Meta-análisis: Intervalos de confianza e Intervalos de predicción / Meta-analysis: Confidence intervals and Prediction intervals

En los informes meta-analíticos se suelen reportar varios tipos de intervalos, hecho que ha generado cierta confusión a la hora de interpretarlos. Los intervalos de confianza reflejan la incertidumbre relacionada con un número, el tamaño del efecto medio paramétrico. Los intervalos de predicción reflejan el tamaño paramétrico probable en cualquier estudio de la misma clase que los incluidos en un meta-análisis. Su interpretación y aplicaciones son diferentes. En este artículo explicamos su diferente naturaleza y cómo se pueden utilizar para responder preguntas específicas. Se incluyen ejemplos numéricos, así como su cálculo con el paquete metafor en R.(AU)

Several types of intervals are usually employed in meta-analysis, a fact that has generated some confusion when interpreting them. Confidence intervals reflect the uncertainty related to a single number, the parametric mean effect size. Prediction intervals reflect the probable parametric effect size in any study of the same class as those included in a meta-analysis. Its interpretation and applications are different. In this article we explain in de-tail their different nature and how they can be used to answer specific ques-tions. Numerical examples are included, as well as their computation with the metafor Rpackage.(AU)

Disease burden and attributable risk factors of lip and oral cavity cancer in China from 1990 to 2021 and its prediction to 2031.

Aims: This study addresses the essential need for updated information on the burden of lip and oral cavity cancer (LOC) in China for informed healthcare planning. We aim to estimate the temporal trends and the attributable burdens of selected risk factors of LOC in China (1990-2021), and to predict the possible trends (2022-2031). Subject and methods: Analysis was conducted using data from the Global Burden of Disease study (GBD) 2021, encompassing six key metrics: incidence, mortality, prevalence, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs). Absolute number and age-standardized rates, alongside 95% uncertainty intervals, were computed. Forecasting of disease burden from 2022 to 2031 was performed using an autoregressive integrated moving average (ARIMA) model. Results: Over the observed period (1990-2021), there were notable increases in the number of deaths (142.2%), incidence (283.7%), prevalence (438.0%), DALYs (109.2%), YLDs (341.2%), and YLLs (105.1%). Age-standardized rates demonstrated notable changes, showing decreases and increases of -5.8, 57.3, 143.7, -8.9%, 85.8%, and - 10.7% in the respective metrics. The substantial majority of LOC burden was observed among individuals aged 40-79 years, and LOC may exhibit a higher burden among males in China. From 2022 to 2031, the age-standardized rate of incidence, prevalence, and YLDs of LOC showed upward trends; while mortality, DALYs, and YLLs showed downward trends, and their estimated values were predicted to change to 2.72, 10.47, 1.11, 1.10, 28.52, and 27.43 per 100,000 in 2031, respectively. Notably, tobacco and high alcohol use emerged as predominant risk factors contributing to the burden of LOC. Conclusion: Between 1990 and 2021, the disability burden from LOC in China increased, while the death burden decreased, and projections suggest these trends will persist over the next decade. A significant portion of this disease burden to modifiable risk factors, specifically tobacco use and excessive alcohol consumption, predominantly affecting males and individuals aged 40-79 years. Attention to these areas is essential for implementing targeted interventions and reducing the impact of LOC in China.

A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model.

Background: This paper asks whether Dynamic Causal modelling (DCM) can predict the long-term clinical impact of the COVID-19 epidemic. DCMs are designed to continually assimilate data and modify model parameters, such as transmissibility of the virus, changes in social distancing and vaccine coverage-to accommodate changes in population dynamics and virus behavior. But as a novel way to model epidemics do they produce valid predictions? We presented DCM predictions 12 months ago, which suggested an increase in viral transmission was accompanied by a reduction in pathogenicity. These changes provided plausible reasons why the model underestimated deaths, hospital admissions and acute-post COVID-19 syndrome by 20%. A further 12-month validation exercise could help to assess how useful such predictions are. Methods: we compared DCM predictions-made in October 2022-with actual outcomes over the 12-months to October 2023. The model was then used to identify changes in COVID-19 transmissibility and the sociobehavioral responses that may explain discrepancies between predictions and outcomes over this period. The model was then used to predict future trends in infections, long-COVID, hospital admissions and deaths over 12-months to October 2024, as a prelude to future tests of predictive validity. Findings: Unlike the previous predictions-which were an underestimate-the predictions made in October 2022 overestimated incidence, death and admission rates. This overestimation appears to have been caused by reduced infectivity of new variants, less movement of people and a higher persistence of immunity following natural infection and vaccination. Interpretation: despite an expressive (generative) model, with time-dependent epidemiological and sociobehavioral parameters, the model overestimated morbidity and mortality. Effectively, the model failed to accommodate the "law of declining virulence" over a timescale of years. This speaks to a fundamental issue in long-term forecasting: how to model decreases in virulence over a timescale of years? A potential answer may be available in a year when the predictions for 2024-under a model with slowly accumulating T-cell like immunity-can be assessed against actual outcomes.

