Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.

Métodos de aprendizaje automático para predecir el comportamiento epidemiológico de enfermedades arbovirales: revisión estructurada de literatura

Introducción: Los métodos de aprendizaje automático permiten manejar datos estructurados y no estructurados para construir modelos predictivos y apoyar la toma de decisiones. Objetivo: Identificar los métodos de aprendizaje automático aplicados para predecir el comportamiento epidemiológico de enfermedades arbovirales utilizando datos de vigilancia epidemiológica. Metodología: Se realizó búsqueda en EMBASE y PubMed, análisis bibliométrico y síntesis de la información. Resultados: Se seleccionaron 41 documentos, todos publicados en la última década. La palabra clave más frecuente fue dengue. La mayoría de los autores (88,3 %) participó en un artículo de investigación. Se encontraron 16 métodos de aprendizaje automático, el más frecuente fue Red Neuronal Artificial, seguido de Máquinas de Vectores de Soporte. Conclusiones: En la última década se incrementó la publicación de trabajos que pretenden predecir el comportamiento epidemiológico de arbovirosis por medio de diversos métodos de aprendizaje automático que incorporan series de tiempo de los casos, variables climatológicas, y otras fuentes de información de datos abiertos.

Introduction: Machine learning methods allow to manipulate structured and unstructured data to build predictive models and support decision-making. Objective: To identify machine learning methods applied to predict the epidemiological behavior of vector-borne diseases using epidemiological surveillance data. Methodology: A literature search in EMBASE and PubMed, bibliometric analysis, and information synthesis were performed. Results: A total of 41 papers were selected, all of them were published in the last decade. The most frequent keyword was dengue. Most authors (88.3 %) participated in a research article. Sixteen machine learning methods were found, the most frequent being Artificial Neural Network, followed by Support Vector Machines. Conclusions: In the last decade there has been an increase in the number of articles that aim to predict the epidemiological behavior of vector-borne diseases using by means of various machine learning methods that incorporate time series of cases, climatological variables, and other sources of open data information.

Evaluación de la disfunción del endotelio para el pronóstico de riesgo cardiovascular / Evaluation of endothelial dysfunction for cardiovascular risk prediction i: en

Introducción: Las enfermedades cardiovasculares constituyen la primera causa de muerte en el mundo, por lo que la identificación y modificación de los factores de riesgo asociados a ellas constituyen estrategias priorizadas por la Organización Mundial de la Salud. Contar con un modelo de predicción del riesgo cardiovascular enriquecido con la evaluación de la disfunción endotelial influiría positivamente en estas metas. Objetivos: Identificar la presencia de disfunción endotelial en pacientes con enfermedades cardiovasculares o sin estas y determinar la asociación entre ambas. Métodos: Se realizó un estudio observacional y descriptivo, de serie de casos, en el Centro de Cardiología y Cirugía Cardiovascular del Hospital Provincial Docente Clínico-Quirúrgico Saturnino Lora de Santiago de Cuba, desde enero del 2022 hasta igual mes del 2023, donde se analizaron como variables los factores de riesgo cardiovascular tradicionales y los biomarcadores de disfunción endotelial. Secundariamente, se llevó a cabo un estudio analítico de casos y controles en el cual se aplicó la regresión logística binaria multivariada. Resultados: Se confirmó la presencia de disfunción endotelial asociada a la aparición de las enfermedades cardiovasculares, lo que se evaluó a través del índice de vasodilatación, mediado por el flujo de la arteria braquial y las concentraciones plasmáticas de fibrinógeno. Conclusiones: Las características epidemiológicas y clínicas de los pacientes con enfermedades cardiovasculares o sin estas no difirieron de lo registrado en la literatura especializada acerca de la base de identificación de los factores de riesgo tradicionales.

Introduction: Cardiovascular diseases constitute the first death cause worldwide, reason why the identification and modification of associated risk factors constitute prioritized strategies by the World Health Organization. To have a prediction model of cardiovascular risk enriched with the evaluation of the endothelial dysfunction would influence positively in these goals. Objectives: To identify the presence of endothelial dysfunction in patients with or without cardiovascular diseases and to determine the association between them. Methods: An observational and descriptive cases series study was carried out in the Cardiology and Cardiovascular Surgery Center at Saturnino Lora Teaching Clinical Surgical Provincial Hospital in Santiago de Cuba, from January, 2022 to the same month, 2023, where the traditional cardiovascular risk factors and endothelial dysfunction biomarkers were analyzed as variables. Secondarily, an analytic case-control study was carried out in which multivariate binary logistic regression was applied. Results: The presence of endothelial dysfunction associated with the onset of cardiovascular diseases was confirmed, what was evaluated through the vasodilatation index, mediated by the brachial artery flow and the fibrinogen plasmatic concentrations. Conclusions: The clinical and epidemiological pattern of patients with or without cardiovascular diseases did not differ from that reported in the specialized literature on the base of the identification of traditional risk factors.

