Scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair in rheumatic mitral disease.

OBJECTIVE: We aimed to evaluate the mitral valve calcification and mitral structure detected by cardiac computed tomography (cardiac CT) and establish a scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair (EGMR) and guide surgical strategy in rheumatic mitral disease (RMD). MATERIALS AND METHODS: This is a retrospective bi-center cohort study. Based on cardiac CT, mitral valve calcification and mitral structure in RMD were quantified and evaluated. The primary outcome was EGMR. A logical regression algorithm was applied to the scoring model. RESULTS: A total of 579 patients were enrolled in our study from January 1, 2019, to August 31, 2022. Of these, 443 had baseline cardiac CT scans of adequate quality. The calcification quality score, calcification and thinnest part of the anterior leaflet clean zone, and papillary muscle symmetry were the independent CT factors of EGMR. Coronary artery disease and pulmonary artery pressure were the independent clinical factors of EGMR. Based on the above six factors, a scoring model was established. Sensitivity = 95% and specificity = 95% were presented with a cutoff value of 0.85 and 0.30 respectively. The area under the receiver operating characteristic of external validation set was 0.84 (95% confidence interval [CI] 0.73-0.93). CONCLUSIONS: Mitral valve repair is recommended when the scoring model value > 0.85 and mitral valve replacement is prior when the scoring model value < 0.30. This model could assist in guiding surgical strategies for RMD. CLINICAL RELEVANCE STATEMENT: The model established in this study can serve as a reference indicator for surgical repair in rheumatic mitral valve disease. KEY POINTS: • Cardiac CT can reflect the mitral structure in detail, especially for valve calcification. • A model based on cardiac CT and clinical factors for predicting early good mitral valve repair was established. • The developed model can help cardiac surgeons formulate appropriate surgical strategies.

Prediction of symptomatic anastomotic leak after rectal cancer surgery: A machine learning approach.

INTRODUCTION: Anastomotic leakage (AL) remains the most dreaded and unpredictable major complication after low anterior resection for mid-low rectal cancer. The aim of this study is to identify patients with high risk for AL based on the machine learning method. METHODS: Patients with mid-low rectal cancer undergoing low anterior resection were enrolled from West China Hospital between January 2008 and October 2019 and were split by time into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) method and stepwise method were applied for variable selection and predictive model building in the training cohort. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to evaluate the performance of the models. RESULTS: The rate of AL was 5.8% (38/652) and 7.2% (15/208) in the training cohort and validation cohort, respectively. The LASSO-logistic model selected almost the same variables (hypertension, operating time, cT4, tumor location, intraoperative blood loss) compared to the stepwise logistic model except for tumor size (the LASSO-logistic model) and American Society of Anesthesiologists score (the stepwise logistic model). The predictive performance of the LASSO-logistics model was better than the stepwise-logistics model (AUC: 0.790 vs. 0.759). Calibration curves showed mean absolute error of 0.006 and 0.013 for the LASSO-logistics model and stepwise-logistics model, respectively. CONCLUSION: Our study developed a feasible predictive model with a machine-learning algorithm to classify patients with a high risk of AL, which would assist surgical decision-making and reduce unnecessary stoma diversion. The involved machine learning algorithms provide clinicians with an innovative alternative to enhance clinical management.

Predictive Value of Jugulo-omohyoid Lymph Nodes in Lateral Lymph Node Metastasis of Papillary Thyroid Cancer.