Parametric seasonal-trend autoregressive neural network for long-term crop price forecasting.

Crop price forecasting is difficult in that supply is not as elastic as demand, therefore, supply and demand should be stabilized through long-term forecasting and pre-response to the price. In this study, we propose a Parametric Seasonal-Trend Autoregressive Neural Network (PaSTANet), which is a hybrid model that includes both a multi-kernel residual convolution neural network model and a Gaussian seasonality-trend model. To compare the performance of the PaSTANet, we used daily data from the Garak market for four crops: onion, radish, Chinese cabbage, and green onion, and performed long-term price forecasts for one year in 2023. The PaSTANet shows good performance on all four crops compared to other conventional statistical and deep learning-based models. In particular, for onion, the (mean absolute error (MAE) for the long-term forecast of 2023 is 107, outperforming the second-best Prophet (152) by 29.6%. Chinese cabbage, radish, and green onion all outperform the existing models with MAE of 2008, 3703, and 557, respectively. Moreover, using the confidence interval, the predicted price was categorized into three intervals: probability, caution, and warning. Comparing the percentage of classified intervals about the true prices in our test set, we found that they accurately detect the large price volatility.

How Effective Are Forecasting Models in Predicting Effects of Exoskeletons on Fatigue Progression?

Forecasting can be utilized to predict future trends in physiological demands, which can be beneficial for developing effective interventions. This study implemented forecasting models to predict fatigue level progression when performing exoskeleton (EXO)-assisted tasks. Specifically, perceived and muscle activity data were utilized from nine recruited participants who performed 45° trunk flexion tasks intermittently with and without assistance until they reached medium-high exertion in the low-back region. Two forecasting algorithms, Autoregressive Integrated Moving Average (ARIMA) and Facebook Prophet, were implemented using perceived fatigue levels alone, and with external features of low-back muscle activity. Findings showed that univariate models without external features performed better with the Prophet model having the lowest mean (SD) of root mean squared error (RMSE) across participants of 0.62 (0.24) and 0.67 (0.29) with and without EXO-assisted tasks, respectively. Temporal effects of BSIE on delaying fatigue progression were then evaluated by forecasting back fatigue up to 20 trials. The slope of fatigue progression for 20 trials without assistance was ~48-52% higher vs. with assistance. Median benefits of 54% and 43% were observed for ARIMA (with external features) and Prophet algorithms, respectively. This study demonstrates some potential applications for forecasting models for workforce health monitoring, intervention assessment, and injury prevention.

Climate Change Projections for Stroke Incidence in Taiwan: Impact of 2 °C and 4 °C Global Warming Level.

OBJECTIVES: This study aimed to establish the exposure-lag-response effect between daily maximum temperature and stroke-related emergency department visits and to project heat-induced stroke impacts under global warming levels (GWL) of 2 °C and 4 °C. METHODS: Stroke-related emergency department visits in Taiwan from 2001 to 2020 were identified using the National Health Insurance Research Database (NHIRD). The study population consisted of 1,100,074 initial stroke cases matched with 2,200,148 non-stroke controls. We employed Distributed Lag Nonlinear Models (DLNM) in a case-crossover study to investigate the association between temperature and stroke. Generalized Estimating Equations (GEE) models with a Poisson function were used to correlate high-temperature exposure with annual stroke incidence rates. Projections were made under two global warming scenarios, GWL 2.0 °C and 4.0 °C, using Coupled General Circulation Model (GCMs). Baseline data from 1995 to 2014 were transformed for spatial distribution at the township level. Geographic Information System (GIS) spatial analysis was performed using Quantum GIS 3.2.0 software. RESULTS: DLNM exposure-lag-response effect revealed that daily maximum temperature exceeding 34 °C significantly increased the risk of stroke-related emergency department visits, particularly for ischemic stroke. Under the 2 °C GWL scenario, the frequency of days with temperatures surpassing 34 °C is projected to rise substantially by the median year of 2042, with a further increase to 92.6 ± 18.0 days/year by 2065 under the 4 °C GWL scenario. Ischemic stroke showed the highest increase in temperature-related incidence rates, notably rising from 7.80% under the GWL 2 °C to 36.06% under the GWL 4 °C. Specifically, the annual temperature-related incidence rate for ischemic stroke is expected to increase significantly by 2065. Regions such as Taichung, Hsinchu, Yilan, and Taitung demonstrated pronounced changes in heat-related ischemic stroke incidence under the GWL 4 °C. CONCLUSIONS: The findings emphasize the importance of addressing temperature-related stroke risks, particularly in regions projected to experience significant temperature increases. Effective mitigation strategies are crucial to reduce the impact of rising temperatures on stroke incidence and safeguard public health.

