Higher Net Benefit Research Articles

PSMA PET/CT based multimodal deep learning model for accurate prediction of pelvic lymph-node metastases in prostate cancer patients identified as candidates for extended pelvic lymph node dissection by preoperative nomograms.

To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pelvic lymph node dissection (ePLND) by preoperative nomograms. [68Ga]Ga-PSMA-617 PET/CT scan of 116 eligible PCa patients (82 in the training cohort and 34 in the test cohort) who underwent radical prostatectomy with ePLND were analyzed in our study. The Med3D deep learning network was utilized to extract discriminative features from the entire prostate volume of interest on the PET/CT images. Subsequently, a multimodal model i.e., Multi kernel Support Vector Machine was constructed to combine the PET/CT deep learning features, quantitative PET and clinical parameters. The performance of the multimodal models was assessed using final histopathology as the reference standard, with evaluation metrics including area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis, and compared with available nomograms and PET/CT visual evaluation result. Our multimodal model incorporated clinical information, maximum standardized uptake value (SUVmax), and PET/CT deep learning features. The AUC for predicting LNI was 0.89 (95% confidence interval [CI] 0.81-0.97) for the final model. The proposed model demonstrated superior predictive accuracy in the test cohort compared to PET/CT visual evaluation result, the Memorial Sloan Kettering Cancer Center (MSKCC) and the Briganti-2017 nomograms (AUC 0.85 [95% CI 0.69-1.00] vs. 0.80 [95% CI 0.64-0.95] vs. 0.79 [95% CI 0.61-0.97] and 0.69 [95% CI 0.50-0.88], respectively). The proposed model showed similar calibration and higher net benefit as compared to the traditional nomograms. Our multimodal deep learning model, which incorporates preoperative PSMA PET/CT imaging, shows enhanced predictive capabilities for LNI in clinically localized PCa compared to PSMA PET/CT visual evaluation result and existing nomograms like the MSKCC and Briganti-2017 nomograms. This model has the potential to reduce unnecessary ePLND procedures while minimizing the risk of missing cases of LNI.

A new prognostic model for predicting overall survival and progression-free survival in unresectable hepatocellular carcinoma treated with the FOLFOX-HAIC regimen based on patient clinical characteristics and blood biomarkers

BackgroundWe developed a prognostic model to evaluate the overall survival (OS) and progression-free survival (PFS) of patients with unresectable hepatocellular carcinoma (u-HCC) treated with Hepatic arterial infusion chemotherapy of infusion oxaliplatin, fluorouracil and leucovorin (FOLFOX-HAIC).MethodsThis model was based on a retrospective study of u-HCC patients treated with the FOLFOX-HAIC (oxaliplatin 130 mg/m2, leucovorin 400 mg/m2, fluorouracil bolus 400 mg/m2 on day 1, and fluorouracil infusion 2,400 mg/m2 for 23–46 h, once every 3–4 weeks). We divided the patients into a training cohort and a validation cohort, used LASSO regression construct prognostic models, predict patient’s OS and PFS based on nomograms of models. Patients were divided into high-risk, medium-risk, and low-risk groups according to their respective model risk scores. Kaplan-Meier survival analysis was used to assess the survival time between the three patient cohorts.ResultsA total of 333 patients were enrolled in the study and divided into a training cohort and a verification cohort at a ratio of 7:3 (233 in the training cohort and 100 in the validation cohort). The prognostic model we established contained nine prognostic variables. The results of concordance index (C-index) of the OS and PFS prognostic model was 0.75 and 0.71, respectively, higher than that of the TNM staging (0.57 and 0.55, p < 0.001), time-dependent ROC (td-ROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) also showed that the model was better than the TNM staging for u-HCC predicting OS and PFS. Subsequently, the model was used to develop a nomogram to predict the individualized prognosis of patients with u-HCC treated with the FOLFOX-HAIC, with a higher net benefit than the TMN staging. According to the risk score, patients were divided into a low-risk group (risk score ≤ 0.458), the medium-risk group (risk score: 0.458–0.799) and the high-risk group (risk score > 0.799). There were significant differences in the OS and PFS between the three groups.ConclusionsThe model developed by our team enables risk stratification and personalized prognosis assessment for u-HCC patients undergoing FOLFOX-HAIC treatment, exhibiting superior predictive accuracy and discriminative capability compared to TNM staging.

Development and internal validation of a diagnostic prediction model for life-threatening events in callers with shortness of breath: a cross-sectional study in out-of-hours primary care.

