Evaluation of Four Predictive Models for Identifying Malignancy of Solitary Pulmonary Nodules in Health Check-up Population / 肿瘤防治研究

Objective To compare and validate the efficiency of four models predicting the malignancy of solitary pulmonary nodules (SPN). Methods Patients diagnosed with SPN during health check-up were selected as the research subjects. Risk assessment was conducted using four predictive models. Outcomes were obtained through prospective follow-up. Statistical description and univariate analysis were performed for all risk factors of the four models. ROC curve was applied to compare the efficiency of the four predictive models. Results A total of 479 cases were included in this study. Among these patients, 82 were diagnosed with lung tumor, and the malignant rate was 17.12%. Age, nodule diameter, smoking, family history of tumor, history of extrapulmonary tumor ≥5 years, upper lobe site, unclear boundary, and spiculation rates were higher in the malignancy group than those in the benign group (P < 0.05). The efficiency of Brock model was the best. Its AUC was 0.833, sensitivity was 80.49%, and specificity was 74.31%. Its Youden index, positive likelihood ratio, positive predictive value, and negative predictive value were the highest, and its negative likelihood ratio was the lowest. The AUC, sensitivity, and specificity of Mayo model were 0.815, 81.71%, and 67.51%, respectively; those of PKUPH model were 0.754, 69.51%, and 73.55%, respectively; and those of VA model were 0.738, 68.29%, and 67.55%, respectively. Conclusion The Brock model might be the most appropriate predictive model for the risk assessment of SPN among the health check-up population, and the VA model is the worst. The combination of Brock, Mayo, and PKUPH models requires further study.

Risk factors and prediction model of postoperative atrial fibrillation surgery after esophageal cancer surgery / 中华胸心血管外科杂志

Objective:To develop a risk prediction lineogram of neooperative atrial fibrillation in patients with esophageal cancer.Methods:The clinical data of 1 509 patients undergoing esophageal cancer surgery admitted to the department of esophageal surgery of our hospital from December 2019 to April 2022 were gathered, and they were divided into two layers according to whether they had new atrial fibrillation after surgery. In each layer, they were randomly divided into training set and test set in a ratio of 7∶3. In the training population, the multi-factor logistic regression method was used to establish the prediction model, and the line graph of the prediction model was drawn. The ROC curve and calibration curve were drawn to assess the differentiation ability and calibration ability of the prediction model. The test set population is used to validate the prediction model. Results:A total of 1 509 patients with esophageal cancer were included in the study, and the incidence of new atrial fibrillation after surgery was 247 patients(16.4%). A total of 1 039 patients(68.9%) were enrolled in the training set. The multivariate logistic regression model indicated that age, gender, BMI, pulmonary infection, the use of invasive ventilator, and the need for additional drainage of fluid accumulation were the influencing factors for new postoperative atrial fibrillation. The AUC of the training set prediction model under ROC curve was 0.775(95% CI: 0.737-0.812, P<0.001), indicating that the model has high predictive discrimination ability. Calibration curve and Hosmer- Lemeshow test results P=0.796, indicating that the model has good consistency of prediction ability. There were 470 subjects(31.1%) in the test set. The results showed that the AUC of the prediction model under the ROC curve was 0.773(95% CI: 0.719-0.826, P<0.001), indicating that the prediction model still has a high discriminative ability in the test set population. Conclusion:Patients with age, gender, BMI, pulmonary infection, the use of invasive ventilator, and the need for additional drainage of effusion are at higher risk of new atrial fibrillation after surgery. The timely prediction, prevention and management of POAF are crucial to improve the prognostic quality of postoperative patients with esophageal cancer by constructing clinical prediction models.

