Analysis of risk factors and construction of nomogram model for arthroscopic single-row rivet repair.

BACKGROUND: The factors influencing the clinical outcome of arthroscopic rotator cuff repair are not fully understood. PURPOSE: To explore the factors related to the postoperative outcome of arthroscopic single-row rivet rotator cuff repair in patients with rotator cuff injury and to construct the related nomogram risk prediction model. METHODS: 207 patients with rotator cuff injury who underwent arthroscopic single-row rivet rotator cuff repair were reviewed. The differences of preoperative and postoperative Visual Analogue Score (VAS) scores and University of California, Los Angeles (UCLA) scores were analyzed and compared. The postoperative UCLA score of 29 points was taken as the critical point, and the patients were divided into good recovery group and poor recovery group, and binary logstic regression analysis was performed. According to the results of multivariate logistic regression analysis, the correlation nomogram model was constructed, and the calibration chart was used, AUC, C-index. The accuracy, discrimination and clinical value of the prediction model were evaluated by decision curve analysis. Finally, internal validation is performed using self-random sampling. RESULTS: The mean follow-up time was 29.92 ± 17.20 months. There were significant differences in VAS score and UCLA score between preoperative and final follow-up (p < 0.05); multivariate regression analysis showed: Combined frozen shoulder (OR = 3.890, 95% CI: 1.544 ∼ 9.800), massive rotator cuff tear (OR = 3.809, 95%CI: 1.218 ∼ 11.908), More rivets number (OR = 2.118, 95%CI: 1.386 ∼ 3.237), lower preoperative UCLA score (OR = 0.831, 95%CI: 0.704-0.981) were adverse factors for the postoperative effect of arthroscopic rotator cuff repair. Use these factors to build a nomogram. The nomogram showed good discriminant and predictive power, with AUC of 0.849 and C-index of 0.900 (95% CI: 0.845 ∼ 0.955), and the corrected C index was as high as 0.836 in internal validation. Decision curve analysis also showed that the nomogram could be used clinically when intervention was performed at a threshold of 2%∼91%. CONCLUSION: Combined frozen shoulders, massive rotator cuff tears, and increased number of rivets during surgery were all factors associated with poor outcome after arthroscopic rotator cuff repair, while higher preoperative UCLA scores were factors associated with good outcome after arthroscopic rotator cuff repair. This study provides clinicians with a new and relatively accurate nomogram model.

Unifying mortality forecasting model: an investigation of the COM-Poisson distribution in the GAS model for improved projections.

Forecasting mortality rates is crucial for evaluating life insurance company solvency, especially amid disruptions caused by phenomena like COVID-19. The Lee-Carter model is commonly employed in mortality modelling; however, extensions that can encompass count data with diverse distributions, such as the Generalized Autoregressive Score (GAS) model utilizing the COM-Poisson distribution, exhibit potential for enhancing time-to-event forecasting accuracy. Using mortality data from 29 countries, this research evaluates various distributions and determines that the COM-Poisson model surpasses the Poisson, binomial, and negative binomial distributions in forecasting mortality rates. The one-step forecasting capability of the GAS model offers distinct advantages, while the COM-Poisson distribution demonstrates enhanced flexibility and versatility by accommodating various distributions, including Poisson and negative binomial. Ultimately, the study determines that the COM-Poisson GAS model is an effective instrument for examining time series data on mortality rates, particularly when facing time-varying parameters and non-conventional data distributions.

Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems.

This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting. Up-to-date literature shows a lack of research on selecting practical ML algorithms for short-term forecasting in real-time industrial applications. This research uses a quantitative and qualitative mixed method combining two rounds of literature reviews, a case study, and a comparative analysis. Ten widely used ML algorithms are selected to conduct a comparative study of gas warning systems in a case study mine. We propose a new assessment visualization tool: a 2D space-based quadrant diagram can be used to visually map prediction error assessment and predictive performance assessment for tested algorithms. Overall, this visualization tool indicates that LR, RF, and SVM are more efficient ML algorithms with overall prediction performance for short-term forecasting. This research indicates ten tested algorithms can be visually mapped onto optimal (LR, RF, and SVM), efficient (ARIMA), suboptimal (BP-SOG, KNN, and Perceptron), and inefficient algorithms (RNN, BP_Resilient, and LSTM). The case study finds results that differ from previous studies regarding the ML efficiency of ARIMA, KNN, LR, LSTM, and SVM. This study finds different views on the prediction performance of a few paired algorithms compared with previous studies, including RF and LR, SVM and RF, KNN and ARIMA, KNN and SVM, RNN and ARIMA, and LSTM and SVM. This study also suggests that ARIMA, KNN, LR, and LSTM should be investigated further with additional prediction error assessments. Overall, no single algorithm can fit all applications. This study raises 20 valuable questions for further research.

Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics.

Introduction: Accurate prediction of tumor dynamics following Gamma Knife radiosurgery (GKRS) is critical for optimizing treatment strategies for patients with brain metastases (BMs). Traditional machine learning (ML) algorithms have been widely used for this purpose; however, recent advancements in deep learning, such as autoencoders, offer the potential to enhance predictive accuracy. This study aims to evaluate the efficacy of autoencoders compared to traditional ML models in predicting tumor progression or regression after GKRS. Objectives: The primary objective of this study is to assess whether integrating autoencoder-derived features into traditional ML models can improve their performance in predicting tumor dynamics three months post-GKRS in patients with brain metastases. Methods: This retrospective analysis utilized clinical data from 77 patients treated at the "Prof. Dr. Nicolae Oblu" Emergency Clinic Hospital-Iasi. Twelve variables, including socio-demographic, clinical, treatment, and radiosurgery-related factors, were considered. Tumor progression or regression within three months post-GKRS was the primary outcome, with 71 cases of regression and 6 cases of progression. Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. The study further explored the impact of incorporating features derived from autoencoders, particularly focusing on the effect of compression in the bottleneck layer on model performance. Results: Traditional ML models achieved accuracy rates ranging from 0.91 (KNN) to 1.00 (Extra Trees). Integrating autoencoder-derived features generally enhanced model performance. Logistic Regression saw an accuracy increase from 0.91 to 0.94, and SVM improved from 0.85 to 0.96. XGBoost maintained consistent performance with an accuracy of 0.94 and an AUC of 0.98, regardless of the feature set used. These results demonstrate that hybrid models combining deep learning and traditional ML techniques can improve predictive accuracy. Conclusion: The study highlights the potential of hybrid models incorporating autoencoder-derived features to enhance the predictive accuracy and robustness of traditional ML models in forecasting tumor dynamics post-GKRS. These advancements could significantly contribute to personalized medicine, enabling more precise and individualized treatment planning based on refined predictive insights, ultimately improving patient outcomes.

Zoonotic outbreak risk prediction with long short-term memory models: a case study with schistosomiasis, echinococcosis, and leptospirosis.

BACKGROUND: Zoonotic infections, characterized with huge pathogen diversity, wide affecting area and great society harm, have become a major global public health problem. Early and accurate prediction of their outbreaks is crucial for disease control. The aim of this study was to develop zoonotic diseases risk predictive models based on time-series incidence data and three zoonotic diseases in mainland China were employed as cases. METHODS: The incidence data for schistosomiasis, echinococcosis, and leptospirosis were downloaded from the Scientific Data Centre of the National Ministry of Health of China, and were processed by interpolation, dynamic curve reconstruction and time series decomposition. Data were decomposed into three distinct components: the trend component, the seasonal component, and the residual component. The trend component was used as input to construct the Long Short-Term Memory (LSTM) prediction model, while the seasonal component was used in the comparison of the periods and amplitudes. Finaly, the accuracy of the hybrid LSTM prediction model was comprehensive evaluated. RESULTS: This study employed trend series of incidence numbers and incidence rates of three zoonotic diseases for modeling. The prediction results of the model showed that the predicted incidence number and incidence rate were very close to the real incidence data. Model evaluation revealed that the prediction error of the hybrid LSTM model was smaller than that of the single LSTM. Thus, these results demonstrate that using trending sequences as input sequences for the model leads to better-fitting predictive models. CONCLUSIONS: Our study successfully developed LSTM hybrid models for disease outbreak risk prediction using three zoonotic diseases as case studies. We demonstrate that the LSTM, when combined with time series decomposition, delivers more accurate results compared to conventional LSTM models using the raw data series. Disease outbreak trends can be predicted more accurately using hybrid models.