Análisis temporal de la mortalidad por causas evitables en las primeras 24 horas de vida, 2000-2021 / Temporal analysis of mortality from preventable causes in the first 24 hours of life, 2000-2021 / Análise temporal da mortalidade por causas evitáveis nas primeiras 24 horas de vida, 2000-2021

Objetivo: analizar el patrón temporal y estimar las tasas de mortalidad en las primeras 24 horas de vida y por causas evitables en el estado de Pernambuco en el período de 2000 a 2021. Método: estudio ecológico, teniendo como unidad de análisis el trimestre. La fuente de datos se constituyó por el Sistema de Informaciones sobre Mortalidad y el Sistema de Informaciones sobre Nacidos Vivos. El modelado de series temporales se realizó según el Modelo Autorregresivo Integrado de Promedio Móvil. Resultados: se registraron 14.462 óbitos en las primeras 24 horas de vida, siendo 11.110 (el 76,8%) evitables. Se observa para los pronósticos ( forecasts) que la tasa de mortalidad en las primeras 24 horas de vida registro una variación de 3,3 a 2,4 por 1.000 nacidos vivos, y la tasa de mortalidad por causas evitables de 2,3 a 1,8 por 1.000 nacidos vivos. Conclusión: la predicción sugirió avances en la reducción de la mortalidad en las primeras 24 horas de vida en el estado y por causas evitables. Los modelos ARIMA presentaron estimaciones satisfactorias para las tasas de mortalidad y por causas evitables en las primeras 24 horas de vida.

Objective: to analyze the temporal pattern and estimate mortality rates in the first 24 hours of life and from preventable causes in the state of Pernambuco from 2000 to 2021. Method: an ecological study, using the quarter as the unit of analysis. The data source was made up of the Mortality Information System and the Live Birth Information System. The time series modeling was conducted according to the Autoregressive Integrated Moving Average Model. Results: 14,462 deaths were recorded in the first 24 hours of life, 11,110 (76.8%) of which being preventable. It is observed from the forecasts that the mortality rate in the first 24 hours of life ranged from 3.3 to 2.4 per 1,000 live births, and the mortality rate from preventable causes ranged from 2.3 to 1.8 per 1,000 live births. Conclusion: the prediction suggested progress in reducing mortality in the first 24 hours of life in the state and from preventable causes. The ARIMA models presented satisfactory estimates for mortality rates and preventable causes in the first 24 hours of life.

Objetivo: analisar o padrão temporal e estimar as taxas de mortalidade nas primeiras 24 horas de vida e por causas evitáveis no estado de Pernambuco no período de 2000 a 2021. Método: estudo ecológico, tendo como unidade de análise o trimestre. A fonte de dados foi constituída pelo Sistema de Informações sobre Mortalidade e pelo Sistema de Informações sobre Nascidos Vivos. A modelagem da série temporal foi conduzida segundo o Modelo Autorregressivo Integrado de Médias Móveis. Resultados: foram registrados 14.462 óbitos nas primeiras 24 horas de vida, sendo 11.110 (76,8%) evitáveis. Observa-se para os forecasts que a taxa de mortalidade nas primeiras 24 horas de vida variou de 3,3 a 2,4 por 1.000 nascidos vivos, e a taxa de mortalidade por causas evitáveis variou de 2,3 a 1,8 por 1.000 nascidos vivos. Conclusão: a previsão sugeriu avanços na redução da mortalidade nas primeiras 24 horas de vida no estado e por causas evitáveis. Os modelos ARIMA apresentaram estimativas satisfatórias para as taxas de mortalidade e por causas evitáveis nas primeiras 24 horas de vida.

Rendimiento en los Juegos Panamericanos de Santiago 2023: Predicción mediante método de suavización exponencial simple

El objetivo del presente trabajo es analizar el desempeño deportivo de la delegación chilena en los Juegos Panamericanos celebrados entre los años 1951 y 2023, haciendo uso de datos retrospectivos y proyectivos a través de series temporales de tiempo. Para esto se empleó un diseño cuantitativo, no experimental y longitudinal de tendencias y un método de suavización exponencial simple, que utiliza promedios históricos y que permite realizar una predicción o comportamiento futuro basado en una media ponderada de los valores actuales y de los pasados. A partir de los resultados obtenidos, fue posible concluir que, en las últimas décadas, la ubicación de Chile en el ranking de los Juegos Panamericanos se ha estabilizado en torno a un onceavo lugar, posición pronosticada para Santiago 2023. Manteniéndose condiciones similares, el desempeño deportivo general y específico no tendría un quiebre exponencial de la tendencia y los resultados no resultan favorables, específicamente en lo que respecta a la obtención de medallas de oro y la posición general de la delegación.

The objective of this paper is to analyze the sports performance of the Chilean delegation in the Pan American Games held between 1951 and 2023, using retrospective and projective data through time series. For this purpose, a quantitative, non-experimental and longitudinal design of trends and a simple exponential smoothing method was used, which uses historical averages and allows a prediction or future behavior based on a weighted average of current and past values. From the results obtained, it was possible to conclude that, in recent decades, Chile's position in the Pan American Games ranking has stabilized around eleventh place, a position predicted for Santiago 2023. Maintaining similar conditions, the general and specific sporting performance would not have an exponential break in the trend and the results are not favorable, specifically in terms of obtaining gold medals and the overall position of the delegation.

O objetivo deste artigo é analisar o desempenho esportivo da delegação chilena nos Jogos Pan-Americanos realizados entre 1951 e 2023, usando dados retrospectivos e projetivos por meio de séries temporais. Para isso, foi utilizado um desenho quantitativo, não experimental e longitudinal de tendências e um método de suavização exponencial simples, que utiliza médias históricas e permite uma previsão do comportamento futuro com base em uma média ponderada dos valores atuais e passados. Com base nos resultados obtidos, foi possível concluir que, nas últimas décadas, a posição do Chile no ranking dos Jogos Pan-Americanos se estabilizou em torno do 11º lugar, posição prevista para Santiago 2023. Mantendo-se condições semelhantes, o desempenho esportivo geral e específico não teria uma quebra exponencial na tendência e os resultados não são favoráveis, especificamente em termos de conquista de medalhas de ouro e posição geral da delegação.