Estimation of potential epidemic risk in the landslide zone based on physical factors in the Sillapa district / Estimación de riesgo potencial epidémico en la zona de deslave basado en factores físicos en el distrito Sillapa

Almost 17% of causes of death due to natural hazards are the product of landslides. Most of them occur in the most deprived places of less developed countries, coexisting a lethal combination of factors that point to this type of tragedies: the natural and the human factor. On the other hand, after a disaster, health care needs and priorities may change; in this sense, the food security of refugees, the supply of drinking water, the disposal of excreta and solid waste, the need for shelters, attention to personal hygiene needs, vector control, attention to injuries after the cleanup activities and the conduct of public health surveillance becomes a priority. To mitigate the disruption, public health authorities must act promptly to avert the adverse effects of the disaster, prevent further damage, and restore public service delivery as soon as possible. In this sense, public health surveillance, epidemiology, can identify local problems and establish priorities for decision-making in the health area. In this article, mention is made of one of the most alarming events that occurred in Sillapata, Peru, where a level 4 landslide affected the infrastructure of the population. Considering an established statistical model, it is possible to predict the zoning of higher risks, and thus establish the most appropriate territorial planning and epidemiological surveillance when similar events reach this population or other populations of the Peruvian State(AU)

Casi el 17 % de causas de muerte por amenazas naturales es producto de los deslizamientos de masa. La mayoría de ellas ocurre en los sitios más deprimidos de los países menos desarrollados coexistiendo una combinación letal de factores que apuntan a este tipo de tragedias: el factor natural y el humano. Por otra parte, después de un desastre, las necesidades y prioridades de cuidado de salud pueden cambiar; en ese sentido, el aseguramiento alimenticio de los refugiados, el suministro de agua de potable, la disposición de excretas y desechos sólidos, la necesidad de albergues, la atención de las necesidades de higiene personal, el control de vectores, la atención de las lesiones después de las actividades de limpieza y la conducción de la vigilancia en salud pública se hace prioritarias. Para mitigar el trastorno, las autoridades de salud pública deben actuar con prontitud para evitar los efectos advesos del desastre, prevenir más daños y restaurar la prestación de servicios públicos lo más pronto posible. En ese sentido, la vigilancia en salud pública, la epidemiología, puede identificar los problemas del lugar y establecer prioridades para la toma de decisiones en el área de la salud. En este artículo, se hace mención a uno de los eventos más alarmante ocurrido en Sillapata, Perú, donde un deslizamiento nivel 4 afectó la infraestructura de la población. Tomando en cuenta, un modelo estadístico establecido es posible predecir la zonificación de mayores riesgos, y de esta manera establecer la planificación territorial y de vigilancia epidemiológica más adecuada cuando eventos similares alcance a esta población o a otras poblaciones del Estado Peruano(AU)

Prediction of heart failure decompensations using artificial intelligence and machine learning techniques / Predicción de las descompensaciones de la insuficiencia cardíaca mediante técnicas de inteligencia artificial y aprendizaje automático

Abstract Introduction: Heart failure (HF) is a major concern in public health. We have used artificial intelligence to analyze information and improve patient outcomes. Method: An Observational, retrospective, and non-randomized study with patients enrolled in our telemonitoring program (May 2014-February 2018). We collected patients’ clinical data, telemonitoring transmissions, and HF decompensations. Results: A total of 240 patients were enrolled with a follow-up of 13.44 ± 8.65 months. During this interval, 527 HF decompensations in 148 different patients were detected. Significant weight increases, desaturation below 90% and perception of clinical worsening are good predictors of HF decompensation. We have built a predictive model applying machine learning (ML) techniques, obtaining the best results with the combination of "Weight + Ankle + well-being plus alerts of systolic and diastolic blood pressure, oxygen saturation, and heart rate." Conclusions: ML techniques are useful tools for the analysis of HF datasets and the creation of predictive models that improve the accuracy of the actual remote patient telemonitoring programs.