BACKGROUND: Jugulo-omohyoid lymph nodes (JOHLN) metastasis has proven to be associated with lateral lymph node metastasis (LLNM). This study aimed to reveal the clinical features and evaluate the predictive value of JOHLN in PTC to guide the extent of surgery. METHODS: A total of 550 patients pathologically diagnosed with PTC between October 2015 and January 2020, all of whom underwent thyroidectomy and lateral lymph node dissection, were included in this study. RESULTS: Thyroiditis, tumor location, tumor size, extra-thyroidal extension, extra-nodal extension, central lymph node metastasis (CLNM), and LLMM were associated with JOHLN. Male, upper lobe tumor, multifocality, extra-nodal extension, CLNM, and JOHLN metastasis were independent risk factors from LLNM. A nomogram based on predictors performed well. Nerve invasion contributed the most to the prediction model, followed by JOHLN metastasis. The area under the curve (AUC) was 0.855, and the p-value of the Hosmer-Lemeshow goodness of fit test was 0.18. Decision curve analysis showed that the nomogram was clinically helpful. CONCLUSION: JOLHN metastasis could be a clinically sensitive predictor of further LLM. A high-performance nomogram was established, which can provide an individual risk assessment of LNM and guide treatment decisions for patients.

A mathematical model to predict "low-lying" posterior communicating artery aneurysms in neurosurgical practice.

"Low-lying" posterior communicating artery (PCoA) aneurysms require great attention in surgical clipping due to their distinct anatomical characteristics. In this study, we propose an easy method to immediately recognize "low-lying" PCoA aneurysms in neurosurgical practice. A total of 89 cases with "low-lying" PCoA aneurysms were retrospectively analyzed. All patients underwent preoperative digital subtraction angiography (DSA) examinations and microsurgical clipping. Cases were classified into the "low-lying" and regular groups based on intraoperative findings. The distance- and angle-relevant parameters that reflected the relative location of the aneurysms and tortuosity of the internal carotid artery were measured using 3D-DSA images. The data were sequentially integrated into a mathematical analysis to obtain the prediction model. Finally, we proposed a novel mathematical formula to preoperatively predict the existence of "low-lying" PCoA aneurysms with great accuracy. Neurosurgeons might benefit from this model, which enables them to directly identify "low-lying" PCoA aneurysms and make appropriate surgical decisions accordingly.

Distinct risk factors of lateral lymph node metastasis in patients with papillary thyroid cancer based on age stratification.

INTRODUCTION: Studies have revealed that age is associated with the risk of lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC). This study aimed to identify the optimal cut point of age for a more precise prediction model of LLNM and to reveal differences in risk factors between patients of distinct age stages. METHODS: A total of 499 patients who had undergone thyroidectomy and lateral neck dissection (LND) for PTC were enrolled. The locally weighted scatterplot smoothing (LOWESS) curve and the 'changepoint' package were used to identify the optimal age cut point using R. Multivariate logistic regression analysis was performed to identify independent risk factors of LLNM in each group divided by age. RESULTS: Younger patients were more likely to have LLNM, and the optimal cut points of age to stratify the risk of LLNM were 30 and 45 years old. Central lymph node metastasis (CLNM) was a prominent risk factor for further LNM in all patients. Apart from CLNM, sex(p = 0.033), tumor size(p = 0.027), and tumor location(p = 0.020) were independent predictors for patients younger than 30 years old; tumor location(p = 0.013), extra-thyroidal extension(p < 0.001), and extra-nodal extension(p = 0.042) were independent risk factors for patients older than 45 years old. CONCLUSIONS: Our study could be interpreted as an implication for a change in surgical management. LND should be more actively performed when CLNM is confirmed; for younger patients with tumors in the upper lobe and older patients with extra-thyroidal extension tumors, more aggressive detection of the lateral neck might be considered.

Independent predictors and clinical predictive score of postanesthetic reintubation after general anesthesia: A time-matched, case control study.

