GOIZ ZAINDU study: a FINGER-like multidomain lifestyle intervention feasibility randomized trial to prevent dementia in Southern Europe.

BACKGROUND: GOIZ ZAINDU ("caring early" in Basque) is a pilot study to adapt the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) methodology to the Basque population and evaluate the feasibility and adherence to a FINGER-like multidomain intervention program. Additional aims included the assessment of efficacy on cognition and data collection to design a large efficacy trial. METHOD: GOIZ ZAINDU is a 1-year, randomized, controlled trial of a multidomain intervention in persons aged 60+ years, with Cardiovascular Risk Factors, Aging and Dementia (CAIDE) risk score ≥ 6, no diagnosis of dementia, and below-than-expected performance in at least one of three cognitive screening tests. Randomization to a multidomain intervention (MD-Int) or regular health advice (RHA) was stratified by sex, age (>/≤ 75), and cognitive status (mild cognitive impairment (MCI)/normal cognition). MD-Int included cardiovascular risk factor control, nutritional counseling, physical activity, and cognitive training. The primary outcomes were retention rate and adherence to the intervention program. Exploratory cognitive outcomes included changes in the Neuropsychological Test Battery z-scores. Analyses were performed according to the intention to treat. RESULTS: One hundred twenty-five participants were recruited (mean age: 75.64 (± 6.46); 58% women). The MD-Int (n = 61) and RHA (n = 64) groups were balanced in terms of their demographics and cognition. Fifty-two (85%) participants from the RHA group and 56 (88%) from the MD-Int group completed the study. More than 70% of the participants had high overall adherence to the intervention activities. The risk of cognitive decline was higher in the RHA group than in the MD-Int group in terms of executive function (p =.019) and processing speed scores (p =.026). CONCLUSIONS: The GOIZ-ZAINDU study proved that the FINGER methodology is adaptable and feasible in a different socio-cultural environment. The exploratory efficacy results showed a lower risk of decline in executive function and processing speed in the intervention group. These results support the design of a large-scale efficacy trial. TRIAL REGISTRATION: GOIZ ZAINDU feasibility trial was approved and registered by the Euskadi Drug Research Ethics Committee (ID: PI2017134) on 23 January 2018. Retrospectively registered in ClinicalTrials.gov (NCT06163716) on 8 December 2023.

Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models.

PURPOSE: To describe a procedure for incorporating parametric functions into individual-level simulation models to sample time to event when age-specific rates are available but not the individual data. METHODS: Using age-specific event rates, regression analysis was used to parametrize parametric survival distributions (Weibull, Gompertz, log-normal, and log-logistic), select the best fit using the R2 statistic, and apply the corresponding formula to assign random times to events in simulation models. We used stroke rates in the Spanish population to illustrate our procedure. RESULTS: The 3 selected survival functions (Gompertz, Weibull, and log-normal) had a good fit to the data up to 85 y of age. We selected Gompertz distribution as the best-fitting distribution due to its goodness of fit. CONCLUSIONS: Our work provides a simple procedure for incorporating parametric risk functions into simulation models without individual-level data. HIGHLIGHTS: We describe the procedure for sampling times to event for individual-level simulation models as a function of age from parametric survival functions when age-specific rates are available but not the individual dataWe used linear regression to estimate age-specific hazard functions, obtaining estimates of parameter uncertainty.Our approach allows incorporating parameter (second-order) uncertainty in individual-level simulation models needed for probabilistic sensitivity analysis in the absence of individual-level survival data.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data.