Resumen Introducción: La insuficiencia cardíaca (IC) es un motivo de gran preocupación en la salud pública. Hemos utilizado técnicas de aprendizaje automático para analizar información y mejorar los resultados. Métodos: Estudio observacional, retrospectivo y no aleatorizado, con los pacientes incluidos en el programa de telemonitorización de IC de nuestro centro desde mayo 2014 hasta febrero 2018. Se han analizado datos clínicos, transmisiones de telemonitorización y descompensaciones de IC. Resultados: 240 pacientes incluidos con un seguimiento de 13.44 ± 8.65 meses. En este intervalo se han detectado 527 descompensaciones de IC en 148 pacientes diferentes. Los aumentos significativos de peso, la desaturación inferior al 90% y la percepción de empeoramiento clínico, han resultado buenos predictores de la descompensación de IC. Hemos construido un modelo predictivo aplicando técnicas de aprendizaje automático obteniendo los mejores resultados con la combinación de "Peso + Edemas en EEII + empeoramiento clínico + alertas de tensión arterial sistólica y diastólica, saturación de oxígeno y frecuencia cardiaca". Conclusiones: Las técnicas de inteligencia artificial son herramientas útiles para el análisis de las bases de datos de IC y para la creación de modelos predictivos que mejoran la precisión de los programas de telemonitorización actuales.

Modelos predictivos de mortalidad utilizados en el adulto mayor con abdomen agudo quirúrgico

RESUMEN Introducción: durante las últimas décadas se han desarrollado diversos modelos predictivos de mortalidad, pero solo un limitado número de ellos se han diseñado específicamente para estimar la mortalidad quirúrgica en el adulto mayor. Objetivo: analizar las características de los modelos predictivos de mortalidad utilizados en el adulto mayor con abdomen agudo quirúrgico. Desarrollo: la revisión se realizó con la utilización de motores de búsqueda como el Google Académico, fueron consultados 112 artículos en español e inglés en las bases de SciELO, Pubmed y Dialnet. Conclusiones: El score APACHE II y la escala POSSUM son los modelos predictivos de mortalidad más fiables, difundidos y utilizados a nivel mundial en el adulto mayor con abdomen agudo quirúrgico. Será necesario unificar variables de estos modelos y agregar la fragilidad fisiológica del adulto mayor para así lograr un modelo más fiable y seguro en esta población de pacientes específica.

ABSTRACT Introduction: during the last decades, various predictive models of mortality have been developed, but only a limited number of them have been specifically designed to estimate surgical mortality in the elderly. Objective: analyze the characteristics of the predictive models of mortality used in the elderly with acute abdomen surgical. Development: the review was carried out using search engines such as Google Scholar, were consulted 112 articles in spanish and english in the databases of SciELO, Pubmed and Dialnet. Conclusions: APACHE II score and the POSSUM scale are the more reliable mortality predictive models, disseminated and used worldwide in the older adult with acute surgical abdomen. It will be necessary to unify variables of these models and add the physiological fragility of the elderly in order to achieve a more reliable and safe in this specific patient population.

RESUMO Introdução: Durante as últimas décadas, vários modelos preditivos de mortalidade foram desenvolvidos, mas apenas um número limitado deles foi projetado especificamente para estimar a mortalidade cirúrgica em idosos. Objetivo: analisar as características dos modelos preditivos de mortalidade utilizados em idosos com abdome cirúrgico agudo. Desenvolvimento: a revisão foi realizada por meio de buscadores como o Google Acadêmico, foram consultados 112 artigos em espanhol e inglês nas bases de dados SciELO, Pubmed e Dialnet. Conclusões: O escore APACHE II e a escala POSSUM são os modelos preditivos de mortalidade mais confiáveis, difundidos e utilizados mundialmente em idosos com abdome cirúrgico agudo. Será necessário unificar as variáveis ​​desses modelos e agregar a fragilidade fisiológica dos idosos a fim de alcançar um modelo mais confiável e seguro nesta população específica de pacientes.