Development and external validation of temporal fusion transformer models for continuous intraoperative blood pressure forecasting.

Background: During surgery, intraoperative hypotension is associated with postoperative morbidity and should therefore be avoided. Predicting the occurrence of hypotension in advance may allow timely interventions to prevent hypotension. Previous prediction models mostly use high-resolution waveform data, which is often not available. Methods: We utilised a novel temporal fusion transformer (TFT) algorithm to predict intraoperative blood pressure trajectories 7 min in advance. We trained the model with low-resolution data (sampled every 15 s) from 73,009 patients who were undergoing general anaesthesia for non-cardiothoracic surgery between January 1, 2017, and December 30, 2020, at the General Hospital of Vienna, Austria. The data set contained information on patient demographics, vital signs, medication, and ventilation. The model was evaluated using an internal (n = 8113) and external test set (n = 5065) obtained from the openly accessible Vital Signs Database. Findings: In the internal test set, the mean absolute error for predicting mean arterial blood pressure was 0.376 standard deviations-or 4 mmHg-and 0.622 standard deviations-or 7 mmHg-in the external test set. We also adapted the TFT model to binarily predict the occurrence of hypotension as defined by mean arterial blood pressure < 65 mmHg in the next one, three, five, and 7 min. Here, model discrimination was excellent, with a mean area under the receiver operating characteristic curve (AUROC) of 0.933 in the internal test set and 0.919 in the external test set. Interpretation: Our TFT model is capable of accurately forecasting intraoperative arterial blood pressure using only low-resolution data showing a low prediction error. When used for binary prediction of hypotension, we obtained excellent performance. Funding: No external funding.

Accurate PM2.5 urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions.

Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM2.5), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-h PM2.5 concentrations, considering meteorological conditions, the rolling mean of PM2.5 time series, and temporal features. PM2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings.

Nonlinear model predictive control of a conductance-based neuron model via data-driven forecasting.

Objective. Precise control of neural systems is essential to experimental investigations of how the brain controls behavior and holds the potential for therapeutic manipulations to correct aberrant network states. Model predictive control, which employs a dynamical model of the system to find optimal control inputs, has promise for dealing with the nonlinear dynamics, high levels of exogenous noise, and limited information about unmeasured states and parameters that are common in a wide range of neural systems. However, the challenge still remains of selecting the right model, constraining its parameters, and synchronizing to the neural system.Approach. As a proof of principle, we used recent advances in data-driven forecasting to construct a nonlinear machine-learning model of a Hodgkin-Huxley type neuron when only the membrane voltage is observable and there are an unknown number of intrinsic currents.Main Results. We show that this approach is able to learn the dynamics of different neuron types and can be used with model predictive control (MPC) to force the neuron to engage in arbitrary, researcher-defined spiking behaviors.Significance.To the best of our knowledge, this is the first application of nonlinear MPC of a conductance-based model where there is only realistically limited information about unobservable states and parameters.

Forecasting model for qualitative prediction of the results of patient-specific quality assurance based on planning and complexity metrics and their interrelations. Pilot study.

Background: The purpose was to analyse the interrelations between planning and complexity metrics and gamma passing rates (GPRs) obtained from VMAT treatments and build the forecasting models for qualitative prediction (QD) of GPRs results. Materials and method: 802 treatment arcs from the plans prepared for the head and neck, thorax, abdomen, and pelvic cancers were analysed. The plans were verified by portal dosimetry and analysed twice using the gamma method with 3%|2mm and 2%|2mm acceptance criteria. The tolerance limit of GPR was 95%. Red, yellow, and green QDs were established for GPR examination. The interrelations were examined, as well as the analysis of effective differentiation of QD. Three models for QD forecasting based on discriminant analysis (DA), random decision forest (RDF) methods, and the hybrid model (HM) were built and evaluated. Results: Most of the interrelations were small or moderate. The exception is correlations of the join function with the average number of monitor units per control point (R = 0.893) and the beam aperture with planning target volume (R = 0.897). While many metrics allow for the effective separation of the QDs from each other, the study shows that predicting the values of the QD is possible only through multi-component forecasting models, of which the HM is the most accurate (0.894). Conclusion: Of the three models explored in this study, the HM, which uses DA methods to predict red QD and RDF methods to predict green and yellow QDs, is the most promising one.