INTRODUCTION: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS. METHODS: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia. RESULTS: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms. CONCLUSIONS: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data

Introduction: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS.Methods: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia.Results: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms.Conclusions: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families’ suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research. (AU)

Introducción: Se desconocen las tasas de incidencia de los síntomas neuropsiquiátricos (SN) asociados a la demencia, lo cual dificulta la evaluación de su carga para la población. El objetivo de este estudio fue obtener una estimación aproximada de la incidencia y prevalencia en la población tanto de la demencia como de los SN.Métodos: Dada la naturaleza dinámica de la población con demencia, se realizó un estudio dentro de la base de datos del Servicio Vasco de Salud (datos del mundo real) a comienzos y finales de 2019. Se utilizaron modelos de bosques aleatorios validados para identificar por separado los clústeres depresivos y psicóticos, con arreglo a su presencia en los registros sanitarios electrónicos de todos los pacientes con diagnóstico de demencia.Resultados: Entre los 631.949 individuos mayores de 60 años registrados, 28.563 fueron diagnosticados de demencia, de los cuales 15.828 (55,4%) mostraron síntomas psicóticos y 19.461 (68,1%) síntomas depresivos. La incidencia de la demencia en 2019 fue de 6,8/1.000 personas-años. Muchos de los casos incidentes de SN depresivos (72,3%) y psicóticos (51,9%) se produjeron en casos de demencia incidente. El riesgo de SN de tipo depresivo se incrementa con factores tales como los años transcurridos desde que se diagnostica la demencia, la residencia en un sanatorio, y el sexo femenino, pero desciende con la edad avanzada. En el modelo de clúster psicótico, los efectos del sexo masculino y la edad avanzada se invierten, incrementando ambos la probabilidad de este tipo de síntomas.Conclusiones: El factor de estigmatización condiciona el entorno social y actitudinal, demorando el diagnóstico de la demencia, impidiendo que los pacientes reciban los cuidados adecuados, y exacerbando el sufrimiento de las familias. Este estudio evidencia la sinergia entre los grandes datos y los datos del mundo real para la investigación epidemiológica psiquiátrica. (AU)

Development and Validation of a Discrete Event Simulation Model to Evaluate the Cardiovascular Impact of Population Policies for Obesity.

INTRODUCTION: Our aim was to describe the development and validation of an obesity model representing the cardiovascular risks associated with different body mass index (BMI) categories, through simulation, designed to evaluate the epidemiological and economic impact of population policies for obesity. METHODS: A discrete event simulation model was built in R considering the risk of cardiovascular events (heart failure, stroke, coronary heart disease, and diabetes) associated with BMI categories in the Spanish population. The main parameters included in the model were estimated from Spanish hospital discharge records and the Spanish Health Survey and allowed both first-order and second-order (probabilistic sensitivity analysis) uncertainty to be programmed into the model. The simulation yielded the incidence and prevalence of cardiovascular events as validation outputs. To illustrate the capacity of the model, we estimated the reduction in cardiovascular events and cost-utility (incremental cost/incremental quality-adjusted life-years [QALYs]) of a hypothetical intervention that fully eliminated the cardiovascular risks associated with obesity and overweight. RESULTS: The Validation Status of Health-Economic decision models (AdViSHE) tool was applied. Internal validation plots showed adequate goodness of fit for the Spanish population. External validation was achieved by comparing the simulated and real incidence by age group for stroke, acute myocardial infarction, and heart failure. The intervention reduced the population hazard ratios of stroke, acute myocardial infarction, and heart failure to 0.81, 0.74, and 0.78, respectively, and added 0.74 QALYs to the whole population. CONCLUSIONS: This obesity simulation model evidenced good properties for estimating the long-term epidemiological and economic impact of policies to tackle obesity in Spain. The conceptual model could be implemented for other counties using country-specific input data.

[Low transmission of SARS-CoV-2 at school settings: a population-based study in the Basque Country.] / Baja transmisión del SARS-CoV-2 en el ámbito escolar: estudio poblacional en Euskadi.