To develop and internally validate a model predicting life-threatening events for out-of-hours primary care callers with shortness of breath. This cross-sectional study includes data from 1,952 patients with shortness of breath who called out-of-hours primary care between September 2020 and August 2021. Four logistic regression models were developed with life-threatening events as the outcome. We started with a model of age and gender (model 1) and successively added call characteristics (calling at night and someone else calling on behalf of the patient; model 2), symptoms (cough, fever, inability to speak full sentences and wheezing; model 3), and medical history and medication use (cardiovascular and/or pulmonary; model 4). The models were internally validated using optimism correction via bootstrap with 1000 repetitions. Performance measures of discrimination (c-statistic) and calibration (calibration intercept and slope) were determined. Approximately 17% of callers with shortness of breath had a life-threatening event. Model 3 performed best. This model exhibited good discriminative ability (internal validation c-statistic of 0.764 (95% CI: 0.739 to 0.792)) and was well calibrated. All models had a high net benefit compared to using no model. Models 3 and 4 had a higher net benefit compared with models 1 and 2. As models 3 and 4 were similar in terms of net benefit, the model with fewer parameters (model 3) is preferred. A prediction model consisting of age, gender, call characteristics, and symptoms holds promise for improving telephone triage of callers to out-of-hours primary care with shortness of breath.

Optimization of cropping patterns in the Bhimsagar canal irrigation scheme using linear programming approach

ABSTRACT Optimization techniques can be employed to determine the best cropping patterns to ensure optimum net benefits, food production, and labor employment. Therefore, the current investigation employed the linear programming module in LINGO 14.0 software to develop optimal crop area allocation plans in the Right Main Canal (RMC) of the Bhimsagar irrigation project. The optimal cropping patterns (OCPs) were generated for 2013–2014 under different canal run (CR) periods in a month, i.e., 30, 24, and 21 days using three objective functions, viz., net benefit, food production, and labor-days maximization. In addition, the OCP scenarios were generated for long-term agriculture sustainability scenarios, viz., 2015, 2020, and 2025. Results revealed that the net benefit was increased by 122.2, 93.4, and 95.5% for Ratanpura (RT), Chaplada (CP), and Maraita II (MT) minor, respectively, under OCP estimated for 30-day CR in 2013–2014, whereas food production was increased by 22.4, 33.8, and 25.9% for RT, CP, and MT, respectively, under OCP over existing cropping patterns. Similarly, under OCP, labor employment had increased by 40.9, 33.8, and 33.1% for RT, CP, and MT, respectively, for 30-day CR. These findings infer that higher net benefits, food production, and labor employment may be achieved by shifting to OCPs.

An Explanatory Mixed-methods Study of "ICU net benefit": Triage and Trajectory for Sepsis and Acute Respiratory Failure.

Patients with sepsis and/or acute respiratory failure are at high risk for death or long hospital stays, yet limited evidence exists to guide triage to intensive care units (ICUs) or general medical wards for the majority of these patients who do not initially require life support. To identify factors that influence how hospitals triage patients with capacity-sensitive conditions and those factors that may account for observed ICU relative to ward, or ward relative to ICU, benefits for such patients. We conducted an explanatory sequential mixed-methods study. As part of a 27-hospital, two-health system retrospective cohort study, we calculated hospital-specific measurements of ICU net benefit for patients with sepsis and/or acute respiratory failure. Hospitals among the highest ICU net benefit and lowest ICU net benefit (or highest ward net benefit) from each study health system were selected for in-depth qualitative study. At each hospital interviews were conducted with emergency department (ED), ward, and ICU clinicians and administrators. Interview transcripts were analyzed using flexible coding and the framework method. Interviews were conducted with 118 respondents (46 physicians, 43 nurses, 5 advanced practice providers, and 24 administrators) from four hospitals. Respondents across hospitals agreed that the prediction of patient trajectory is central to triage decisions, but there was variation in opinion across work locations about optimal pre-triage ED interventions in terms of intensity, repetition, clinical reassessment, and observation duration. The main difference observed between high and low ICU net benefit hospitals related to the way respondents working in the ICU and ward described their responses to patients who experience rapid clinical deviations from triage-expected trajectories including sustained lack of critical care needs after admission to the ICU and acute critical care needs after admission to the ward. Hospitals with low ICU net benefit (or high ward net benefit) had particularly robust and proactive rapid response and clinical decompensation surveillance practices for ward-admitted patients. Particularly proactive rapid response programs that deliver on-location critical care may quantitatively increase ward net benefit by bringing ICU benefits without ICU-associated harms to ward patients who become critically ill.

