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BACKGROUND: To develop and validate a serum protein nomogram for colorectal cancer (CRC) screening. METHODS: The serum protein characteristics were extracted from an independent sample containing 30 colorectal cancer and 12 polyp tissues along with their paired samples, and different serum protein expression profiles were validated using RNA microarrays. The prediction model was developed in a training cohort that included 1345 patients clinicopathologically confirmed CRC and 518 normal participants, and data were gathered from November 2011 to January 2017. The lasso logistic regression model was employed for features selection and serum nomogram building. An internal validation cohort containing 576 CRC patients and 222 normal participants was assessed. RESULTS: Serum signatures containing 27 secreted proteins were significantly differentially expressed in polyps and CRC compared to paired normal tissue, and REG family proteins were selected as potential predictors. The C-index of the nomogram1 (based on Lasso logistic regression model) which contains REG1A, REG3A, CEA and age was 0.913 (95% CI, 0.899 to 0.928) and was well calibrated. Addition of CA199 to the nomogram failed to show incremental prognostic value, as shown in nomogram2 (based on logistic regression model). Application of the nomogram1 in the independent validation cohort had similar discrimination (C-index, 0.912 [95% CI, 0.890 to 0.934]) and good calibration. The decision curve (DCA) and clinical impact curve (ICI) analysis demonstrated that nomogram1 was clinically useful. CONCLUSIONS: This study presents a serum nomogram that included REG1A, REG3A, CEA and age, which can be convenient for screening of colorectal cancer.

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This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study's results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.

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BACKGROUND: Breast cancer is a leading cause of cancer-related deaths in females, and the hormone receptor-positive subtype is the most frequent. Breast cancer is a common source of brain metastases; therefore, we aimed to generate a brain metastases prediction model in females with hormone receptor-positive breast cancer. METHODS: The primary cohort included 3,682 females with hormone receptor-positive breast cancer treated at a single center from May 2009 to May 2020. Patients were randomly divided into a training dataset (n = 2,455) and a validation dataset (n = 1,227). In the training dataset, simple logistic regression analyses were used to measure associations between variables and the diagnosis of brain metastases and to build multivariable models. The model with better calibration and discrimination capacity was tested in the validation dataset to measure its predictive performance. RESULTS: The variables incorporated in the model included age, tumor size, axillary lymph node status, clinical stage at diagnosis, HER2 expression, Ki-67 proliferation index, and the modified Scarff-Bloom-Richardson grade. The area under the curve was 0.81 (95 % CI 0.75-0.86), p < 0.001 in the validation dataset. The study presents a guide for the clinical use of the model. CONCLUSION: A brain metastases prediction model in females with hormone receptor-positive breast cancer helps assess the individual risk of brain metastases.

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Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

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Chile had a violent military coup (1973-1990) that resulted in 3,000 victims declared detained, missing or killed; many are still missing and unidentified. Currently, the Human Rights Unit of the Forensic Medical Service in Chile applies globally recognised forensic anthropological approaches, but many of these methods have not been validated in a Chilean sample. As current research has demonstrated population-specificity with extant methods, the present study aims to validate sex estimation methods in a Chilean population and thereafter establish population-specific equations. A sample of 265 os coxae of known age and sex of adult Chileans from the Santiago Subactual Osteology Collection were analysed. Visual assessment and scoring of the pelvic traits were performed in accordance with the Phenice (1969) and Klales et al. (2012) methods. The accuracy of Phenice (1969) in the Chilean sample was 96.98%, with a sex bias of 7.68%. Klales et al. (2012) achieved 87.17% accuracy with a sex bias of -15.39%. Although both methods showed acceptable classification accuracy, the associated sex bias values are unacceptable in forensic practice. Therefore, six univariate and eight multivariate predictive models were formulated for the Chilean population. The most accurate univariate model was the ventral arc at 96.6%, with a sex bias of 5.2%. Classification accuracy using all traits was 97.0%, with a sex bias of 7.7%. This study provides Chilean practitioners a population-specific morphoscopic standard with associated classification probabilities acceptable to accomplish legal admissibility requirements in human rights and criminal cases specific to the second half of the 20th century.