Global burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050.

BACKGROUND: Antimicrobial resistance (AMR) poses an important global health challenge in the 21st century. A previous study has quantified the global and regional burden of AMR for 2019, followed with additional publications that provided more detailed estimates for several WHO regions by country. To date, there have been no studies that produce comprehensive estimates of AMR burden across locations that encompass historical trends and future forecasts. METHODS: We estimated all-age and age-specific deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 22 pathogens, 84 pathogen-drug combinations, and 11 infectious syndromes in 204 countries and territories from 1990 to 2021. We collected and used multiple cause of death data, hospital discharge data, microbiology data, literature studies, single drug resistance profiles, pharmaceutical sales, antibiotic use surveys, mortality surveillance, linkage data, outpatient and inpatient insurance claims data, and previously published data, covering 520 million individual records or isolates and 19 513 study-location-years. We used statistical modelling to produce estimates of AMR burden for all locations, including those with no data. Our approach leverages the estimation of five broad component quantities: the number of deaths involving sepsis; the proportion of infectious deaths attributable to a given infectious syndrome; the proportion of infectious syndrome deaths attributable to a given pathogen; the percentage of a given pathogen resistant to an antibiotic of interest; and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden attributable to and associated with AMR, which we define based on two counterfactuals; respectively, an alternative scenario in which all drug-resistant infections are replaced by drug-susceptible infections, and an alternative scenario in which all drug-resistant infections were replaced by no infection. Additionally, we produced global and regional forecasts of AMR burden until 2050 for three scenarios: a reference scenario that is a probabilistic forecast of the most likely future; a Gram-negative drug scenario that assumes future drug development that targets Gram-negative pathogens; and a better care scenario that assumes future improvements in health-care quality and access to appropriate antimicrobials. We present final estimates aggregated to the global, super-regional, and regional level. FINDINGS: In 2021, we estimated 4·71 million (95% UI 4·23-5·19) deaths were associated with bacterial AMR, including 1·14 million (1·00-1·28) deaths attributable to bacterial AMR. Trends in AMR mortality over the past 31 years varied substantially by age and location. From 1990 to 2021, deaths from AMR decreased by more than 50% among children younger than 5 years yet increased by over 80% for adults 70 years and older. AMR mortality decreased for children younger than 5 years in all super-regions, whereas AMR mortality in people 5 years and older increased in all super-regions. For both deaths associated with and deaths attributable to AMR, meticillin-resistant Staphylococcus aureus increased the most globally (from 261 000 associated deaths [95% UI 150 000-372 000] and 57 200 attributable deaths [34 100-80 300] in 1990, to 550 000 associated deaths [500 000-600 000] and 130 000 attributable deaths [113 000-146 000] in 2021). Among Gram-negative bacteria, resistance to carbapenems increased more than any other antibiotic class, rising from 619 000 associated deaths (405 000-834 000) in 1990, to 1·03 million associated deaths (909 000-1·16 million) in 2021, and from 127 000 attributable deaths (82 100-171 000) in 1990, to 216 000 (168 000-264 000) attributable deaths in 2021. There was a notable decrease in non-COVID-related infectious disease in 2020 and 2021. Our forecasts show that an estimated 1·91 million (1·56-2·26) deaths attributable to AMR and 8·22 million (6·85-9·65) deaths associated with AMR could occur globally in 2050. Super-regions with the highest all-age AMR mortality rate in 2050 are forecasted to be south Asia and Latin America and the Caribbean. Increases in deaths attributable to AMR will be largest among those 70 years and older (65·9% [61·2-69·8] of all-age deaths attributable to AMR in 2050). In stark contrast to the strong increase in number of deaths due to AMR of 69·6% (51·5-89·2) from 2022 to 2050, the number of DALYs showed a much smaller increase of 9·4% (-6·9 to 29·0) to 46·5 million (37·7 to 57·3) in 2050. Under the better care scenario, across all age groups, 92·0 million deaths (82·8-102·0) could be cumulatively averted between 2025 and 2050, through better care of severe infections and improved access to antibiotics, and under the Gram-negative drug scenario, 11·1 million AMR deaths (9·08-13·2) could be averted through the development of a Gram-negative drug pipeline to prevent AMR deaths. INTERPRETATION: This study presents the first comprehensive assessment of the global burden of AMR from 1990 to 2021, with results forecasted until 2050. Evaluating changing trends in AMR mortality across time and location is necessary to understand how this important global health threat is developing and prepares us to make informed decisions regarding interventions. Our findings show the importance of infection prevention, as shown by the reduction of AMR deaths in those younger than 5 years. Simultaneously, our results underscore the concerning trend of AMR burden among those older than 70 years, alongside a rapidly ageing global community. The opposing trends in the burden of AMR deaths between younger and older individuals explains the moderate future increase in global number of DALYs versus number of deaths. Given the high variability of AMR burden by location and age, it is important that interventions combine infection prevention, vaccination, minimisation of inappropriate antibiotic use in farming and humans, and research into new antibiotics to mitigate the number of AMR deaths that are forecasted for 2050. FUNDING: UK Department of Health and Social Care's Fleming Fund using UK aid, and the Wellcome Trust.

