Receiver Operating Characteristic Curve Values Research Articles

The prognostic value of HEART score in patients with known heart failure evaluated at the emergency department for acute chest pain

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Lung and Blood Institute (United States). Background Chest pain is a common and concerning symptom in emergency care, often necessities further risk stratification for adverse cardiovascular events on short-term follow up. Patients with known heart failure (HF) constitutes a unique sub-population that would greatly benefit from short-term risk stratification to guide appropriate clinical interventions and optimize resource allocation. HEART (History, ECG, Age, Risk factors, Troponin) is a well-validated risk score that has been shown to outperform other clinical risk scores in patients with chest pain. It remains unknown if HEART score can be safely used in patients with known HF presenting to the emergency department with chest pain. Purpose We sought to evaluate the prognostic value of HEART score in patients with known HF compared to their counterparts. Method This was a prospective observational cohort study of consecutive adult patients evaluated at the emergency department for non-traumatic chest pain. HEART score was computed from charts by independent physician reviewers. The primary study outcome was major adverse cardiac events (MACE) defined as acute coronary syndrome, invasive revascularization, reinfarction, death, cardiac arrest, ventricular tachyarrhythmia, or acute exacerbation within 30 days of indexed admission. Outcomes were adjudicated by two independent reviewers (Kappa = 0.908). Classification performance between groups was compared using area under receiver operator characteristic curve (AUROC), sensitivity, and negative predictive value (NPV). Results Our cohort included 4,132 patients (age 59±16, 47% female, 33% Black). Overall, 921 patients (22.3%) experienced 30-day MACE. The classification performance of HEART score in those without HF (n = 3,329, 81%) was AUROC of 0.831 (95% CI 0.813-0.848), compared to only 0.633 (95% CI 0.592-0.673) in those with known HF (n = 803, 19%) (z score = 8.771, p < 0.001, Figure). The sensitivity and NPV of HEART score among those without HF were 87.4% and 95.3%, compared to 81.2% and 73.3%, respectively, among those with known HF. Conclusion Our findings suggest that the prognostic value of HEART score for predicting 30-day MACE is inferior and suboptimal in patients with known HF evaluated for chest pain at the emergency department compared to their counterparts. These findings emphasize the need for more tailored risk assessment and management strategies in this specific patient population. Further research is warranted to better understand the limitations and potential enhancements of the HEART score in patients with HF.Classification Performance of HEART score

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models.

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated. A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model. The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model. PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study

BackgroundAccurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability. Materials and methodsPatients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival. ResultsA total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71–0.84; test: 0.77, 95%CI 0.68–0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates. ConclusionsOur study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

A new prediction nomogram of non-sentinel lymph node metastasis in cT1-2 breast cancer patients with positive sentinel lymph nodes

We aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training (n = 410), internal validation (n = 298), and external validation cohorts (n = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) (P < 0.001), the proportion of positive SLNs (P = 0.029), lymph-vascular invasion (P = 0.029), perineural invasion (P = 0.023), and estrogen receptor (ER) status (P = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676–0.785) for the training, 0.701 (95% CI 0.630–0.773) for internal validation, and 0.813 (95% CI 0.734–0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.

Ovarian Weight and Uterine Volume Index Are Useful for Age Estimation in Adult Women

Practically, when only parts of an unidentified human body are found, age estimation with limited materials is required. The purpose of this study was to investigate methods for estimating age using the uterus and ovaries. Among forensic autopsies performed between January 2011 and March 2022, 211 uteruses and 521 ovaries of 322 women were used for this study. Measured values for ovarian weight and uterine volume index were corrected by body surface area to consider the effect of body size. The corrected uterine volume index increased in teenage years and achieved its maximum in the 40–49 group, then gradually decreased with increasing age. The corrected ovarian weight increased until the twenties, after which it decreased with age. For women aged 20 years or more, receiver operating characteristic (ROC) curve analysis suggested that a uterine volume index of 41.2 cm3/m2 was the cutoff value for classifying the age as ≥60 years or &lt;60 years, with an area under the ROC curve (AUC) value of 0.751. Ovarian weights of 2.27 g/m2 and 1.92 g/m2 were the cutoff values for classifying the age as ≥40 years or &lt;40 years, or ≥50 years or &lt;50 years, with AUC values of 0.935 and 0.930, respectively. These methods can help determine an unknown individual’s age group simply and quickly, even for incomplete cadavers.