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After COVID-19 emerged, alternative methods to laboratory tests for the individualized prediction of SARS-CoV-2 were developed in several world regions. The objective of this investigation was to develop models for the individualized prediction of SARS-CoV-2 infection in a large municipality of Mexico. The study included data from 36,949 patients with suspected SARS-CoV-2 infection who received a diagnostic tested at health centers of the Alvaro Obregon Jurisdiction in Mexico City registered in the Epidemiological Surveillance System for Viral Respiratory Diseases (SISVER-SINAVE). The variables that were different between a positive test and a negative test were used to generate multivariate binary logistic regression models. There was a large variation in the prediction variables for the models of different pandemic waves. The models obtained an overall accuracy of 73% (63-82%), sensitivity of 52% (18-71%), and specificity of 84% (71-92%). In conclusion, the individualized prediction models of a positive COVID-19 test based on SISVER-SINAVE data had good performance. The large variation in the prediction variables for the models of different pandemic waves highlights the continuous change in the factors that influence the spread of COVID-19. These prediction models could be applied in early case identification strategies, especially in vulnerable populations.

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A biomarker is a measured indicator of a variety of processes, and is often used as a clinical tool for the diagnosis of diseases. While the developmental process of biomarkers from lab to clinic is complex, initial exploratory stages often focus on characterizing the potential of biomarkers through utilizing various statistical methods that can be used to assess their discriminatory performance, establish an appropriate cut-off that transforms continuous data to apt binary responses of confirming or excluding a diagnosis, or establish a robust association when tested against confounders. This review aims to provide a gentle introduction to the most common tools found in diagnostic biomarker studies used to assess the performance of biomarkers with an emphasis on logistic regression.

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(1) Background: Spinocerebellar ataxias (SCA) is a term that refers to a group of hereditary ataxias, which are neurological diseases characterized by degeneration of the cells that constitute the cerebellum. Studies suggest that magnetic resonance imaging (MRI) supports diagnoses of ataxias, and linear measurements of the aneteroposterior diameter of the midbrain (ADM) have been investigated using MRI. These measurements correspond to studies in spinocerebellar ataxia type 2 (SCA2) patients and in healthy subjects. Our goal was to obtain the cut-off value for ADM atrophy in SCA2 patients. (2) Methods: This study evaluated 99 participants (66 SCA2 patients and 33 healthy controls). The sample was divided into estimations (80%) and validation (20%) samples. Using the estimation sample, we fitted a logistic model using the ADM and obtained the cut-off value through the inverse of regression. (3) Results: The optimal cut-off value of ADM was found to be 18.21 mm. The area under the curve (AUC) of the atrophy risk score was 0.957 (95% CI: 0.895-0.991). Using this cut-off on the validation sample, we found a sensitivity of 100.00% (95% CI: 76.84%-100.00%) and a specificity of 85.71% (95% CI: 42.13%-99.64%). (4) Conclusions: We obtained a cut-off value that has an excellent discriminatory capacity to identify SCA2 patients.

