Behavioral red flags for optimizing castration-induced acute pain diagnosis in cattle.

Unesp-Botucatu Cattle Pain Scale (UCAPS) is widely used in experimental settings, however the high number of UCAPS behaviors might represent a barrier to its implementation in the farm's or hospital's routine. We aimed to identify a smaller combination of UCAPS behaviors that could be used as behavioral red flags for optimizing the acute pain diagnosis in cattle. We hypothesize that a specific set of UCAPS behaviors might be used as behavioral red flags for pain. This would represent a quick and simple pain evaluation and might optimize the acute pain assessment in large-scale systems. Data from two previous studies regarding UCAPS assessments before (pain free condition) and after (painful condition) surgical castration of 59 male cattle was used. We fitted a decision tree, resulting in a logic with two behaviors that we used as behavioral red flags. The logic adapted from the decision tree considered the painful diagnosis when the Activity was scored 2. When Activity was scored less than 2, but Locomotion was scored 1 or 2, the diagnosis was also considered positive for pain. When Activity was below 2 and Locomotion was 0, the diagnosis was considered free pain. Behavioral red flags had an area under the curve of 95.95 % for predicting UCAPS diagnosis and 94.13 % for predicting overall pain free and painful conditions. In conclusion, behaviors in the decision tree logic can work as behavioral red flags for optimizing the acute pain diagnosis in cattle, as a quick assessment in large-scale systems.

Economic analysis of the use of the Flt-1/PlGF preeclampsia ratio compared to the standard of care in Uruguay / Análisis económico del cociente de preeclampsia sFlt- 1/PlGF comparado con el estándar de atención en Uruguay

Objectives: Preeclampsia (PE) is a pregnancyrelated hypertensive disorder that can lead to severe complications and adverse maternal and fetal outcomes. This study aimed to estimate the economic impact of integrating the sFlt-1/PlGF ratio into Uruguay's healthcare system as part of routine clinical practice for diagnosing. Material and methods: A decision tree model was used to estimate the annual economic impact on the Uruguayan healthcare system for a hypothetical cohort of women with suspected PE. This included relevant costs associated with diagnosis, monitoring, and treatment from the initial presentation of suspected PE until childbirth. The study analyzed the annual costs under two scenarios: the standard of care and a scenario incorporating the sFlt-1/PlGF ratio for PE, using 2022 as the reference year. Various deterministic and probabilistic sensitivity analyses were performed. Results: The economic model estimated that the implementation of the sFlt-1/PlGF ratio could save the Uruguayan healthcare system $95,432,678 Uruguayan pesos (2,320,269 United States Dollars [USD]) annually, representing a 5 % reduction in costs compared with the standard of care. These savings were primarily due to a reduction in hospitalizations of women with suspected PE. The estimated economic impact equated to an annual net saving of approximately $10,602 Uruguayan pesos (258 USD) per patient. Conclusions: The introduction of the sFlt-1/PlGF ratio into the Uruguayan healthcare system is likely to generate savings due to the optimization of the management of hospitalizations for women with suspected preeclampsia (PE). However, the potential for savings will primarily depend on the current hospitalization rate of these women (the efficiency of managing high-risk PE pregnancies) and the length of stay for hospitalized women.