OBJECTIVE: Studies about SARS-CoV-2 transmission at school settings have been outbreaks or schools clusters. There are scarce population-based studies has been studied. We aimed at describing SARS-CoV-2 school-related transmission and its relationship with baseline community cumulative incidence rate in the Basque Country after school reopening in order to inform Public Health decision-making. METHODS: We conducted a scholar surveillance population-based study of SARS-CoV-2 transmission from 7 September to 31 October 2020. We calculated percentages of cases in school-age population, secondary attack rates by education level among close contacts and correlation between population´s and scholars´ incidence rates at municipal level. RESULTS: There were 35,477 SARS-CoV-2 laboratory confirmed cases. Among them, 7.65% happened at school settings. Secondary attack rate at schools ranged from 2.9%, in preschools to 7.1% in high schools; Scholars caused a household and social secondary attack rate from 13% (high scholars) to 23.2% (elementary scholars). We found a low correlation between population´s and scholars´ incidence rates at municipal level (R2=0.047). CONCLUSIONS: Secondary attack rate at school settings increased as educational level did; conversely, to social and family secondary attack rate, that decreased with higher educational level. School attendance, during a SARS-CoV-2 high transmission period showed feasible and did not rise transmission. These findings happened under strict non-pharmaceutical measures at school settings and proper epidemiological surveillance, including tracing of laboratory confirmed cases of SARS-CoV-2 looking for close contacts, isolation and testing of close contacts during isolation period. The different degree of transmission of the circulating variants in the different periods of the pandemic must also be taken into account.

OBJETIVO: La transmisión del SARS-CoV-2 en escolares se ha estudiado en brotes o en conjuntos de escuelas y apenas hay estudios poblacionales. El objetivo de este estudio fue describir la transmisión de SARS-CoV-2 relacionada con el ámbito escolar y su relación con la incidencia acumulada comunitaria en Euskadi tras la reapertura de las escuelas para contribuir a la toma de decisiones en salud pública. METODOS: Se trató de un estudio poblacional, basado en el sistema de vigilancia epidemiológica, que analizó la transmisión de SARS-CoV-2 en el ámbito escolar tras la reapertura escolar el 7 de septiembre de 2020 hasta el 31 octubre de 2020. Se calcularon porcentajes de casos en población escolar, tasas de ataque secundaria por nivel educativo entre contactos estrechos, así como la correlación entre tasas de incidencia acumulada municipal y tasa en escolares. RESULTADOS: Se diagnosticaron 35.477 casos confirmados de SARS-CoV-2. Entre ellos el 7,65% sucedieron en el ámbito escolar. La tasa de ataque secundaria en dicho ámbito osciló entre un 2,9%, en educación infantil y un 7,1% en bachiller; los alumnos causaron, en el ámbito familiar y social, tasas de ataque secundarias de entre un 13% (bachiller) y un 23,2% (educación primaria). Encontramos una baja correlación entre las tasas de incidencia acumulada a nivel municipal y la de los escolares (R2=0,047). CONCLUSIONES: La tasa de ataque secundaria en ámbito escolar aumentó según el grado escolar, al contrario que la del ámbito social y familiar que disminuyó. La educación presencial no condujo a un incremento de la transmisión de SARS-CoV-2. Estos hallazgos sucedieron bajo estrictas medidas no farmacológicas en el ámbito escolar y una vigilancia epidemiológica adecuada que incluyó la búsqueda de contactos estrechos de casos de SARS-CoV-2 confirmados por laboratorio, así como el aislamiento y testeo de los contactos estrechos durante el periodo de aislamiento. Ha de tenerse en cuenta también, el diferente grado de transmisión de las variantes circulantes en los diferentes periodos de la pandemia.

Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study.

OBJECTIVE: To analyze the performance of adenosine deaminase in pleural fluid combined with other parameters routinely measured in clinical practice and assisted by machine learning algorithms for the diagnosis of pleural tuberculosis in a low prevalence setting, and secondly, to identify effusions that are non-tuberculous and most likely malignant. PATIENTS AND METHODS: We prospectively analyzed 230 consecutive patients diagnosed with lymphocytic exudative pleural effusion from March 2013 to June 2020. Diagnosis according to the composite reference standard was achieved in all cases. Pre-test probability of pleural tuberculosis was 3.8% throughout the study period. Parameters included were: levels of adenosine deaminase, pH, glucose, proteins, and lactate dehydrogenase, red and white cell counts and lymphocyte percentage in pleural fluid, as well as age. We tested six different machine learning-based classifiers to categorize the patients. Two different classifications were performed: a) tuberculous/non-tuberculous and b) tuberculous/malignant/other. RESULTS: Out of a total of 230 patients with pleural effusion included in the study, 124 were diagnosed with malignant effusion and 44 with pleural tuberculosis, while 62 were given other diagnoses. In the tuberculous/non-tuberculous classification, and taking into account the validation predictions, the support vector machine yielded the best result: an AUC of 0.98, accuracy of 97%, sensitivity of 91%, and specificity of 98%, whilst in the tuberculous/malignant/other classification, this type of classifier yielded an overall accuracy of 80%. With this three-class classifier, the same sensitivity and specificity was achieved in the tuberculous/other classification, but it also allowed the correct classification of 90% of malignant cases. CONCLUSION: The level of adenosine deaminase in pleural fluid together with cell count, other routine biochemical parameters and age, combined with a machine-learning approach, is suitable for the diagnosis of pleural tuberculosis in a low prevalence scenario. Secondly, non-tuberculous effusions that are suspected to be malignant may also be identified with adequate accuracy.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data.