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BACKGROUND: Due to its unique advantages over radical cystectomy (RC), trimodality therapy (TMT) is increasingly being utilized by patients diagnosed with muscle-invasive bladder cancer (MIBC) who are not suitable for or refuse RC. However, achieving a satisfactory oncological outcome with TMT requires strict patient selection criteria, and the comparative oncological outcomes of TMT versus RC remain controversial. METHODS: Patients diagnosed with non-metastatic MIBC who underwent TMT or RC were identified from the SEER database during 2004-2015. Before one-to-one propensity score matching (PSM), logistic regression was utilized to identify predictors of TMT. After matching, K-M curves were generated to estimate cancer-specific survival (CSS) and overall survival (OS) with log-rank to test the significance. Finally, we conducted univariate and multivariate Cox analyses to identify independent prognostic factors for CSS and OS. RESULTS: The RC and TMT groups included 5812 and 1260 patients, respectively, and the TMT patients were significantly older than the RC patients. Patients with advanced age, separated, divorced, or widowed (SDW) or unmarried marital status (married as reference), and larger tumor size (< 40 mm as reference) were more likely to be treated with TMT. After PSM, TMT was found to be associated with worse CSS and OS, and it was identified as an independent risk factor for both CSS and OS. CONCLUSION: MIBC patients may not be carefully evaluated prior to TMT, and some non-ideal candidates underwent TMT. TMT resulted in worse CSS and OS in the contemporary era, but these results may be biased. Strict TMT candidate criteria and TMT treatment modality should be required.

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Objective.to develop an optimization and training pipeline for a classification model based on principal component analysis and logistic regression using neuroimages from PET with 2-[18F]fluoro-2-deoxy-D-glucose (FDG PET) for the diagnosis of Alzheimer's disease (AD).Approach.as training data, 200 FDG PET neuroimages were used, 100 from the group of patients with AD and 100 from the group of cognitively normal subjects (CN), downloaded from the repository of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Regularization methods L1 and L2 were tested and their respective strength varied by the hyperparameter C. Once the best combination of hyperparameters was determined, it was used to train the final classification model, which was then applied to test data, consisting of 192 FDG PET neuroimages, 100 from subjects with no evidence of AD (nAD) and 92 from the AD group, obtained at the Centro de Diagnóstico por Imagem (CDI).Main results.the best combination of hyperparameters was L1 regularization andC≈ 0.316. The final results on test data were accuracy = 88.54%, recall = 90.22%, precision = 86.46% and AUC = 94.75%, indicating that there was a good generalization to neuroimages outside the training set. Adjusting each principal component by its respective weight, an interpretable image was obtained that represents the regions of greater or lesser probability for AD given high voxel intensities. The resulting image matches what is expected by the pathophysiology of AD.Significance.our classification model was trained on publicly available and robust data and tested, with good results, on clinical routine data. Our study shows that it serves as a powerful and interpretable tool capable of assisting in the diagnosis of AD in the possession of FDG PET neuroimages. The relationship between classification model output scores and AD progression can and should be explored in future studies.

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Improvement in treatment options has increased the survival of people living with HIV (PLHIV). Thus, we evaluated the factors associated with better health-related quality of life (HRQoL) among PLHIV in Brazil. This was a cross-sectional study carried out among 349 PLHIV. Data were collected using an interview-based questionnaire, and HRQoL was assessed by the Brazilian version of the WHOQOL HIV BREF instrument. We used non-hierarchical cluster analysis (K-means) to compile the WHOQOL HIV BREF's overall and domain scores into a unique more multidimensional measure for HRQoL consisting of three clusters: poor, fair and good; associations with clusters of better HRQoL were assessed using multinomial logistic regression models. The mean and median overall HRQoL scores were 15.13 (SD = 3.39) and 16, respectively. The reliability and validity of the Brazilian version of the WHOQOL HIV BREF instrument was confirmed among PLHIV in a non-metropolitan, medium-sized municipality of Brazil, which reaffirmed the cross-cultural validity of this instrument. The factors male sex; heterosexual and asexual orientations; higher individual income; undetectable viral load; absence of any comorbidity and presence of an infectious or a chronic comorbidity, with mental illness as the reference; and never having consumed illegal substances were independently associated with good HRQoL. Thus, the compilation of the WHOQOL HIV BREF's overall and domain scores into a unique multidimensional measure for HRQoL, which this study proposed for the first time, may facilitate more robust interpretations and models of predictors. These differentials could simplify HRQoL as an indicator of health and wellbeing to be routinely used as a key outcome in the clinical management of patients and in the global monitoring of health system responses to HIV.