Health policy implications for cardiovascular disease, type 2 diabetes mellitus, and stroke in Central Asia: a decadal forecast of their impact on women of reproductive age.

Introduction: Cardiovascular disease, type 2 diabetes, and stroke are significant global health concerns. However, gaps persist in understanding the impact of these disorders on women of reproductive age in Central Asia. This study aimed to analyze the health policies implemented in Central Asian countries to address the healthcare needs of this demographic and to forecast future trends in prevalence rates. Methodology: We forecasted future trends in prevalence rates, years of life lost, years lived with disability, and disability-adjusted life years for cardiovascular disease, type 2 diabetes, and stroke using publicly available data. Two data sources were utilized: health policy documents issued by the governments of Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, and Turkmenistan, and data from the Institute for Health Metrics and Evaluation. Forecasting models, including ARIMA, were employed to predict trends until 2030. Results: The results indicate an anticipated increase in cardiovascular disease prevalence from 1856.55 in 2020 to 2007.07 by 2029 in Kazakhstan, a subtle increase in Kyrgyzstan from 2492.22 to 2558.69 over 10 years, and similar trends in other countries. Conclusion: The analysis of policy documents revealed a lack of specific focus on addressing cardiovascular disease, stroke, or type 2 diabetes outside the contexts of pregnancy and childbirth. Understanding these trends is crucial for informing targeted health interventions and resource allocation to mitigate the impact of these diseases on women's health in Central Asia.

Industry visions of technology for older adults: A futures anthropology perspective.

The negative portrayal of ageing as a human decline burdening society has prompted Ageing Technology industries (AgeTech) to foresee solutions rooted in the Ageing in Place paradigm. These ostensibly neutral future interventions are intertwined with socio-technical dynamics. While Science and Technology Studies (STS) and anthropology scholars have questioned these AgeTech practices, limited literature explores industry's predictions of future AgeTech. Drawing on STS and futures-anthropology literature, I interrogate AgeTech industry visions of future assemblages involving older people, smart home technology, and socio-material discourses rooted in their own discrepancies and dilemmas. To unpack AgeTech futures, my methods include a review of 49 industry reports and 29 interviews with industry experts. Based on the reports, I designed comics to be used in interviews with experts spanning CEOs and managers of companies designing technology for older people, consultants, and aged-care workers based in 12 countries. Ageing futures are far from being neutral or a chronological process, instead they are non-consensual and fragmented. In the review and interviews, I captured future assemblages of a fragmented AgeTech industry in relationships with governments and industry giants. The fragmentation continues unfolding in participants from diverse countries and professions contesting dominant AgeTech narratives. In dissecting future assemblages, I also unpack non-consensual futures based on diverging experts' values (e.g. safety versus activity) and humans' values like control and improvisation challenging predictive and surveillance technology. AgeTech Futures transcend physical matters or assemblages of technologies and humans. They encompass future normativities, tensions, divergent values, and ideological concepts. I propose not only alternatives to the visions found in industry narratives, but also encourage scholars to understand the AgeTech industry's dilemmas.