A superior tool for predicting sepsis in SAH patients: The nomogram outperforms SOFA score.

This study aimed to develop and validate a nomogram to predict the risk of sepsis in non-traumatic subarachnoid hemorrhage (SAH) patients using data from the MIMIC-IV database. A total of 803 SAH patients meeting the inclusion criteria were randomly divided into a training set (563 cases) and a validation set (240 cases). Independent prognostic factors were identified through forward stepwise logistic regression, and a nomogram was created based on these factors. The discriminative ability of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC) and compared with the SOFA score. The model's consistency was evaluated using the C-index, and the improvement in performance over the SOFA score was calculated using integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Five independent predictive factors were identified through LASSO regression analysis: mechanical ventilation, hyperlipidemia, temperature, white blood cell count, and red blood cell count. The AUC of the nomogram in the training and validation sets were 0.854 and 0.824, respectively, both higher than the SOFA score. NRI and IDI results indicated that the nomogram outperformed the SOFA score in identifying sepsis risk. Calibration curves and the Hosmer-Lemeshow test demonstrated good calibration of the nomogram. Decision curve analysis showed that the nomogram had higher net benefit in clinical application. The nomogram developed in this study performed excellently in predicting the risk of sepsis in SAH patients, surpassing the traditional SOFA scoring system, and has significant clinical application value.

Nomogram for predicting nutritional risk of cognitive impairment

Background Cognitive impairment patients are prone to malnutrition, which further promotes cognitive decline. Cognitive impairment patients are unable to accurately answer subjective questions in the nutrition screening scale. Therefore, it is crucial to establish a nutritional risk prediction model using objective evaluation indicators to evaluate the nutritional status of cognitive impairment patients during hospitalization. Objective To develop a nomogram for prediction of the nutritional risk in cognitive impairment patients. Methods The least absolute shrinkage and selection operator (LASSO) was used for regression analysis, and predictive factors were selected based on 10-fold cross validation. Then, using the selected predictive factors, multivariable logistic regression analysis was performed to obtain the final clinical prediction model. Moreover, the performance of the model was evaluated from receiver operating characteristic curve, calibration curve, and decision curve analysis. Further assessment was conducted through internal validation. Results Six predictive factors were selected from 20 variables through LASSO, including body mass index, age, triglyceride, taking cognitive-improving drugs, controlling nutritional status, and geriatric nutritional risk index. The area under the receiver operating characteristic curve of the training cohort was 0.91, while the validation cohort was 0.88, indicating that the model constructed with 6 predictors had moderate predictive ability. The decision curve analysis showed that the threshold range for both groups was 0.00–0.80, with the highest net benefit 0.76 for training cohort, while 0.77 for validation cohort. Conclusions Introducing six predictive factors, the risk nomogram is useful for predicting nutritional risk of cognitive impairment.

Predicting High-Grade Acute Urinary Toxicity and Lower Gastrointestinal Toxicity After Postoperative Volumetric Modulated Arc Therapy for Cervical and Endometrial Cancer Using a Normal Tissue Complication Probability Model.

(1) Background: Volumetric modulated arc therapy (VMAT) can deliver more accurate dose distribution and reduce radiotherapy-induced toxicities for postoperative cervical and endometrial cancer. This study aims to retrospectively analyze the relationship between dosimetric parameters of organs at risk (OARs) and acute toxicities and provide suggestions for the dose constraints. (2) Methods: A total of 164 postoperative cervical and endometrial cancer patients were retrospectively analyzed, and the endpoints were grade ≥ 2 acute urinary toxicity (AUT) and acute lower gastrointestinal toxicity (ALGIT). The normal tissue complication probability (NTCP) model was established using the logistic regression model. Restricted cubic spline (RCS) curves were used to explore the association between dosimetric parameters and toxicities. The receiver operating characteristic (ROC) curve, calibration curve, Akaike's corrected information criterion (AICc), decision curve analysis (DCA), and clinical impact curve (CIC) were analyzed to evaluate the performance of NTCP models. (3) Results: Bladder V40Gy was identified to develop the NTCP model of AUT, and the mean AUC was 0.69 (CI: 0.58-0.80). Three candidate predictors, namely the small intestine V30Gy, colon D45%, and rectum D55%, were identified to develop the NTCP model of ALGIT, and the mean AUC was 0.71 (CI: 0.61-0.80). Both models were considered to have relatively good discriminative accuracy and could provide a high net benefit in clinical applications. (4) Conclusions: We developed NTCP models to predict the probability for grade ≥ 2 AUT and ALGIT. We recommend that bladder V40Gy, the small intestine V30Gy, colon D45%, and rectum D55% be controlled below 42%, 20.4%, 16.9 Gy, and 32.0 Gy, respectively.