Endocannabinoids and cortisol in plasma, aqueous and tear samples of primary angle closure glaucoma versus controls.

To compare the levels of endocannabinoids (EC) in plasma, aqueous humor and tears, cortisol in plasma and aqueous, in primary angle closure glaucoma (PACG) and controls, while comparing the quality of life in both groups. A total of 60 patients, ≥40years of age, with a diagnosis of PACG or cataract, 30 in each group were recruited. They were subjected to a detailed ophthalmic evaluation, a WHO Quality of Life Brief Version (WHOQOL-BREF) questionnaire answering and collection of tear, aqueous and blood samples. The levels of endocannabinoids-anandamide (AEA), 2-arachidonoylglycerol (2AG) in plasma, aqueous humor and tears; cortisol in plasma and aqueous humor; and WHO-QOL score in each group were noted. Plasma AEA (p = 0.01) and plasma 2-AG, (p = 0.002) levels were significantly higher in the control group as compared to the PACG group. WHO-QOL score was better in controls (p < 0.001). The EC were in undetectable levels in aqueous. Plasma and aqueous cortisol were significantly higher in PACG and both had the highest Area under the receiver operating characteristics (AUROC) curve value for differentiating PACG from controls. Tear 2AG and tear AEA had a moderately strong positive correlation with plasma 2-AG. Females had insignificantly higher levels of plasma and tear endocannabinoids. Tear endocannabinoids were determined for the first time in PACG and normal with no difference between the two groups. Plasma and aqueous cortisol levels are a differentiating factor between normal and glaucoma patients with plasma endocannabinoids being remarkably higher in normals. Quality of life in glaucoma patients with high cortisol levels is poorer.

Neutrophil-Creatinine Index: A New Prognostic Factor for Severity of Acute Pancreatitis.

Background and Objectives: Determining the severity of acute pancreatitis (AP) is the main goal in the early stage of AP. The aim of this study was to compare laboratory parameters and indices, including the neutrophil to lymphocyte ratio (NLR) and the neutrophil-creatinine index (NCI), at admission in order to predict the severity of AP. Materials and Methods: Data from 421 patients who were admitted with a diagnosis of AP were collected retrospectively. Disease severity was assessed using the Bedside Index of Severity in Acute Pancreatitis (BISAP) and the revised Atlanta classification (RAC). BISAP was graded as mild and severe, and RAC was graded as mild (MAP), moderately severe (MSAP), and severe (SAP). The laboratory parameters and indices, including the NLR and NCI, were compared. Results: Of the patients, 70 (16.6%) had severe AP according to BISAP; the AP subgroups according to the RAC were as follows: MAP (n = 213), MSAP (n = 158), and SAP (n = 50). The NCI had the highest area under the receiver operator characteristic (AUROC) curve value (0.862), demonstrating severe disease according to BISAP, with a sensitivity of 78.6% and a specificity of 79.8%. Age (OR:1.046), white blood cell count (WBC) (OR:1.141), hematocrit (OR:1.081), blood urea nitrogen (BUN) (OR:1.040), and NCI (OR:1.076) were independently associated with severe disease, according to the multivariate analysis results, and were determined as components of the newly developed nomogram. The AUROC of the nomogram (0.891) was superior to the AUROCs of all the components of the nomogram except the NCI. Moreover, the NCI was the only parameter to distinguish MSAP from MAP (OR:1.119, 95% CI: 1.015-1.235, p = 0.023) and SAP from MSAP (OR:1.095, 95% CI: 1.031-1.162, p = 0.003). Conclusions: The present study enabled the identification of the neutrophil-creatinine index as a new prognostic tool for the assessment of AP severity at hospital admission.

An Adaptive Combined Learning of Grading System for Early Stage Emerging Diseases