Background and Aims: Postanesthetic reintubation is associated with increased morbidities and mortality; however, it can be reduced with defined predictors and using a score as a tool. This study aimed to identify independent predictors and develop a reliable predictive score. Material and Methods: A retrospective, time-matched, case control study was conducted on patients who underwent general anesthesia between October 2017 and September 2021. Using stepwise multivariable logistic regression analysis, predictors were determined and the predictive score was developed and validated. Results: Among 230 patients, 46 were in the reintubated group. Significant independent predictors included age >65 years (odds ratio [OR] 2.96 [95% confidence interval {CI} 1.23, 7.10]), the American Society of Anesthesiologists physical status III-IV (OR 6.60 [95%CI 2.50 17.41]), body mass index (BMI) ≥30 kg/m2 (OR 4.91 [95% CI 1.55, 15.51]), and head and neck surgery (OR 4.35 [95% CI 1.46, 12.87]). The predictive model was then developed with an area under the receiver operating characteristic curve (AUC) of 0.84 (95% CI 0.78, 0.90). This score ranged from 0 to 29 and was classified into three subcategories for clinical practicability, in which the positive predictive values were 6.01 (95% CI 2.63, 11.50) for low risk, 18.64 (95% CI 9.69, 30.91) for moderate risk, and 71.05 (95% CI 54.09, 84.58) for high risk. Conclusion: The independent predictors for postanesthetic reintubation according to this simplified risk-based scoring system designed to aid anesthesiologists before extubation were found to be advanced age, higher American Society of Anesthesiologists physical status, obesity, and head and neck surgery.

Nomogram for the acute exacerbation of acetylcholine receptor antibody-positive generalized myasthenia gravis.

BACKGROUND AND OBJECTIVE: An acute exacerbation of myasthenia gravis (MG) can lead to the life-threatening myasthenia crisis which can increase the in-hospital mortality. This study aimed to clarify the correlative factor of the severity and activity of MG and the predictors of its exacerbation. METHODS: A prospective study was conducted to compare the clinical characteristics of acetylcholine receptor antibody (AChR-Ab)-positive generalized MG during acute exacerbation (AE) and in a stable state (SS). Logistic regression was used to determine risk factors, and a nomogram was developed. RESULTS: A total of 97 AChR-Ab MG patients were enrolled, of whom 44 had AE and 53 were in SS. The concentrations of AChR-Ab were 35.24 (23.26, 42.52) nmol/L and 19.51 (8.30, 36.93) nmol/L in the AE and SS groups (P = 0.005), respectively. The receiver operating characteristic curve showed that a single AChR-Ab predicted severity and acute exacerbation, with an area under the curve (AUC) of 0.679. Logistic regression analysis showed that, in addition to AChR-Ab (P = 0.018), bulbar symptoms (P = 0.001), interleukin (IL)-6 (P = 0.025), CD4+/CD8+ T cell ratio (P = 0.031), and CD19+ B cell proportion (P = 0.019) were independent risk factors for acute exacerbation of MG. The developed nomogram had an AUC of 0.878. The Hosmer and Lemeshow chi-square test was 4.37 (P = 0.929). CONCLUSION: AChR-Ab concentration was positively correlated with the severity and activity of MG. AChR-Ab concentration, alongside bulbar symptoms, IL-6 concentration, CD4+/CD8+ T cell ratio, and CD19+ B cell proportion can predict the acute exacerbation of MG.

Risk factors for postpartum hemorrhage in patients with retained placenta: building a predict model.

OBJECTIVES: Among patients with placenta retention, the risk factors of massive blood loss remain unclear. In this study, a secondary data analysis was conducted to construct a predictive risk model for postpartum hemorrhage (PPH) in this particular population. METHODS: A prediction model based on the data of 13 hospitals in the UK, Uganda, and Pakistan, from December 2004, to May 2008 was built. A total of 516 patients and 14 potential risk factors were analyzed. The least absolute shrinkage and selection operator regression (LASSO) model was used to optimize feature selection for the PPH risk model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the LASSO model. Discrimination and calibration were assessed using C-index and calibration plot. RESULTS: Among patients with placenta retention, the incidence of PPH was 62.98% (325/526). Risk factors in the model were country, number of past deliveries, previous manual removal of placenta, place of placenta delivery, and how the placenta was delivered. In these factors, patients in the low-income country (i.e., Uganda) (OR: 1.753, 95% CI=1.055-2.915), retained placentas delivered in the theater (OR: 2.028, 95% CI=1.016-4.050), and having placentas partially removed by controlled cord traction (cct), completely removed manually (OR: 4.722, 95% CI=1.280-17.417) were independent risk factors. The C-statistics was 0.702. CONCLUSIONS: By secondary data analysis, our study constructed a prediction model for PPH in patients with placenta retention, and identified the independent risk factors.