INTRODUCTION: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS. METHODS: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia. RESULTS: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms. CONCLUSIONS: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.

Dynamic evaluation of the comparative effectiveness of an integrated program for heart failure care.

RATIONALE, AIMS AND OBJECTIVES: An integrated care program for heart failure (HF) was developed in the Basque Country in 2013. The objective of this research was to evaluate its effectiveness through the number of hospital admissions in three integrated healthcare organizations (IHOs), taking into account the longitudinal nature of the disease and the intensity of the implementation. METHODS: A retrospective observational study was carried out, based on data entered in administrative and clinical databases between 2014 and 2018 for a total population of 230 000. In addition to conventional statistical analyses, Andersen-Gill models for recurrent events were used, incorporating dynamic variables that allowed assessment of the intervention's intensity before each hospitalization. RESULTS: A total of 6768 patients were analysed. Age (hazard ratio [HR] = 1.016; 95% confidence interval [CI] 1.011-1.022), the Charlson index (HR = 1.067, 95% CI 1.047-1.087), and the number of previous hospitalizations (HR = 1.632, 95% CI 1.557-1.712) were risk factors for readmission. Differences between IHOs were also statistically significant. Greater intervention intensity was associated with a lower hospitalization rate (HR = 0.995, 95% CI 0.990-1.000). As indicated by the interaction between intervention intensity and IHO, differences between IHOs disappeared when intensity rose. No inequities in hospitalization were found as a function of deprivation index or sex. Nonetheless, inequity in the implementation of the program by sex was clear, women with HF receiving less intense intervention than men with the same level of comorbidity and age. CONCLUSIONS: The extent of program implementation measured by intervention intensity is a main driver of the effectiveness of an educational and monitoring program for HF. The evaluation of HF program effectiveness on readmissions must take into account the entire natural history of the disease. Implementation intensity explains differences between IHOs.

Impact of an organised population screening programme for colorectal cancer: Measurement after first and second rounds.

OBJECTIVE: The first and second rounds of the Basque programme for organised colorectal cancer screening were implemented between 2009 and 2014. Our objective was to measure the changes in incidence, tumour, node, metastasis staging distribution and tumour, node, metastasis-adjusted survival of patients with colorectal cancer from 2003 to 2014. METHOD: Colorectal cancer cases with screening (patients <70 years old) and without screening (patients ≥70 years old) were compared during three four-year periods: 2003-2006, 2007-2010 and 2011-2014 (fully implemented phase). Cox regression, five-year relative survival and cancer probability of death were calculated for each four-year period, age group and tumour, node, metastasis stage. Adjusted incidence rates were analysed by joinpoint regression. RESULTS: In an analysis of 23,301 cases of colorectal cancer, the incidence in patients younger than 70 years in 2013 showed a 17% annual decrease. The survival hazard ratios for stages I, II and III for 2003-2006 and 2007-2010 were compared to those for 2011-2014. From the first to the third period, diagnosis in the early stages (I and II) rose from 45.1% to 50.9% in the younger patient group and remained stable in the older group (49.6% and 49.4%). Additionally, the five-year relative survival rate increased significantly from 0.67 to 0.82 in those patients younger than 70 years, whereas in patients 70 years or older the rate did not change significantly (0.61 and 0.65). CONCLUSION: The screening reduced incidence and improved survival by anticipating the diagnosis and by reducing mortality for each tumour, node, metastasis stage in the target population. The effect on survival could also be due to lead-time bias.

