Multivariable Prediction Model Research Articles

Etiology and characteristics of non-aneurysmal thunderclap headache presenting to an acute setting

ObjectivesTo describe the characteristics and causes of non-aneurysmal thunderclap headache (TCH) and compare serious from benign underlying causes. MethodsRetrospective cohort study of consecutive adult patients with TCH presenting to a tertiary care academic medical center between 2010 and 2020. Aneurysmal subarachnoid hemorrhage cases were excluded. Cases were categorized into serious or benign; serious TCH was defined as any condition in which delayed diagnosis and treatment could result in neurological disability or death. Risk factors for serious TCH were analyzed. We adhere to standardized guidelines for reporting observational studies. ResultsA total of 932 patients presented with TCH. After exclusion of 393 patients with aneurysmal-type subarachnoid hemorrhage, 539 were included in the analysis. One-half (n = 275, 51.0 %) had a serious cause. Median age was 51 years, 69.0 % were female. Most frequent diagnoses were intracranial hemorrhage (n = 102, 18.9 %), reversible cerebral vasoconstriction syndrome (n = 97, 18.0 %), and idiopathic TCH (n = 102, 38.6 %). A multivariable logistic regression model for prediction of serious TCH included age, hypertension, migraines, recurrent TCH, level of consciousness and other clinical exam findings, and achieved an AUROC of 0.732. This score had a sensitivity of 79.9 % (95 % CI 73.5–83.5 %) for the identification of serious TCH. A 0.5-point increase in the risk score was associated with a 73 % increase in the odds of serious TCH (odds ratio 1.73, 95 % CI 1.53–1.95, p < 0.001). ConclusionOur study describes the relative frequency of presentation and etiologies among patients with TCH This score can aide clinicians in recognising patients with potentially serious cause of TCH, for whom additional imaging and neurological consultation is necessary.

Emergency delivery in case of suspected placenta accreta spectrum: Can it be predicted?

The main goal of placenta accreta spectrum (PAS) screening is to enable delivery in an expert center in the presence of an experienced team at an appropriate time. Our study aimed to identify independent risk factors for emergency deliveries within the IS-PAS 2.0 database cohort and establish a multivariate predictive model. A retrospective analysis of prospectively collected PAS cases from the IS-PAS database between January 2020 and June 2022 by 23 international expert centers was performed. All PAS cases (singleton and multiple pregnancies) managed according to local protocols were included. Individuals with emergent delivery were identified and compared to those with scheduled delivery. A multivariate analysis was conducted to identify the possible risk factors for emergency delivery and was used to establish a predictive model. Maternal outcomes were compared. Overall, 315 women were included in the study. Of these, 182 participants (89 with emergent and 93 with scheduled delivery) were included in the final analysis after exclusion of those with unsuspected PAS antenatally or who lacked information about the urgency of delivery. Gestational age at delivery was higher in the scheduled group (34.7 vs. 32.9, p < 0.001). Antenatal bleeding (OR 2.9, p = 0.02) and a placenta located over a uterine scar (OR 0.38, p = 0.001) were the independent predictive factors for emergent delivery (AUC 0.68). Ultrasound (US) markers: loss of clear zone (p = 0.001), placental lacunae (p = 0.01), placental bulge (p = 0.02), and presence of bridging vessels (p = 0.02) were more frequently documented in the scheduled group. None of these markers improved the predictive values of the model. Higher PAS grades were identified in the scheduled group (p = 0.01). There were no significant differences in maternal outcomes. Antenatal bleeding and the placental location away from the uterine scar remained the most significant predictors for emergent delivery among patients with PAS, even when combining more predictive risk factors, including US markers. Based on these results, patients who bleed antenatally may benefit from transfer to an expert center, as we found no differences in maternal outcomes between groups delivered in expert centers. Earlier-scheduled delivery is not supported due to the low predictive value of our model.