Evaluación cuantitativa de un modelo SEIR para predecir casos de COVID-19 / Quantitative evaluation of a SEIR model for forecasting COVID-19 cases

Resumen Introducción : Los modelos epidemiológicos han sido ampliamente utilizados durante la pandemia de COVID-19, aunque la evaluación de su desempeño ha sido limitada. El objetivo del presente trabajo fue evaluar de forma retrospectiva un modelo SEIR para la predicción de casos a corto plazo (1 a 3 semanas), cuantificando su desempeño real y potencial, me diante la optimización de los parámetros del modelo. Métodos : Se realizaron proyecciones para cada día de la primera ola de casos (31 de julio de 2020 al 11 de marzo de 2021) en el municipio de General Pueyrredón (Argentina), cuantificando el desempeño del modelo en términos de incertidumbre, inexactitud e imprecisión. La evaluación se realizó con los parámetros originales del modelo (utilizados en proyecciones que fueron oportunamente publicadas), y luego variando distintos parámetros a fin de identificar valores óptimos. Resultados : El análisis del desempeño del modelo mostró que valores alternativos de algunos parámetros, y la corrección de los valores de entrada utilizando un filtro de "media móvil" para eliminar las variaciones semanales en los reportes de casos, habrían otorgado mejores resultados. El modelo con los parámetros opti mizados logró disminuir desde casi 40% a menos de 15% la incertidumbre, con valores similares de inexactitud, y con una imprecisión levemente mayor. Discusión : Modelos epidemiológicos sencillos, sin grandes requerimientos para su implementación, pue den ser de utilidad para la toma de decisiones rápi das en localidades pequeñas o con recursos limitados, siempre y cuando se tenga en cuenta la importancia de su evaluación y la consideración de sus alcances y limitaciones.

Abstract Introduction : Epidemiological models have been widely used during the COVID-19 pandemic, although performance evaluation has been limited. The objec tive of this work was to thoroughly evaluate a SEIR model used for the short-term (1 to 3 weeks) predic tion of cases, quantifying its actual past performance, and its potential performance by optimizing the model parameters. Methods : Daily case forecasts were obtained for the first wave of cases (July 31, 2020 to March 11, 2021) in the district of General Pueyrredón (Argentina), quantifying the model performance in terms of uncertainty, inac curacy and imprecision. The evaluation was carried out with the original parameters of the model (used in the forecasts that were published), and also varying different parameters in order to identify optimal values. Results : The analysis of the model performance showed that alternative values of some parameters, and the correction of the input values using a "mov ing average" filter to eliminate the weekly variations in the case reports, would have yielded better results. The model with the optimized parameters was able to reduce the uncertainty from almost 40% to less than 15%, with similar values of inaccuracy, and with slightly greater imprecision. Discussion : Simple epidemiological models, without large requirements for their implementation, can be very useful for making quick decisions in small cities or cities with limited resources, as long as the importance of their evaluation is taken into account and their scope and limitations are considered.

Modelo de inteligencia artificial para la detección temprana de diabetes

Introducción. La diabetes es una enfermedad crónica que se caracteriza por el aumento de la concentración de la glucosa en sangre. Puede generar complicaciones que afectan la calidad de vida y aumentan los costos de la atención en salud. En los últimos años, las tasas de prevalencia y mortalidad han aumentado en todo el mundo. El desarrollo de modelos con gran desempeño predictivo puede ayudar en la identificación temprana de la enfermedad. Objetivo. Desarrollar un modelo basado en la inteligencia artificial para apoyar la toma de decisiones clínicas en la detección temprana de la diabetes. Materiales y métodos. Se llevó a cabo un estudio de corte transversal, utilizando un conjunto de datos que incluía edad, signos y síntomas de pacientes con diabetes y de individuos sanos. Se utilizaron técnicas de preprocesamiento para los datos. Posteriormente, se construyó el modelo basado en mapas cognitivos difusos. El rendimiento se evaluó mediante tres parámetros: exactitud, especificidad y sensibilidad. Resultados. El modelo desarrollado obtuvo un excelente desempeño predictivo, con una exactitud del 95 %. Además, permitió identificar el comportamiento de las variables involucradas usando iteraciones simuladas, lo que proporcionó información valiosa sobre la dinámica de los factores de riesgo asociados con la diabetes. Conclusiones. Los mapas cognitivos difusos demostraron ser de gran valor para la identificación temprana de la enfermedad y en la toma de decisiones clínicas. Los resultados sugieren el potencial de estos enfoques en aplicaciones clínicas relacionadas con la diabetes y respaldan su utilidad en la práctica médica para mejorar los resultados de los pacientes.

Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decision-making in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.

Modelo explicativo de ansiedad generalizada y bullying como predictores de autolesiones no suicidas en adolescentes peruanos