Plasma lipids are associated with white matter microstructural changes and axonal degeneration.

Dislipidemia is a risk factor for cognitive impairment. We studied the association between interindividual variability of plasma lipids and white matter (WM) microstructure, using diffusion tensor imaging (DTI) in 273 healthy adults. Special focus was placed on 7 regions of interest (ROI) which are structural components of cognitive neurocircuitry. We also investigated the effect of plasma lipids on cerebrospinal fluid (CSF) neurofilament light chain (NfL), an axonal degeneration marker. Low density lipoprotein (LDL) and triglyceride (TG) levels showed a negative association with axial diffusivity (AxD) in multiple regions. High density lipoproteins (HDL) showed a positive correlation. The association was independent of Apolipoprotein E (APOE) genotype, blood pressure or use of statins. LDL moderated the relation between NfL and AxD in the body of the corpus callosum (p = 0.041), right cingulum gyrus (p = 0.041), right fornix/stria terminalis (p = 0.025) and right superior longitudinal fasciculus (p = 0.020) and TG in the right inferior longitudinal fasciculus (p = 0.004) and left fornix/stria terminalis (p = 0.001). We conclude that plasma lipids are associated to WM microstructural changes and axonal degeneration and might represent a risk factor in the transition from healthy aging to disease.

Economic evaluation of supplementing the diet with Souvenaid in patients with prodromal Alzheimer's disease.

BACKGROUND: The LipiDiDiet trial showed that Souvenaid, a medical food, might delay progression to dementia in prodromal Alzheimer's disease (AD). The objective of this study was to assess the cost-utility of Souvenaid compared to placebo in patients with prodromal AD under the conditions applied in that trial. METHODS: A discrete event simulation model was developed based on the LipiDiDiet trial and a literature review to assess the cost-utility of Souvenaid from a societal perspective considering direct and indirect costs. For both intervention and control groups, patient trajectories in terms of functional decline on the Clinical Dementia Rating Sum of Boxes (CDR-SB) scale in LipiDiDiet were reproduced statistically with mixed models by assigning time until events to simulated patients. From the societal perspective, four scenarios were analysed by combining different options for treatment duration and diagnostic test cost. Univariate sensitivity analysis assessed parameter uncertainties. RESULTS: Validation results at year 2 of disease progression fit with CDR-SB progression in LipiDiDiet. The incremental cost-utility ratio (ICUR) in the baseline case was €22,743/quality-adjusted life year (QALY). All scenarios rendered an ICUR lower than €25,000/QALY (the societal threshold). Moreover, the treatment option was cost-saving and increased health benefits when diagnostic costs were not considered and treatment was only administered during the prodromal stage. CONCLUSIONS: Treating prodromal AD with Souvenaid is a cost-effective intervention in all scenarios analysed. The LipiDiDiet trial showed a modest improvement in disease course but as the social costs of AD are very high, the intervention was efficient. Assessing small benefits at specific stages of AD is relevant because it is reasonable to expect that no effective, safe and affordable disease-modifying therapies will become available in the short to medium term.

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.

BACKGROUND: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. OBJECTIVE: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers. METHODS: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models. RESULTS: Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age. CONCLUSION: Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.

Cerebrospinal Fluid 7-Ketocholesterol Level is Associated with Amyloid-ß42 and White Matter Microstructure in Cognitively Healthy Adults.