The Pre-ECPR Score: Developing and Validating a Multivariable Prediction Model for Favorable Neurological Outcomes in Patients Undergoing Extracorporeal Cardiopulmonary Resuscitation

The Pre-ECPR Score: Developing and Validating a Multivariable Prediction Model for Favorable Neurological Outcomes in Patients Undergoing Extracorporeal Cardiopulmonary Resuscitation

Addressing multidimensional highly correlated data for forecasting in precision beekeeping

In recent years, there have been relevant advances in precision beekeeping. These advances are mainly focused on proposing sensor systems that collect crucial information for bee welfare, creating integrated architectures that allow beekeepers to monitor the current state of their hive through real-time data. However, there is a lack of predictive models that would allow beekeepers to anticipate specific events that endanger bee welfare and lead to a decline in productivity. Specifically, predictive approaches accounting for the high correlation among internal variables of beehives have not been developed to date. To address this research gap, multivariate predictive models, including auto-regressive state-space and time series models, have been implemented and applied to four different hives from the we4bee project. These models aim to predict the internal variables of beehives (four different temperatures, humidity, and weight) by utilizing the meteorological conditions to which the hives are exposed. A cross-validation adapted to time series data was employed for model generalization assessment. Prediction models based on vector time series exhibited superior performance in forecasting internal hive variables compared to multivariate auto-regressive state-space models. Overall, the approach based on the vector error correction model yielded the best balance between fit, prediction, and computational cost. The VEC-based approach produces predictions with maximum mean absolute errors of 177 (312)g in weight, 3.366 (3.802)% in humidity, and 1.122 (1.685)°C in temperature at 1 (3)-days ahead when dealing with beehives exhibiting a high degree of correlation in their internal variables. Moreover, the VEC-based approach requires less than a second to perform the time series fitting process, which makes it particularly interesting for application in big data environments. The integration of such models into a decision support system would meet the need of beekeepers to anticipate potential threats to the welfare of their bee colonies, streamlining their monitoring processes while eliminating the need for continuous inspections.

Development and validation of supervised machine learning multivariable prediction models for the diagnosis of Pneumocystis jirovecii pneumonia using nasopharyngeal swab PCR in adults in a low-HIV prevalence setting.

The global burden of the opportunistic fungal disease Pneumocystis jirovecii pneumonia (PJP) remains substantial. Polymerase chain reaction (PCR) on nasopharyngeal swabs (NPS) has high specificity and may be a viable alternative to the gold standard diagnostic of PCR on invasively collected lower respiratory tract specimens, but has low sensitivity. Sensitivity may be improved by incorporating NPS PCR results into machine learning models. Three supervised multivariable diagnostic models (random forest, logistic regression and extreme gradient boosting) were constructed and validated using a 111-person Australian dataset. The predictors were age, gender, immunosuppression type and NPS PCR result. Model performance metrics such as accuracy, sensitivity, specificity and predictive values were compared to select the best-performing model. The logistic regression model performed best, with 80% accuracy, improving sensitivity to 86% and maintaining acceptable specificity of 70%. Using this model, positive and negative NPS PCR results indicated post-test probabilities of 84% (likely PJP) and 26% (unlikely PJP), respectively. The logistic regression model should be externally validated in a wider range of settings. As the predictors are simple, routinely collected patient variables, this model may represent a diagnostic advance suitable for settings where collection of lower respiratory tract specimens is difficult but PCR is available.

Optimization of process parameters in laser cladding multi channel forming using MVBM-NSGA-II method

ABSTRACT Laser cladding multi-channel forming is a complex, nonlinear process characterized by multi-parameter coupling. To determine the technology parameters, an optimization method of multivariate black-box prediction model (MVBM) matching non-dominated sequential genetic algorithm (NSGA-II) was constructed. Multi-channel LC orthogonal experiments were designed. The MVBM was established with process technology parameters as inputs and characteristic parameters reflecting the quality (surface hardness, dilution rate) as responses. The set of optimal solutions for technology parameters by the NSGA-II. The experimental results showed that, under the optimum parameters, the average hardness of the optimized increased by 6.1%, and the dilution rate was reduced by 49.8%. The dilution rate was maintained within the optimal range. The study’s findings can provide theoretical support for optimizing LC multi-objective technology parameters and improving coating quality control.

Multivariate time series approaches to extract predictive asthma biomarkers from prospectively patient-collected diary data: a systematic review

ObjectivesLongitudinal data are common in asthma studies, to assess asthma progression in patients and identify predictors of future outcomes, including asthma exacerbations and asthma control. Different methods can quantify temporal...