Currently, individual artificial intelligence (AI) algorithms face significant challenges in effectively diagnosing and predicting early stage emerging serious diseases. Our investigation indicates that these challenges primarily arise from insufficient clinical treatment data, leading to inadequate model training and substantial disparities among algorithm outcomes. Therefore, this study introduces an adaptive framework aimed at increasing prediction accuracy and mitigating instability by integrating various AI algorithms. In analyzing two cohorts of early cases of the coronavirus disease 2019 (COVID-19) in Wuhan, China, we demonstrate the reliability and precision of the adaptive combined learning algorithm. Employing an adaptive combination with three feature importance methods (Random Forest (RF), Scalable end-to-end Tree Boosting System (XGBoost), and Sparsity Oriented Importance Learning (SOIL)) for two cohorts, we identified 23 clinical features with significant impacts on COVID-19 outcomes. Subsequently, the adaptive combined prediction leveraged and enhanced the advantages of individual methods based on three forecasting algorithms (RF, XGBoost, and Logistic regression). The average accuracy for both cohorts exceeded 0.95, with the area under the receiver operating characteristics curve (AUC) values of 0.983 and 0.988, respectively. We established a severity grading system for COVID-19 based on the combined probability of death. Compared to the original classification, there was a significant decrease in the number of patients in the severe and critical levels, while the levels of mild and moderate showed a substantial increase. This severity grading system provides a more rational grading in clinical treatment. Clinicians can utilize this system for effective and reliable preliminary assessments and examinations of patients with emerging diseases, enabling timely and targeted treatment.

Development of a predictive model for risk stratification of acute kidney injury in patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy

Acute kidney injury (AKI) following hyperthermic intraperitoneal chemotherapy (HIPEC) is common. Identifying patients at risk could have implications for surgical and anesthetic management. We aimed to develop a predictive model that could predict AKI based on patients’ preoperative characteristics and intraperitoneal chemotherapy regimen. We retrospectively gathered data of adult patients undergoing HIPEC at our health system between November 2013 and April 2022. Next, we developed a model predicting postoperative AKI using multivariable logistic regression and calculated the performance of the model (area under the receiver operating characteristics curve [AUC]) via tenfold cross-validation. A total of 412 patients were included, of which 36 (8.7%) developed postoperative AKI. Based on our multivariable logistic regression model, multiple preoperative and intraoperative characteristics were associated with AKI. We included the total intraoperative cisplatin dose, body mass index, male sex, and preoperative hemoglobin level in the final model. The mean area under the receiver operating characteristics curve value was 0.82 (95% confidence interval 0.71–0.93). Our risk model predicted AKI with high accuracy in patients undergoing HIPEC in our institution. The external validity of our model should now be tested in independent and prospective patient cohorts.

Risk of cardiovascular death in patients with hepatocellular carcinoma based on the Fine-Gray model.

Hepatocellular carcinoma (HCC) is one of the most common types of cancers worldwide, ranking fifth among men and seventh among women, resulting in more than 7 million deaths annually. With the development of medical technology, the 5-year survival rate of HCC patients can be increased to 70%. However, HCC patients are often at increased risk of cardiovascular disease (CVD) death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients. Moreover, CVD and cancer have become major disease burdens worldwide. Thus, further research is needed to lessen the risk of CVD death in HCC patient survivors. To determine the independent risk factors for CVD death in HCC patients and predict cardiovascular mortality (CVM) in HCC patients. This study was conducted on the basis of the Surveillance, Epidemiology, and End Results database and included HCC patients with a diagnosis period from 2010 to 2015. The independent risk factors were identified using the Fine-Gray model. A nomograph was constructed to predict the CVM in HCC patients. The nomograph performance was measured using Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) value. Moreover, the net benefit was estimated via decision curve analysis (DCA). The study included 21545 HCC patients, of whom 619 died of CVD. Age (< 60) [1.981 (1.573-2.496), P < 0.001], marital status (married) [unmarried: 1.370 (1.076-1.745), P = 0.011], alpha fetoprotein (normal) [0.778 (0.640-0.946), P = 0.012], tumor size (≤ 2 cm) [(2, 5] cm: 1.420 (1.060-1.903), P = 0.019; > 5 cm: 2.090 (1.543-2.830), P < 0.001], surgery (no) [0.376 (0.297-0.476), P < 0.001], and chemotherapy(none/unknown) [0.578 (0.472-0.709), P < 0.001] were independent risk factors for CVD death in HCC patients. The discrimination and calibration of the nomograph were better. The C-index values for the training and validation sets were 0.736 and 0.665, respectively. The AUC values of the ROC curves at 2, 4, and 6 years were 0.702, 0.725, 0.740 in the training set and 0.697, 0.710, 0.744 in the validation set, respectively. The calibration curves showed that the predicted probabilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities. DCA demonstrated that the prediction model has a high net benefit. Risk factors for CVD death in HCC patients were investigated for the first time. The nomograph served as an important reference tool for relevant clinical management decisions.