BACKGROUND: Abnormal cholesterol metabolism changes the neuronal membrane and may promote amyloidogenesis. Oxysterols in cerebrospinal fluid (CSF) are related to Alzheimer's disease (AD) biomarkers in mild cognitive impairment and dementia. Cholesterol turnover is important for axonal and white matter (WM) microstructure maintenance. OBJECTIVE: We aim to demonstrate that the association of oxysterols, AD biomarkers, and WM microstructure occurs early in asymptomatic individuals. METHODS: We studied the association of inter-individual variability of CSF 24-hydroxycholesterol (24-OHC), 27-hydroxycholesterol (27-OHC), 7-ketocholesterol (7-KC), 7ß-hydroxycholesterol (7ß-OHC), amyloid-ß42 (Aß42), total-tau (t-tau), phosphorylated-tau (p-tau), neurofilament (NfL), and WM microstructure using diffusion tensor imaging, generalized linear models and moderation/mediation analyses in 153 healthy adults. RESULTS: Higher 7-KC levels were related to lower Aß42, indicative of greater AD pathology (p = 0.041) .  Higher 7-KC levels were related to lower fractional anisotropy (FA) and higher mean (MD), axial (AxD), and radial (RD) diffusivity. 7-KC modulated the association between AxD and NfL in the corpus callosum splenium (B = 39.39, p = 0.017), genu (B = 68.64, p = 0.000), and fornix (B = 10.97, p = 0.000). Lower Aß42 levels were associated to lower FA and higher MD, AxD, and RD in the fornix, corpus callosum, inferior longitudinal fasciculus, and hippocampus. The association between AxD and Aß42 was moderated by 7K-C (p = 0.048). CONCLUSION: This study adds clinical evidence to support the role of 7K-C on axonal integrity and the involvement of cholesterol metabolism in the Aß42 generation process.

Probabilistic cost-utility analysis and expected value of perfect information for the Oncotype multigenic test: a discrete event simulation model / Análisis de coste-utilidad probabilístico y valor esperado de la información perfecta para el test multigénico Oncotype: un modelo de simulación de eventos discretos

OBJECTIVE: To carry out a cost-utility analysis of the application of the Oncotype genomic test to inform the decision to use or not to use chemotherapy in the Basque Country (Spain). METHOD: The cost-utility study was carried out using a discrete event simulation model representing the natural history of breast cancer. The decision of treatment with chemotherapy based on Oncotype was compared with the standard of treatment based on clinical-pathological criteria. The model included clinical data from Basque hospitals and the literature and was processed by deterministic and probabilistic analysis to calculate the incremental cost-effectiveness ratio (ICER), the cost-effectiveness plane, the acceptability curve and the expected value of perfect information. The study adopted both a health and societal perspective. RESULTS: From a health perspective, the deterministic analysis estimated an ICER for Oncotype of 17,453 euros/quality-adjusted life year (QALY), discount included, and 9,613 euros/QALY without the discount. Eighty five percent (85%) of the simulations were below the efficiency threshold for Spain. The parametric variability associated with the Oncotype results was the main uncertainty factor in the decision. CONCLUSIONS: Oncotype is a cost-effective intervention from a health system perspective since each QALY gained costs less than 25,000 euros. From a societal perspective, it is dominant since it provides greater health and is accompanied by cost savings

OBJETIVO: Llevar a cabo un análisis de coste-utilidad del uso del test genómico Oncotype en el País Vasco (España) para informar la decisión de uso de quimioterapia. MÉTODO: El estudio de coste-utilidad se realizó mediante un modelo de simulación de eventos discretos que representó la evolución natural del cáncer de mama. La decisión de tratamiento con quimioterapia basada en Oncotype se comparó con el estándar de tratamiento basado en criterios clínico-patológicos. El modelo incluyó datos clínicos de hospitales vascos y la literatura para calcular la ratio de coste-efectividad incremental (RCEI) mediante análisis determinista y probabilístico, el plano coste-efectividad, la curva de aceptabilidad y el valor esperado de la información perfecta. El estudio adoptó una perspectiva tanto sanitaria como social. RESULTADOS: El análisis determinista estimó una RCEI para Oncotype de 17.453 euros/año de vida ajustado por calidad (AVAC) con descuento y 9613 euros euros/AVAC sin descuento, desde la perspectiva sanitaria. El 85% de las simulaciones estuvieron por debajo el umbral de aceptabilidad para España. La variabilidad paramétrica asociada a los resultados de Oncotype fue el mayor factor de incertidumbre de la decisión. CONCLUSIONES: Oncotype constituye una intervención coste-efectiva, ya que cada AVAC ganado tiene asociado un coste inferior a 25.000 euros. El test es dominante desde una perspectiva social al lograr mayor salud acompañada de ahorros