CT-based Machine Learning Radiomics Modeling: Survival Prediction and Mechanism Exploration in Ovarian Cancer Patients.

To create a radiomics model based on computed tomography (CT) to predict overall survival in ovarian cancer patients. To combine Rad-score with genomic data to explore the association between gene expression and Rad-score. Imaging and clinical data from 455 patients with ovarian cancer were retrospectively analyzed. Patients were categorized into training cohort, validation cohort and test cohort. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) methods were utilized to identify characteristics and develop the Rad-score. Radiomics models were developed and evaluated for predictive efficacy and clinical incremental value. Application of genomic data from the cancer genome atlas (TCGA) to reveal differential genes in different Rad-score groups. Screening hub genes and exploring the functions of hub genes through bioinformatics analysis and machine learning. Prognostic models based on FIGO, tumor residual disease and Rad-score were developed. The receiver operating characteristic (ROC) curves showed that the 1, 3, and 5 year area under curves (AUCs) of the model were in the training group (0.816, 0.865 and 0.862, respectively), validation group (0.845, 0.877, 0.869, respectively) and test group (0.899, 0.906 and 0.869, respectively) had good predictive accuracy. Calibration curves showed good agreement between observations and predictions. Decision curve analysis revealed a high net benefit of the clinical-radiomics model. The clinical impact curve (CIC) showed good clinical applicability of the clinical-radiomics model. Analysis of sequencing data from the TCGA database revealed EMP1 as a hub gene for radiomics modeling. It revealed that its biological function may be associated with extracellular matrix organization and focal adhesion. Prognostic models based on FIGO, Tumor residual disease, and Rad-score can effectively predict the overall survival (OS) of ovarian cancer patients. Rad-score may enable prognostic prediction of ovarian cancer patients by revealing the expression level of EMP1 and its biological function.

Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery.

Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and perioperative management plans, but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study developed and validated a machine learning (ML) model using accessible preoperative clinical data to predict perioperative MACEs in stable coronary artery disease (SCAD) patients undergoing noncardiac surgery (NCS). We collected data from 9171 adult SCAD patients who underwent NCS and extracted 64 preoperative variables. First, the optimal data imputation, resampling, and feature selection methods were compared and selected to deal with missing data values and imbalances. Then, nine independent machine learning models (logistic regression (LR), support vector machine, Gaussian Naive Bayes (GNB), random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine, categorical boosting (CatBoost), and deep neural network) and a stacking ensemble model were constructed and compared with the validated Revised Cardiac Risk Index's (RCRI) model for predictive performance, which was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), calibration curve, and decision curve analysis (DCA). To reduce overfitting and enhance robustness, we performed hyperparameter tuning and 5-fold cross-validation. Finally, the Shapley additive interpretation (SHAP) method and a partial dependence plot (PDP) were used to determine the optimal ML model. Of the 9,171 patients, 514 (5.6 %) developed MACEs. 24 significant preoperative features were selected for model development and evaluation. All ML models performed well, with AUROC above 0.88 and AUPRC above 0.39, outperforming the AUROC (0.716) and AUPRC (0.185) of RCRI (P < 0.001). The best independent model was XGBoost (AUROC = 0.898, AUPRC = 0.479). The calibration curve accurately predicted the risk of MACEs (Brier score = 0.040), and the DCA results showed that XGBoost had a high net benefit for predicting MACEs. The top-ranked stacking ensemble model, consisting of CatBoost, GBDT, GNB, and LR, proved to be the best (AUROC 0.894, AUPRC 0.485). We identified the top 20 most important features using the mean absolute SHAP values and depicted their effects on model predictions using PDP. This study combined missing-value imputation, feature screening, unbalanced data processing, and advanced machine learning methods to successfully develop and verify the first ML-based perioperative MACEs prediction model for patients with SCAD, which is more accurate than RCRI and enables effective identification of high-risk patients and implementation of targeted interventions to reduce the incidence of MACEs.

Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage (ICH) in real-world settings. ML-based models were developed to predict in-hospital mortality in 527 patients with ICH using raw brain imaging data from brain computed tomography and clinical data. The models' performances were evaluated using the area under the receiver operating characteristic curves and calibration plots, comparing them with traditional risk scores such as the ICH score and ICH grading scale. Kaplan-Meier curves were used to examine the post-ICH survival rates, stratified by ML-based risk assessment. The net benefit of ML-based models was evaluated using decision curve analysis. The area under the receiver operating characteristic curves were 0.91 (95% CI, 0.86-0.95) for the ICH score, 0.93 (95% CI, 0.89-0.97) for the ICH grading scale, 0.83 (95% CI, 0.71-0.91) for the ML-based model fitted with raw image data only, and 0.87 (95% CI, 0.76-0.93) for the ML-based model fitted using clinical data without specialist expertise. The area under the receiver operating characteristic curve increased significantly to 0.97 (95% CI, 0.94-0.99) when the ML model was fitted using clinical and image data assessed by specialists. All ML-based models demonstrated good calibration, and the survival rates showed significant differences between risk groups. Decision curve analysis indicated the highest net benefit when utilizing the findings assessed by specialists. ML-based prediction models exhibit satisfactory performance in predicting post-ICH in-hospital mortality when utilizing raw imaging data or nonspecialist input. Nevertheless, incorporating specialist expertise notably improves performance.

Development and validation of predictive models of early immune effector cell-associated hematotoxicity (eIPMs).

Immune effector cell-associated hematotoxicity (ICAHT) is associated with morbidity and mortality after chimeric antigen receptor (CAR) T-cell therapy. To date, the factors associated with ICAHT are poorly characterized, and there is no validated predictive model of ICAHT specifically. Therefore, we performed comprehensive univariate analyses to identify factors associated with severe (grade 3-4) early ICAHT (eICAHT) in 691 patients who received commercial or investigational CAR T-cell therapy for hematologic malignancies. In univariate logistic regression, pre-infusion factors associated with severe eICAHT included disease type (acute lymphoblastic leukemia), pre-lymphodepletion (LD) blood counts including absolute neutrophil count (ANC), lactate dehydrogenase (LDH), and inflammatory (C-reactive protein [CRP]; ferritin, interleukin-6 [IL-6]), and coagulopathy biomarkers (D-dimer). Post-infusion laboratory markers associated with severe eICAHT included early and peak levels of inflammatory biomarkers (CRP, ferritin, IL-6), coagulopathy biomarkers (D-dimer), peak cytokine release syndrome (CRS) grade, and peak neurotoxicity grade. We trained (n = 483) and validated (n = 208) two Early ICAHT Prediction Models (eIPM): eIPMPre including pre-infusion factors only (disease type and pre-LD ANC, platelet count, LDH, and ferritin) and eIPMPost containing both pre- (disease type and pre-LD ANC, platelet count, and LDH) and post-infusion (day +3 ferritin) factors. Both models generated calibrated predictions and high discrimination (area under the receiver operating characteristic curve [AUROC] in test set: 0.87 for eIPMPre and 0.88 for eIPMPost), with higher net benefit in decision curve analysis for eIPMPost. Individualized predictions of severe eICAHT can be generated from both eIPMs utilizing our online tool (available at https://eipm.fredhutch.org).

Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database.

The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict the mortality of AHF patients. The Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-Ⅳ database and randomly divided into a training set (n = 3,580, 70%) and a validation set (n = 1,534, 30%). The variates collected include demographic data, vital signs, comorbidities, laboratory test results, and treatment information within 24 hours of ICU admission. By using the least absolute shrinkage and selection operator (LASSO) regression model in the training set, variates that affect the in-hospital mortality of AHF patients were screened. Subsequently, in the training set, five common machine learning (ML) algorithms were applied to construct models using variates selected by LASSO to predict the in-hospital mortality of AHF patients. The predictive ability of the models was evaluated for sensitivity, specificity, accuracy, the area under the curve of receiver operating characteristics, and clinical net benefit in the validation set. To obtain a model with the best predictive ability, the predictive ability of common scoring systems was compared with the best ML model. Among the 5,114 patients, in-hospital mortality was 12.5%. Comparing the area under the curve, the XGBoost model had the best predictive ability among all ML models, and the XGBoost model was chosen as the final model for its higher net benefit. Its predictive ability was superior to common scoring systems. The XGBoost model can effectively predict the in-hospital mortality of AHF patients admitted to the ICU, which may assist clinicians in precise management and early intervention for patients with AHF to reduce mortality.