Predicting lower limb lymphedema after cervical cancer surgery using artificial neural network and decision tree models.

PURPOSE: This study aimed to develop and validate accessible artificial neural network and decision tree models to predict the risk of lower limb lymphedema after cervical cancer surgery. METHODS: We selected 759 patients who underwent cervical cancer surgery at the Hunan Cancer Hospital from January 2010 to January 2020, collecting demographic, behavioral, clinicopathological, and disease-related data. The artificial neural network and decision tree techniques were used to construct prediction models for lower limb lymphedema after cervical cancer surgery. Then, the models' predictive efficacies were evaluated to select the optimal model using several methods, such as the area under the receiver operating characteristic curve and accuracy, sensitivity, and specificity tests. RESULTS: In the training set, the artificial neural network and decision tree model accuracies for predicting lower limb lymphedema after cervical cancer surgery were 99.80% and 88.14%, and the sensitivities 99.50% and 74.01%, respectively; the specificities were 100% and 95.20%, respectively. The area under the receiver operating characteristic curve was 1.00 for the artificial neural network and 0.92 for the decision tree model. In the test set, the artificial neural network and decision tree models' accuracies were 86.70% and 82.02%, and the sensitivities 65.70% and 67.11%, respectively; the specificities were 96.00% and 89.47%, respectively. CONCLUSION: Both models had good predictive efficacy for lower limb lymphedema after cervical cancer surgery. However, the predictive performance and stability were superior in the artificial neural network model than in the decision tree model.

Predictive Value of Neutrophil-to-Lymphocyte Ratio in Differentiating Perforated from Non-perforated Appendicitis: A Cross-Sectional Study in a Tertiary Care Hospital, Tamil Nadu.

Introduction Acute appendicitis is a common reason for acute abdominal pain. It has a high perforation rate of 20%. Diagnosis of acute appendicitis is usually through well-known clinical signs and symptoms. Radiologic imaging is by and large carried out in peculiar cases with indistinct signs and symptoms. Although various scoring methods are available for screening and diagnosis, those have inadequate validity to accurately predict the severity of acute appendicitis. From the differential counts, the neutrophil-to-lymphocyte ratio (NLR) is an economical and straightforward measure of subclinical inflammation. NLR may be a useful marker for predicting the onset and severity of appendicitis because of the insight it gives into immunological and inflammatory pathways. In this study, we aimed to determine the association between NLR and acute appendicitis among adult patients to differentiate between perforated and non-perforated appendicitis in a tertiary care hospital in Tamil Nadu, India. Methods This was a cross-sectional study conducted in the Department of General Surgery of a deemed university in Chennai, Tamil Nadu. The study was conducted from March 2022 to December 2022. Patients aged 18 years and above undergoing appendicectomy surgery were included in the study. Patients with hematology disorders, chronic kidney disease, chronic liver disease, chronic obstructive pulmonary disease, asthma, cancer, or auto-immune diseases, and any viral, bacterial, or parasitic infections were excluded. Pregnant women were also excluded from the study. After obtaining informed consent from the patients, blood samples were collected as and when they were diagnosed as acute appendicitis. Laboratory analysis for complete hemogram including white blood cell (WBC) count, neutrophil, and lymphocyte count was carried out using an automated hematology analyzer. Prevalence of perforated appendicitis was reported as a percentage. The receiver-operating characteristic (ROC) curve was developed for NLR in differentiating perforated and non-perforated appendicitis. Data were entered in Microsoft Excel 2023. These analyses were carried out in STATA 12.0 (StataCorp, College Station, Texas, USA). Results A total of 212 patients aged 18 years and above were included in the study. Among them 93 (43.9%) were male and 119 (56.1%) were female. Prevalence of perforated appendicitis observed intra-operatively was 29.7% and non-perforated appendicitis was 70.3%. The mean (SD) of NLR among patients with perforated appendicitis was 8.8 (5.1) and non-perforated appendicitis was 3.2 (2.4) with a statistically significant difference (p-value < 0.0001). ROC curve with a cut-off value of 3.78 NLR, had sensitivity of 65.9% and specificity of 93.1% in differentiating perforated and non-perforated appendicitis. The positive predictive value (PPV) and negative predictive values (NPV) were reported as 85.7% and 81.2%, respectively. Conclusion NLR has a reasonable validity in differentiating perforated and non-perforated appendicitis. NLR may be useful in low-resource settings where routine confirmatory radiological procedures like computed tomography scans are not available.