Application of two-part models and Cholesky decomposition to incorporate covariate-adjusted utilities in probabilistic cost-effectiveness models / Aplicación de los modelos en dos etapas y la descomposición de Cholesky para incorporar las utilidades ajustadas en los modelos probabilísticos de coste-efectividad

This study aimed to explain a joint statistical procedure (two-part models and Cholesky decomposition) to incorporate second-order uncertainty from covariate adjusted mean utility functions in probabilistic cost-effectiveness models. First, two-part models were applied to obtain parameters for the utility function. Second, a new set of correlated parameters for each simulation was obtained by Cholesky decomposition. The procedure was applied to EuroQol5D-5L in the Spanish Health Survey (21,007 adults). An example for the first simulation showed that 71% of men aged 60 years, high social status and normal weight were in perfect health, and in those not in perfect health, the expected utility was 0.8474 (= 1 − 0.1526). Therefore, their estimated mean utility value was 0.9559. Mean utility values in the interval (− ∞1] were calculated and their associated uncertainty incorporated in the cost-effectiveness models, based on the uncertainty related to correlated parameters in the utilities function

El objetivo de este estudio fue explicar el procedimiento estadístico conjunto (modelos en dos etapas y descomposición de Cholesky) para incorporar la incertidumbre de segundo orden asociada a la función de utilidad media ajustada por variables en los modelos probabilísticos de coste-efectividad. Primero se aplicaron los modelos en dos etapas para obtener los parámetros de la función de utilidad. Segundo, para cada simulación, se obtuvo un nuevo conjunto de parámetros correlacionados utilizando la descomposición de Cholesky. El procedimiento se aplicó al cuestionario EuroQol5D-5L de la Encuesta Nacional de Salud en España (21.007 adultos). El ejemplo de una primera simulación mostró que el 71% de los hombres de 60 años, con clase social alta y peso normal tenían una salud perfecta, y en aquellos que no tenían una salud perfecta la utilidad esperada fue de 0,8474 (= 1 − 0,1526). Por lo tanto, su utilidad media estimada fue de 0,9559. Se calcularon utilidades medias con valores comprendidos en el intervalo (-∞1] y se incorporó la incertidumbre asociada a ellos en los modelos de coste-efectividad, basándose en la incertidumbre correspondiente a los parámetros correlacionados de la función de utilidad

Changes in lung cancer survival by TNM stage in the Basque country from 2003 to 2014 according to period of diagnosis.

INTRODUCTION: The objective of this study was to analyze the survival of patients with lung cancer by TNM stage in the 4-year periods 2003-2006, 2007-2010 and 2011-2014, treated in the Basque Health Service, and to compare this with survival in an equivalent sample of the general population. METHODS: A retrospective observational design was applied to cases from the Hospital Cancer Registry of Euskadi. A cohort of 11,635 patients had complete data for the following variables: TNM stage, age, sex, histology, date of diagnosis, vital status and date of death. Relative survival and Cox and parametric regression models were used to assess changes in survival. RESULTS: The lung cancer 5-year survival probability decreased with increasing stage, from 50-65% in patients with stage I disease to 2-3% in those with stage IV disease. Comparing patients diagnosed from 2011-2014 and 2003-2006, we found that survival improved: (a) the risk of death (hazard ratio) in 2003-2006 was 1.66 for stage I, 1.51 for stage II, 1.21 for stage III, and 1.10 for stage IV; (b) the 5-year relative survival increased from 11.0% to 17.8% in the period 2011-2014; and (c) the years of life lost decreased significantly from 2003-2006 to 2011-2014, varying between 6.16 (stage I) and 16.21 (stage IV). CONCLUSIONS: Survival from lung cancer by stage in the Basque Country has lengthened significantly across all disease stages. Nonetheless, since we estimated that lung cancer patients still have significantly lower mean survival times than the general population, there is a need for more research to improve these outcomes.