Fundamento las autolesiones no suicidas se consideran un problema de salud pública y social durante la última década, el cual afecta en mayor medida a los adolescentes. La ansiedad generalizada y el bullying pueden ser factores desencadenantes para su desarrollo. Objetivo analizar un modelo explicativo de la ansiedad generalizada y el bullying como predictores de autolesiones no suicidas en adolescentes peruanos. Métodos estudio de diseño explicativo, transversal, con participación de 1 249 adolescentes peruanos, de edad promedio de 15 años (desviación estándar = 1,49) quienes respondieron escalas sobre ansiedad generalizada, bullying y autolesiones no suicidas. Para el análisis de datos, se aplicó la potencia estadística, la correlación y un modelo de regresión estructural basado en covarianzas para confirmar el modelo explicativo. Resultados las variables psicológicas se correlacionaron de manera positiva y estadísticamente significativa. El modelo propuesto presentó índices de ajuste adecuados (CFI = 0,94; RMSEA = 0,03 [IC del 90 %: 0,02-0,03] y SRMR = 0,04) y se evidenció que la ansiedad generalizada (β = 0,26, p = 0,001) y las dimensiones del bullying, como la agresión (β = 0,25, p = 0,001) y la victimización (β = 0,21, p = 0,003) predijeron de manera estadísticamente significativa las autolesiones no suicidas. Conclusiones los hallazgos sugieren que tanto la ansiedad generalizada como el bullying predicen las autolesiones no suicidas en adolescentes. La evidencia proporciona información útil para desarrollar y evaluar programas de prevención basados en estas variables psicológicas, con vistas a disminuir el riesgo de las autolesiones no suicidas.

Background non-suicidal self-harm has been considered a public and social health problem during the last decade, which affects adolescents to a greater extent. Generalized anxiety and bullying can be trigger factors for its development. Objective to analyze a generalized anxiety and bullying explanatory model as non-suicidal self-harm predictors in Peruvian adolescents. Methods cross-sectional, explanatory design study, with 1,249 Peruvian adolescents, average age 15 years old (standard deviation = 1.49), who answered scales on generalized anxiety, bullying, and non-suicidal self-harm. For data analysis, statistical power, correlation, and a structural regression model based on covariances were applied to confirm the explanatory model. Results the psychological variables were positively and statistically significantly correlated. The proposed model had adequate fit indices (CFI = 0.94; RMSEA = 0.03 [90% CI: 0.02-0.03] and SRMR = 0.04) and it was evidenced that generalized anxiety (β = 0.26, p = 0.001) and bullying dimensions such as aggression (β = 0.25, p = 0.001) and victimization (β = 0.21, p = 0.003) statistically significantly predicted self-harm not suicidal. Conclusions The findings suggest that both generalized anxiety and bullying predict non suicidal self-harm in adolescents. The evidence provides useful information for developing and evaluating prevention programs based on these psychological variables, to reduce the non-suicidal self-harm risks.

Effect of Soil and Climatic Conditions on Brown Spot Occurrence in Rice Lowland across Four Agro-climatic Zones of Côte d’Ivoire

Aims: To evaluate interaction of soil pH and acidity with weather on Rice Brown spot (BS) occurrence in rice lowlands. Study Design: Cross sectional study. Place and Duration of Study: Four distinct rice lowlands belonging to different climatic zones (forest, transitional and savanna) of Côte d’Ivoire during cropping seasons of 2021. Methodology: BS characterization were done in different farmer fields where soil samples were also collected during dry and rainy seasons. Soil silicon and acidity were determined in those samples and rice grain yield at harvest time were recorded in different sites. Weather data related to sites and seasons were used to find out correlations. Results: Occurrence of BS was found in forest zones with scores of 4 and 3 compared to 1 and 2 in savanna and transitional zones, respectively, with seasonal variation. Both rice production and the occurrence of BS were explained by soil parameters in conjunction with climatic parameters. Rainfall (R=0.38) and relative humidity (R=0.64) leaded BS occurrence and decrease of yield. Wind speed (R=0.62) and air maximum temperature (R= 0.63) were the determinant factors affecting rice yields. Si was found to be a component of sustainable soil management that interferes with soil pH in all climatic zones. Combined with Temperature, both soil parameters predicted BS occurrence over 50%. Conclusion: Temperature decrease BS pathogens occurrence whereas high humidity increases its spread. Those parameters combined with silicon which interferes with pH could leads sustainable solutions in BS control. Furthermore, having a deep understanding with rice varietal considerations can significantly improve strategies related to rice cultivation and protection.

The predictive value of renal artery resistance index combined with cystatin C in contrast-induced nephropathy after coronary artery CT angiography / 中国医师进修杂志

Objective:To investigate the predictive value of serum cystatin C (Cys-C) and renal artery resistance index (RRI) 24 h before coronary CT angiography (CTA) examination in contrast-induced nephropathy(CIN).Methods:Sixty-four patients with coronary heart disease who received coronary CTA examination in Hebei Petro China Central Hospital from January 2020 to March 2021 were selected as the research subjects. According to the incidence of CIN after coronary CTA examination, they were divided into the case group (25 patients) and the normal group(39 patients). Serum Cys-C level was measured by automatic biochemical analyzer at 24 h before CTA examination, and RRI value was measured by color Doppler ultrasound. Risk factors of CIN after CTA examination were analyzed by Logistic regression. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of serum Cys-C, RRI and the combination of the two indexes in CIN.Results:Compared with the control group, the dosage of contrast agent, patients with hypertension, serum Cys-C level at 24 h before CTA examination and RRI value in the case group were higher than those in the normal group: (85.53 ± 16.27) ml vs. (64.37 ± 15.08) ml, 80.00%(20/25) vs. 56.41%(22/39), (1.36 ± 0.18)mg/L vs.(1.02 ± 0.21) mg/L, 0.743 ± 0.093 vs. 0.632 ± 0.081, there were statistical differences ( P<0.05). Multivariate Logistic regression analysis showed that the amount of contrast agent, hypertension, serum Cys-C level at 24 h before CTA examination and RRI value were independent risk factor for CIN after CTA examination ( P<0.05). The results of ROC curve analysis showed that serum Cys-C (>1.318 mg/L) combined with RRI value (>0.653) at 24 h before CTA examination predicted CIN with the highest area under the curve was 0.922, sensitivity was 92.5% and specificity was 81.6%. Conclusions:The incidence of CIN after CTA is related to the dosage of contrastant, hypertension, serum Cys-C level and RRI value at 24 h before CTA. The combination of Cys-C level and RRI value has a high predictive value for the occurrence of CIN.