Multiple myeloma incidence, mortality, and prevalence estimates and projections, Australia, 1982-2043: a statistical modelling study.

OBJECTIVES: To examine changes in multiple myeloma incidence and mortality rates during 1982-2018, and to estimate its incidence, mortality, and prevalence for 2019-2043. STUDY DESIGN: Population-based statistical modelling study; analysis of and projections based on Australian Institute of Health and Welfare multiple myeloma incidence, mortality, and survival data. SETTING: Australia, 1982-2018 (historical data) and projections to 2043. MAIN OUTCOME MEASURES: Changes in multiple myeloma incidence and mortality rates, 1982-2018, determined by joinpoint regression analysis (age-standardised to 2021 Australian population); projection of rates to 2043 based on age-period-cohort models; estimated 5- and 30-year prevalence of multiple myeloma (modified counting method). RESULTS: The incidence of multiple myeloma increased during 1982-2018 (eg, annual percentage change [APC], 2006-2018, 1.9%; 95% confidence interval [CI], 1.7-2.2%), but the mortality rate declined during 1990-2018 (APC, -0.4%; 95% CI, -0.5% to -0.2%). The age-standardised incidence rate was projected to increase by 14.9% during 2018-2043, from 8.7 in 2018 to 10.0 (95% CI, 9.4-10.7) new cases per 100 000 population in 2043; the mortality rate was projected to decline by 27.5%, from 4.0 to 2.9 (95% CI, 2.6-3.3) deaths per 100 000 population. The annual number of people newly diagnosed with multiple myeloma was estimated to increase by 89.2%, from 2120 in 2018 to 4012 in 2043; the number of deaths from multiple myeloma was projected to increase by 31.7%, from 979 to 1289. The number of people living with multiple myeloma up to 30 years after initial diagnosis was projected to increase by 163%, from 10 288 in 2018 to 27 093 in 2043, including 13 019 people (48.1%) diagnosed during the preceding five years. CONCLUSION: Although the decline in the mortality rate was projected to continue, the projected increases in the incidence and prevalence of multiple myeloma in Australia over the next 25 years indicate that investment in prevention and early detection research, and planning for prolonged treatment and care, are needed.

A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios.

We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

Forecasting the need for palliative and hospice care using the creeping trend method with segment smoothing.

OBJECTIVE: Aim: To determine the limits of refinement of the forecast of the need for palliative and hospice care (PHC) among adults and children, made by the methods of linear, logarithmic and exponential trends, using the improved forecasting method. PATIENTS AND METHODS: Materials and Methods: Based on the calculated demand for 2018-2020, a demand forecast was made using the linear trend method for 2021 and 2022, which was verified by comparing it with the calculation based on available statistical data for 2022. To improve the forecasting result, the creeping trend method with a smoothing segment was used. RESULTS: Results: The estimated need for PHC by the linear trend method for 2022 was 87,254 adults and 46,122 children. The predicted need for this year by the linear trend method was 172,303 for adults and 45,517 for children. The prediction using the sliding trend method with segment smoothing was found to be 4.7 times more accurate and reliable for adults and all age groups combined, but was less accurate and not reliable for children. It was found out that in order to achieve a reliable forecast, it is necessary to clarify the data of medical statistics regarding of malignant neoplasms and congenital malformations, as well as demographic statistics. CONCLUSION: Conclusions: The method of a creeping trend gave more accurate results and made it possible to determine the reliability of the forecast, allowed to take into account the simultaneous influence of various input parameters.