Objetivos: la preeclampsia (PE) es un trastorno hipertensivo del embarazo que puede causar complicaciones graves y resultados adversos maternos y fetales. El objetivo del estudio fue estimar el impacto económico de la incorporación del cociente sFlt-1/PlGF (factor tirosinkinasa -1 soluble tipo fms / Placenta Growth Factor ­ Factor de Crecimiento Placentario) al sistema de salud uruguayo. Materiales y métodos: se utilizó un árbol de decisión para estimar, en una cohorte hipotética de mujeres con sospecha de PE, el impacto económico anual incluidos los costos relevantes asociados con el diagnóstico, el seguimiento y el tratamiento de la presentación inicial de la PE clínicamente sospechada hasta el parto. Se analizaron los costos anuales para un escenario estándar de atención y un escenario que incluye el cociente sFlt-1/PlGF para PE. Se realizaron diversos análisis de sensibilidad determinísticos y probabilísticos. Resultados: el modelo económico estimó que el uso del cociente sFlt-1/PlGF permitiría al sistema de salud uruguayo ahorrar 95.432.678 pesos uruguayos (2.320.269 dólares) anualmente: una reducción del 5 % en comparación con el estándar de atención, principalmente como resultado de la reducción de las hospitalizaciones de mujeres con sospecha de PE. El cálculo del impacto económico estimó un ahorro neto anual de aproximadamente 10.602 pesos uruguayos (258 dólares) por paciente. Conclusiones: la introducción del cociente sFlt-1/PlGF en el sistema de salud uruguayo probablemente genere ahorros debido a la optimización del manejo de las hospitalizaciones de mujeres con sospecha de PE, aunque la posibilidad de obtener ahorros dependerá principalmente de la tasa actual de hospitalización de estas (la eficiencia del manejo de los embarazos de alto riesgo de PE), y de los días de internación de las mujeres hospitalizadas.

Using machine learning to classify temporomandibular disorders: a proof of concept.

BACKGROUND: the escalating influx of patients with temporomandibular disorders and the challenges associated with accurate diagnosis by non-specialized dental practitioners underscore the integration of artificial intelligence into the diagnostic process of temporomandibular disorders (TMD) as a potential solution to mitigate diagnostic disparities associated with this condition. OBJECTIVES: In this study, we evaluated a machine-learning model for classifying TMDs based on the International Classification of Orofacial Pain, using structured data. METHODOLOGY: Model construction was performed by the exploration of a dataset comprising patient records from the repository of the Multidisciplinary Orofacial Pain Center (CEMDOR) affiliated with the Federal University of Santa Catarina. Diagnoses of TMD were categorized following the principles established by the International Classification of Orofacial Pain (ICOP-1). Two independent experiments were conducted using the decision tree technique to classify muscular or articular conditions. Both experiments uniformly adopted identical metrics to assess the developed model's performance and efficacy. RESULTS: The classification model for joint pain showed a relevant potential for general practitioners, presenting 84% accuracy and f1-score of 0.85. Thus, myofascial pain was classified with 78% accuracy and an f1-score of 0.76. Both models used from 2 to 5 clinical variables to classify orofacial pain. CONCLUSION: The use of decision tree-based machine learning holds significant support potential for TMD classification.

Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Study of machine learning techniques for outcome assessment of leptospirosis patients.

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.

Utilization of machine learning for dengue case screening.

Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years 2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual information technique was used to assess which variables were most related to laboratory-confirmed dengue cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache, vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be suitable for building an application and implementing it on smartphones. This resource would be available to healthcare professionals such as doctors and nurses.

Exploratory analysis of new craniometric measures for the investigation of biological sex using open-access statistical and machine-learning tools on a cone-beam computed tomography sample.

Investigation of the biological sex of human remains is a crucial aspect of physical anthropology. However, due to varying states of skeletal preservation, multiple approaches and structures of interest need to be explored. This research aims to investigate the potential use of distances between bifrontal breadth (FMB), infraorbital foramina distance (IOD), nasal breadth (NLB), inter-canine width (ICD), and distance between mental foramina (MFD) for combined sex prediction through traditional statistical methods and through open-access machine-learning tools. Ethical approval was obtained from the ethics committee, and out of 100 cone beam computed tomography (CBCT) scans, 54 individuals were selected with all the points visible. Ten extra exams were chosen to test the predictors developed from the learning sample. Descriptive analysis of measurements, standard deviation, and standard error were obtained. T-student and Mann-Whitney tests were utilized to assess the sex differences within the variables. A logistic regression equation was developed and tested for the investigation of the biological sex as well as decision trees, random forest, and artificial neural networks machine-learning models. The results indicate a strong correlation between the measurements and the sex of individuals. When combined, the measurements were able to predict sex using a regression formula or machine learning based models which can be exported and added to software or webpages. Considering the methods, the estimations showed an accuracy rate superior to 80% for males and 82% for females. All skulls in the test sample were accurately predicted by both statistical and machine-learning models. This exploratory study successfully established a correlation between facial measurements and the sex of individuals, validating the prediction potential of machine learning, augmenting the investigative tools available to experts with a high differentiation potential.