Validation of a stratification system of surgical risk for valve surgery: VMCP score / Validación de un sistema de estratificación de riesgo quirúrgico para cirugía valvular: Escala VMCP

Abstract: Introduction: One of the various instruments that can be used to evaluate the impact of risk factors on the survival of patients undergoing valve surgery is the VMCP score. This work evaluates the performance of this tool. Objective: To validate the surgical risk score for heart valve surgery (VMCP score) in our hospital unit. Material and method: A prospective cohort study was conducted on 239 patients undergoing heart valve surgery, estimating the risk with the VMCP score. The sample was divided into two groups at a cut-off point of 8. The discriminating power of the score was analyzed based on the area under the ROC curve. A value of p < 0.05 was considered significant. The data were processed using SPSS v.25.0. Results: The score stratified the samples as follows: 40.6% of patients were without risk and 59.4% were at risk. The evaluation of the calibration component showed that the score was not appropriate for our sample (Cronbach's alpha coefficient: 0.59). The discrimination component of the score showed a poor capacity to distinguish between the population at risk of mortality (0.630) and/or morbidity (0.655). Conclusion: It is not valid to use the surgical risk score for heart valve surgery (VMCP score) in our hospital unit.

Resumen: Introducción: Existen diversos instrumentos para evaluar el impacto de los factores de riesgo sobre la supervivencia del paciente sometido a cirugía valvular, entre los que encontramos la escala VMCP, por lo que conminaremos a una evaluación del desempeño. Objetivo: Validar la escala de riesgo quirúrgico para cirugía valvular: Escala VMCP en nuestra unidad hospitalaria. Material y métodos: Se realizó un estudio de cohortes prospectivo en 239 pacientes sometidos a cirugía valvular y se les estimó el riesgo mediante la escala VMCP. La muestra se dividió en dos grupos de acuerdo con un punto de corte de 8. La capacidad de discriminación se analizó mediante el área bajo la curva ROC. Una p < 0.05 fue significativa. Los datos se procesaron con SPSS v-25.0. Resultados: La estratificación de la escala mostró: 40.6% de pacientes sin riesgo y 59.4% con riesgo. La evaluación del componente de calibración mostró que la escala no se ajusta a nuestra muestra (Coeficiente Alfa de Cronbach 0.59). La evaluación del componente de discriminación mostró que no puede distinguir la población con riesgo de mortalidad (0.630) y/o morbilidad (0.655). Conclusión: No es válido el uso del sistema de estratificación de riesgo quirúrgico para cirugía valvular, la escala VMCP, en nuestra unidad hospitalaria.

Combination of socio-demographic and clinical co-variates for predicting treatment responses and outcomes in patients with chronic myeloid leukemia in the chronic phase / 中华血液学杂志

Objective: To explore the impacts of socio-demographic and clinical co-variates on treatment responses and outcomes in patients with chronic myeloid leukemia in the chronic phase (CML-CP) receiving tyrosine kinase inhibitor (TKI) and identified the predictive models for them. Methods: Data of newly diagnosed adult patients with CML-CP receiving first-line TKI and having complete socio-demographic data and clinical information were reviewed. Cox model was used to identify the independent variables associated with complete cytogenetic response (CCyR) , major molecular response (MMR) , molecular response 4 (MR(4)) and molecular response 4.5 (MR(4.5)) , as well as failure-free survival (FFS) , progression-free survival (PFS) , overall survival (OS) and CML-related OS. Results: A total of 1414 CML-CP patients treated with first-line imatinib (n=1176) , nilotinib (n=170) or dasatinib (n=68) were reviewed. Median age was 40 (18-83) years and 873 patients (61.7% ) were males. Result of the multivariate analysis showed that lower educational level (P<0.001-0.070) and EUTOS long-term survival intermediate or high-risk (P<0.001-0.009) were significantly associated with lower cumulative incidences of CCyR, MMR, MR(4) and MR(4.5), as well as the inferior FFS, PFS, OS and CML-related OS. In addition, those who were males, from rural households, had white blood cells (WBC) ≥120×10(9)/L, hemoglobin (HGB) <115 g/L and treated with first-line imatinib had significantly lower cumulative incidences of cytogenetic and/or molecular responses. Being single, divorced or widowed, having, rural household registration, WBC≥120×10(9)/L, HGB<15 g/L, and comorbidity (ies) was significantly associated with inferior FFS, PFS, OS, and/or CML-related OS. Thereafter, the patients were classified into several subgroups using the socio-demographic characteristics and clinical variables by cytogenetic and molecular responses, treatment failure and disease progression, as well as overall survival and CML-related OS, respectively. There were significant differences in treatment responses and outcomes among the subgroups (P<0.001) . Conclusion: Except for clinical co-variates, socio-demographic co-variates significantly correlated with TKI treatment responses and outcomes in CML-CP patients. Models established by the combination of independent socio-demographic and clinical co-variates could effectively predict the responses and outcome.