Global forecasting of chronic kidney disease mortality rates and numbers with the generalized additive model.

BACKGROUND: Chronic kidney disease (CKD) is an important public health problem worldwide; therefore, forecasting CKD mortality rates and death numbers globally is vital for planning CKD prevention programs. This study aimed to characterize the temporal trends in CKD mortality at the international level from 1990 to 2019 and predict CKD mortality rates and numbers until 2030. METHODS: Data were obtained from the Global Burden of Disease 2019 Study. A joinpoint regression model was used to estimate the average annual percentage change in CKD mortality rates and numbers. Finally, we used a generalized additive model to predict CKD mortality through 2030. RESULTS: The number of CKD-related deaths worldwide increased from 591.80 thousand in 1990 to 1425.67 thousand in 2019. The CKD age-adjusted mortality rate increased from 15.95 per 100,000 people to 18.35 per 100,000 people during the same period. Between 2020 and 2030, the number of CKD deaths is forecasted to increase further to 1812.85 thousand by 2030. The CKD age-adjusted mortality rate is expected to decrease slightly to 17.76 per 100,000 people (95% credible interval (CrI): 13.84 to 21.68). Globally, it is predicted that in the next decade, the CKD mortality rate will decrease in men, women, all subgroups of disease etiology except glomerulonephritis, people younger than 40 years old, and all groupings of countries based on the sociodemographic index (SDI) except high-middle-SDI countries. CONCLUSIONS: The CKD mortality rate is predicted to decrease in the next decade. However, more attention should be given to people with glomerulonephritis, people over 40 years old, and people in high- to middle-income countries because the mortality rate due to CKD in these subgroups is expected to increase until 2030.

Disease burden of AIDS in last 30-year period and its predicted level in next 25-years based on the global burden disease 2019.

BACKGROUND: This study examines global trends in acquired immune deficiency syndrome (AIDS) incidence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2019, focusing on regional disparities in AIDS incidence, mortality, and DALYs across various levels of socio-demographic index (SDI). It also investigates variations in AIDS incidence, mortality, and DALYs across different age groups, and projects specific trends for the next 25 years. METHODS: Comprehensive data on AIDS from 1990 to 2019 in 204 countries and territories was obtained from a GBD study. This included information on AIDS incidence, mortality, DALYs, and age-standardized rates (ASRs). Projections for AIDS incidence and mortality over the next 25 years were generated using the Bayesian age-period-cohort model. RESULTS: From 1990 to 2019, the global incidence of HIV cases increased from 1,989,282 to 2,057,710, while the age-standardized incidence rate (ASIR) decreased from 37.59 to 25.24 with an estimated annual percentage change (EAPC) of -2.38. The ASIR exhibited an upward trend in high SDI and high-middle SDI regions, a stable trend in middle SDI regions, and a downward trend in low-middle SDI and low SDI regions. In regions with higher SDI, the ASIR was higher in males than in females, while the opposite was observed in lower SDI regions. Throughout 1990 to 2019, the age-standardized death rate (ASDR) and age-standardized DALY rate remained stable, with EAPCs of 0.24 and 0.08 respectively. Countries with the highest HIV burden affecting women and children under five years of age are primarily situated in lower SDI regions, particularly in sub-Saharan Africa. Projections indicate a significant continued decline in the age-standardized incidence and mortality rates of AIDS over the next 25 years, for both overall and by gender. CONCLUSIONS: The global ASIR decreased from 1990 to 2019. Higher incidence and death rates were observed in the lower SDI region, indicating a greater susceptibility to AIDS among women and < 15 years old. This underscores the urgent need for increased resources to combat AIDS in this region, with focused attention on protecting women and < 15 years old as priority groups. The AIDS epidemic remained severe in sub-Saharan Africa. Projections for the next 25 years indicate a substantial and ongoing decline in both age-standardized incidence and mortality rates.