Maize Fertilizer Requirement Estimates Through Nutrient Use Efficiency and Economic Analysis from Assosa, Western Ethiopia

Scientific information with regards to the response of maize (Zea mays L.) to different blended fertilizer rates for its optimum production in Nitisols of Assosa area is limited. Therefore, a field experiment was conducted on Nitisols of Assosa Agricultural Research Centre during 2016/17 cropping season to investigate the response of growth, yield and nutrient use efficiency of maize (Zea mays L.) to different blended fertilizer rates and types. The treatments consists of: control, three rates of N and P combined (92/46, 115/57 and 138/69 N/P&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;5&amp;lt;/sub&amp;gt; kg ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; and two formula of blended fertilizers with different rates, formula 2 consists of 100 kg NPSB+ 73.9 N, 150 kg NPSB +110.8 N and 200 kg NPSB + 147.8 N kg ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; and formula 4 consists of 100 kg NPSZnB + 75.1 N, 150 kg NPSZnB + 112.6 N &amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt; and 200 kg NPSZnB +150.2 N kg ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;. The treatments were laid out as a Randomized Complete Block Design with three replications. Application of blended fertilizers (NPSB, NPSZnB) hastened days to tasseling silking and maturity by 10, 7 and 15 days, respectively as compared to combined N and P rates. Application of blended fertilizer increases significantly (p &amp;lt; 0.01) the plant height, cob weight, ear length, 100 kernels weight, number of kernels per row and ear height as compared to combined N and P and the control. The analysis of variance revealed that fertilizer types and rates significantly (P &amp;lt; 0.01) affected on grain yield, straw yield and harvest index. However there was no significant difference between the two blended fertilizer types. Maximum grain yield (7056.2 kg ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;) was recorded with 200 Kg NPSZnB + 150.2 N kg ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; application, while minimum grain yield 2996.0 kg ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; was recorded from control treatment. The application of 150 kg NPSB + 110.8 N kg ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; had highest Marginal rate of return (MRR%) and net benefit. Therefore, we recommended the treatment (150 Kg NPSB + 110.8N kg ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;) since it produced high marginal rate of return, high net benefit and relatively small total cost of production, for maize production in Asossa area. Furthermore, based on yield, net benefit and relatively low total cost of production the farmer of Asossa area also can use 150 kg NPSZnB + 112.6 N in case of absence of NPSB in market.

Discrimination Model Construction for Non-Lactational Mastitis and Breast Cancer Based on Imaging Features.

Aims/Background The clinical presentation of non-lactational mastitis (NLM) shares similarities with some symptoms and examination results of breast cancer (BC), which can lead to misdiagnosis or delayed treatment. Current studies on breast lesions mostly focus on the diagnostic performance of a single imaging technique. This study aims to construct a discrimination diagnostic model for NLM and BC based on such imaging features as ultrasound and magnetic resonance imaging (MRI) and to validate the application value of the model, assisting clinicians in improving disease diagnosis and refining medical decisions. Methods This study is a retrospective analysis. Clinical data of 108 patients suspected of NLM based on imaging diagnosis, admitted to The First Affiliated Hospital with Nanjing Medical University between May 2018 and August 2023, were collected. Among them, 94 cases were pathologically confirmed as NLM and 14 cases as BC. Univariate and multivariate logistic regression analyses were performed on the patients' clinical data, ultrasound features, and MRI features to select the risk factors for discriminating NLM and BC, and construct a discrimination model. The discrimination performance of the model was analyzed with the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve. Results In the NLM group, there were 24 cases of granulomatous lobular mastitis (25.53%) and 70 cases of plasma cell mastitis (74.47%). In the BC group, there were 2 cases of infiltrating ductal carcinoma, 2 cases of atypical hyperplasia, 3 cases of papillary carcinoma, and 7 cases of ductal carcinoma in situ. Age, internal blood flow, calcification, edge, enhancement characteristics, apparent diffusion coefficient (ADC) values, and time-intensity curve (TIC) type were independent factors for differentiating NLM and BC (p < 0.05). The ROC analysis showed that the area under the curve of the model for discriminating NLM and BC was 0.920. The DCA results showed that the model had high net benefits for discriminating NLM and BC. The calibration curve analysis showed that the model had good consistency with the actual diagnosis of NLM and BC, with a chi-square value of 4.545 and a p-value of 0.155 according to the Hosmer-Lemeshow test. Conclusion Age, internal blood flow, calcification, edge, enhancement characteristics, ADC, and TIC curve types are important factors in distinguishing NLM and BC, and the model based on the above characteristics to distinguish NLM and BC has a high net benefit in distinguishing the two.

Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation.

Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention. We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (≥2 blood pressure [BP] readings ≥140/90 mm Hg or ≥1 BP reading ≥180/120 mm Hg) and one to predict hypertensive crisis (≥1 BP reading ≥180/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded). Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record-based predictors were based on the 1-year period before a patient's index visit. The dataset was randomly split (80:20) into a training set and a validation set. In total, 4 machine learning frameworks were considered: L2-regularized logistic regression, multilayer perceptron, gradient boosting machines, and random forest. Model selection was performed with 10-fold cross-validation. The final models were assessed on discrimination (C-statistic), calibration (eg, integrated calibration index), and net benefit (with decision curve analysis). Additionally, internal-external cross-validation was performed at the county level to assess performance with new populations and summarized using random-effect meta-analyses. In internal validation, the C-statistic and integrated calibration index were 0.72 (95% CI 0.71-0.72) and 0.015 (95% CI 0.012-0.020) for the sustained, uncontrolled hypertension model, and 0.81 (95% CI 0.79-0.82) and 0.009 (95% CI 0.007-0.011) for the hypertensive crisis model. The models had higher net benefit than the default policies (ie, treat-all and treat-none) across different decision thresholds. In internal-external cross-validation, the pooled performance was consistent with internal validation results; in particular, the pooled C-statistics were 0.70 (95% CI 0.69-0.71) and 0.79 (95% CI 0.78-0.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively. An electronic health record-based model predicted hypertensive crisis reasonably well in internal and internal-external validations. The model can potentially be used to support population health surveillance and hypertension management. Further studies are needed to improve the ability to predict sustained, uncontrolled hypertension.

Nomogram Predictive Model for Vaginal Birth after One Prior Cesarean Section: A Retrospective Study

Background: Repeat cesareans pose significant maternal risks, whereas vaginal birth after cesarean (VBAC) offers better outcomes, with a complication rate of 2.4% compared with 3.6% for elective repeat cesareans and 14.1% for failed trial of labor after cesarean (TOLAC). TOLAC rates are low in China, and success rates vary between 60% and 80%. This study aims to develop a nomogram-based predictive model for VBAC in China and improve existing, less rigorous scoring models. Methods: This retrospective cohort study was conducted at Hangzhou Women's Hospital from February 2015 to March 2020, and included 159 parturient attempts at labor after one prior low transverse cesarean section. The participants were divided into two groups based on their mode of delivery for comparison: the VBAC and the TOLAC failure group. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors for VBAC and develop a nomogram predictive model to estimate the success rate of TOLAC. The bootstrap method was used for internal validation of the models. Three different VBAC prediction models were evaluated by plotting receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: A total of 127 women had VBAC and 32 women failed TOLAC, with a success rate of 79.9%. Three independent factors affecting the VBAC were identified: gestational age at delivery, Bishop score and newborn birth weight. A predictive nomogram model for the VBAC was constructed that incorporates these three factors. The model showed a good fit (χ2 = 11.94, p = 0.154) with an overall prediction accuracy of 81.1%. The area under the ROC curve was 0.83 (95% CI (confidence interval), 0.76–0.90) (p &lt; 0.001) and the optimal cut-off value was 83.4%. The bootstrap internal validation showed that our predictive model maintained high overall accuracy and specificity, but exhibited low sensitivity and a low Kappa coefficient. Compared to the Grobman model and Jiaming Rao et al. model, our developed prediction model possesses the strongest discriminatory ability and the highest net benefit, followed by the model by Jiaming Rao et al. All three models demonstrate a high degree of fit. Conclusions: Shorter gestational age at delivery, lower newborn birth weight and higher cervical Bishop score are favorable factors for VBAC. The predictive nomogram model for the VBAC after a single cesarean section, constructed with these three factors, has good predictive efficacy. The model is simple to calculate and has practical value in the clinical selection of suitable candidates for TOLAC after a single cesarean section.

Influence of Plant Density and Fungicides on Downy Mildew (&amp;lt;i&amp;gt;Peronospora Destructor&amp;lt;/i&amp;gt;) and Bulb Yield of Onion in Ethiopia