Predictive value of albumin-bilirubin score combined with Glasgow-Blatchfordscale in the short-term prognosis of patients with acute upper gastrointestinal hemorrhage / 中国医师进修杂志

Objective:To investigate the predictive value of albumin-bilirubin score combined with Glasgow-Blatchfordscale(GBS) in the short-term prognosis of patients with acute upper gastrointestinal hemorrhage.Methods:Eighty-one patients with acute upper gastrointestinal hemorrhage who were treated in JingzhouHospital Affiliated to Yangtze University from May 2020 to May 2022 were selected as the research subjects, according to the prognosis of patients within 30 d, they were divided into poor prognosis group (35 cases) and fair prognosis group (46 cases). Clinical data were collected and the levels of albumin (ALB), creatinine (Cr), hemoglobin (Hb), total bilirubin (TBIL), urea nitrogen (BUN) and the scores of ALBI, GBS were compared between the two groups. The independent risk factors of short-term prognosis in patients with acute upper gastrointestinal hemorrhage were analyzed by Logistic multivariate regression analysis. The predictive value of ALBI score and GBS score for short-term prognosis of acute upper gastrointestinal hemorrhage was evaluated. Receiver operating characteristic (ROC) curve were drawn, and the area under the curve was calculated and compared.Results:There were no significant differences in baseline data such as gender, heart rate, systolic blood pressure, smoking history, drinking history, drug use, syncope, mental changesand comorbidities between the two groups ( P>0.05). The age in the poor prognosis group was higher than that in the fair prognosis group: (65.60 ± 7.90) years vs. (62.60 ± 7.50) years, there was statistical difference ( P<0.05). The levels of BUN, TBIL and GBS scores in the poor prognosis group were higher than those in the fair prognosis group: (9.86 ± 2.94) mmol/L vs.(8.56 ± 2.66) mmol/L, (20.70 ± 12.31) μmol/L vs. (11.71 ± 8.11) μmol/L, (10.77 ± 1.59) scores vs. (7.91 ± 1.91) scores; the levels of Hb, Cr, ALB and ALBI scores were lower than those in the fair prognosis group: (74.97 ± 16.47) g/L vs.(84.01 ± 19.44) g/L, (65.72 ± 12.08) μmol/L vs. (70.37 ± 11.52) μmol/L, (25.67 ± 4.30) g/L vs. (32.62 ± 5.07) g/L, (0.75 ± 0.47) scores vs. (1.37 ± 0.43) scores, there were statistical differences ( P<0.05). Logistic regression analysis showed that ALB, TBIL and ALBI, GSB scores were independent risk factors for death within 30 din patients with acute upper gastrointestinal hemorrhage ( P<0.05). ROC curve analysis showed that the area under the curve of ALBI score and GBS score were 0.922 and 0.875, while the area under the curve of combined was 0.958, the sensitivity was 94.29%, and the specificity was 84.78%, which were significantly higher than predicted alone ( Z = 1.87, 2.44; P<0.05). Conclusions:ALBI score combined with GBS has good predictive value for short-term prognosis in patients with acute upper gastrointestinal hemorrhage.

Effect of different thrombus risk assessment tools in obstetric inpatients / 中国实用护理杂志

Objective:To compare the application value of Caprini thrombosis risk assessment scale and Shanghai venous thrombosis risk factor scale in predicting venous thromboembolism in hospitalized maternal patients.Methods:This was a case-control study. A total of 67 pregnant women complicated with venous thromboembolism in the Obstetrics Department of Shandong Provincial Hospital Affiliated to Shandong First Medical University from January 2010 to September 2021 were retrospectively analyzed. And 144 pregnant women without venous thromboembolism in the same period were selected. Two venous thrombosis risk assessment tools were used to evaluate the pregnant women, and the predictive effectiveness and accuracy of the two assessment tools were compared.Results:The effects of the two risk assessment tools for venous thrombosis were different before and after delivery with statistical difference ( Z=8.15, 5.97, both P<0.01), but the Shanghai venous thrombosis risk factor scale (83.9%, 67.3%) was superior to Caprini thrombosis risk assessment scale (52.1%, 45.0%) in the accuracy of prenatal and postnatal prediction. The area under receiver operating characteristic curve showed that the Shanghai venous thrombosis risk factor rating scale (0.863) was significantly superior to the Caprini thrombosis risk assessment scale (0.748) after delivery. Conclusions:The Shanghai venous thrombosis risk factor scale is more valuable than the Caprini thrombosis risk assessment scale in the early risk identification of venous thromboembolism in pregnant women.

Construction of wound drainage tube time delay prediction model for patients with breast cancer / 中国实用护理杂志

Objective:To investigate the independent influencing factors of wound drainage tube time delay in patients with breast cancer and establish a predictive model.Methods:Patients admitted to Tianjin Medical University Cancer Hospital from January to November 2021 were selected as the research objects. They were divided into sword modeling group (156 cases) and verification group (86 cases) according to the admission time. Delayed time to postoperative wound drainage and extubation in breast cancer patients was the end point, 95 cases of 156 patients in the modeling group whose extubation time was less than or equal to 7 days were set as the normal group, 61 cases whose extubation time were more than 7 days were set as delayed group, and the influencing factors of the two groups were compared to establish the prediction model, Hosmer-Lemeshow test was conduct to verify the fitting effect, used the ROC curve to verify the prediction model performance.Results:Univariate and multivariate Cox proportional hazards regression analysis showed that patients' high BMI, related basic disease history, operation mode, axillary lymph node dissection, breast tumor size (T3, T4) and drainage fluid volume 48 hours (≥50 ml) after operation were independent influencing factors for wound drainage tube time delay ( P<0.05). The prediction model was P=0.822, and the area under the ROC curve was 0.877, and the Youden index was 0.605, the sensitivity was 0.736, and the specificity was 0.869. The research data of 86 cases in the validation group were used as the test set for internal and external validation of the model, and the model verification was 96.51%. Conclusion:This prediction model has a good effect, providing a reference basis for clinical medical workers.