Past trends and future projections of palliative care needs in Chile: analysis of routinely available death registry and population data.

BACKGROUND: The number of people with palliative care needs is projected to increase globally. Chile has recently introduced legislation for universal access to palliative care services for patients with severe and terminal illnesses, including non-cancer conditions. We aimed to estimate the number of people affected by serious health-related suffering and need for palliative care in Chile to 2050. METHODS: We used data on all deaths registered in Chile between 1997-2019 and population estimates for 1997-2050. We used Poisson regression to model past trends in causes of death adjusted by age, sex and population estimates, to project the number of deaths for each cause from 2021 to 2050. We applied the Lancet Commission on Palliative Care and Pain Relief weights to these projections to identify decedents and non-decedents with palliative care needs. RESULTS: Population palliative care needs in Chile are projected to increase from 117 (95% CI 114 to 120) thousand people in 2021 to 209 (95% CI 198 to 223) thousand people in 2050, a 79% increase (IRR 1.79; 95% CI 1.78-1.80). This increase will be driven by non-cancer conditions, particularly dementia (IRR 2.9, 95% CI 2.85-2.95) and cardiovascular conditions (IRR 1.86, 95% CI 1.83-1.89). By 2050, 50% of those estimated to need palliative care will be non-decedents (not expected to die within a year). CONCLUSIONS: Chile will experience a large increase in palliative care needs, particularly for people with dementia and other non-cancer conditions. Improved availability of high-quality services, expanded clinician training and new sustainable models of care are urgently required to ensure universal access to palliative care.

Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency.

Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant Rt. These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ∼0.6% median absolute error and ∼6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.

Shaping the future of heart health.

For World Heart Day on September 24, 2024, the World Heart Federation urges nations to endorse national strategies for enhancing cardiovascular health. While advancements show promise in reducing atherosclerosis, addressing healthcare inequalities and ensuring equitable access to tools remain crucial. This collection of voices touches on the intricate relationship between type 2 diabetes and cardiovascular disease, highlights innovative treatments for rare cardiomyopathies and heart failure, and explores the potentially transformative role of artificial intelligence in cardiovascular medicine, showcasing the dedication and innovation that are shaping the future of heart health.

Computational surgery in the management of patients with abdominal aortic aneurysms: Opportunities, challenges, and future directions.

Computational surgery (CS) is an interdisciplinary field that uses mathematical models and algorithms to focus specifically on operative planning, simulation, and outcomes analysis to improve surgical care provision. As the digital revolution transforms the surgical work environment through broader adoption of artificial intelligence and machine learning, close collaboration between surgeons and computational scientists is not only unavoidable, but will become essential. In this review, the authors summarize the main advances, as well as ongoing challenges and prospects, that surround the implementation of CS techniques in vascular surgery, with a particular focus on the care of patients affected by abdominal aortic aneurysms (AAAs). Several key areas of AAA care delivery, including patient-specific modelling, virtual surgery simulation, intraoperative imaging-guided surgery, and predictive analytics, as well as biomechanical analysis and machine learning, will be discussed. The overarching goals of these CS applications is to improve the precision and accuracy of AAA repair procedures, while enhancing safety and long-term outcomes. Accordingly, CS has the potential to significantly enhance patient care across the entire surgical journey, from preoperative planning and intraoperative decision making to postoperative surveillance. Moreover, CS-based approaches offer promising opportunities to augment AAA repair quality by enabling precise preoperative simulations, real-time intraoperative navigation, and robust postoperative monitoring. However, integrating these advanced computer-based technologies into medical research and clinical practice presents new challenges. These include addressing technical limitations, ensuring accuracy and reliability, and managing unique ethical considerations associated with their use. Thorough evaluation of these aspects of advanced computation techniques in AAA management is crucial before widespread integration into health care systems can be achieved.

Prediction of acute onset of chronic cor pulmonale: comparative analysis of Holt-Winters exponential smoothing and ARIMA model.

BACKGROUND: The aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan'an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases. METHODS: The data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan'an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set. RESULTS: The number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan'an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1)12, while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error. CONCLUSION: The Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models.

Spatial and temporal analysis and forecasting of TB reported incidence in western China.

OBJECTIVE: Tuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies. METHODS: We collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China. RESULTS: The reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran's I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05). CONCLUSIONS: As a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different.