Predictive Risk Factors for Intraoperative Hypothermia During Endoscopic Retrograde Cholangiopancreatography Under General Anesthesia / 胃肠病学

Background: Unintended intraoperative hypothermia is a common complication of general anesthesia surgery, which can cause pain, coagulation dysfunction, wound infection, delayed recovery, and other adverse consequences. There are few studies related to intraoperative hypothermia during endoscopic retrograde cholangiopancreatography (ERCP). Aims: To analyze the risk factors of intraoperative hypothermia during ERCP under general anesthesia and establish a predictive model. Methods: A total of 121 patients underwent ERCP under general anesthesia from September 2021 to November 2021 at Shanghai General Hospital were recruited, and relevant clinical data were collected. Logistic regression analysis was used to screen risk factors, and a predictive model was constructed. The model was externally validated by independent datasets with ROC curve and Hosmer⁃Lemeshow goodness of fit test. Results: A total of 114 patients were enrolled in modeling group. The incidence of intraoperative hypothermia was 11.40% (13/114). There were more women in the hypothermia group (P<0.05). The temperature of entering the operating room and operating room temperature were relatively lower in the hypothermia group (P<0.05). Gender was an independent risk factor for intraoperative hypothermia in ERCP under general anesthesia (P<0.05). The predictive model constructed by using gender and temperature of entering the operating room screened by Logistic regression analysis had a good discrimination and calibration, area under the ROC curve by external validation was 0.78. Conclusions: Gender and temperature of entering the operating room can effectively predict the occurrence of intraoperative hypothermia and assist perioperative monitoring and management.

Predictive modeling of 30-day readmission risk of diabetes patients by logistic regression, artificial neural network, and EasyEnsemble

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes. Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and EasyEnsemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features (4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods (oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models (logistic regression and EasyEnsemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets. Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.

Development & cross-validation of anthropometric predictive models to estimate the appendicular skeletal muscle mass in middle-aged women in Sri Lanka

Background & objectives: Attempts have been made to estimate appendicular skeletal muscle mass (ASMM) using anthropometric indices and most of these are country specific. This study was designed to develop and cross-validate simple predictive models to estimate the ASMM based on anthropometry in a group of healthy middle-aged women in Sri Lanka. Methods: The study was conducted on a randomly selected group of community-dwelling women aged 30-60 years. ASMM (kg) quantified with dual-energy X-ray absorptiometry (DXA) (ASMMDXA) was used as the reference standard. Anthropometric measurements such as body weight (kg), height (m), limb circumferences (cm) and skinfold thickness (mm) which showed significant correlations with ASMMDXA, were used to develop the models. The models were developed using a group of 165 women (aged 30-60 yr) and were cross-validated using a separate sample of women (n=167) (mean age: 48.9±8.56 yr), selected randomly. Results: Nine anthropometry-based models were developed using weight, height, skinfold thicknesses, circumferences, body mass index, menopausal status (MS) and age as independent variables. Four models which were based on height, weight, triceps skinfold thickness (TSFT), age and MS met all the validation criteria with high correlations (ranged 0.89-0.92) and high predictive values explaining high variance (80-84%) with low standard error of estimate (1.10-1.24 kg). Interpretation & conclusions: The four models (ASMM 1-ASMM 4) developed based on height, weight, TSFT, age and MS showed a high accuracy in estimating the ASMM in middle-aged women.

