Towards better mortality prediction in cancer patients in the ICU: a comparative analysis of prognostic scales: systematic literature review.

OBJECTIVE: To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs). DESIGN: A systematic review of the literature was conducted using a search algorithm in October 2022. The following databases were searched: PubMed, Scopus, Virtual Health Library (BVS), and Medrxiv. The risk of bias was assessed using the QUADAS-2 scale. SETTING: ICUs admitting cancer patients. PARTICIPANTS: Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU. INTERVENTIONS: Integrative study without interventions. MAIN VARIABLES OF INTEREST: Mortality prediction, standardized mortality, discrimination, and calibration. RESULTS: Seven mortality risk prediction models were analyzed in cancer patients in the ICU. Most models (APACHE II, APACHE IV, SOFA, SAPS-II, SAPS-III, and MPM II) underestimated mortality, while the ICMM overestimated it. The APACHE II had the SMR (Standardized Mortality Ratio) value closest to 1, suggesting a better prognostic ability compared to the other models. CONCLUSIONS: Predicting mortality in ICU cancer patients remains an intricate challenge due to the lack of a definitive superior model and the inherent limitations of available prediction tools. For evidence-based informed clinical decision-making, it is crucial to consider the healthcare team's familiarity with each tool and its inherent limitations. Developing novel instruments or conducting large-scale validation studies is essential to enhance prediction accuracy and optimize patient care in this population.

Predictive Models for Non-Alcoholic Fatty Liver Disease Diagnosis in Mexican Patients with Gallstone Disease: Sex-Specific Insights.

(1) Background: Evidence regarding Non-Alcoholic Fatty Liver Disease (NAFLD) diagnosis is limited in the context of patients with gallstone disease (GD). This study aimed to assess the predictive potential of conventional clinical and biochemical variables as combined models for diagnosing NAFLD in patients with GD. (2) Methods: A cross-sectional study including 239 patients with GD and NAFLD diagnosed by ultrasonography who underwent laparoscopic cholecystectomy and liver biopsy was conducted. Previous clinical indices were also determined. Predictive models for the presence of NAFLD stratified by biological sex were obtained through binary logistic regression and sensitivity analyses were performed. (3) Results: For women, the model included total cholesterol (TC), age and alanine aminotransferase (ALT) and showed an area under receiver operating characteristic curve (AUC) of 0.727 (p < 0.001), sensitivity of 0.831 and a specificity of 0.517. For men, the model included TC, body mass index (BMI) and aspartate aminotransferase (AST), had an AUC of 0.898 (p < 0.001), sensitivity of 0.917 and specificity of 0.818. In both sexes, the diagnostic performance of the designed equations was superior to the previous indices. (4) Conclusions: These models have the potential to offer valuable guidance to healthcare providers in clinical decision-making, enabling them to achieve optimal outcomes for each patient.

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy.

Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

A Dynamic Fitting Strategy for Physiological Models: A Case Study of a Cardiorespiratory Model for the Simulation of Incremental Aerobic Exercise.

Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system's temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system's parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models.

Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical-chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains.

Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks.

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001-12.41% MAPE, 0.001-5.82 mg Nm-3 MAE, and 0.03-52.30 mg Nm-3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10-6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.

Comparative Analysis of Three Predictive Models of Performance Indicators with Results-Based Management: Cancer Data Statistics in a National Institute of Health.

Predictive models play a crucial role in RBMs to analyze performance indicator results to manage unexpected events and make timely decisions to resolve them. Their use in Mexico is deficient, and monitoring and evaluation are among the weakest pillars of the model. In response to these needs, the aim of this study was to perform a comparative analysis of three predictive models to analyze 10 medical performance indicators and cancer data related to children with cancer. To accomplish these purposes, a comparative and retrospective study with nonprobabilistic convenience sampling was conducted. The predictive models were exponential smoothing, autoregressive integrated moving average, and linear regression. The lowest mean absolute error was used to identify the best model. Linear regression performed best regarding nine of the ten indicators, with seven showing p < 0.05. Three of their assumptions were checked using the Shapiro-Wilk, Cook's distance, and Breusch-Pagan tests. Predictive models with RBM are a valid and relevant instrument for monitoring and evaluating performance indicator results to support forecasting and decision-making based on evidence and must be promoted for use with cancer data statistics. The place numbers obtained by cancer disease inside the main causes of death, morbidity and hospital outpatients in a National Institute of Health were presented as evidence of the importance of implementing performance indicators associated with children with cancer.

Early Prediction of Asthma.

The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. It is a disease that generally begins in the first five years of life, and it is essential to promptly identify patients at high risk of developing asthma by using different prediction models. The aim of this review regarding the early prediction of asthma is to summarize predictive factors for the course of asthma, including lung function, allergic comorbidity, and relevant data from the patient's medical history, among other factors. This review also highlights the epigenetic factors that are involved, such as DNA methylation and asthma risk, microRNA expression, and histone modification. The different tools that have been developed in recent years for use in asthma prediction, including machine learning approaches, are presented and compared. In this review, emphasis is placed on molecular mechanisms and biomarkers that can be used as predictors of asthma in children.