Tracking the epidemiological trends of female breast cancer in Saudi Arabia since 1990 and forecasting future statistics using global burden of disease data, time-series analysis.

BACKGROUND: Female breast cancer stands as the prime type of cancer in the Kingdom of Saudi Arabia (KSA), with a high incidence and mortality rates. This study assessed the burden of female breast cancer in KSA by analyzing and forecasting its incidence, mortality, and disability-adjusted life years (DALYs). METHODS: We retrieved data from the Global Burden of Disease (GBD) about female breast cancer from 1990 to 2021. Time-series analysis used the autoregressive integrated moving average (ARIMA) model to forecast female breast cancer statistics from 2022 to 2026. RESULTS: From 1990 to 2021, KSA reported 77,513 cases of female breast cancer. The age groups with the highest number of cases are 45-49 years, followed by 40-44 years, 50-54 years, and 35-39 years. The analysis also showed fewer cases in the younger age groups, with the lowest number in the less than 20-year-old age group. From 1990 to 2021, KSA reported 19,440 deaths due to breast cancer, increasing from 201 cases in 1990 to 1,190 cases in 2021. The age-standardized incidence rate/100,000 of breast cancer increased from 15.4 (95% confidence interval (CI) 11.2-21.0) in 1990 to 46.0 (95%CI 34.5-61.5) in 2021. The forecasted incidence rate of female breast cancer will be 46.5 (95%CI 45.8-46.5) in 2022 and 49.6 (95%CI 46.8-52.3) in 2026. The age-standardized death rate per 100,000 Saudi women with breast cancer increased from 6.73 (95%CI 6.73-9.03) in 1990 to 9.77 (95%CI 7.63-13.00) in 2021. The forecasted female breast cancer death rate will slightly decrease to 9.67 (95%CI 9.49-9.84) in 2022 and to 9.26 (95%CI 8.37-10.15) in 2026. DALYs increased from 229.2 (95%CI 165.7-313.6) in 1990 to 346.1 (95%CI 253.9-467.2) in 2021. The forecasted DALYs of female breast cancer will slightly decrease to 343.3 (95%CI 337.2-349.5) in 2022 reaching 332.1 (95%CI 301.2-363.1) in 2026. CONCLUSIONS: Female breast cancer is still a significant public health burden that challenges the health system in KSA, current policies and interventions should be fashioned to alleviate the disease morbidity and mortality and mitigate its future burden.

Design of application-oriented disease diagnosis model using a meta-heuristic algorithm.

BACKGROUND: Healthcare is crucial to patient care because it provides vital services for maintaining and restoring health. As healthcare technology evolves, cutting-edge tools facilitate faster diagnosis and more effective patient treatment. In the present age of pandemics, the Internet of Things (IoT) offers a potential solution to the problem of patient safety monitoring by creating a massive quantity of data about the patient through the linked devices around them and then analyzing it to estimate the patient's current status. Utilizing the IoT-based meta-heuristic algorithm allows patients to be remotely monitored, resulting in timely diagnosis and improved care. Meta-heuristic algorithms are successful, resilient, and effective in solving real-world enhancement, clustering, predicting, and grouping. Healthcare organizations need an efficient method for dealing with big data since the prevalence of such data makes it challenging to analyze for diagnosis. The current techniques used in medical diagnostics have limitations due to imbalanced data and the overfitting issue. OBJECTIVE: This study introduces the particle swarm optimization and convolutional neural network to be used as a meta-heuristic optimization method for extensive data analysis in the IoT to monitor patients' health conditions. METHOD: Particle Swarm Optimization is used to optimize the data used in the study. Information for a diabetes diagnosis model that includes cardiac risk forecasting is collected. Particle Swarm Optimization and Convolutional Neural Networks (PSO-CNN) results effectively make illness predictions. Support Vector Machine has been used to predict the possibility of a heart attack based on the classification of the collected data into projected abnormal and normal ranges for diabetes. RESULTS: The results of the simulations reveal that the PSO-CNN model used to predict diabetic disease increased in accuracy by 92.6%, precision by 92.5%, recall by 93.2%, F1-score by 94.2%, and quantization error by 4.1%. CONCLUSION: The suggested approach could be applied to identify cancer cells.