Situación futura de la cardiopatía isquémica en el estado de San Luis Potosí: un modelo dinámico predictivo / Future status of ischaemic heart disease in the state of San Luis Potosí: A predictive dynamic model

Resumen Objetivo: Desarrollar un modelo dinámico predictivo para generar y analizar la situación futura de la tasa de incidencia de la enfermedad isquémica del corazón en población de 25 años y mayores en México, en función de la variación en el tiempo de algunos factores de riesgo. Método: Estudio ecológico retrospectivo durante el periodo 2013-2015, en la ciudad de San Luis Potosí (México). Se utilizaron bases de datos secundarias con indicadores oficiales de los 58 municipios que conforman el estado de San Luis Potosí, los cuales corresponden a los años 2000, 2005 y 2010. Se analizaron 8 indicadores a nivel municipio, por medio de los métodos de análisis de componentes principales, modelos de ecuaciones estructurales, modelaje dinámico y software de simulación. Resultados: Fueron extraídos 3 componentes que en conjunto explican el 80.43% de la varianza total de los indicadores oficiales utilizados; el segundo componente tiene un peso de 16.36 unidades que favorecen el incremento de la enfermedad analizada; este componente está integrado solo por el indicador EDAD 60-64 y el escenario esperado del mismo va en aumento. El modelo estructural confirma que los indicadores explican el 42% de la variación de esta enfermedad; los posibles escenarios para los años 2015, 2020 y 2025 son de 195.7, 240.7 y 298, respectivamente, por cada 100,000 habitantes de 25 años y mayores. Conclusiones: Se espera un incremento exponencial en la tasa de incidencia de la enfermedad isquémica del corazón; la edad de 60-64 años se identificó como el factor de riesgo de más peso. © 2017 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. Este es un artículo Open Access bajo la licencia CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract Objective: To develop a predictive dynamic model to generate and analyse the future status of the incidence rate of ischaemic heart disease in a population of 25 years and over in Mexico, according to the variation in time of some risk factors. Method: Retrospective ecological study performed during the period 2013-2015, in San Luis Potosí City, Mexico. Secondary databases that corresponded to the years 2000, 2005, and 2010, were used along with official indicators of the 58 municipalities of the state of San Luis Potosí. Eight indicators were analysed at municipality level, using principal components analysis, structural equation modelling, dynamic modelling, and simulation software methods. Results: Three components were extracted, which together explained 80.43% of the total variance of the official indicators used. The second component had a weight of 16.36 units that favoured an increase of the disease analysed. This component was integrated only by the indicator AGE 60-64 and the expected stage of it increasing. The structural model confirmed that the indicators explain 42% of the variation of this disease. The possible stages for the years 2015, 2020, and 2025 are 195.7, 240.7, and 298.0, respectively for every 100,000 inhabitants aged 25 and over. Conclusions: An exponential increase in the incidence rate of ischaemic heart disease is expected, with the age of 60-64 years being identified as the highest risk factor. © 2017 Instituto Nacional de Cardiología Ignacio Chávez. Published by Masson Doyma México S.A. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Actualización del modelo predictivo SEGRAV 23, intervenciones diagnósticas terapéuticas de mayor riesgo / Updating the predictive model SEGRAV 23, higher risk diagnostic and therapeutic interventions