Machine Learning-Based Predictive Model for Mortality in Female Breast Cancer Patients Considering Lifestyle Factors.

Purpose: To construct a free and accurate breast cancer mortality prediction tool by incorporating lifestyle factors, aiming to assist healthcare professionals in making informed decisions. Patients and Methods: In this retrospective study, we utilized a ten-year follow-up dataset of female breast cancer patients from a major Chinese hospital and included 1,390 female breast cancer patients with a 7% (96) mortality rate. We employed six machine learning algorithms (ridge regression, k-nearest neighbors, neural network, random forest, support vector machine, and extreme gradient boosting) to construct a mortality prediction model for breast cancer. Results: This model incorporated significant lifestyle factors, such as postsurgery sexual activity, use of totally implantable venous access ports, and prosthetic breast wear, which were identified as independent protective factors. Meanwhile, ten-fold cross-validation demonstrated the superiority of the random forest model (average AUC = 0.918; 1-year AUC = 0.914, 2-year AUC = 0.867, 3-year AUC = 0.883). External validation further supported the model's robustness (average AUC = 0.782; 1-year AUC = 0.809, 2-year AUC = 0.785, 3-year AUC = 0.893). Additionally, a free and user-friendly web tool was developed using the Shiny framework to facilitate easy access to the model. Conclusion: Our breast cancer mortality prediction model is free and accurate, providing healthcare professionals with valuable information to support their clinical decisions and potentially promoting healthier lifestyles for breast cancer patients.

Predictive model for post-induction hypotension in patients undergoing transcatheter aortic valve implantation: a retrospective observational study.

PURPOSE: Post-induction hypotension (PIH) is an independent risk factor for prolonged postoperative stay and hospital death. Patients undergoing transcatheter aortic valve implantation (TAVI) are prone to develop PIH. This study aimed to develop a predictive model for PIH in patients undergoing TAVI. METHODS: This single-center retrospective observational study included 163 patients who underwent TAVI. PIH was defined as at least one measurement of systolic arterial pressure <90 mmHg or at least one incident of norepinephrine infusion at a rate >6 µg/min from anesthetic induction until 20 min post-induction. Multivariate logistic regression analysis was performed to develop a predictive model for PIH in patients undergoing TAVI. RESULTS: In total, 161 patients were analyzed. The prevalence of PIH was 57.8%. Multivariable logistic regression analysis showed that baseline mean arterial pressure ≥90 mmHg [adjusted odds ratio (aOR): 0.413, 95% confidence interval (95% CI): 0.193-0.887; p=0.023] and higher doses of fentanyl (per 1-µg/kg increase, aOR: 0.619, 95% CI: 0.418-0.915; p=0.016) and ketamine (per 1-mg/kg increase, aOR: 0.163, 95% CI: 0.062-0.430; p=0.002) for induction were significantly associated with lower risk of PIH. A higher dose of propofol (per 1-mg/kg increase, aOR: 3.240, 95% CI: 1.320-7.920; p=0.010) for induction was significantly associated with higher risk of PIH. The area under the curve (AUC) for this model was 0.802. CONCLUSION: The present study developed predictive models for PIH in patients who underwent TAVI. This model may be helpful for anesthesiologists in preventing PIH in patients undergoing TAVI.

Enhancing diabetic foot ulcer prediction with machine learning: A focus on Localized examinations.