A machine learning-based predictive model of nonsuicidal self-injurious behavior among college students in Guizhou Province / 中国学校卫生

Objective@#To explore the effectiveness of machine learning algorithms in predicting non-suicidal self-injury (NSSI) behavior among college students, and to analyze the influencing factors of NSSI behavior, thus providing a reference for promoting psychological well-being.@*Methods@#In December 2022, a stratified random cluster sampling method was used to select 835 college students from a university in Guizhou Province, China. The Adolescent Self-injury Scale, Family Function Assessment Scale, and Emotion Regulation Self-efficacy Scale were used to evaluate the participants. Demographic characteristics, family factors, and emotional factors were taken as independent variables, while the dependent variable was whether college students exhibited NSSI behavior. Machine learning algorithms, including Logistic regression, support vector machine (SVM), decision trees, algorithm gradient boosting trees, random forests, and AdaBoost, were used to construct predictive models.@*Results@#The detection rate of NSSI behavior among the college students was 23.23% (194 individuals). The NSSI behavior group scored higher than the non-NSSI behavior group in total family function, emotional communication, egoism, and family rules ( t=3.02, 3.35 , 2.23,2.87, P <0.05). On the other hand, the non-NSSI behavior group scored higher than the NSSI behavior group in total emotion regulation selfefficacy, managing negative emotion self-efficacy, and expressing positive emotion self-efficacy ( t=-5.04, -5.48 , -2.43, P <0.05). The recall rates of random forests, SVM, Logistic regression, decision trees, algorithm gradient boosting trees, and AdaBoost were 84.3% , 90.6%, 73.4%, 87.5%, 95.3%, 89.0%, respectively. The F1 scores were 84.4%, 92.1%, 71.2 %, 79.4%, 91.7%, 89.1% , respectively. The respective precision rates were 84.4%, 93.5%, 69.1%, 72.7%, 88.4%, 89.1 %. The AUC scores were 0.845, 0.922, 0.706, 0.776, 0.915, and 0.891, respectively.@*Conclusion@#Compared to the algorithm gradient boosting tree, random forest, Logistic regression, and AdaBoost models, the SVM model has a better predictive effect on whether college students in Guizhou Province exhibits NSSI behavior. It is recommended to use an appropriate model to identify students at risk of NSSI behavior as early as possible and provide psychological crisis interventions to promote their mental health.

Diseño y validación de riesgo de síndrome bajo gasto cardíaco en la cirugía valvular

Introducción: La incidencia del síndrome bajo gasto cardíaco postoperatorio es variable en las distintas series publicadas, desde 4 % hasta 15 %, con una mortalidad que se aproxima al 20 %. Si bien en enfermos mayores de 70 años el síndrome puede estar presente hasta en un 63 %, a pesar del desarrollo de mejores técnicas de cardioprotección y cuidados postoperatorios, la incidencia de este síndrome en poblaciones de alto riesgo no se ha modificado en una proporción significativa. Objetivo: Diseñar y validar un modelo predictivo de síndrome de bajo gasto cardíaco postoperatorio a través de factores de riesgo. Métodos: Se realizó un estudio analítico de casos y controles en pacientes con síndrome de bajo gasto cardíaco posoperatorio, atendidos en el Centro de Cardiología y Cirugía Cardiovascular del Hospital Provincial Docente Saturnino Lora de la provincia Santiago de Cuba, en un periodo 2019-2021. Se empleó la regresión logística con ajustes para obtener el modelo. Resultados: Los factores de riesgo predictores de mayor valor fue edad > 65 años, función de ventrículo derecho disminuida, el tiempo de pinzamiento aórtico, sangrado postoperatorio que fueron los que arrojó el modelo de regresión logística. Se realizó su validación interna por división de datos. Conclusiones: El modelo predictivo elaborado a partir de la regresión logística quedó compuesto por los predictores: edad > 65 años, el tiempo de pinzamiento aórtico > 90 minutos y el sangrado posoperatorio prolongado; presentó buen ajuste y poder discriminante, sobre todo valor predictivo positivo.

Introduction: The incidence of postoperative low cardiac output syndrome is variable in the different published series, from 4% to 15%, with a mortality approaching 20%. Although in patients over 70 years of age the syndrome may be present in up to 63%, despite the development of better cardioprotection techniques and postoperative care, the incidence of this syndrome in high-risk populations has not changed in a significant proportion. Objective: To design and validate a predictive model of postoperative low cardiac output syndrome through risk factors. Methods: An analytical, case-control study was conducted in patients with postoperative low cardiac output syndrome attended at the Center for Cardiology and Cardiovascular Surgery of the Saturnino Lora Teaching Provincial Hospital in Santiago de Cuba, in the period 2019-2021. Logistic regression with adjustments to obtain the model was used. Results: The highest value predictor risk factors are: age > 65 years, decreased right ventricular function, aortic clamping time, postoperative bleeding, which were the ones that yielded the logistic regression model. Internal validation was performed by data division. Conclusions: The predictive model developed from logistic regression was composed of the predictors: age > 65 years, aortic clamping time > 90 minutes and prolonged postoperative bleeding. It presented good fit and discriminant power, especially positive predictive value.