Study on the changing patterns of production performance of laying hens and their relationships with environmental factors in a large-scale henhouse

The production performance of laying hens is influenced by various environmental factors within the henhouse. The intricate interactions among these factors make the impact process highly complicated. The exact relationships between production performance and environmental variables are still not well understood. In this study, we measured the production performance of laying hens and various environmental variables across different parts of the henhouse, evaluated the weight of each environmental variable, and constructed a laying rate prediction model. Results displayed that body weight, laying rate, egg weight and eggshell thickness of hens decrease gradually from WCA to FA (P < 0.05). Serum levels of FSH and LH, as well as antibody level of H5 Re-13, gradually decrease from WCA to FA (P < 0.05). Moreover, the values for temperature (T), temperature-humidity index (THI), air velocity (AV), carbon dioxide (CO2), and particulate matter (PM2.5) gradually increase from WCA to FA (P < 0.05). Conversely, the relative humidity (RH) value gradually decreases from FA to WCA (P < 0.05). Additionally, the weights of the environmental variables, determined using a combination of the grey relational analysis (GRA) and analytic hierarchy process (AHP), were as follows in descending order: RH, THI, T, light intensity (LI), AV, PM2.5, NH3, and CO2. When the number of decision trees in the laying rate prediction model was set to 2,500, the results displayed a high level of agreement between the model's predictions and the observed outcomes. The model's performance evaluation yielded an R2 value of 0.89995 for the test set, suggesting strong predictive effects. In conclusion, the current study revealed significant differences in both the production performance of laying hens and the environmental variables across different parts of the henhouse. Furthermore, the study demonstrated that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate.

Prediction of transforaminal epidural injection success in sciatica (POTEISS): a protocol for the development of a multivariable prediction model for outcome after transforaminal epidural steroid injection in patients with lumbar radicular pain due to disc herniation or stenosis

BackgroundTransforaminal epidural injections (TEI) can alleviate symptoms and help to maintain physical functioning and quality of life in patients with lumbar radicular pain. We aim to develop a prediction model for patient outcome after TEI in patients suffering from unilateral lumbar radicular pain due to lumbar disc herniation (LDH) or single-level spinal stenosis (LSS). The secondary aim is to estimate short-term patient outcome differences between LDH and LSS patients, the association between psychological variables and patient outcome, the rate of additional injections, surgery and complications, and to explore the short-term cost-effectiveness of TEI.MethodsThis study is designed as a multi-centre, observational, prospective cohort study in two large regional hospitals in the Netherlands. Patients diagnosed with unilateral lumbar radicular pain secondary to LDH or LSS and congruent with MRI findings, who are referred for TEI along usual care pathways, are eligible for study participation. A total of 388 patients with LDH or LSS will be included. A pre-defined set of demographic, clinical and radiological variables will be used as the predictors in the model. The primary outcome measure is the Numerical Rating Scale (NRS) for leg pain. Secondary outcome measures include back pain, physical functioning, perceived recovery, pain coping strategies, anxiety and depression and use of analgesics and physical therapy. Patients will be evaluated at baseline, 2 weeks and 6 weeks after treatment. NRS leg pain and Likert perceived recovery data will be used as the dependent variables in a generalized linear mixed model for prediction of TEI outcome, with internal validation of performance (explained variation) by bootstrap resampling. Cost-effectiveness for a period of 6 weeks prior to and after treatment will be performed with decision-analytic modelling.DiscussionPatients with severe lumbar radicular pain often request additional treatment when conservative care is insufficient. TEI can offer relief of symptoms. Currently, it is not possible to predict responsiveness to this treatment for individual patients. This study is designed to explore predictors that can differentiate between patients that will and will not have a positive outcome after TEI. This information may support treatment strategies for this patient group.Trial registrationThis study is registered at ClinicalTrials.gov database under registry number NCT04540068 on September 1, 2020.

Radiomic study of antenatal prediction of severe placenta accreta spectrum from MR imaging.