Cost-effectiveness of restorative treatments for permanent molars with severe molar incisor hypomineralization: perspectives for the Brazilian public health system.

OBJECTIVES: To undertake a cost-effectiveness analysis of restorative treatments for a first permanent molar with severe molar incisor hypomineralization from the perspective of the Brazilian public system. MATERIALS AND METHODS: Two models were constructed: a one-year decision tree and a ten-year Markov model, each based on a hypothetical cohort of one thousand individuals through Monte Carlo simulation. Eight restorative strategies were evaluated: high viscosity glass ionomer cement (HVGIC); encapsulated GIC; etch and rinse adhesive + composite; self-etch adhesive + composite; preformed stainless steel crown; HVGIC + etch and rinse adhesive + composite; HVGIC + self-etch adhesive + composite, and encapsulated GIC + etch and rinse adhesive + composite. Effectiveness data were sourced from the literature. Micro-costing was applied using 2022 USD market averages with a 5% variation. Incremental cost-effectiveness ratio (ICER), net monetary benefit (%NMB), and the budgetary impact were obtained. RESULTS: Cost-effective treatments included HVGIC (%NMB = 0%/ 0%), encapsulated GIC (%NMB = 19.4%/ 19.7%), and encapsulated GIC + etch and rinse adhesive + composite (%NMB = 23.4%/ 24.5%) at 1 year and 10 years, respectively. The benefit gain of encapsulated GIC + etch and rinse adhesive + composite in relation to encapsulated GIC was small when compared to the cost increase at 1 year (gain of 3.28% and increase of USD 24.26) and 10 years (gain of 4% and increase of USD 15.54). CONCLUSION: Within the horizon and perspective analyzed, the most cost-effective treatment was encapsulated GIC restoration. CLINICAL RELEVANCE: This study can provide information for decision-making.

Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer.

PURPOSE: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML models to predict biochemical recurrence (BCR) of prostate cancer (PCa). MATERIALS AND METHODS: A total of 647 PCa patients were retrospectively evaluated. Clinical parameters were identified using LASSO regression. Then, cohort was split into training and validation datasets with a ratio of 0.75:0.25 and BCR-related features were included in Cox regression and five ML algorithm to construct BCR prediction models. The clinical utility of each model was evaluated by concordance index (C-index) values and decision curve analyses (DCA). Besides, Shapley Additive Explanation (SHAP) values were used to explain the features in the models. RESULTS: We identified 11 BCR-related features using LASSO regression, then establishing five ML-based models, including random survival forest (RSF), survival support vector machine (SSVM), survival Tree (sTree), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and a Cox regression model, C-index were 0.846 (95%CI 0.796-0.894), 0.774 (95%CI 0.712-0.834), 0.757 (95%CI 0.694-0.818), 0.820 (95%CI 0.765-0.869), 0.793 (95%CI 0.735-0.852), and 0.807 (95%CI 0.753-0.858), respectively. The DCA showed that RSF model had significant advantages over all models. In interpretability of ML models, the SHAP value demonstrated the tangible contribution of each feature in RSF model. CONCLUSIONS: Our score system provide reference for the identification for BCR, and the crafting of a framework for making therapeutic decisions for PCa on a personalized basis.

Cost-Effectiveness Analysis of Pharmacological Treatment for Adult Kidney Transplant Recipients in Colombia.