Introducción: los modelos predictivos constituyen herramienta importante en cuidados intensivos. La escala SEGRAV 23 ha mostrado su validez para establecer pronóstico en pacientes pediátricos. Objetivo: determinar intervenciones de mayor riesgo del SEGRAV 23. Métodos: estudio observacional, analítico, de cohorte retrospectivo, en el cual se aplicó el modelo SEGRAV 23 en pacientes con neumonía adquirida en la comunidad, en la Unidad de Cuidados Intensivos Pediátricos del Hospital Militar Central Dr. Luis Díaz Soto , durante cinco años (2007-2008, 2012-2014). La muestra fue de 356 pacientes. Se calculó chi cuadrado de ajuste, se comprobó independencia a través de chi cuadrado de Pearson y prueba exacta de Fisher, con nivel de significación estadística p< 0,05. Se descartaron variables no puntuables y las de menor influencia en categoría fallecidos (p> 0,05). Se calculó odds ratio (OR) con intervalo de confianza (IC) de 95 por ciento para determinar riesgo. Resultados: la mortalidad fue 2,53 por ciento (356 ingresos/9 fallecidos), los pacientes no graves predominaron (222/62,4 por ciento; p< 0,05). Entre los muy graves (12/3,4 por ciento) y críticos (9/2,5 por ciento) estuvieron todos los fallecidos. Una vía venosa central (71/19,9 por ciento y tratamiento de trastornos hidroelectrolíticos severos (63/17,7 por ciento) fueron más frecuentes. El doppler transcraneal, la nutrición parenteral total y el tratamiento de la coagulación intravascular diseminada, no puntuaron. Realización de tomografía, una vía venosa central, intervención quirúrgica y pleurotomía mostraron relación poco significativa con mortalidad (p> 0,05). La realización de reanimación cardiopulmonar (OR= 1 380; IC 95 por ciento [113,198-16 823,63]) y uso de FiO2≥ de 60 por ciento (OR= 454,67; IC 95 por ciento [48,89-4 228,57]) presentaron mayor riesgo. Conclusiones: de las 23 intervenciones diagnósticas y terapéuticas del SEGRAV 23, se determinaron 15 asociadas a mayor riesgo de mortalidad(AU)

Introduction: predictive models are an important tool in intensive care. The SEGRAV 23 scale has proven to be useful to establish a prognosis in pediatric patients. Objective: determine SEGRAV 23 higher risk interventions. Methods: an observational analytical retrospective cohort study based on application of the SEGRAV 23 model was conducted with community-acquired pneumonia patients at the Pediatric Intensive Care Unit of Dr. Luis Díaz Soto Central Military Hospital during five years (2007-2008, 2012-2014). The sample consisted of 356 patients. Adjustment chi square was estimated, and independence verified by Pearson's chi-squared test and Fisher's exact test, with a statistical significance level of p< 0.05. Nonpoint variables and those with a lesser influence on the deceased category (p> 0.05) were discarded. Odds ratio (OR) was estimated with a confidence interval (CI) of 95 percent to determine risk. Results: mortality was 2.53 percent (356 admissions/9 deaths), with a predominance of non-critical patients (222/62.4 percent; p< 0.05). All the deaths were among very critical (12/3.4 percent) and critical (9/2.5 percent) patients. The most frequent procedures were one central venous route (71/19.9 percent) and treatment for severe hydroelectrolitic disorders (63/17.7 percent). Transcranial Doppler, total parenteral nutrition, and the treatment for disseminated intravascular coagulation did not score. Tomography, one central venous route, surgery and pleurotomy exhibited a not very significant relationship to mortality (p> 0.05). Cardiopulmonary resuscitation (OR= 1 380; CI 95 percent [113.198-16 823.63]) and the use of FiO2≥ 60 percent (OR= 454.67; IC 95 percent [48.89-4 228.57]) displayed higher risk. Conclusions: of the 23 SEGRAV 23 diagnostic and therapeutic interventions, 15 were found to be associated with a higher risk of mortality(AU)

Thermal requirements and estimated number of generations of Neopamera bilobata (Say) in strawberry-producing regions of Brazil / Exigências térmicas e estimativa do número de gerações de Neopamera bilobata (Say) em regiões produtoras de morango do Brasil