Stage- and age-adjusted cost-effectiveness analysis of laparoscopic surgery in rectal cancer.

BACKGROUND: It has been argued that laparoscopy should be a standard treatment in rectal cancer due to its greater technical complexity. The objective of this study was to conduct a cost-effectiveness analysis to compare laparoscopy with open surgery for rectal cancer adjusting for age and clinical stage. METHODS: A real-world prospective cost-effectiveness cohort study was conducted with data on costs and effectiveness at individual patient level. A "genetic matching" algorithm was used to correct for selection bias. After balancing the sample groups, combined multivariate analysis of total costs and quality-adjusted life years (QALYs) was performed using seemingly unrelated regression (SUR) models. These models were first constructed without interactions and, subsequently, effects of any age-stage interaction were analyzed. RESULTS: The sample included 601 patients (400 by laparoscopy and 201 by open surgery). Crude cost-effectiveness analysis indicated that overall laparoscopy was cheaper and associated with higher QALYs. The SUR models without interactions showed that while laparoscopy remained dominant, the incremental effectiveness decreased to the point that it offered no statistically significant benefits over open surgery. In the subgroup analysis, at advanced stages of the disease, although none of the coefficients were significant, the mean incremental effectiveness (QALYs value) for laparoscopy was positive in younger patients and negative in older patients. Further, for advanced stages, the mean cost of open surgery was lower in both age subgroups but differences did not reach statistical significance. In early stages, laparoscopy cost was significantly lower in the subgroup younger than 70 and higher in the older subgroup. CONCLUSIONS: The cost-effectiveness of laparoscopy in surgery for rectal cancer justifies this being the standard surgical procedure in young patients and those at initial stages. The choice of procedure should be discussed with patients who are older and/or in advanced stages of the disease. Trial registration ClinicalTrials.gov Identifier: NCT02488161.

Application of two-part models and Cholesky decomposition to incorporate covariate-adjusted utilities in probabilistic cost-effectiveness models.

This study aimed to explain a joint statistical procedure (two-part models and Cholesky decomposition) to incorporate second-order uncertainty from covariate adjusted mean utility functions in probabilistic cost-effectiveness models. First, two-part models were applied to obtain parameters for the utility function. Second, a new set of correlated parameters for each simulation was obtained by Cholesky decomposition. The procedure was applied to EuroQol5D-5L in the Spanish Health Survey (21,007 adults). An example for the first simulation showed that 71% of men aged 60 years, high social status and normal weight were in perfect health, and in those not in perfect health, the expected utility was 0.8474 (= 1 - 0.1526). Therefore, their estimated mean utility value was 0.9559. Mean utility values in the interval (- ∞1] were calculated and their associated uncertainty incorporated in the cost-effectiveness models, based on the uncertainty related to correlated parameters in the utilities function.

Probabilistic cost-utility analysis and expected value of perfect information for the Oncotype multigenic test: a discrete event simulation model.

OBJECTIVE: To carry out a cost-utility analysis of the application of the Oncotype genomic test to inform the decision to use or not to use chemotherapy in the Basque Country (Spain). METHOD: The cost-utility study was carried out using a discrete event simulation model representing the natural history of breast cancer. The decision of treatment with chemotherapy based on Oncotype was compared with the standard of treatment based on clinical-pathological criteria. The model included clinical data from Basque hospitals and the literature and was processed by deterministic and probabilistic analysis to calculate the incremental cost-effectiveness ratio (ICER), the cost-effectiveness plane, the acceptability curve and the expected value of perfect information. The study adopted both a health and societal perspective. RESULTS: From a health perspective, the deterministic analysis estimated an ICER for Oncotype of 17,453 euros/quality-adjusted life year (QALY), discount included, and 9,613 euros/QALY without the discount. Eighty five percent (85%) of the simulations were below the efficiency threshold for Spain. The parametric variability associated with the Oncotype results was the main uncertainty factor in the decision. CONCLUSIONS: Oncotype is a cost-effective intervention from a health system perspective since each QALY gained costs less than 25,000 euros. From a societal perspective, it is dominant since it provides greater health and is accompanied by cost savings.