OBJECTIVES: To evaluate cost-effective pharmacological treatment in adult kidney transplant recipients from the perspective of the Colombian health system. METHODS: A decision tree model for the induction phase and a Markov model for the maintenance phase were built. A review of the clinical literature was conducted to extract probabilities, and the life-years were used as the outcome. Costs were calculated using the administrative databases. The evaluating treatment schemes are organized by groups of evidence with direct comparisons. RESULTS: In the induction phase, anti-thymocyte immunoglobulin+ methylprednisolone is dominant, more effective, and less expensive, compared with basiliximab+methylprednisolone. In the maintenance phase, azathioprine (AZA) is dominant in contrast to mycophenolate mofetil (MFM) both with cyclosporine (CIC)+ corticosteroids (CE); CIC is dominant relative to sirolimus (SIR) and tacrolimus (TAC) (both with MFM+CE or AZA+CE), and TAC is dominant compared with SIR (in addition with MFM+CE or mycophenolate sodium [MFS]+CE); MFM is dominant in relation to MFS and everolimus, and SIR is more effective MFM but it does not exceed the threshold (in sum with TAC+CE); MFS and MFM are dominant relative to everolimus, and SIR is more effective than MFM, but it does not exceed the threshold (in addiction with CIC+CE); MFM is dominant in relation to TAC (in sum with SIR+CE), and CIC+AZA+CE is dominant in relation to TAC+MFM+CE. CONCLUSIONS: The base-case results for all evidence groups are consistent with the different sensitivity analyses.

Electronic Decision-Making Tool for Smoking Cessation (Pare de Fumar Conosco) Versus Standard of Care: A Cost-Effectiveness Analysis.

OBJECTIVES: The study aimed to evaluate the cost-effectiveness of the Pare de Fumar Conosco software compared with the standard of care adopted in Brazil for the treatment of smoking cessation. METHODS: In the cohort of smokers with multiple chronic conditions, we developed an decision tree model for the benefit measures of smoking cessation. We adopted the perspectives of the Brazilian Unified Health System and the service provider. Resources and costs were measured by primary and secondary sources and effectiveness by a randomized clinical trial. The incremental cost-effectiveness ratio (ICER) was calculated, followed by deterministic and probabilistic sensitivity analyses and deterministic and probabilistic sensitivity analyses. No willingness to pay threshold was adopted. RESULTS: The software had a lower cost and greater effectiveness than its comparator. The ICER was dominant in all of the benefits examined (-R$2 585 178.29 to -R$325 001.20). The cost of the standard of care followed by that of the electronic tool affected the ICER of the benefit measures. In all probabilistic analyses, the software was superior to the standard of care (53.6%-82.5%). CONCLUSION: The Pare de Fumar Conosco software is a technology that results in cost savings in treating smoking cessation.

A model based on PDCA and data mining approach for the prevention of occupational accidents in the plumbing activity in the construction sector.

BACKGROUND: Occupational accidents in the plumbing activity in the construction sector in developing countries have high rates of work absenteeism. The productivity of enterprises is heavily influenced by it. OBJECTIVE: To propose a model based on the Plan, Do, Check, and Act cycle and data mining for the prevention of occupational accidents in the plumbing activity in the construction sector. METHODS: This cross-sectional study was administered on a total of 200 male technical workers in plumbing. It considers biological, biomechanical, chemical, and, physical risk factors. Three data mining algorithms were compared: Logistic Regression, Naive Bayes, and Decision Trees, classifying the occurrences occupational accident. The model was validated considering 20% of the data collected, maintaining the same proportion between accidents and non-accidents. The model was applied to data collected from the last 17 years of occupational accidents in the plumbing activity in a Colombian construction company. RESULTS: The results showed that, in 90.5% of the cases, the decision tree classifier (J48) correctly identified the possible cases of occupational accidents with the biological, chemical, and, biomechanical, risk factors training variables applied in the model. CONCLUSION: The results of this study are promising in that the model is efficient in predicting the occurrence of an occupational accident in the plumbing activity in the construction sector. For the accidents identified and the associated causes, a plan of measures to mitigate the risk of occupational accidents is proposed.

Predictive model for temporomandibular disorder in adolescents: Decision tree.