Web App for prediction of hospitalisation in Intensive Care Unit by covid-19 / Web App para la predicción de hospitalización em la Unidad de Cuidados Intensivos por covid-19 / Web App para a predição de internação em Unidade de Terapia Intensiva por covid-19

ABSTRACT Objective: To develop a Web App from a predictive model to estimate the risk of Intensive Care Unit (ICU) admission for patients with covid-19. Methods: An applied technological production research was carried out with the development of Streamlit using Python, considering the decision tree model that presented the best performance (AUC 0.668). Results: Based on the variables associated with Precision Nursing, Streamlit stratifies patients admitted to clinical units who are most likely to be admitted to the Intensive Care Unit, serving as a decision-making support tool for healthcare professionals. Final considerations: The performance of the model may have been influenced by the start of vaccination during the data collection period, however, the Web App via Streamlit proved to be a feasible tool for presenting research results, due to the ease of understanding by nurses and its potential for supporting clinical decision-making.

RESUMEN Objetivo: Desarrollar una Web App a partir de un modelo predictivo para estimar el riesgo de ingreso a la Unidad de Cuidados Intensivos (UCI) para pacientes con covid-19. Métodos: Se realizó una investigación de producción tecnológica aplicada con el desarrollo de Streamlit utilizando Python, considerando el modelo de árbol de decisiones que presentó el mejor rendimiento (AUC 0.668). Resultados: Basado en las variables asociadas con la Enfermería de Precisión, Streamlit estratifica a los pacientes ingresados en unidades clínicas que tienen más probabilidades de ser admitidos en la Unidad de Cuidados Intensivos, sirviendo como una herramienta de apoyo para la toma de decisiones para los profesionales de la salud. Consideraciones finales: El rendimiento del modelo puede haber sido influenciado por el inicio de la vacunación durante el período de recolección de datos. La Web App a través de Streamlit demostró ser una herramienta factible para presentar los resultados, debido a la facilidad de comprensión y su potencial para apoyar la toma de decisiones clínicas.

RESUMO Objetivo: Desenvolver um Web App a partir de um modelo preditivo para estimar o risco de internação de pacientes com covid-19 em UTI. Métodos: Realizou-se uma pesquisa aplicada de produção tecnológica com o desenvolvimento do Streamlit a partir do Python, considerando o modelo de árvore de decisão que apresentou o melhor desempenho (AUC 0.668). Resultados: A partir das variáveis associadas à Enfermagem de Precisão, o Streamlit estratifica os pacientes internados nas unidades clínicas com maior probabilidade de internação em Unidade de Terapia Intensiva, funcionando como uma ferramenta de apoio à tomada de decisão dos profissionais de saúde. Considerações finais: A performance do modelo pode ter sido influenciada pelo início da vacinação no período de coleta de dados, no entanto, o Web App via Streamlit mostrou-se uma ferramenta viável para a apresentação dos resultados de pesquisa, devido à facilidade de entendimento por parte dos enfermeiros e pelo potencial de apoio à decisão clínica.

Scientific writing with artificial intelligence: key considerations and alerts / Escrita científica com inteligência artificial: principais considerações e alertas

A integração de geradores de texto de inteligência artificial (IA) em relatórios científicos exige uma avaliação cuidadosa de considerações éticas específicas. Embora essas tecnologias de IA ofereçam suporte à geração de texto, abordar as implicações éticas é fundamental. Este editorial destaca a necessidade de uma abordagem ponderada e responsável, enfatizando o estabelecimento de diretrizes e melhores práticas por parte de pesquisadores e comunidades científicas. Esforços colaborativos entre desenvolvedores de IA, pesquisadores e comitês éticos podem garantir a integração perfeita das tecnologias de IA, ao mesmo tempo em que mantêm a integridade, qualidade e padrões éticos da divulgação científica. Este texto oferece um resumo abrangente considerações-chave ao se utilizar geradores de texto de inteligência artificial em relatórios científicos (AU)

The integration of artificial intelligence (AI) text generators in scientific reports demands careful evaluation of specific ethical considerations. While these AI technologies offer text generation support, addressing the ethical implications is vital. This editorial highlights the need for a thoughtful and responsible approach, emphasizing the establishment of guidelines and best practices by researchers and scientific communities. Collaborative efforts between AI developers, researchers, and ethical committees can ensure the seamless integration of AI technologies while upholding the integrity, quality, and ethical standards of scientific reporting. This text comprehensively summarizes the key considerations to be followed when utilizing artificial intelligence text generators in scientific reports.(AU)

Uso de técnicas de aprendizado de máquina para classificação de fatores que influenciam a ocorrência de dermatites ocupacionais / Using machine learning techniques to classify factors that influence the occurrence of occupational dermatitis

Resumo Introdução: realizar a predição de doenças relacionadas ao trabalho é um desafio às organizações e ao poder público. Com as técnicas de aprendizado de máquina (AM), é possível identificar fatores determinantes para a ocorrência de uma doença ocupacional, visando direcionar ações mais efetivas à proteção dos trabalhadores. Objetivo: predizer, a partir da comparação de técnicas de AM, os fatores com maior influência para a ocorrência de dermatite ocupacional. Métodos: desenvolveu-se um código em linguagem R e uma análise descritiva dos dados e identificaram-se os fatores de influência de acordo com a técnica de AM que demonstrou melhor desempenho. O banco de dados foi disponibilizado pelo Serviço de Dermatologia Ocupacional da Fundação Oswaldo Cruz e contém informações de trabalhadores que apresentaram alterações cutâneas sugestivas de dermatite ocupacional no período de 2000-2014. Resultados: as técnicas com melhor desempenho foram: neural network, random forest, support vector machine e naive Bayes. As variáveis sexo, escolaridade e profissão foram as mais adequadas para os modelos de previsão de dermatite ocupacional. Conclusão: as técnicas de AM possibilitam predizer os fatores que influenciam a segurança e a saúde dos trabalhadores, os parâmetros que subsidiam a implantação de procedimentos e as políticas mais efetivas para prevenir a dermatite ocupacional.