Responsiveness of the Zurich Claudication Questionnaire in Patients with Lumbar Spinal Stenosis Undergoing Nonsurgical Treatment: A Secondary Analysis of a Randomized Controlled Trial.

Secondary analysis of a randomized controlled trial. We investigated the ability to distinguish patients with lumbar spinal stenosis (LSS) who improved from those who did not after receiving nonsurgical treatment. We used the disorder-specific Zurich Claudication Questionnaire (ZCQ) satisfaction subscale as an external anchor and estimated the minimal clinically important differences (MCIDs) for the ZCQ symptom severity and physical function subscales. The ZCQ satisfaction subscale effectively distinguishes surgical patients who improved from those who did not for LSS. However, its responsiveness in nonsurgical treatment has not been evaluated yet. Eighty-four patients with LSS who received supervised physical therapy or a home exercise program were included. Patients were classified as responders or nonresponders according to the cutoff of 2.5 for the ZCQ satisfaction subscales at 6 weeks and 1 year. The external responsiveness of the ZCQ satisfaction subscale was assessed using correlational and receiver-operating characteristic (ROC) curve analyses. MCIDs for the ZCQ symptom severity and physical function subscales were estimated using anchor and distribution approaches. Pearson's correlation coefficients between the changes in outcomes and the ZCQ satisfaction subscale at 6 weeks and 1 year were 0.37-0.58 (symptom severity) and 0.40-0.45 (physical function subscales) (>0.30 is considered a good anchor). The area under the ROC curve values were 0.66-0.72 and 0.63-0.71 for the symptom severity and physical function subscales, respectively (>0.7 is considered acceptable). The MCIDs at 6 weeks and 1 year estimated from anchor-based approaches were -0.64 to -0.13 (symptom severity) and -0.39 to 0.10 (physical function), and those from the distribution-based approaches were -0.31 to -0.30 and -0.29 to -0.27, respectively. The findings of this study suggest that the ZCQ satisfaction subscale has less ability to distinguish patients with LSS who improved in the ZCQ symptom severity and physical function subscales from those who did not after nonsurgical treatment, compared to those after surgical treatment.

A novel radiomics nomogram based on T2-sampling perfection with application-optimized contrasts using different flip-angle evolutions (SPACE) images for predicting cochlear and vestibular endolymphatic hydrops in Meniere's disease patients.

To construct and validate a radiomics nomogram based on T2-sampling perfection with application-optimized contrasts using different flip-angle evolutions (SPACE) images for predicting cochlear and vestibular endolymphatic hydrops (EH) in Meniere's disease patients. A total of 156 patients (312 affected ears) with bilateral definite Meniere's disease who underwent delayed enhancement MRI scans were enrolled in this study. All ears of the patients were divided into a training set (n = 218) and an internal validation set (n = 94). A radiomics nomogram was constructed from radiomics features extracted from the T2-SPACE images, and a radiomics score was calculated. Performance of the radiomics nomogram was assessed using receiver operating characteristics analysis. Five features were selected for the construction of the cochlear radiomics nomogram, and seven features for the vestibular radiomics nomogram. The radiomics nomograms exhibited robust performance in differentiating between EH-positive and EH-negative statuses in both training and validation cohorts, with the area under the receiver operating characteristics curve values for cochlear and vestibular radiomic nomograms being 0.703 and 0.728 in the training set, and 0.718 and 0.701 in the validation set, respectively. The novel radiomics nomograms based on T2-SPACE images were successfully constructed to predict cochlear and vestibular EH in Meniere's disease. The models showed a solid and superior performance and may play an important role in the EH prediction. We constructed a novel radiomics nomogram, which can be a very useful tool for predicting cochlear and vestibular endolymphatic hydrops in Meniere's disease patients. • This is the first T2-SPACE-based nomogram to predict cochlear and vestibular endolymphatic hydrops. • The nomogram is of great value to patients who are unable to undergo delayed enhancement MRI scans.

Predictors of Significant Histological Hepatic Abnormality in Treatment-Naive Patients Infected with HBeAg-Negative Chronic Hepatitis B.