BACKGROUND: Temporomandibular disorders (TMD) do not only occur in adults but also in adolescents, with negative impacts on their development. AIM: To propose a predictive model for TMD in adolescents using a decision tree (DT) analysis and to identify groups at high and low risk of developing TMD in the city of Recife, PE, Brazil. DESIGN: This cross-sectional study was conducted in Recife on 1342 schoolchildren of both sexes aged 10-17 years. The analyses were performed using Pearson's chi-squared test and Fisher's exact test, as well as the CHAID algorithm for the construction of the DT. The SPSS statistical program was used. RESULTS: The prevalence of TMD was 33.2%. Statistically significant associations were observed between TMD and sex, depression, self-reported orofacial pain, and orofacial pain on clinical examination. The DT consisted of self-reported orofacial pain, orofacial pain on physical examination, and depression, with an overall predictive power of 73.0%. CONCLUSION: The proposed tree has a good predictive capacity and permits to identify groups at high risk of developing TMD among adolescents, such as those with self-reported orofacial pain or orofacial pain on examination associated with depression.

Normatização dos escores da escala eHealth Literacy Scale para avaliação do letramento digital em saúde / Standardization of eHealth Literacy Scale scores for assessing digital health literacy / Estandarización de las puntuaciones de la escala eHealth Literacy Scale para evaluar la alfabetización digital en salud

Objetivo: analisar os dados de normatização dos escores da versão brasileira do instrumento eHealth Literacy Scale (eHeals) para avaliação do letramento digital em saúde. Método: estudo transversal com 502 adultos brasileiros, realizado em 2019. Dados coletados pelo instrumento eHeals e questionário sociodemográfico. Foram aplicadas árvores de decisão e análise discriminante. Estudo aprovado pelo Comite de Ética em Pesquisa. Resultados: a análise discriminante determinou as faixas de classificação do eHeals a partir da distribuição dos escores. A árvore de decisão indicou que a escolaridade afetou de forma relevante os resultados da escala. Os indivíduos com escolaridade até o ensino fundamental II incompleto: baixo (até 10), médio (11 a 25), alto (27 a 40), e escolaridade acima: baixo (até 25), médio (25 a 32) e alto LDS (33 a 40). Conclusão: a classificação dos níveis de letramento digital em saúde de adultos pelo eHeals deve ser controlada pelos níveis de escolaridade dos participantes(AU)

Objective: to analyze the normative data of the scores of the Brazilian version of the eHealth Literacy Scale (eHeals) instrument for assessing digital health literacy. Method: cross-sectional study with 502 Brazilian adults in 2019. Data collected using the eHeals instrument and sociodemographic questionnaire. Decision trees and discriminant analysis were applied. Study approved by the Research Ethics Committee. Results: Discriminant analysis determined the eHeals classification ranges based on the distribution of scores. The decision tree indicated that education significantly affected the scale results. Thus, individuals with incomplete elementary school education up to II: low (up to 10), medium (11 to 25), high (27 to 40), and higher education: low (up to 25), medium (25 to 32) and high LDS (33 to 40). Conclusion: the classification of digital health literacy levels using eHeals in adults should be controlled by the participants' education levels(AU)

Objetivo: analizar los datos de estandarización de las puntuaciones de la versión brasileña del instrumento eHealth Literacy Scale (eHeals) para evaluar la alfabetización digital en salud. Método: estudio transversal con 502 adultos brasileños que tuvo lugar en 2019. La recolección de datos se hizo mediante el instrumento eHeals y un cuestionario sociodemográfico. Se aplicaron árboles de decisión y análisis discriminante. El Comité de Ética en Investigación aprobó el estudio. Resultados: El análisis discriminante determinó los rangos de clasificación de eHeals con base en la distribución de puntuaciones. El árbol de decisión indicó que la educación afectó significativamente los resultados de la escala. Individuos con educación primaria incompleta: baja (hasta 10), media (11 a 25), alta (27 a 40), y educación superior a esa mencionada: baja (hasta 25), media (25 a 32) y alto LDS (33 a 40). Conclusión: la clasificación de los niveles de alfabetización en salud digital en adultos con eHeals debe ser controlada por los niveles de educación de los participantes(AU)

Clinical Guidelines for the Surgical/Orthodontic Management of Impacted Maxillary Central Incisors Based on a Decision Tree.