Bacteriological and eutrophication risk assessment of an Argentinian temperate shallow urban lagoon.

The urban lagoons receive strong anthropic pressures and the tensions often coexist between the "urban" and the "natural," and this consequently generates pollution and risks to the environment and human health. Our main objective was to study the water quality and to assess the bacteriological and eutrophication risks in the temperate shallow urban lagoon of the Parque Unzué (Gualeguaychú, Argentina), and to predict these risks in climate change scenarios considering the temperature and the rains as indicators. This urban shallow lagoon is in a recreative multiuse park (Gualeguaychú city), in the floodplain of the Gualeguaychú river in the Center-East of Argentina (Neotropical region). Twenty-seven sampling in 3 sampling points (n = 81) were carried out during 2015-2019, and physicochemical and bacteriological parameters were measured. Phosphorus, organic matter, chlorophyll-a (Chl-a), and total coliforms (TC) frequently had a moderate and very high contamination factor (CF), and the pollution load index (PLI) indicated contamination with a frequency of 74.1 %. Moreover, the index (WQI) indicated poor (66.7 %) and good (33.3 %) water quality. Bacteriological and eutrophication predictive risk models showed an increase of the TC and the Chl-a concentration generating a current and future high risk of contamination of the lagoon under climate change scenarios that could generate ecosystemic function losses in the short-term.

Composite predictive models for asthma exacerbations or asthma deterioration in pediatric asthmatic patients: A systematic review of the literature.

A variety of factors have shown to be useful in predicting which children are at high risk for future asthma exacerbations, some of them combined into composite predictive models. The objective of the present review was to systematically identify all the available published composite predictive models developed for predicting which children are at high risk for future asthma exacerbations or asthma deterioration. A systematic search of the literature was performed to identify studies in which a composite predictive model developed for predicting which children are at high risk for future asthma exacerbations or asthma deterioration was described. Methodological quality assessment was performed using accepted criteria for prediction rules and prognostic models. A total of 18 articles, describing a total of 17 composite predictive models were identified and included in the review. The number of predictors included in the models ranged from 2-149. Upon analyzing the content of the models, use of healthcare services for asthma and prescribed or dispensed asthma medications were the most frequently used items (in 8/17, 47.0% of the models). Seven (41.2%) models fulfilled all the quality criteria considered in our evaluation. The identified models may help clinicians dealing with asthmatic children to identify which children are at a higher risk for future asthma exacerbations or asthma deterioration, therefore targeting and/or reinforcing specific interventions for these children in an attempt to prevent exacerbations or deterioration of the disease.

Link between Flavor Perception and Volatile Compound Composition of Dark Chocolates Derived from Trinitario Cocoa Beans from Dominican Republic.

The chemical composition of dark chocolate has a significant impact on its complex flavor profile. This study aims to investigate the relationship between the volatile chemical composition and perceived flavor of 54 dark chocolate samples made from Trinitario cocoa beans from the Dominican Republic. The samples were evaluated by a trained panel and analyzed using gas chromatography-mass spectrometry (GC-MS) to identify and quantify the volatile compounds. Predictive models based on a partial least squares regression (PLS) allowed the identification of key compounds for predicting individual sensory attributes. The models were most successful in classifying samples based on the intensity of bitterness and astringency, even though these attributes are mostly linked to non-volatile compounds. Acetaldehyde, dimethyl sulfide, and 2,3-butanediol were found to be key predictors for various sensory attributes, while propylene glycol diacetate was identified as a possible marker for red fruit aroma. The study highlights the potential of using volatile compounds to accurately predict chocolate flavor potential.

Effects of chlorine and peroxyacetic acid wash treatments on growth kinetics of Salmonella in fresh-cut lettuce.

Fresh-cut produces are often consumed uncooked, thus proper sanitation is essential for preventing cross contamination. The reduction and subsequent growth of Salmonella enterica sv Thompson were studied in pre-cut iceberg lettuce washed with simulated wash water (SWW), sodium hypochlorite (SH, free chlorine 25 mg/L), and peroxyacetic acid (PAA, 80 mg/L) and stored for 9 days under modified atmosphere at 9, 13, and 18 °C. Differences in reduction between SH and PAA were non-existent. Overall, visual quality, dehydration, leaf edge and superficial browning and aroma during storage at 9 °C were similar among treatments, but negative effects increased with temperature. These results demonstrated that PAA can be used as an effective alternative to chlorine for the disinfection of Salmonella spp. in fresh-cut lettuce. The growth of Salmonella enterica sv Thompson was successfully described with the Baranyi and Roberts growth model in the studied storage temperature range, and after treatment with SWW, chlorine, and PAA. Subsequently, predictive secondary models were used to describe the relationship between growth rates and temperature based on the models' family described by Belehrádek. Interestingly, the exposure to disinfectants biased growth kinetics of Salmonella during storage. Below 12 °C, growth rates in lettuce treated with disinfectant (0.010-0.011 log CFU/h at 9 °C) were lower than those in lettuce washed with water (0.016 log CFU/h at 9 °C); whereas at higher temperatures, the effect was the opposite. Thus, in this case, the growth rate values registered at 18 °C for lettuce treated with disinfectant were 0.048-0.054 log CFU/h compared to a value of 0.038 log CFU/h for lettuce treated with only water. The data and models developed in this study will be crucial to describing the wash-related dynamics of Salmonella in a risk assessment framework applied to fresh-cut produce, providing more complete and accurate risk estimates.