We previously demonstrated the potential of radiomics for prediction of severe histological Placenta Accreta Spectrum (PAS) subtypes using T2-weighted MRI. We aim is to validate our model using an additional dataset. Secondly, we explore whether performance is improved using a new approach to develop a new multivariate radiomics model. Multi-centre retrospective analysis conducted between 2018-2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomised feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including Area Under the Curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity and specificity. We present 100 women (mean age 34.6 (±3.9) with PAS, 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found a Support Vector Machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922) and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990) and specificity of 0.447 (95% CI 0.167; 0.667). We validated our previous model and present a new multivariate radiomic model for prediction of severe placenta increta from a well-defined, cohort of PAS cases. Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.

Long-Term Nitrogen Dioxide Exposure as a Possible 5-Year Mortality Risk Factor in Diabetic Patients Treated Using Off-Pump Surgical Revascularization-A Retrospective Analysis.

Background: There is mounting evidence that diabetic-related cardiac metabolism abnormalities with oxidative stress and inflammatory mechanism activation align with the functional impairments that result in atherosclerotic lesion formation. Among the possible non-traditional coronary lesion risk factors, environmental exposure may be significant, especially in diabetic patients. Methods: A total of 140 diabetic patients (115 (82%) males and 25 (18%) females) with a mean age of 65 (60-71) underwent surgical revascularization due to multivessel coronary disease. The possible all-cause mortality risk factors, including demographical and clinical factors followed by chronic air pollution exposure, were identified. Results: All patients were operated on using the off-pump technique and followed for 5.6 (5-6.1) years. The multivariable model for 5-year mortality prediction presented the nitrogen dioxide chronic exposure (HR: 3.99, 95% CI: 1.16-13.71, p = 0.028) and completeness of revascularization (HR: 0.19, 95% CI: 0.04-0.86, p = 0.031) as significant all-cause mortality risk factors. Conclusions: Ambient air pollutants such as an excessive chronic nitrogen dioxide concentration (>15 µg/m3) may increase 5-year all-cause mortality in diabetic patients following surgical revascularization.

Natriuretic Peptides to Classify Risk of Atrial Fibrillation Detection After Stroke: Analysis of the BIOSIGNAL and PRECISE Cohort Studies.

Prolonged cardiac monitoring (PCM) increases atrial fibrillation (AF) detection after ischemic stroke, but access is limited, and it is burdensome for patients. Our objective was to assess whether midregional proatrial natriuretic peptide (MR-proANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) could classify people who are unlikely to have AF after ischemic stroke and allow better targeting of PCM. We analyzed people from the Biomarker Signature of Stroke Aetiology (BIOSIGNAL) study with ischemic stroke, no known AF, and ≥3 days cardiac monitoring. External validation was performed in the Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient (PRECISE) study of 28 days of cardiac monitoring in people with ischemic stroke or transient ischemic attack and no known AF. The main outcome is no AF detection. We assessed the discriminatory value of MR-proANP and NT-proBNP combined with clinical variables to identify people with no AF. A decision curve analysis was performed with combined data to determine the net reduction in people who would undergo PCM using the models based on a 15% threshold probability for AF detection. We included 621 people from the BIOSIGNAL study. The clinical multivariable prediction model included age, NIH Stroke Scale score, lipid-lowering therapy, creatinine, and smoking status. The area under the receiver-operating characteristic curve (AUROC) for clinical variables was 0.68 (95% CI 0.62-0.74), which improved with the addition of log10MR-proANP (0.72, 0.66-0.78; p = 0.001) or log10NT-proBNP (0.71, 0.65-0.77; p = 0.009). Performance was similar for the models with log10MR-proANP vs log10NT-proBNP (p = 0.28). In 239 people from the PRECISE study, the AUROC for clinical variables was 0.68 (0.59-0.76), which improved with the addition of log10NT-proBNP (0.73, 0.65-0.82; p < 0.001) or log10MR-proANP (0.79, 0.72-0.86; p < 0.001). Performance was better for the model with log10MR-proANP vs log10NT-proBNP (p = 0.03). The models could reduce the number of people who would undergo PCM by 30% (clinical and log10MR-proANP), 27% (clinical and log10NT-proBNP), or 20% (clinical only). MR-proANP and NT-proBNP help classify people who are unlikely to have AF after ischemic stroke. Measuring MR-proANP or NT-proBNP could reduce the number of people who need PCM by 30%, without reducing the amount of AF detected. NCT02274727; clinicaltrials.gov/study/NCT02274727.