This study aimed to determine the predictors for significant hepatic abnormality (SHA), a treatment indication, by assessing demographic, laboratory, and radiological results of chronic hepatitis B (CHB) patients who underwent liver biopsy. In this retrospective study, individuals with untreated hepatitis B e-antigen (HBeAg)-negative CHB infection were enrolled. Multivariate analysis modeling was conducted with parameters identified as predictors for SHA in univariate analysis. Optimal threshold levels for variables to predict SHA in patients with chronic hepatitis B were determined based on receiver operating characteristic (ROC) curve analysis. A total of 566 patients with untreated chronic hepatitis B were included in the cohort; 61% (345/566) were male, and the median age was 41 years (interquartile range [IQR]=34-50). Notably, 36.9% (209/566) had SHA. In the multivariate analysis, utilizing different models, age, gender, HBV-DNA, LDL, ALT, and platelet count were identified as the most reliable predictors for SHA in CHB patients. For predicting SHA, the area under the ROC curve values of HBV-DNA, AST, and ALT were 0.704 (sensitivity=62.8%, specificity=76.2%; p<0.0001), 0.747 (sensitivity=51.9%, specificity=88.9%; p<0.0001), and 0.737 (sensitivity=68.6%, specificity=68.4%; p<0.0001), respectively. In our study, age, male gender, ALT, AST, HBV-DNA, LDL cholesterol, platelet count, and FIB-4 score were independent predictors of SHA in HBeAg-negative chronic hepatitis B. The most sensitive parameters for SHA were LDL and ALT. The most specific parameters were age, AST, and APRI score. SHA may occur in patients with high HBV-DNA levels, even if ALT values are normal in HBeAg-negative patients.

Wrist-Based Fall Detection: Towards Generalization across Datasets.

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence of the older adults, as well as financial impact on the health systems. Thus, many studies have developed fall detectors from several types of sensors. Previous studies related to the creation of fall detection systems models use only one dataset that usually has a small number of samples. Training and testing machine learning models in this small scope: (i) yield overoptimistic classification rates, (ii) do not generalize to real-life situations and (iii) have very high rate of false positives. Given this, the proposal of this research work is the creation of a new dataset that encompasses data from three different datasets, with more than 1300 fall samples and 28 K negative samples. Our new dataset includes a standard way of adding samples, which allow the future addition of other data sources. We evaluate our dataset by using classic cost-sensitive Machine Leaning methods that deal with class imbalance. For the training and validation of this model, a set of temporal and frequency features were extracted from the raw data of an accelerometer and a gyroscope using a sliding window of 2 s with an overlap of 50%. We study the generalization properties of each dataset, by testing on the other datasets and also the performance of our new dataset. The model showed a good ability to distinguish between activities of daily living and falls, achieving a recall of 90.57%, a specificity of 96.91% and an Area Under the Receiver Operating Characteristic curve (AUC-ROC) value of 98.85% against the combination of three datasets.

Do metal artifact reduction algorithms influence the detection of implant-related injuries to the inferior alveolar canal in CBCT images?

BackgroundThe routine application of dental implants for replacing missing teeth has revolutionized restorative and prosthetic dentistry. However, cone beam computed tomography (CBCT) evaluations of structures adjacent to the implants are limited by metal artifacts. There are several methods for reducing metal artifacts, but this remains a challenging task. This study aimed to examine the effectiveness of metal artifact reduction (MAR) algorithms in identifying injuries of implants to the inferior alveolar canal in CBCT images.MethodIn this in vitro study, mono-cortical bone windows were created and the inferior alveolar canal was revealed. Using 36 implants, pilot drill and penetration damage of the implant tip into the canal was simulated and compared to the control implants with distance from the canal. CBCT images were evaluated by four experienced observers with and without the MAR algorithm and compared to direct vision as the gold standard. The values of accuracy, sensitivity, and specificity were obtained and compared by receiver operating characteristic (ROC) curve (α = 0.05).ResultThe area under the ROC curve values for detection of pilot drill injuries varied between 0.840–0.917 and 0.639–0.854 in the active and inactive MAR conditions, respectively. The increase in ROC area was only significant for one of the observers (P = 0.010). For diagnosing penetrative injuries, the area under the ROC curve values was between 0.990–1.000 and 0.722–1.000 in the active and inactive MAR conditions, respectively. The improvement of ROC curve values in active MAR mode was only significant for one of the observers (P = 0.006).ConclusionActivation of MAR improved the diagnostic values of CBCT images in detecting both types of implant-related injuries to the inferior alveolar canal. However, for most observers, this increase was not statistically significant.