RESUMEN: La mejora en las opciones de tratamiento ha aumentado la supervivencia de las personas que viven con el VIH (PVVIH). Por lo tanto, evaluamos los factores asociados con una mejor calidad de vida relacionada con la salud (CVRS) entre las PVVIH en Brasil. Se trata de un estudio transversal realizado con 349 PVVIH. Los datos se recopilaron mediante un cuestionario basado en entrevistas y la CVRS se evaluó mediante la versión brasileña del instrumento WHOQOL VIH BREF. Usamos un análisis de conglomerados no jerárquico (K-medias) para compilar las puntuaciones generales y de dominios del WHOQOL HIV BREF en una medida única más multidimensional para la CVRS que consta de tres conglomerados: deficiente, regular y bueno; y las asociaciones con grupos de mejor CVRS se evaluaron mediante modelos de regresión logística multinomial. Las puntuaciones de la CVRS global media y mediana fueron 15,13 (DE = 3,39) y 16. La confiabilidad y validez del WHOQOL VIH BREF versión brasileña fue confirmada entre personas que viven con el VIH en un municipio no metropolitano de mediana población de Brasil, lo que reafirma la validez transcultural de este instrumento. Los factores sexo masculino; orientaciones heterosexuales y asexuales; mayores ingresos individuales; carga viral indetectable; ausencia de comorbilidad y presencia de comorbilidad infecciosa o crónica, teniendo como referencia la enfermedad mental; y nunca haber consumido sustancias ilegales se asociaron de forma independiente con una buena CVRS. Por lo tanto, la compilación de las puntuaciones generales y de dominio del WHOQOL HIV BREF en una medida multidimensional única para la CVRS, que este estudio propuso por primera vez, puede facilitar interpretaciones y modelos de predictores más robustos. Estos diferenciales podrían simplificar la HRQoL como un indicador de salud y bienestar para ser utilizado de forma rutinaria como un resultado clave en el manejo clínico de los pacientes y en el monitoreo global de las respuestas del sistema de salud al VIH.

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In Brazil, the fatality rate for visceral leishmaniasis is high, and it is important to investigate its associated factors. The aim of this study was to analyze the clinical-epidemiological profile and prognostic factors associated with death from visceral leishmaniasis (VL) in the Central-West region of Brazil, between 2010 and 2019. A study of series of VL cases was carried out using data obtained from the Sistema de Informação de Agravos de Notificação (SINAN). Multivariate logistic regression was performed to identify variables associated with deaths. Male (64.96%); age group ≤5 years (28.51%); mixed race/color (59.20%); and level of education incomplete primary education (45.16%) were the most affected. The most frequent symptoms were fever (87.65%), weakness (77.56%), enlarged spleen (70.22%) and liver (67.33%), weight loss (67.22%) and pallor (63.41%). Co-infection with HIV was observed in 15.84% of patients. The parasitological diagnosis was positive in 74.17% and the Indirect Immunofluorescence (IIF) in 82.80%. The drug most used for treatment was pentavalent antimony (41.96%). Regarding the evolution of VL, cure was recorded for 82.90% of patients and death from VL in 8.30%. Factors associated with death from VL were: age group ≥20 and <60 (OR 2.95; 95% CI 1.98-4.38) and ≥60 (OR 5.84; 95% CI 3.63-9.38), edema (OR 2.27; 95% CI 1.64-3.13), pallor (OR 1.53; 95% CI 1.06-2.20), infectious condition (OR 1.56; 95% CI 1.12-2.15) and hemorrhagic phenomena (OR 2.87; 95% CI 2.02-4.08). New studies are needed in order to better manage VL control, monitoring, prevention and primary care strategies.