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)

Generalized Property-Based Encoders and Digital Signal Processing Facilitate Predictive Tasks in Protein Engineering.

Computational methods in protein engineering often require encoding amino acid sequences, i.e., converting them into numeric arrays. Physicochemical properties are a typical choice to define encoders, where we replace each amino acid by its value for a given property. However, what property (or group thereof) is best for a given predictive task remains an open problem. In this work, we generalize property-based encoding strategies to maximize the performance of predictive models in protein engineering. First, combining text mining and unsupervised learning, we partitioned the AAIndex database into eight semantically-consistent groups of properties. We then applied a non-linear PCA within each group to define a single encoder to represent it. Then, in several case studies, we assess the performance of predictive models for protein and peptide function, folding, and biological activity, trained using the proposed encoders and classical methods (One Hot Encoder and TAPE embeddings). Models trained on datasets encoded with our encoders and converted to signals through the Fast Fourier Transform (FFT) increased their precision and reduced their overfitting substantially, outperforming classical approaches in most cases. Finally, we propose a preliminary methodology to create de novo sequences with desired properties. All these results offer simple ways to increase the performance of general and complex predictive tasks in protein engineering without increasing their complexity.

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.

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions.

Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry.

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.

In silico toxicity evaluation for transformation products of antimicrobials, from aqueous photolysis degradation.

This study investigates degradation processes of three antimicrobials in water (norfloxacin, ciprofloxacin, and sulfamethoxazole) by photolysis, focusing on the prediction of toxicity endpoints via in silico quantitative structure-activity relationship (QSAR) of their transformation products (TPs). Photolysis experiments were conducted in distilled water with individual solutions at 10 mg L-1 for each compound. Identification of TPs was performed by means of LC-TOF-MS, employing a method based on retention time, exact mass fragmentation pattern, and peak intensity. Ten main compounds were identified for sulfamethoxazole, fifteen for ciprofloxacin, and fifteen for norfloxacin. Out of 40 identified TPs, 6 have not been reported in the literature. Based on new data found in this work, and TPs already reported in the literature, we have proposed degradation pathways for all three antimicrobials, providing reasoning for the identified TPs. QSAR risk assessment was carried out for 74 structures of possible isomers. QSAR predictions showed that all 19 possible structures of sulfamethoxazole TPs are non-mutagenic, whereas 16 are toxicant, 18 carcinogenic, and 14 non-readily biodegradable. For ciprofloxacin, 28 out of the 30 possible structures for the TPs are mutagenic and non-readily biodegradable, and all structures are toxicant and carcinogenic. All 25 possible norfloxacin TPs were predicted mutagenic, toxicant, carcinogenic, and non-readily biodegradable. Results obtained from in silico QSAR models evince the need of performing risk assessment for TPs as well as for the parent antimicrobial. An expert analysis of QSAR predictions using different models and degradation pathways is imperative, for a large variety of structures was found for the TPs.

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.

Modeling the formation of trihalomethanes in rural and semi-urban drinking water distribution networks of Costa Rica.

Chlorination is one of the most important stages in the treatment of drinking water due to its effectiveness in the inactivation of pathogenic organisms. However, the reaction between chlorine and natural organic matter (NOM) generates harmful disinfection by-products (DBPs), such as trihalomethanes (THMs). In this research, drinking water quality data was collected from the distribution networks of 19 rural and semi-urban systems that use water sources as springs, surfaces, and a mixture of both, in three provinces of Costa Rica from April 2018 to September 2019. Twelve models were developed from four data sets: all water sources, spring, surface, and a mixture of spring and surface waters. Linear, logarithmic, and exponential multivariate regression models were developed for each data set to predict the concentration of total trihalomethanes (TTHMs) in the distribution networks. Concentrations of TTHMs were found between < 0.20 and 91.31 µg/L, with chloroform being the dominant species accounting for 62% of TTHMs on average. Turbidity, free residual chlorine, total organic carbon (TOC), dissolved organic carbon (DOC), and ultraviolet absorbance at 254 nm (UV254) showed a significant correlation with TTHMs. In all the data sets the linear models presented the best goodness-of-fit and were moderately robust. Four models, the best of each data set, were validated with data from the same systems, and, according to the criteria of R2, standard error (SE), mean square error (MSE), and mean absolute error (MAE), spring water and mixed spring/surface water models showed a satisfactory level of explanation of the variability of the data. Moreover, the models seem to better predict TTHM concentrations below 30 µg/L. These models were satisfactory and could be useful for decision-making in drinking water supply systems.