Maxillary central incisors are critical to occlusal function, smile esthetics, and even one's self-image. Furthermore, their impaction at an early age could have harmful psychological consequences on the individual. Maxillary central incisors can be impacted due to early dentoalveolar trauma to the upper anterior region that displaces the incisor in formation and, in rare instances, tooth germs are deformed. The aftermath of trauma during primary dentition is seen later during mixed dentition. Other causes are either an impediment in the eruption pathway of the maxillary central incisor due to the presence of odontomas or supernumerary teeth, an insufficient eruption space, or, very rarely, syndromic and/or other general medical conditions. Diagnosis is completed through a detailed medical/dental history, clinical evaluation, and appropriate imaging. Arch width increase, space opening, removal of obstructions if present, suitable soft-tissue management, well-designed orthodontic traction mechanics, and long-term periodontal follow-up are all essential elements in resolving cases of impacted maxillary central incisors.

Decision tree-based classification as a support to diagnosis in the Alzheimer's disease continuum using cerebrospinal fluid biomarkers: insights from automated analysis.

OBJECTIVE: Cerebrospinal fluid (CSF) biomarkers add accuracy to the diagnostic workup of cognitive impairment by illustrating Alzheimer's disease (AD) pathology. However, there are no universally accepted cutoff values for the interpretation of AD biomarkers. The aim of this study is to determine the viability of a decision-tree method to analyse CSF biomarkers of AD as a support for clinical diagnosis. METHODS: A decision-tree method (automated classification analysis) was applied to concentrations of AD biomarkers in CSF as a support for clinical diagnosis in older adults with or without cognitive impairment in a Brazilian cohort. In brief, 272 older adults (68 with AD, 122 with mild cognitive impairment [MCI], and 82 healthy controls) were assessed for CSF concentrations of Aß1-42, total-tau, and phosphorylated-tau using multiplexed Luminex assays; biomarker values were used to generate decision-tree algorithms (classification and regression tree) in the R statistical software environment. RESULTS: The best decision tree model had an accuracy of 74.65% to differentiate the three groups. Cluster analysis supported the combination of CSF biomarkers to differentiate AD and MCI vs. controls, suggesting the best cutoff values for each clinical condition. CONCLUSION: Automated analyses of AD biomarkers provide valuable information to support the clinical diagnosis of MCI and AD in research settings.

Comparación de la costo-efectividad de la nefrolitotomía percutánea y de la nefrolitotomía retrógrada flexible con láser de holmio en pacientes con litiasis renal de 20 a 30 mm en Colombia / Comparison of the Cost-effectiveness of Percutaneous Nephrolithotomy and Flexible Retrograde Holmium Laser Nephrolithotomy in Patients with Kidney Stones of 20 to 30 mm in Colombia

Introducción y Objetivo Con el advenimiento de nuevas tecnologías, vienen controversias respecto al espectro de sus aplicaciones. El costo derivado de estas tecnologías juega un papel muy importante en el momento de la toma de decisiones terapéuticas. Es por esto que consideramos relevante estimar la costo-efectividad de la nefrolitotomía percutánea comparada con la nefrolitotomía retrógrada flexible con láser de holmio en pacientes con litiasis renal de 20 mm a 30 mm en Colombia. Materiales y Métodos Por medio de la construcción de un modelo de árbol de decisión usando el programa Treeage (TreeAge Software, LLC, Williamstown, MA, EE.UU.), se realizó una comparación entre la nefrolitotomía percutánea y la nefrolitotomía retrógrada flexible con láser de holmio en pacientes con litiasis renal de 20 mm a 30 mm. La perspectiva fue la del tercer pagador, y se incluyeron los costos directos. Las cifras fueron expresadas en pesos colombianos de 2018. La mejoría clínica, definida como el paciente libre de cálculos, fue la unidad de resultado. Se hizo una extracción de datos de efectividad y seguridad por medio de una revisión sistemática de la literatura. La razón de costo-efectividad incremental fue calculada. Resultados El modelo final indica que la nefrolitotomía percutánea puede ser considerada como la alternativa más costo-efectiva. Los hallazgos fueron sensibles a la probabilidad de mejoría clínica de la nefrolitotomía percutánea. Conclusión Teniendo en cuenta las variables económicas, los supuestos del modelo y desde la perspectiva del tercer pagador, la nefrolitotomía percutánea para el tratamiento de pacientes con cálculos renales de 20 mm a 30 mm es costo-efectiva en nuestro país. Estos hallazgos fueron sensibles a los costos y a la efectividad de los procedimientos quirúrgicos.