Maximum Entropy Model Prediction of the Distributions of Two Sympatric Bean Weevil Species, Megabruchidius dorsalis (Fahraeus, 1839) and Bruchidius coreanus (Chûjô, 1937), under Various Climate Scenarios in Guizhou Province, China

Megabruchidius dorsalis and Bruchidius coreanus are sympatric bean weevil species that bore into Gleditsia sinensis seeds, seriously affecting the commercial development of this plant. Here, we aimed to understand potential changes in the distribution of these two sympatric pests under current and future climate conditions to provide a reference for the prediction of their occurrence and facilitate their prevention and control. Based on empirical field data, we predicted the potential distribution of M. dorsalis and B. coreanus in suitable habitat areas using the MaxEnt model and explored the relationships among different spatiotemporal distributions using change analysis. Our findings showed that compared with the current situation, the suitable areas for M. dorsalis and B. coreanus were predicted to increase by 4.8141% and 3.1009%, respectively, in the future. Isothermality (BIO3), min temperature of coldest month (BIO6), and variance in precipitation (BIO15) in the coldest month were determined to be the main factors restricting the current distribution of M. dorsalis and B. coreanus. Areas currently suitable for the two species are mainly in the central region of Guizhou and are predicted to move eastward in the future. Significant area under the receiver operating characteristics curve values for M. dorsalis (0.878) and B. coreanus (0.833) indicated that MaxEnt could be used to predict the potential habitats of these weevils, providing valuable information to inform their control in Guizhou Province.

Analysis of Risk Factors for Severe Acute Pancreatitis in the Early Period (

BackgroundSevere acute pancreatitis (SAP) has high mortality. Early identification of high-risk factors that may progress to SAP and active intervention measures may improve the prognosis of SAP patients. ObjectiveClinical data within 24 h after admission were retrospectively analyzed to provide an evidence for early screening of high-risk factors in patients with SAP. MethodsA review of clinical data of acute pancreatitis patients from January 1, 2018, to December 31, 2022, was conducted. We compared the clinical data of SAP and non-SAP patients, and a multivariable logistic regression model was used to identify the independent predictors of SAP. The receiver operating characteristic (ROC) curve of SAP was drawn for continuous numerical variables to calculate the optimal clinical cutoff value of each variable, and the predictive value of each variable was compared by the area under the ROC curve. ResultsBased on the multivariate logistic regression analysis of Age (odds ratio (OR), 1.032;95% confident interval (CI),1.018–1.046, p < 0.001), body mass index (BMI) (OR, 1.181; 95% CI,1.083–1.288, p < 0.001), Non-HTGAP (nonhypertriglyceridemic acute pancreatitis) (OR, 2.098; 95% CI,1.276–3.45, p = 0.003), white blood cell count (WBC) (OR,1.072; 95% CI,1.034–1.111, p < 0.001), procalcitonin (PCT) (OR, 1.060; 95% CI, 1.027–1.095, p < 0.001), serum calcium (Ca) (OR,0.121; 95% CI, 0.050–0.292, p < 0.001), computed tomography severity index (CTSI) ≥4 (OR,12.942;95% CI,7.267–23.049, p < 0.001) were identified as independent risk factors for SAP. The area under the ROC curve (AUC) and optimal CUT-OFF values of continuous numerical variables for predicting SAP were Age (0.6079,51.5), BMI (0.6,23.25), WBC (0.6701,14.565), PCT (0.7086, 0.5175), Ca (0.7787,1.965), respectively. ConclusionAge, BMI, non-HTGAP, WBC, PCT, serum Ca and CTSI≥4 have good predictive value for SAP.

Prognostic nomogram in patients with gastrointestinal stromal tumors: a SEER-based study.

Gastrointestinal stromal tumor (GIST) is a common mesenchymal tumor of the gastrointestinal system. They originate from the interstitial cells of Cajal located within the muscle layer and are characterized by over-expression of the tyrosine kinase receptor KIT. Data from the Surveillance Epidemiology, and End Results (SEER) database of 1,213 patients diagnosed with GIST between 2010 and 2019 were dichotomized into a modeling set and a validation set at a 2:1 ratio. For the modeling set, both univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. A nomogram was then constructed based on these determinants. Model efficacy was tested using receiver operating characteristic (ROC) curves, calibration curves, clinical decision curves, and risk stratification analysis in both subsets. Identified prognostic determinants included age, sex, pathological differentiation level, tumor-node-metastasis (TNM) stage, surgical intervention, radiotherapy, and marital status. The constructed nomogram showed area under the ROC curve (AUC) values of 0.822, 0.793, and 0.779 for 1-, 3-, and 5-year overall survival (OS) in the modeling set, respectively, while in the validation set, the values were 0.796, 0.823, and 0.806, respectively. Calibration plots from both sets confirmed the concordance between predicted and observed survival. Decision curve analysis (DCA) indicated significant clinical utility for the nomogram. Risk stratification of the patient data revealed distinct survival differences between high-risk and low-risk cohorts in both sets (P<0.001). A novel and potent nomogram for the prognosis of GIST has been introduced. This model's precision offers crucial insights for clinical decisions, yet further external validation remains essential.