[Severe hypereosinophilia as a paraneoplastic syndrome in a patient with differentiated thyroid cancer] / Hipereosinofilia severa como síndrome paraneoplásico en un paciente con cáncer diferenciado de tiroides.

In solid tumors, hypereosinophilia is a rare phenomenon and is mainly associated with mucin-secreting carcinomas. Thyroid tumors associated with neutrophilia and/or eosinophilia have been described exclusively in patients with anaplastic thyroid cancer. Eosinophilia associated with papillary thyroid cancer is extremely rare and there are very few cases currently described. It has been suggested that three cytokines, namely interleukin-3 (IL-3), interleukin-5 (IL-5), and granulocyte-macrophage colony-stimulating factor (GM-CSF), may act as a peptide potential eosinophilic. To date, only three patients with differentiated thyroid cancer associated with eosinophilia have been reported, two of the papillary type and one of the medullary type. A 48-year-old patient consulted in 2022 due to bilateral cervical lymphadenopathy of 3 years' duration associated with wasting syndrome and hypereosinophilia. PET CT was requested, which showed hypermetabolic focus in the right thyroid lobe and lymph node, lung, bone, and liver metastases; Thyroid ultrasound showing a nodule of high suspicion of malignancy and a conglomerate of lymphadenopathy in the right lobe with positive needle wash for thyroglobulin. Hypereosinophilia was evaluated with initial leukocytosis values of GB 30,310/mm3 (10,608/mm3 of eosinophils) to maximum values of GB 77,090/mm3 (eosinophils 20,814/mm3). It was interpreted as paraneoplastic syndrome and corticosteroid therapy was started at immunosuppressive doses without response. Our observations presented in this article are in line with most studies reflecting that paraneoplastic hypereosinophilia is characterized by more advanced disease and poor prognosis.

En los tumores sólidos la hipereosinofilia es un fenómeno raro y se asocia principalmente con carcinomas secretores de mucina. Los tumores tiroideos asociados a neutrofilia y/o eosinofilia se han descrito exclusivamente en pacientes con cáncer anaplásico de tiroides. La eosinofilia asociada con cáncer papilar de tiroides es extremadamente rara y se encuentran muy pocos casos descriptos actualmente. Se ha sugerido que tres citocinas, a saber, la interleucina-3 (IL-3), la interleucina-5 (IL-5) y el factor estimulante de colonias de granulocitos y macrófagos (GM-CSF), pueden actuar como un péptido eosinofílico potencial. Hasta el momento solo se han reportado tres pacientes con cáncer diferenciado de tiroides asociados a eosinofilia, dos de tipo papilar y uno de tipo medular. Paciente de 48 años consultó en el año 2022 por adenopatías cervicales bilaterales de 3 años de evolución asociado a síndrome consuntivo e hipereosinofilia. Se solicitó PET CT que evidenció foco hipermetabólico en lóbulo tiroideo derecho y metástasis ganglionares, pulmonares, óseas y hepáticas; ecografía tiroidea que evidencia en lóbulo derecho nódulo de alta sospecha de malignidad y conglomerado de adenopatías con lavado de aguja positivo para tiroglobulina. Evaluada la hipereosinofilia con valores iniciales de leucocitosis de GB 30310/mm3 (10608/mm3 de eosinófilos) hasta valores máximos de GB 77090/mm3 (eosinófilos 20814/mm3) se interpretó como síndrome paraneoplásico y se inició corticoterapia en dosis inmunosupresoras sin respuesta. Nuestras observaciones presentadas en este artículo están en línea con la mayoría de los estudios que reflejan que la hipereosinofilia paraneoplásica se caracteriza por una enfermedad más avanzada y un mal pronóstico.

Towards reliable forecasting of healthcare capacity needs: A scoping review and evidence mapping.