Onion is a popular vegetable crop, which has been produced for its daily uses and economic benefits. Downey mildew caused by the fungus &amp;lt;i&amp;gt;Peronospora&amp;lt;/i&amp;gt; &amp;lt;i&amp;gt;destructor&amp;lt;/i&amp;gt; is among the most important yield-reducing factor in onion production. The effect of three different levels of plant population and fungicide frequency with two different fungicides was studied on downy mildew severity and yield of onion. Nineteen treatment of this Experiment included two fungicides mancozeb + metalaxyl and copper hydrox-ide, three different spraying interval with (10 days, 15 days and 21 days) and three level of plant population (0.71, 0.95 and 1.2 million plants/ha). The experiment was laid out in a randomized block design with three replications for two year at Fogera and for one year at Dera districts of South Gondar zone Ethiopia. Data on disease severity, bulb yield, bulb number and bulb size were recorded during the time of harvest. The price of bulb yield was assessed from the local market and the total price of the yield obtained from each treatment was computed on hectare basis. Input costs like seed, fungicides and labor were converted into hectare basis according to their frequencies used. Fungicides cost was estimated based on the price of the local market. Cost of the labor was in Birr per man-days; cost of spray and spray equipment to spray per hectare were also calculated. Based on the obtained data from the above mentioned parameters economic analysis was performed. The lowest disease severity was recorded in treatment were lowest level of plant population (0.71 million plant/hectare) spraying with fungicide mancozeb + metalaxyl within 15days interval. In this treatment bulb yield and bulb weight were the highest. The economic evaluation showed that the highest net benefit with acceptable marginal rate of return was obtained from T3 (0.71million plants/ha spraying with fungicide mancozeb + metalaxyl within 15days interval).

Predictive model for acute radiation esophagitis in esophageal carcinoma based on prognostic nutritional index and systemic inflammatory index and its application.

Acute radiation esophagitis (ARE) is a common complication in patients with esophageal cancer undergoing radiotherapy. Therefore, it is important to construct an effective ARE risk-prediction model for clinical treatment. The present study performed a retrospective analysis of 225 patients with esophageal cancer who received radiotherapy at the First Affiliated Hospital of Anhui Medical University (Hefei, China) from January 2018 to December 2022. Univariate and logistic regression analyses were performed to screen patients with esophageal cancer after radiotherapy. The results revealed that 147 patients developed radiation esophagitis. Logistic regression analysis results demonstrated that the prognostic nutritional index [odds ratio (OR), 0.864; 95% confidence interval (CI), 0.809-0.924], neutrophil to lymphocyte ratio (OR, 1.795; 95% CI, 1.209-2.667) and platelet to lymphocyte ratio (OR, 1.011; 95% CI, 1.000-1.022) were independent predictors of ARE in patients receiving intensity-modulated conformal radiotherapy for esophagus cancer (P<0.05). A nomogram model for predicting the occurrence of ARE was established based on the three risk factors. The decision curve suggested a high net benefit value when the threshold probability was within 0.25-1.0. External verification confirmed the reproducibility and generalizability of the nomogram model. In general, the calibration curve of this model was close to the ideal curve and had excellent prediction accuracy. Therefore, it may be used as a new tool for early prediction of the ARE risk.

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study.

Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physical, psychological and financial burden of patients. Our study aims to construct and validate a predictive model for CRT risk in patients with cancer. It offers the possibility to identify independent risk factors for CRT and prevent CRT in patients with cancer. We prospectively followed patients with cancer and CVAD at Xiangya Hospital of Central South University from January 2021 to December 2022 until catheter removal. Patients with CRT who met the criteria were taken as the case group. Two patients with cancer but without CRT diagnosed in the same month that a patient with cancer and CRT was diagnosed were selected by using a random number table to form a control group. Data from patients with CVAD placement in Qinghai University Affiliated Hospital and Hainan Provincial People's Hospital (January 2023 to June 2023) were used for the external validation of the optimal model. The incidence rate of CRT in patients with cancer was 5.02% (539/10 736). Amongst different malignant tumour types, head and neck (9.66%), haematological (6.97%) and respiratory (6.58%) tumours had the highest risks. Amongst catheter types, haemodialysis (13.91%), central venous (8.39%) and peripherally inserted central (4.68%) catheters were associated with the highest risks. A total of 500 patients with CRT and 1000 without CRT participated in model construction and were randomly assigned to the training (n = 1050) or testing (n = 450) groups. We identified 11 independent risk factors, including age, catheterisation method, catheter valve, catheter material, infection, insertion history, D-dimer concentration, operation history, anaemia, diabetes and targeted drugs. The logistic regression model had the best discriminative ability amongst the three models. It had an area under the curve (AUC) of 0.868 (0.846-0.890) for the training group. The external validation AUC was 0.708 (0.618-0.797). The calibration curve of the nomogram model was consistent with the ideal curve. Moreover, the Hosmer-Lemeshow test showed a good fit (P > 0.05) and high net benefit value for the clinical decision curve. The nomogram model constructed in this study can predict the risk of CRT in patients with cancer. It can help in the early identification and screening of patients at high risk of cancer CRT.