Introduction and Objective The advent of new technologies leads to controversies regarding the spectrum of their applications and their cost. The cost of these technologies plays a very important role when making therapeutic decisions. Therefore, we consider it relevant to estimate the cost-effectiveness of percutaneous nephrolithotomy compared with flexible retrograde holmium laser nephrolithotomy in patients with kidney stones of 20 mm to 30 mm in Colombia. Materials and Methods Through the development of a decision tree model using the Treeage (TreeAge Software, LLC, Williamstown, MA, US) software, we compared percutaneous nephrolithotomy with flexible holmium laser retrograde nephrolithotomy in patients with kidney stones of 20 mm to 30 mm. The perspective was that of the third payer, and all direct costs were included. The figures were expressed in terms of 2018 Colombian pesos. Clinical improvement, which was defined as a stone-free patient, was the outcome unit. We extracted data on effectiveness and safety through a systematic review of the literature. The incremental cost-effectiveness ratio was calculated. Results In terms of cost-effectiveness the final model indicates that percutaneous nephrolithotomy may be considered the best alternative. These findings were sensitive to the probability of clinical improvement of the percutaneous nephrolithotomy. Conclusion Taking into account the economic variables, the assumptions of the model, and through the perspective of the third payer, percutaneous nephrolithotomy for the treatment of patients with kidney stones of 20 mm to 30mm is cost-effective in our country. These findings were sensitive to the costs and effectiveness of the surgical procedures.

Trends and factors related to adolescent pregnancies: an incidence trend and conditional inference trees analysis of northern Nicaragua demographic surveillance data.

BACKGROUND: We aimed to identify the 2001-2013 incidence trend, and characteristics associated with adolescent pregnancies reported by 20-24-year-old women. METHODS: A retrospective analysis of the Cuatro Santos Northern Nicaragua Health and Demographic Surveillance 2004-2014 data on women aged 15-19 and 20-24. To calculate adolescent birth and pregnancy rates, we used the first live birth at ages 10-14 and 15-19 years reported by women aged 15-19 and 20-24 years, respectively, along with estimates of annual incidence rates reported by women aged 20-24 years. We conducted conditional inference tree analyses using 52 variables to identify characteristics associated with adolescent pregnancies. RESULTS: The number of first live births reported by women aged 20-24 years was 361 during the study period. Adolescent pregnancies and live births decreased from 2004 to 2009 and thereafter increased up to 2014. The adolescent pregnancy incidence (persons-years) trend dropped from 2001 (75.1 per 1000) to 2007 (27.2 per 1000), followed by a steep upward trend from 2007 to 2008 (19.1 per 1000) that increased in 2013 (26.5 per 1000). Associated factors with adolescent pregnancy were living in low-education households, where most adults in the household were working, and high proportion of adolescent pregnancies in the local community. Wealth was not linked to teenage pregnancies. CONCLUSIONS: Interventions to prevent adolescent pregnancy are imperative and must bear into account the context that influences the culture of early motherhood and lead to socioeconomic and health gains in resource-poor settings.

Application of Data Mining Algorithms for Dementia in People with HIV/AIDS.

Dementia interferes with the individual's motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.