BACKGROUND: The COVID-19 pandemic has highlighted the critical importance of robust healthcare capacity planning and preparedness for emerging crises. However, healthcare systems must also adapt to more gradual temporal changes in disease prevalence and demographic composition over time. To support proactive healthcare planning, statistical capacity forecasting models can provide valuable information to healthcare planners. This systematic literature review and evidence mapping aims to identify and describe studies that have used statistical forecasting models to estimate healthcare capacity needs within hospital settings. METHOD: Studies were identified in the databases MEDLINE and Embase and screened for relevance before items were defined and extracted within the following categories: forecast methodology, measure of capacity, forecast horizon, healthcare setting, target diagnosis, validation methods, and implementation. RESULTS: 84 studies were selected, all focusing on various capacity outcomes, including number of hospital beds/ patients, staffing, and length of stay. The selected studies employed different analytical models grouped in six items; discrete event simulation (N = 13, 15 %), generalized linear models (N = 21, 25 %), rate multiplication (N = 15, 18 %), compartmental models (N = 14, 17 %), time series analysis (N = 22, 26 %), and machine learning not otherwise categorizable (N = 12, 14 %). The review further provides insights into disease areas with infectious diseases (N = 24, 29 %) and cancer (N = 12, 14 %) being predominant, though several studies forecasted healthcare capacity needs in general (N = 24, 29 %). Only about half of the models were validated using either temporal validation (N = 39, 46 %), cross-validation (N = 2, 2 %) or/and geographical validation (N = 4, 5 %). CONCLUSION: The forecasting models' applicability can serve as a resource for healthcare stakeholders involved in designing future healthcare capacity estimation. The lack of routine performance validation of the used algorithms is concerning. There is very little information on implementation and follow-up validation of capacity planning models.

Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis.

This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) in forecasting tumor dynamics within three months following Gamma Knife radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC, and other metrics derived from confusion matrices. The CNN model showcased notable performance with an accuracy of 98% and an AUC of 0.97, effectively complementing the broader model analysis. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like "control at one year", "age of the patient", and "beam-on time for volume V1 treated" were consistently influential across various models, albeit their impacts were interpreted differently depending on the model's underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial. Our research explores the effects of staged Gamma Knife radiosurgery (GKRS) on larger tumors, revealing no significant outcome differences across protocols. It uniquely considers the impact of beam-on time and fraction intervals on treatment efficacy. However, the investigation is limited by a small patient cohort and data from a single institution, suggesting the need for future multicenter research.

Forecasting the Burden of Cardiovascular Disease and Stroke in the United States Through 2050-Prevalence of Risk Factors and Disease: A Presidential Advisory From the American Heart Association.

BACKGROUND: Cardiovascular disease and stroke are common and costly, and their prevalence is rising. Forecasts on the prevalence of risk factors and clinical events are crucial. METHODS: Using the 2015 to March 2020 National Health and Nutrition Examination Survey and 2015 to 2019 Medical Expenditure Panel Survey, we estimated trends in prevalence for cardiovascular risk factors based on adverse levels of Life's Essential 8 and clinical cardiovascular disease and stroke. We projected through 2050, overall and by age and race and ethnicity, accounting for changes in disease prevalence and demographics. RESULTS: We estimate that among adults, prevalence of hypertension will increase from 51.2% in 2020 to 61.0% in 2050. Diabetes (16.3% to 26.8%) and obesity (43.1% to 60.6%) will increase, whereas hypercholesterolemia will decline (45.8% to 24.0%). The prevalences of poor diet, inadequate physical activity, and smoking are estimated to improve over time, whereas inadequate sleep will worsen. Prevalences of coronary disease (7.8% to 9.2%), heart failure (2.7% to 3.8%), stroke (3.9% to 6.4%), atrial fibrillation (1.7% to 2.4%), and total cardiovascular disease (11.3% to 15.0%) will rise. Clinical CVD will affect 45 million adults, and CVD including hypertension will affect more than 184 million adults by 2050 (>61%). Similar trends are projected in children. Most adverse trends are projected to be worse among people identifying as American Indian/Alaska Native or multiracial, Black, or Hispanic. CONCLUSIONS: The prevalence of many cardiovascular risk factors and most established diseases will increase over the next 30 years. Clinical and public health interventions are needed to effectively manage, stem, and even reverse these adverse trends.