ABSTRACT: The thermal threshold and thermal requirements of Neopamera bilobata were determined, and the number of generations that this species may produce in the main strawberry-producing regions of Brazil was estimated. In a climate chamber (70±10% RH and 12h photophase) at 16, 19, 22, 25, 28, or 30±1°C, the development of 120 eggs was monitored until the adult stage, at each temperature. Nymphs were maintained in individual cages and fed on strawberry fruits of the cultivar Aromas. The mean duration and viability of the egg and nymph stages were calculated by estimating the lower and upper developmental thresholds and the thermal constant, and this information was used to estimate the number of generations per year in different strawberry-producing regions of Brazil. The egg-to-adult duration decreased as temperatures increased, up to 28°C (93.4, 83.2, 43.9, and 31.4 days at 19, 22, 25, and 28°C, respectively). Viability of nymphs was highest between 22 and 28°C. At 30°C, the egg-to-adult duration increased (36 days), while the viability decreased (11.11%). The lower egg-to-adult developmental threshold was 15.2°C and the thermal constant was 418.4 degree-days. Calculating the number of generations indicated that the largest number (5.1 generations yr-1) was obtained for the municipality of Jaboti, Paraná, and the smallest for Caxias do Sul, Rio Grande do Sul (1.9 generations yr-1). Our findings demonstrated that important strawberry-producing regions in Brazil are suitable for the development of N. bilobata.

RESUMO: A temperatura base e as exigências térmicas de Neopamera bilobata foram determinadas sendo estimado o número de gerações que a espécie realiza nas principais regiões produtoras de morango no Brasil. Em uma câmara climatizada (UR 70±10% e fotofase de 12h) nas temperaturas de 16, 19, 22, 25, 28 ou 30±1°C, o desenvolvimento de 120 ovos por temperatura foi monitorado até a fase adulta. As ninfas foram individualmente mantidas em gaiolas alimentando-as com frutos de morangueiro da cultivar Aromas. A duração média e a viabilidade da fase de ovo e de ninfa foi calculada estimando-se os valores de temperatura base inferior, temperatura base superior, constante térmica e número provável de gerações anuais que o percevejo completa em diferentes regiões produtoras de morangueiro do Brasil. A duração do desenvolvimento ovo-adulto de N. bilobata diminui com a elevação da temperatura até 28°C (93,4; 83,2; 43,9 e 31,4 dias para 19, 22, 25 e 28°C, respectivamente) apresentando maior viabilidade da fase de ninfa entre 22 a 28°C. A 30°C, a duração da fase ovo-adulto aumentou (36 dias), enquanto a viabilidade diminuiu (11,11%). A temperatura base inferior para o desenvolvimento ovo-adulto foi de 15,2°C e a constante térmica de 418,4 graus dia. O maior número de gerações (5,1 gerações/ano) foi obtido para a cidade de Jaboti, PR, e o menor para Caxias do Sul, RS (1,9 gerações/ano). Os valores estimados demonstram que as regiões de importância na produção do morangueiro no Brasil são aptas ao desenvolvimento de N. bilobata.

Prediction of acid lactic-bacteria growth in turkey ham processed by high hydrostatic pressure

High hydrostatic pressure (HHP) has been investigated and industrially applied to extend shelf life of meat-based products. Traditional ham packaged under microaerophilic conditions may sometimes present high lactic acid bacteria population during refrigerated storage, which limits shelf life due to development of unpleasant odor and greenish and sticky appearance. This study aimed at evaluating the shelf life of turkey ham pressurized at 400 MPa for 15 min and stored at 4, 8 and 12 ºC, in comparison to the non pressurized product. The lactic acid bacteria population up to 10(7) CFU/g of product was set as the criteria to determine the limiting shelf life According to such parameter the pressurized sample achieved a commercial viability within 75 days when stored at 4 ºC while the control lasted only 45 days. Predictive microbiology using Gompertz and Baranyi and Roberts models fitted well both for the pressurized and control samples. The results indicated that the high hydrostatic pressure treatment greatly increased the turkey ham commercial viability in comparison to the usual length, by slowing down the growth of microorganisms in the product.