTC using ultrasound (US) video keyframes. The bicentral study retrospective enrolled 518 PTC patients, who were then randomly divided into the training (Hospital 1, n=340), internal test (Hospital 1, n=83), and external test cohorts (Hospital 2, n=95). The FADLM integrated mask region-based convolutional neural network (Mask R-CNN) for automatic thyroid primary tumor segmentation and ResNet34 with Bayes strategy for cervical LNM diagnosis. A radiomics model (RM) using the same automated segmentation method, a traditional radiomics model (TRM) using manual segmentation, and a clinical-semantic model (CSM) were developed for comparison. The dice similarity coefficient (DSC) was used to evaluate segmentation performance. The prediction performance of the models was validated in terms of discrimination and clinical utility with the area under the receiver operator characteristic curve (AUC), heatmap analysis, and decision curve analysis (DCA). The comparison of the predictive performance among different models was conducted by DeLong test. The performances of two radiologists compared with FADLM and the diagnostic augmentation with FADLM's assistance were analyzed in terms of accuracy, sensitivity and specificity using McNemar's x2 test. The p-value less than 0.05 was defined as a statistically significant difference. The Benjamini-Hochberg procedure was applied for multiple comparisons to deal with Type I error. The FADLM yielded promising segmentation results in training (DSC: 0.88±0.23), internal test (DSC: 0.88±0.23), and external test cohorts (DSC: 0.85±0.24). The AUCs of FADLM for cervical LNM prediction were 0.78 (95% CI: 0.73, 0.83), 0.83 (95% CI: 0.74, 0.92), and 0.83 (95% CI: 0.75, 0.92), respectively. It all significantly outperformed the RM (AUCs: 0.78vs. 0.72; 0.83vs. 0.65; 0.83vs. 0.68, all adjusted p-values<0.05) and CSM (AUCs: 0.78vs. 0.71; 0.83vs. 0.62; 0.83vs. 0.68, all adjusted p-values<0.05) across the three cohorts. The RM offered similar performance to that of TRM (AUCs: 0.61vs. 0.63, adjusted p-value =0.60) while significantly reducing the segmentation time (3.3±3.8 vs. 14.1±4.2 s, p-value <0.001). Under the assistance of FADLM, the accuracies of junior and senior radiologists were improved by 18% and 15% (all adjusted p-values<0.05) and the sensitivities by 25% and 21% (all adjusted p-values<0.05) in the external test cohort. The FADLM with elaborately designed automated strategy using US video keyframes holds good potential to provide an efficient and consistent prediction of cervical LNM in PTC. The FADLM displays superior performance to RM, CSM, and radiologists with promising efficacy.