Background: diabetices foot ulcer (DFU) are serious complications. It is crucial to detect and diagnose DFU early in order to provide timely treatment, improve patient quality of life, and avoid the social and economic consequences. Machine learning techniques can help identify risk factors associated with DFU development. Objective: The aim of this study was to establish correlations between clinical and biochemical risk factors of DFU through local foot examinations based on the construction of predictive models using automated machine learning techniques. Methods: The input dataset consisted of 566 diabetes cases and 50 DFU risk factors, including 9 local foot examinations. 340 patients with Class 0 labeling (low-risk DFU), 226 patients with Class 1 labeling (high-risk DFU). To divide the training group (consisting of 453 cases) and the validation group (consisting of 113 cases), as well as preprocess the data and develop a prediction model, a Monte Carlo cross-validation approach was employed. Furthermore, potential high-risk factors were analyzed using various algorithms, including Bayesian BYS, Multi-Gaussian Weighted Classifier (MGWC), Support Vector Machine (SVM), and Random Forest Classifier (RF). A three-layer machine learning training was constructed, and model performance was estimated using a Confusion Matrix. The top 30 ranking feature variables were ultimately determined. To reinforce the robustness and generalizability of the predictive model, an independent dataset comprising 248 cases was employed for external validation. This validation process evaluated the model's applicability and reliability across diverse populations and clinical settings. Importantly, the external dataset required no additional tuning or adjustment of parameters, enabling an unbiased assessment of the model's generalizability and its capacity to predict the risk of DFU. Results: The ensemble learning method outperformed individual classifiers in various performance evaluation metrics. Based on the ROC analysis, the AUC of the AutoML model for assessing diabetic foot risk was 88.48 % (74.44-97.83 %). Other results were found to be as follows: 87.23 % (63.33 %-100.00 %) for sensitivity, 87.43 % (70.00 %-100.00 %) for specificity, 87.33 % (76.66 %-95.00 %) for accuracy, 87.69 % (75.00 %-100.00 %) for positive predictive value, and 87.70 % (71.79 %-100.00 %) for negative predictive value. In addition to traditional DFU risk factors such as cardiovascular disorders, peripheral artery disease, and neurological damage, we identified new risk factors such as lower limb varicose veins, history of cerebral infarction, blood urea nitrogen, GFR (Glomerular Filtration Rate), and type of diabetes that may be related to the development of DFU. In the external validation set of 158 samples, originating from an initial 248 with exclusions due to missing labels or features, the model still exhibited strong predictive accuracy. The AUC score of 0.762 indicated a strong discriminatory capability of the model. Furthermore, the Sensitivity and Specificity values provided insights into the model's ability to correctly identify both DFU cases and non-cases, respectively. Conclusion: The predictive model, developed through AutoML and grounded in local foot examinations, has proven to be a robust and practical instrument for the screening, prediction, and diagnosis of DFU risk. This model not only aids medical practitioners in the identification of potential DFU cases but also plays a pivotal role in mitigating the progression towards adverse outcomes. And the recent successful external validation of our DFU risk prediction model marks a crucial advancement, indicating its readiness for clinical application. This validation reinforces the model's efficacy as an accessible and reliable tool for early DFU risk assessment, thereby facilitating prompt intervention strategies and enhancing overall patient outcomes.

Development and Validation of a Risk Prediction Model for Female Stress Urinary Incontinence in Rural Fujian, China.

Purpose: With China's rapidly aging population and the rising proportion of obese people, an increase in the number of women suffering from urinary incontinence (UI) is to be expected. In order to identify high-risk groups before leakage occurs, we aimed to develop and validate a model to predict the risk of stress UI (SUI) in rural women. Patients and methods: This study included women aged 20-70 years in rural Fujian who participated in an epidemiologic survey of female UI conducted between June and October 2022. Subsequently the data was randomly divided into training and validation sets in a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to identify independent risk factors as well as to further construct a nomogram for risk prediction. Finally, concordance index (C-index), calibration curve and decision curve analysis were applied to evaluate the performance of the predictive models. Results: A total of 5290 rural females were enrolled, of whom 771 (14.6%) had SUI. Age, body mass index (BMI), postmenopausal status, number of vaginal deliveries, vaginal delivery of large infant, constipation and family history of pelvic organ prolapse (POP) and SUI were included in the nomogram. C-index of this prediction model for the training and validation sets was 0.835 (95% confidence interval [CI] = 0.818-0.851) and 0.829 (95% CI = 0.796-0.858), respectively, and the calibration curves and decision analysis curves for both the training and validation sets showed that the model was well-calibrated and had a positive net benefit. Conclusion: This model accurately estimated the SUI risk of rural women in Fujian, which may serve as an effective primary screening tool for the early identification of SUI risk and provide a basis for further implementation of individualized early intervention. Moreover, the model is concise and intuitive, which makes it more operational for rural women with scarce medical resources.