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The aim of this study was to use latent profile analysis to identify specific profiles of burnout syndrome in combination with work engagement and to identify whether job satisfaction, psychological well-being, and other sociodemographic and work variables affect the probability of presenting a profile of burnout syndrome and low work enthusiasm. A total of 355 healthcare professionals completed the Spanish Burnout Inventory, the Utrecht Work Engagement Scale, the Job Satisfaction Scale, and the Psychological Well-Being Scale for Adults. Latent profile analysis identified four profiles: (1) burnout with high indolence (BwHIn); (2) burnout with low indolence (BwLIn); (3) high engagement, low burnout (HeLb); and (4) in the process of burning out (IPB). Multivariate logistic regression showed that a second job in a government healthcare institution; a shift other than the morning shift; being divorced, separated or widowed; and workload are predictors of burnout profiles with respect to the HeLb profile. These data are useful for designing intervention strategies according to the needs and characteristics of each type of burnout profile.

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The monkeypox virus (MPXV) has caused an unusual epidemiological scenario-an epidemic within a pandemic (COVID-19). Despite the inherent evolutionary and adaptive capacity of poxviruses, one of the potential triggers for the emergence of this epidemic was the change in the status of orthopoxvirus vaccination and eradication programs. This epidemic outbreak of HMPX spread worldwide, with a notable frequency in Europe, North America, and South America. Due to these particularities, the objective of the present study was to assess and compare cases of HMPX in these geographical regions through logistic and Gompertz mathematical modeling over one year since its inception. We estimated the highest contagion rates (people per day) of 690, 230, 278, and 206 for the world, Europe, North America, and South America, respectively, in the logistic model. The equivalent values for the Gompertz model were 696, 268, 308, and 202 for the highest contagion rates. The Kruskal-Wallis Test indicated different means among the geographical regions affected by HMPX regarding case velocity, and the Wilcoxon pairwise test indicated the absence of significant differences between the case velocity means between Europe and South America. The coefficient of determination (R2) values in the logistic model varied from 0.8720 to 0.9023, and in the Gompertz model, they ranged from 0.9881 to 0.9988, indicating a better fit to the actual data when using the Gompertz model. The estimated basic reproduction numbers (R0) were more consistent in the logistic model, varying from 1.71 to 1.94 in the graphical method and from 1.75 to 1.95 in the analytical method. The comparative assessment of these mathematical modeling approaches permitted the establishment of the Gompertz model as the better-fitting model for the data and the logistic model for the R0. However, both models successfully represented the actual HMPX case data. The present study estimated relevant epidemiological data to understand better the geographic similarities and differences in the dynamics of HMPX.

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Epilepsy is a chronic neurological disease characterized by the presence of spontaneous seizures, with a higher incidence in the pediatric population. Anti-seizure medication (ASM) may produce adverse drug reactions (ADRs) with an elevated frequency and a high severity. Thus, the objective of the present study was to analyze, through intensive pharmacovigilance over 112 months, the ADRs produced by valproic acid (VPA), oxcarbazepine (OXC), phenytoin (PHT), and levetiracetam (LEV), among others, administered to monotherapy or polytherapy for Mexican hospitalized pediatric epilepsy patients. A total of 1034 patients were interviewed; 315 met the inclusion criteria, 211 patients presented ADRs, and 104 did not. A total of 548 ASM-ADRs were identified, and VPA, LEV, and PHT were the main culprit drugs. The most frequent ADRs were drowsiness, irritability, and thrombocytopenia, and the main systems affected were hematologic, nervous, and dermatologic. LEV and OXC caused more nonsevere ADRs, and PHT caused more severe ADRs. The risk analysis showed an association between belonging to the younger groups and polytherapy with ADR presence and between polytherapy and malnutrition with severe ADRs. In addition, most of the severe ADRs were preventable, and most of the nonsevere ADRs were nonpreventable.