Developing a multi-variate prediction model for COVID-19 from crowd-sourced respiratory voice data

Aim: COVID-19 has affected more than 223 countries worldwide and in the post-COVID era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. This study focuses on the analysis of voice features and machine learning models in the automatic detection of COVID-19. Methods: We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identification from only voice recordings. We use the Cambridge COVID-19 Sound database which contains 893 speech samples, crowd-sourced from 4,352 participants via a COVID-19 Sounds app. Voice features including Mel-spectrograms and Mel-frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) Encoder features are extracted. Based on the voice data, we develop deep learning classification models to detect COVID-19 cases. These models include long short-term memory (LSTM), CNN and Hidden-Unit BERT (HuBERT). Results: We compare their predictive power to baseline machine learning models. HuBERT achieves the highest accuracy of 86% and the highest AUC of 0.93. Conclusions: The results achieved with the proposed models suggest promising results in COVID-19 diagnosis from voice recordings when compared to the results obtained from the state-of-the-art.

Forecasting the architecture billings index (ABI) using machine learning predictive models

PurposeThis research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.Design/methodology/approachThe architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.FindingsThe experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.Practical implicationsThe architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.Originality/valueThe ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

Systematic review of methods used in prediction models with recurrent event data

BackgroundPatients who suffer from chronic conditions or diseases are susceptible to experiencing repeated events of the same type (e.g. seizures), termed ‘recurrent events’. Prediction models can be used to predict the risk of recurrence so that intervention or management can be tailored accordingly, but statistical methodology can vary. The objective of this systematic review was to identify and describe statistical approaches that have been applied for the development and validation of multivariable prediction models with recurrent event data. A secondary objective was to informally assess the characteristics and quality of analysis approaches used in the development and validation of prediction models of recurrent event data.MethodsSearches were run in MEDLINE using a search strategy in 2019 which included index terms and phrases related to recurrent events and prediction models. For studies to be included in the review they must have developed or validated a multivariable clinical prediction model for recurrent event outcome data, specifically modelling the recurrent events and the timing between them.The statistical analysis methods used to analyse the recurrent event data in the clinical prediction model were extracted to answer the primary aim of the systematic review. In addition, items such as the event rate as well as any discrimination and calibration statistics that were used to assess the model performance were extracted for the secondary aim of the review.ResultsA total of 855 publications were identified using the developed search strategy and 301 of these are included in our systematic review. The Andersen-Gill method was identified as the most commonly applied method in the analysis of recurrent events, which was used in 152 (50.5%) studies. This was closely followed by frailty models which were used in 116 (38.5%) included studies. Of the 301 included studies, only 75 (24.9%) internally validated their model(s) and three (1.0%) validated their model(s) in an external dataset.ConclusionsThis review identified a variety of methods which are used in practice when developing or validating prediction models for recurrent events. The variability of the approaches identified is cause for concern as it indicates possible immaturity in the field and highlights the need for more methodological research to bring greater consistency in approach of recurrent event analysis. Further work is required to ensure publications report all required information and use robust statistical methods for model development and validation.PROSPERO registrationCRD42019116031.

Development and validation of the eMCI-CHD tool: A multivariable prediction model for the risk of mild cognitive impairment in patients with coronary heart disease.

This study aimed to develop and validate an eMCI-CHD tool based on clinical data to predict mild cognitive impairment (MCI) risk in patients with coronary heart disease (CHD). This cross-sectional study prospectively collected data from 400 patients with coronary heart disease (aged 55-90 years, 62% men) from July 2022 to September 2023 and randomized (7:3 ratio) them into training and validation sets. After determining the modeling variables through least absolute shrinkage and selection operator regression analysis, four ML classifiers were developed: logistic regression, extreme gradient boosting (XGBoost), support vector machine, and random forest. The performance of the models was evaluated using area under the ROC curve, accuracy, sensitivity, specificity, and F1 score. Decision curve analysis was used to assess the clinical performance of the established models. The SHapley Additive exPlanations (SHAP) method was applied to determine the significance of the features, the predictive model was visualized with a nomogram, and an online web-based calculator for predicting CHD-MCI risk scores was developed. Of 400 CHD patients (average age 70.86±8.74 years), 220 (55%) had MCI. The XGBoost model demonstrated superior performance (AUC: 0.86, accuracy: 78.57%, sensitivity: 0.74, specificity: 0.84, F1: 0.79) and underwent validation. An online tool (https://mr.cscps.com.cn/mci/index.html) with seven predictive variables (APOE gene typing, age, education, TyG index, NT-proBNP, C-reactive protein, and occupation) assessed MCI risk in CHD patients. This study highlights the potential for predicting MCI risk among CHD patients using an ML model-driven nomogram and risk scoring tool based on clinical data.