Developing questions to assess and measure patients' perceived survival benefit from adjuvant endocrine therapy in breast cancer: a mixed methods pilot study.

This mixed method study developed multiple question types to understand and measure women's perceived benefit from adjuvant endocrine therapy. We hypothesis that patients do not understand this benefit and sought to develop the questions needed to test this hypothesis and obtain initial patient estimates. From 8/2022 to 3/2023, qualitative interviews focused on assessing and modifying 9 initial varied question types asking about the overall survival (OS) benefit from adjuvant endocrine therapy. Subsequent focus groups modified and selected the optimal questions. Patients' self-assessment of their OS benefit was compared to their individualized PREDICT model results. Fifty-three patients completed the survey; 42% Hispanic, 30% rural, and 47% with income < $39,999 per year. Patients reported adequate health care literacy (61.5%) and average confidence about treatment and medication decisions 49.4 (95% CI 24.4-59.5). From the original 9 questions, 3 modified questions were ultimately found to capture patients' perception of this OS benefit, focusing on graphical and prose styles. Patients estimated an OS benefit of 42% compared to 4.4% calculated from the PREDICT model (p < 0.001). In this group with considerable representation from ethnic minority, rural and low-income patients, qualitative data showed that more than one modality of question type was needed to clearly capture patients' understanding of treatment benefit. Women with breast cancer significantly overestimated their 10-year OS benefit from adjuvant endocrine therapy compared to the PREDICT model.

Investigation of stair ascending and descending activities on the lifespan of hip implants.

Total hip arthroplasty (THA) surgeries among young patients are on the increase, so it is crucial to predict the lifespan of hip implants correctly and produce solutions to improve longevity. Current implants are designed and tested against walking conditions to predict the wear rates. However, it would be reasonable to include the additional effects of other daily life activities on wear rates to predict convergent results to clinical outputs. In this study, 14 participants are recruited to perform stair ascending (AS), descending (DS), and walking activities to obtain kinematic and kinetic data for each cycle using marker based Qualisys motion capture (MOCAP) system. AnyBody Modeling System using the Calibrated Anatomical System Technique (CAST) full body marker set are performed Multibody simulations. The 3D generic musculoskeletal model used in this study is a marker-based full-body motion capture model (AMMR,2.3.1 MoCapModel) consisting of the upper extremity and the Twente Lower Extremity Model (TLEM2). The dynamic wear prediction model detailing the intermittent and overall wear rates for CoCr-on-XLPE bearing couple is developed to investigate the wear mechanism under 3D loading for AS, DS, and walking activities over 5 million cycles (Mc) by using finite element modelling technique. The volumetric wear rates of XLPE liner under AS, DS, and walking activities over 5-Mc are predicted as 27.43, 23.22, and 18.84 mm3/Mc respectively. Additionally, the wear rate was predicted by combining stair activities and gait cycles based on the walk-to-stair ratio. By adding the effect of stair activities, the volumetric wear rate of XLPE is predicted as 22.02 mm3/Mc which is equivalent to 19.41% of walking. In conclusion, in this study, the effect of including other daily life activities is demonstrated and evidence is provided by matching them to the clinical data as opposed to simulator test results of implants under ISO 14242 boundary conditions.