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Landslides are one of the natural phenomena with more negative impacts on landscape, natural resources, and human health worldwide. Andean geomorphology, urbanization, poverty, and inequality make it more vulnerable to landslides. This research focuses on understanding explanatory landslide factors and promoting quantitative susceptibility mapping. Both tasks supply valuable knowledge for the Andean region, focusing on territorial planning and risk management support. This work addresses the following questions using the province of Azuay-Ecuador as a study area: (i) How do EFA and LR assess the significance of landslide occurrence factors? (ii) Which are the most significant landslide occurrence factors for susceptibility analysis in an Andean context? (iii) What is the landslide susceptibility map for the study area? The methodological framework uses quantitative techniques to describe landslide behavior. EFA and LR models are based on a historical inventory of 665 records. Both identified NDVI, NDWI, altitude, fault density, road density, and PC2 as the most significant factors. The latter factor represents the standard deviation, maximum value of precipitation, and rainfall in the wet season (January, February, and March). The EFA model was built from 7 latent factors, which explained 55% of the accumulated variance, with a medium item complexity of 1.5, a RMSR of 0.02, and a TLI of 0.89. This technique also identified TWI, fault distance, plane curvature, and road distance as important factors. LR's model, with AIC of 964.63, residual deviance of 924.63, AUC of 0.92, accuracy of 0.84, and Kappa of 0.68, also shows statistical significance for slope, roads density, geology, and land cover factors. This research encompasses a time-series analysis of NDVI, NDWI, and precipitation, including vegetation and weather dynamism for landslide occurrence. Finally, this methodological framework replaces traditional qualitative models based on expert knowledge, for quantitative approaches for the study area and the Andean region.

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Monkeypox (Mpox) is an emerging zoonotic disease with the potential for severe complications. Early identification and diagnosis are essential to prompt treatment, control its spread, and reduce the risk of human-to-human transmission. This study aimed to develop a clinical diagnostic tool and describe the clinical and sociodemographic features of 19 PCR-confirmed Mpox cases during an outbreak in a nonendemic region of northwestern Mexico. The median age of patients was 35 years, and most were male. Mpox-positive patients commonly reported symptoms such as fever, lumbago, and asthenia, in addition to experiencing painful ulcers and a high frequency of HIV infection among people living with HIV (PLWH). Two diagnostic models using logistic regression were devised, with the best model exhibiting a prediction accuracy of 0.92 (95% CI: 0.8-1), a sensitivity of 0.86, and a specificity of 0.93. The high predictive values and accuracy of the top-performing model highlight its potential to significantly improve early Mpox diagnosis and treatment in clinical settings, aiding in the control of future outbreaks.

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Delayed cerebral ischemia (DCI) is a common and severe complication after subarachnoid hemorrhage (SAH). Logistic regression (LR) is the primary method to predict DCI, but it has low accuracy. This study assessed whether other machine learning (ML) models can predict DCI after SAH more accurately than conventional LR. PubMed, Embase, and Web of Science were systematically searched for studies directly comparing LR and other ML algorithms to forecast DCI in patients with SAH. Our main outcome was the accuracy measurement, represented by sensitivity, specificity, and area under the receiver operating characteristic. In the six studies included, comprising 1828 patients, about 28% (519) developed DCI. For LR models, the pooled sensitivity was 0.71 (95% confidence interval [CI] 0.57-0.84; p < 0.01) and the pooled specificity was 0.63 (95% CI 0.42-0.85; p < 0.01). For ML models, the pooled sensitivity was 0.74 (95% CI 0.61-0.86; p < 0.01) and the pooled specificity was 0.78 (95% CI 0.71-0.86; p = 0.02). Our results suggest that ML algorithms performed better than conventional LR at predicting DCI.Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42023441586; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=441586.