Construction and validation of a prognostic signature for mucinous colonic adenocarcinoma based on N7-methylguanosine-related long non-coding RNAs.

Mucinous colonic adenocarcinoma remains a challenging disease due to its high propensity for metastasis and recurrence. N7-methylguanosine (m7G) and long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on m7G-related lncRNA in mucinous colonic adenocarcinoma is lacking. Therefore, we sought to explore the prognostic impact of m7G-related lncRNAs in mucinous adenocarcinoma (MC) patients. In this study, Pearson analysis was used to identify m7G-related lncRNAs from transcriptome data in The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to further screen m7G-related lncRNAs and incorporate them into a prognostic signature. Based on the risk model, patients were divided into low- and high-risk groups and randomly assigned to the training set and test sets in a 6:4 ratio. Kaplan-Meier, receiver operating characteristic (ROC) curve, multivariate regression, and nomogram analyses were used to confirm the accuracy of the signature. The CIBERSORT algorithm was used to calculate the degree of immune cell infiltration (ICI). Finally, the correlation of the prognostic signature with tumor mutational burden (TMB) and immunophenotype score (IPS) was evaluated. A total of 432 m7G-related lncRNAs were identified by Pearson analysis. Univariate Cox regression, LASSO regression and survival analysis were performed to further select six m7G-related lncRNAs (P<0.05): AC254629.1, LINC01133, LINC01134, MHENCR, SMIM2-AS1, and XACT. Based on the risk model, heat maps, Kaplan-Meier curves, and ROC curves were constructed, and the results showed that there were significant differences in expression levels and survival status between the two risk groups. The area under the ROC curve (AUC) values for 3-, 5-, and 10-year survival in the training set were 0.944, 0.957, and 1.000, respectively. And in the test set were 0.964, 1.000, and 1.000, respectively. Subsequently, univariate and multivariate regression analyses of clinical characteristics and risk score were performed. The results of risk score were [hazard ratio (HR): 6.458, 95% confidence interval (CI): 2.708-15.403, P<0.001; HR: 7.280, 95% CI: 2.500-21.203, P<0.001], respectively. Using the risk score as an independent prognostic factor, the AUC of it over 3, 5, and 10 years was 0.911, 0.955, and 0.961, respectively. Calibration plots for the nomogram show that the model calibration line is very close to the ideal calibration line, indicating good calibration. The level of ICI was significantly different in the different risk groups. Survival analysis showed that, regardless of TMB risk, patients with MC and a high-risk score consistently had a poor overall survival (OS). The m7G-related lncRNA prognostic signature has potential value for the prognosis of mucinous colonic adenocarcinoma.

Establishment of Nomogram for Prediction of Hearing Preservation after Retrosigmoid Approach in Patients with Vestibular Schwannoma.

To derive and validate a prognostic nomogram for the prediction of hearing preservation (HP) after retrosigmoid approach (RSA) in patients with vestibular schwannoma (VS) and further assist in clinical decision-making. Retrospective study. Tertiary referral center. A total of 111 patients diagnosed with VS with serviceable hearing from January 2013 to March 2023. All patients underwent surgery via RSA, and hearing outcomes were reviewed 2 weeks postoperatively. Preoperative and postoperative hearing were analyzed and stratified according to the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS). In multivariate analysis of the primary group, preoperative hearing, tumor size, and tumor origin were significantly related to postoperative HP ( p = 0.029, p = 0.043, and p = 0.018, respectively). Factors derived from the multivariate analysis were all assembled into the nomogram. The receiver operating characteristic (ROC) curves showed good predictive accuracy of the nomogram model in both primary and validation groups with area under the ROC curve (AUC) values of 0.802 and 0.797, respectively. Independent predictors of postoperative HP in patients with VS were selected to create the nomogram. The nomogram was able to stratify patients into different risk groups and assist in clinical decision making.