High normal alanine aminotransferase is an indicator for better response to antiviral therapy in chronic hepatitis B.

Background: Evidence shows people living with CHB even with a normal ALT (40U/L as threshold) suffer histological disease and there is still little research to evaluate the potential benefit of antiviral benefits in them. Methods: We retrospectively examined 1352 patients who underwent liver biopsy from 2017 to 2021 and then obtained their 1-year follow-up data to analyze. Results: ALT levels were categorized into high and low, with thresholds set at >29 for males and >15 for females through Youden's Index. The high normal ALT group showed significant histological disease at baseline (56.43% vs 43.82%, p< 0.001), and better HBV DNA clearance from treatment using PSM (p=0.005). Similar results were obtained using 2016 AASLD high normals (male >30, female >19). Further multivariate logistic analysis showed that high normal ALT (both criterias) was an independent predictor of treatment (OR 1.993, 95% CI 1.115-3.560, p=0.020; OR 2.000, 95% CI 1.055-3.793, p=0.034) Both of the models had higher AUC compared with current scoring system, and there was no obvious difference between the two models (AUC:0.8840 vs 0.8835). Conclusion: Male >30 or female >19 and Male >29 or female>15 are suggested to be better thresholds for normal ALT. Having a high normal ALT in CHB provides a potential benefit in antiviral therapy.

Development of Nomogram Models Based on Peripheral Blood Score and Clinicopathological Parameters to Predict Preoperative Advanced Stage and Prognosis for Epithelial Ovarian Cancer Patients.

Purpose: Nutritional and inflammatory states are crucial in cancer development. The purpose of this study is to construct a scoring system grounded on peripheral blood parameters associated with nutrition and inflammation and explore its value in stage, overall survival (OS), and progression-free survival (PFS) prediction for epithelial ovarian cancer (EOC) patients. Patients and Methods: Four hundred and fifty-three EOC patients were retrospectively identified and their clinical data and relevant peripheral blood parameters were collected. The ratio of neutrophil to lymphocyte, lymphocyte to monocyte, fibrinogen to lymphocyte, total cholesterol to lymphocyte and albumin level were calculated and dichotomized. A scoring system named peripheral blood score (PBS) was constructed. Univariate and multivariate Logistic or Cox regression analyses were used to select independent factors; these factors were then used to develop nomogram models of advanced stage and OS, PFS, respectively. The internal validation and DCA analysis were performed to evaluate models. Results: Lower PBS indicated a better prognosis and higher PBS indicated inferior. High PBS is associated with advanced stage, high CA125, serous histological type, poor differentiation, and accompanied ascites. The logistic regression showed age, CA125, and PBS were independent factors for the FIGO III-IV stage. The nomogram models for advanced FIGO stage based on these factors showed good efficiency. FIGO stage, residual disease, and PBS were independent factors affecting OS and PFS, the nomogram models composed of these factors had good performance. DCA curves revealed the models augmented net benefits. Conclusion: PBS can be a noninvasive biomarker for EOC patients' prognosis. The related nomogram models could be powerful, cost-effective tools to provide information of advanced stage, OS, and PFS for EOC patients.

Tumor size combined with staging systems for thymoma recurrence prediction: A 28-year experience.

Background: We evaluated a new thymoma prognosis prediction model by combining current staging systems with tumor size. Methods: The clinical records of thymoma patients in a single center between January 1993 and December 2021 were collected, and data on tumor size and stage and recurrence-free survival (RFS) was obtained. The prediction model was designed by combining staging with tumor size. Results: During 28 years, 219 thymoma patients were enrolled. Twenty-seven patients had a median RFS of 8.2 years. Further, 153 patients were categorized into limited stage and 66 patients into advanced stage. The RFS was statistically different between these two groups (P = 0.022). The largest area under the curve (AUC) of receiver operating characteristic (ROC) was the dividing group as 5 cm (AUC: 0.804). Conclusions: Combining tumor staging and size improves thymoma recurrence prediction. Patients with advanced stage and tumor size >5 cm may show a poor prognosis.