Cluster analysis of Model for End-stage Liver Disease and European System for Cardiac Operative Risk Evaluation for predicting operative mortality after concomitant tricuspid valve surgery

ObjectivePatients requiring concomitant tricuspid surgery represent a heterogeneous cohort with significant comorbidities and varying degrees of organ and right ventricular dysfunction. However, surgeons can rely on little beyond intuition and experience when discussing operative risks. The objective of the study is to assess how the Model for End-stage Liver Disease score complements the European System for Cardiac Operative Risk Evaluation II in risk assessment. MethodsWe performed a single-center retrospective cohort study of 369 consecutive patients who underwent concomitant tricuspid valve surgery from 2011 to 2020. Multivariate analysis of factors affecting operative mortality was performed, producing 2 multivariate risk prediction models, one consisting of European System for Cardiac Operative Risk Evaluation II components and the other consisting of both European System for Cardiac Operative Risk Evaluation II components and Model for End-stage Liver Disease. The models were compared by measuring c-statistic using the Hanley-McNeil method. This was further evaluated with category-free net reclassification improvement index. K-means clustering was performed using Model for End-stage Liver Disease and European System for Cardiac Operative Risk Evaluation II values after scalar transformation as independent variables and operative mortality as the dependent variable. ResultsThe Model for End-stage Liver Disease is an independent predictor of operative mortality, with an adjusted odds ratio of 1.286 per point. Inclusion of the Model for End-stage Liver Disease improves the discriminatory power of the European System for Cardiac Operative Risk Evaluation II for operative mortality, with a difference in area under the curve of 0.128 [0.0341-0.222] (P = .0076). The net reclassification improvement index of incorporating Model for End-stage Liver Disease with European System for Cardiac Operative Risk Evaluation II was 0.959 [0.515-1.392], indicating significant improvement in risk reclassification. Cluster analysis identified a unique cohort of patients with intermediate-to-high Model for End-stage Liver Disease, not previously identified with European System for Cardiac Operative Risk Evaluation II alone, who experienced high operative mortality. ConclusionsModel for End-stage Liver Disease score as a quantifier of hepatorenal dysfunction complements European System for Cardiac Operative Risk Evaluation II in predicting operative mortality after tricuspid valve surgery.

Development and Validation of the GAASThyriC Model for Predicting Patients with Suboptimal Clinical Response After Laparoscopic Sleeve Gastrectomy and a Practical Calculator: A Retrospective Cohort Study.

It might not be possible to achieve the desired outcome in every patient following bariatric surgery, even though every patient is thoroughly examined before surgery. This study aimed to develop a regression model based on parameters that affect weight loss success in patients scheduled for laparoscopic sleeve gastrectomy (LSG) and thus preoperatively predict whether the patients will have an optimal clinical response in terms of weight loss at the end of the first year. Between January 2018 and August 2022, patients who underwent LSG were analyzed retrospectively. Age, sex, comorbidities, smoking status, alcohol use status, preoperative weight, preoperative body mass index (BMI), preoperative laboratory data, weight, and total weight loss (TWL)% values at the end of the first year were recorded. At the end of the first year following LSG, patients with TWL% above 20% were defined as having an optimal clinical response in terms of weight loss. This study is designed, conducted, and reported regarding the "transparent reporting of a multivariable prediction model for individual prognosis or diagnosis" (TRIPOD) statement. The final model was used to construct an Excel-based calculator. Four hundred thirty-eight patients underwent the sleeve gastrectomy procedure, and 38 of them were excluded from the study because of a lack of 1-year follow-up information, resulting in 400 eligible patients for our study. Age, glucose, thyroid stimulating hormone (TSH), alcohol consumption, systemic immune inflammation index (SII), and tobacco were the independent predictors of optimal clinical response ( P <0.001, P <0.001, P <0.001, P =0.011, P =0.039, P =0.045, respectively). The model was called the GAASThyriC score. When the final model was tested in the validation cohort, the AUC was 0.875 (95% CI, 0.742-0.999), the sensitivity was 83.3% (95% CI, 51.6-97.9), specificity was 86.4% (95% CI, 77.4-92.8), negative likelihood ratio was 0.19 (95% CI, 0.05-0.68), and accuracy was 86% (95% CI, 77.6-92.1) when the cutoff value was set to the optimal threshold (logit = 0.8451). The GAASThyriC score can be used as an effective auxiliary tool to predict the patient population with suboptimal clinical response in terms of TWL% at the end of the first year after LSG.