Abstract Introduction: to predict work related diseases is a challenge for organizations and the governmental authorities. By means of machine learning (ML) techniques it is possible to identify factors that determine the occurrence of an occupational disease, aiming at taking more effective actions to protect workers. Objective: to predict, by comparing ML techniques, the factors which highly influence the occurrence of occupational dermatitis. Methods: we developed a code in R language and a descriptive analysis of the data and identified the influence factors according to the ML technique that presented the best performance. The database was made available by the Occupational Dermatology Service of Oswaldo Cruz Foundation and assembles information of the workers who experienced cutaneous alterations suggestive of occupational dermatitis between 2000-2014. Results: the techniques which presented the best performance were: neural network, random forest, support vector machine, and naive Bayes. Sex, schooling, and profession were the most adequate variables for the occupational dermatitis prediction models. Conclusion: ML techniques allowed to predict the factors that influence the workers' safety and health, as well as the parameters that subsidize the procedures implementation, and the most effective policies to prevent occupational dermatitis.

ZJU index and the risk of nonalcoholic fatty liver disease in the Uygur population in the rural area of Xinjiang Uygur Autonomous Region： A cohort study / 临床肝胆病杂志

ObjectiveTo investigate the association between ZJU index and the onset of nonalcoholic fatty liver disease （NAFLD） in the Uygur population and the value of ZJU index in predicting the risk of NAFLD. MethodsThe Uighur community of The 51st Regiment of The Third Division of Xinjiang Kashgar Corps was selected as the investigation site， and the Uygur residents who lived in this area and had an age of >18 years were selected as subjects. Follow-up studies were conducted in 2019， 2020， and 2021， and the investigation of outcomes was completed in June to August of 2021. Finally 10 597 subjects were enrolled for analysis. The Kruskal-Wallis H test was used for comparison of continuous variables between groups， and the chi-square test was used for comparison of categorical variables between groups. The subjects were divided into Q1-Q4 groups according to the level of ZJU index. The Kaplan-Meier curve was used to predict the incidence rate of NAFLD， and the Cox regression model was used to analyze the association between ZJU index and the risk of NAFLD； the area under the ROC curve （AUC） was used to evaluate the value of ZJU index in predicting the risk of NAFLD. ResultsDuring the median follow-up time of 4.92 years， the incidence rate of NAFLD was 9.4% （992/10 597） among the study population. After adjustment for multiple factors， there was a significant increase in the risk of NAFLD with the increase in ZJU index， with a hazard ratio of 2.55 （95% confidence interval ［CI］： 1.60‍ — ‍4.06）， 7.32 （95%CI： 4.78‍ — ‍11.20）， and 21.74 （95%CI： 14.32‍ — ‍33.00）， respectively （all Ptrend<0.001）. The ROC curve showed that ZJU index had a higher value in predicting NAFLD （AUC=0.816）， and the male subgroup had a significantly higher predictive accuracy of ZJU index than the female subgroup （AUC： 0.829 vs 0.809）. ConclusionZJU index is a predictive factor for the onset of NAFLD in the Uygur population in rural areas of Xinjiang and has a good value in predicting the risk of NAFLD.

Trend and prediction of the disease burden of acute hepatitis B in China / 临床肝胆病杂志

ObjectiveTo investigate the situation and development trend of the disease burden of acute hepatitis B in China in 1990 — 2019. MethodsThe Global Burden of Disease 2019 was used to analyze the incidence rate， mortality rate， and disability-adjusted life year （DALY） rate of acute hepatitis B in different sex and age groups and predict the trend of the incidence rate of acute hepatitis B. ResultsIn 2019， the incidence rate， mortality rate， and DALY rate of acute hepatitis B in China were 1 623.71/100 000， 0.20/100 000， and 10.04/100 000 respectively， which were reduced by 42.03%， 79.38%， and 80.21%， respectively， compared with the data in 1990， and women showed lower incidence rate， mortality rate， and DALY rate of acute hepatitis B than men. In 2019， the 20～<54 years group had the highest incidence rate （2 285.85/100 000） and DALY rate （10.53/100 000）， and the ≥55 years group had the highest mortality rate of 0.52/100 000. The Joinpoint regression model analysis showed that the incidence rate， mortality rate， and DALY rate of acute hepatitis B in China tended to decrease from 1990 to 2019， with an average annual percent change of -1.9%， -5.2%， and -5.5%， respectively （P<0.05）. The grey prediction model GM （1，1） showed that the incidence rate of acute hepatitis B will decrease from 2020 to 2030 in China. ConclusionThe disease burden of acute hepatitis B tended to decrease from 1990 to 2019 in China， indicating that the prevention and treatment measures for acute hepatitis B have achieved a marked effect in China； however， due to the large population base of China， active preventive measures should be further adopted to reduce the disease burden of acute hepatitis B.