Machine learning method for predicting cadmium concentrations in rice near an active copper smelter based on chemical mass balance.

Identification the sources of heavy metals can effectively control and prevent agricultural soil pollution. Here we performed a three-year mass balance study along a gradient of soil pollution near a smelter to quantify the potential contribution and net cadmium (Cd) fluxes and predict Cd concentration in rice grains by multiple regression (MR) and back propagation (BP) neural network. The Cd inputs were mainly from the irrigation water (54.6-60.8%) in the moderately polluted and background sites but from atmospheric deposition (90.9%) in the highly polluted site. The Cd outputs were mainly from the surface runoff (55.8-59.5%) in the moderately polluted and background sites, but from Sedum plumbizincicola phytoextraction (83.6%) in the highly polluted site. The soil Cd concentrations, the annual fluxes of atmospheric deposition, pesticides and fertilizers, irrigation water, surface runoff, and leaching water were selected as the dependent factors to predict Cd concentrations in rice grains. The genetic algorithms (GA)-BP neural network model gives the best prediction accuracy compared to the BP neural network model and multivariate regression analysis. The major implication is that the health risks through the consumption of rice can be rapidly assessed based on the Cd concentrations in rice grains predicted by the model.

Developing Questions to Assess and Measure Patients' Perceived Survival Benefit from Adjuvant Endocrine Therapy in Breast Cancer: A Mixed Methods Pilot Study.

Purpose: This mixed methods study developed multiple question types to understand and measure women's perceived benefit from adjuvant endocrine therapy. We hypothesis that patients do not understand this benefit and sought to develop the questions needed to test this hypothesis and obtain initial patient estimates. Methods: From 8/2022 to 3/2023, qualitative interviews focused on assessing and modifying 9 initial varied question types asking about the overall survival (OS) benefit from adjuvant endocrine therapy. Subsequent focus groups modified and selected the optimal questions. Patients' self-assessment of their OS benefit was compared to their individualized PREDICT model results. Results: Fifty-three patients completed the survey; 42% Hispanic, 30% rural, and 47% with income <$39,999 per year. Patients reported adequate health care literacy (61.5%) and average confidence about treatment and medication decisions 49.4 (95% CI 24.4-59.5). From the original 9 questions, 3 modified questions were ultimately found to capture patients' perception of this OS benefit, focusing on graphical and prose styles. Patients estimated an OS benefit of 42% compared to 4.4% calculated from the PREDICT model (p < 0.001). Conclusion: In this group with considerable representation from ethnic minority, rural and low-income patients, qualitative data showed that more than one modality of question type was needed to clearly capture patients' understanding of treatment benefit. Women with breast cancer significantly overestimated their 10-year OS benefit from adjuvant endocrine therapy compared to the PREDICT model.

Dynamic computational wear model of PEEK-on-XLPE bearing couple in total hip replacements.

Understanding wear mechanisms is a key factor to prevent primary failures causing revision surgery in total hip replacement (THR) applications. This study introduces a wear prediction model of (Polyetheretherketone) PEEK-on-XLPE (cross-linked polyethylene) bearing couple utilized to investigate the wear mechanism under 3D-gait cycle loading over 5 million cycles (Mc). A 32-mm PEEK femoral head and 4-mm thick XLPE bearing liner with a 3-mm PEEK shell are modeled in a 3D explicit finite element modeling (FEM) program. The volumetric and linear wear rates of XLPE liner per every million cycles were predicted as 1.965 mm3/Mc, and 0.0032 mm/Mc respectively. These results are consistent with the literature. PEEK-on-XLPE bearing couple exhibits a promising wear performance used in THR application. The wear pattern evolution of the model is similar to that of conventional polyethylene liners. Therefore, PEEK could be proposed as an alternative material to the CoCr head, especially used in XLPE-bearing couples. The wear prediction model could be utilized to improve the design parameters with the aim of prolonging the life span of hip implants.