Prediction technology of mine water inflow based on entropy weight method and multiple nonlinear regression theory and its application

Mine water inflow is an important basis for the formulation of mining plans and the utilization of groundwater resources. The mine water inflow is the result of the combined influence of many factors. The weight value of the influencing factors is calculated by the entropy method, and the order of importance of the factors is: precipitation > mining depth > cumulative mined-out area > aquifer thickness > mining area > mining height. The optimal univariate nonlinear regression model of mine water inflow to each influencing factor is obtained by factor scatter analysis and Matlab function programming. On this basis, combined with the weight values of factors, a multivariate nonlinear regression prediction model of mine water inflow based on weighting is innovatively established, which overcomes the defect that the traditional water inflow prediction method that cannot reflect the relative importance differences of various influencing factors. The multivariate weighted nonlinear regression model is used to predict the mine water inflow of typical coal mines, and the prediction results are compared with the linear regression model and the measured value. The results show that the prediction model of mine water inflow based on weighted multivariate nonlinear regression is accurate higher, with higher practical application value.

Cardiovascular magnetic resonance parametric mapping in the risk stratification of patients affected by chronic myocarditis.

Chronic myocardial inflammation is the substrate for arrhythmias and dilated cardiomyopathy onset, causing morbidity and mortality. Cardiovascular magnetic resonance (CMR) is the noninvasive gold standard for myocardial inflammation detection, due to the high sensitivity of the parametric mapping techniques. However, the potential prognostic capabilities of CMR mapping have not been studied in the setting of chronic myocarditis. This is a retrospective study on consecutive patients undergoing CMR with suspicion of chronic myocarditis from September 2017 to November 2021. CMR was acquired according to 2018 Lake Louise Criteria recommendations. The outcome (chronic heart failure, recurrent chronic myocarditic chest pain, ICD/PM implantation, arrhythmias [Lown class ≥ 2]) was collected at follow-up. The extent and degree of native T1, T2, and extracellular volume fraction alterations were used to create multivariate binary logistic regression models for outcome prediction, with or without left ventricle ejection fraction; their AUCs were compared with DeLong test. Differences between other parameters were assessed using Chi-square test, Fisher's exact test, or Mann-Whitney U-test. The population included 88 patients (age 43 [32-52] yo), mostly male (53/88, 60%). After a median follow-up of 21 (17-34) months, 31/88 (35%) patients experienced the outcome. The model based on the extension of mapping alterations and LV dysfunction reached a good predictability (AUC 0.71). The model based on the intensity of mapping alterations and LV dysfunction had a very good performance (AUC 0.80). The quantitative analysis of CMR mapping parameters indicative of myocardial damage severity may improve risk stratification in patients with chronic myocarditis. The intensity of myocardial damage, assessed as the degree of native T1, T2, and ECV alteration, together with left ventricle dysfunction, improved patient risk stratification. Further prospective studies will be necessary for validation before clinical application. Risk stratification of patients affected by chronic myocarditis is an unmet clinical need. Cardiovascular MRI (CMR) can role in risk stratification thanks to its multiparametric capabilities of tissue characterization. A model based on CMR parametric mapping and left ventricle ejection fraction can predict arrhythmia, heart failure, and recurrent symptoms.