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BACKGROUND:  The aim of this work is to characterize the processes associated with patient and public involvement (PPI) in the form of public consultations (PC) during the first 10 years of operation of the National Committee for Health Technology Incorporation in the Unified Health System (Conitec) of Brazil, and to identify factors associated with changes in Conitec's recommendations following these PC. METHODS:  This cross-sectional study analysed all processes related to the adoption of technologies that took place in Brazil between 2012 and 2021 based on technical reports and self-reported information collected from PC participants. A multiple logistic regression model identified factors associated with changes in Conitec's recommendations following PC. RESULTS:  A total of 479 technical reports were published, of which 83% (n = 400) were submitted to PC. Demands were made mainly by applicants from the government (n = 262; 55%), regarding the adoption of medicines (n = 366; 76%), in which context neoplasms and infectious diseases were the most frequent indications (n = 66; 14% for each). A total of 264 (55%) processes resulted in a final recommendation in favour of introducing the technology. Over the period of 10 years, 196 483 contributions were received in response to PC. The largest volume of contributions was made by patients and their families or representatives (n = 99 082; 50%), females (122 895; 67%), white individuals (129 165; 71%) and individuals between the ages of 25 and 59 years (145 364; 80%). Alteration of the preliminary recommendation occurred in 13% (n = 53) of the PC, with a higher proportion of recommendations being altered from 2017 onwards. Increased participation by patients had a significant impact on the alteration of the preliminary recommendation (odds ratio 3.87, 95% CI 1.33-13.35, p = 0.02). CONCLUSIONS: Increased engagement of patients and their families and caregivers in PC was associated with changing the preliminary recommendation of Conitec about the adoption of technologies into the public health system in Brazil.

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Introducción: Un aneurisma intracraneal roto provoca una hemorragia subaracnoidea. La enfermedad presenta una alta mortalidad y morbilidad. Sin embargo, no todos se rompen. Mejorar la predicción de rotura permitirá un tratamiento quirúrgico preventivo en un grupo de pacientes y evitará una intervención quirúrgica con riesgos en otro grupo de enfermos. Es necesario identificar factores predictivos para mejorar la estratificación del riesgo de rotura y optimizar el tratamiento de los aneurismas intracraneales incidentales. Objetivo: Identificar factores predictivos de rotura de aneurismas intracraneales. Métodos: En una muestra de 152 pacientes espirituanos con aneurismas intracraneales saculares rotos (n = 138) y no rotos (n = 22) y 160 imágenes de angiografía por tomografía computarizada, se realizaron mensuraciones de los índices o factores morfológicos, los cuales se combinaron mediante análisis de regresión logística con variables demográficas y clínicas. Resultados: El grupo de edad con mayor frecuencia de presentación de aneurismas fue el de mayor de 65 años. La muestra estuvo representada, en su gran mayoría, por el sexo femenino. Se identificaron tres factores clínicos y cuatro factores morfológicos estadísticamente significativos, asociados con la rotura. El índice de no esfericidad (p = 0,002 y el sexo femenino (p = 0,02) fueron los de mayor significación estadística. Conclusiones: Se detectaron siete factores predictivos de rotura de aneurismas intracraneales estadísticamente significativos, de los cuales el índice de no esfericidad resultó el de mayor significación(AU)

Introduction: A ruptured intracranial aneurysm causes a subarachnoid hemorrhage. The disease has high mortality and morbidity. However, not all of them break. Improving the rupture prediction will allow preventive surgical treatment in a group of patients and it will avoid risky surgical intervention in another group of patients. It is necessary to identify predictive factors to improve rupture risk stratification and to optimize treatment of incidental intracranial aneurysms. Objective: To identify rupture predictive factors for intracranial aneurysms. Methods: Measurements of the morphological indices or factors were performed in a sample of 152 patients from Sancti Spiritus with ruptured (n = 138) and unruptured (n = 22) saccular intracranial aneurysms and 160 computed tomography angiography images. They were combined using logistic regression analysis with demographic and clinical variables. Results: The age group with the highest frequency of aneurysm presentation was older than 65. The sample was represented, in its vast majority, by the female sex. Three clinical factors and four statistically significant morphological factors associated with rupture were identified. The non-sphericity index (p = 0.002) and the female sex (p = 0.02) were the most statistically significant. Conclusions: Seven statistically significant predictors of intracranial aneurysm rupture were detected, the non-sphericity index being the most significant(AU)

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