Markov Models for Economic Evaluation in Osteoporosis Treatment

[EN] Osteoporosis is frequent in elderly people, causing bone fractures and lowering their quality of life. The costs incurred by these fractures constitute a problem for public health. Markov chains were used to carry out an incremental cost-utility analysis of the four main drugs used in Spain to treat osteoporosis (alendronate, risedronate, denosumab and teriparatide). We considered 14 clinical transition states, from starting osteoporotic treatment at the age of 50 until death or the age of 100. Cost-effectiveness was measured by quality adjusted life years (QALYs). The values used in the Markov model were obtained from the literature. Teriparatide is the cost-effective alternative in the treatment of osteoporosis in patients with fractures from the age of 50, establishing a payment threshold of 20,000 EUR/QALY. However, it is the most expensive therapy, not appearing cost-effective in cases that do not present fracture and in ages over 80 years with fracture. Alendronate and denosumab therapies are presented as cost-effective osteoporosis treatment alternatives depending on the age of onset and duration of treatment. From the perspective of cost-effectiveness, establishing a payment threshold of 20,000 EUR/QALY, teriparatide is the cost-effective alternative in patients with fracture from the age of 50 to 70 years old in Spain.Osca Guadalajara, M.; Díaz-Carnicero, J.; González-De Julián, S.; Vivas-Consuelo, D. (2021). Markov Models for Economic Evaluation in Osteoporosis Treatment. Mathematics. 9(18):1-20. https://doi.org/10.3390/math9182331S12091

Predicting healthcare cost of diabetes using machine learning models

González-Rodríguez, J.; Díaz Carnicero, J.; Vivas-Consuelo, D.; González-De Julián, S.; Pinzón Espitia, OL. (2019). Predicting healthcare cost of diabetes using machine learning models. R. Company, J. C. Cortés, L. Jódar and E. López-Navarro. 99-104. http://hdl.handle.net/10251/180540S9910

Modelling Deprivation Level and Multimorbidity in a Health District

[EN] Deprivation is associated with an increased risk of developing chronic health conditions and with worse outcomes in multimorbidity. The goal of our study was to develop an integrated population index of deprivation (IPID) to observe the influence of deprivation on morbidity and the subsequent use of healthcare resources in one health district, using the socioeconomic, clinical and geographical data from its administrative health records. Eight socioeconomic indicators were identified and weighted using the methodology of two-phase principal component analysis, providing an index that allowed each census section to be classified into seven deprivation groups. Secondly, the possible relation between the IPID and the variables for multimorbidity and healthcare resources was analysed using the theory of multiple comparisons. It was observed that places with a greater proportion of healthy people presented lower values of deprivation and that, at lower levels of deprivation, there were fewer hospital admissions. The results show that living in an area with a higher deprivation index is associated with greater consumption of healthcare resources and disease burden. Identifying areas of sociosanitary vulnerability can help to identify health inequalities and allow intervention by clinical practices and healthcare management to reduce them.Botija Yagüe, MP.; Sorbet-Santiago, S.; Díaz-Carnicero, J.; González-De Julián, S.; Usó-Talamantes, R. (2022). Modelling Deprivation Level and Multimorbidity in a Health District. Mathematics. 10(4):1-14. https://doi.org/10.3390/math1004065911410

Cost of Type 2 Diabetes Patients with Chronic Kidney Disease Based on Real-World Data: An Observational Population-Based Study in Spain

[EN] This study analyzed the prevalence, costs and economic impact of chronic kidney disease CKD in patients with T2D in a Spanish Health District using real-world data. Observational cross-sectional study in adult patients with T2D was through data extracted from the information systems of the Valencia Clinico-La Malvarrosa Health District in the year 2015. Patients were stratified with the KDIGO classification for CKD. Additionally, patients were assigned to Clinical Risk Groups (CRGs) according to multimorbidity. Direct costs of primary and specialized care, and medication were estimated. The prevalence of T2D in the database population (n = 28,345) was 10.8% (mean age (SD) = 67.8 years (13.9); 51.5% male). Up to 14.935 patients (52.6%) had data on kidney function. According to the KDIGO classification, 66.2% of the patients were at low risk of CKD, 20.6% at moderately increased risk, 7.9% at high risk, and 5.2% at very high risk. The average healthcare costs associated with these four risk groups were EUR 3437, EUR 4936, EUR 5899 and EUR 7389, respectively. The large number of T2D patients with CKD in the early stages of the disease generated a significant increase in direct healthcare costs. The economic impact could be mitigated by early and comprehensive therapeutic approaches.This research was funded by Boehringer-Ingelheim Espana, S.A.Usó-Talamantes, R.; González-De Julián, S.; Díaz-Carnicero, J.; Saurí-Ferrer, I.; Trillo-Mata, JL.; Carrasco-Pérez, M.; Navarro-Pérez, J.... (2021). Cost of Type 2 Diabetes Patients with Chronic Kidney Disease Based on Real-World Data: An Observational Population-Based Study in Spain. International Journal of Environmental research and Public Health (Online). 18(18):1-14. https://doi.org/10.3390/ijerph18189853S114181

Desarrollo de modelos predictivos basados en aprendizaje automático como solución a problemas en la gestión clínica y hospitalaria

[ES] Introducción Los cambios demográficos y sociales están causando una transformación del modelo de asistencia en el Sistema Nacional de Salud, que ve aumentar la demanda de servicios progresivamente. Para garantizar la viabilidad del sistema hace falta plantear un nuevo paradigma, centrado en la cronicidad y la optimización de recursos. Objetivo Diseñar modelos predictivos basados en aprendizaje automático que sirvan de apoyo en algunos de los problemas de gestión más habituales. Metodología Se plantean para este estudio cuatro problemas actuales de la gestión clínica y hospitalaria: identificación de pacientes crónicos complejos, predicción de costes, estimación de la ocupación hospitalaria y predicción de reingresos. Se preparan distintos conjuntos de datos para cada uno de los problemas, incluyendo variables de tipo clínico, económico, de consumo de recursos, demográfico y social. Se diseña un algoritmo de aprendizaje automático del estado del arte (regresión logística, random forest o red neuronal) como solución a cada problema y se implementa en Matlab. Los problemas de clasificación son evaluados con el valor de área bajo la curva ROC, exactitud, sensibilidad, especificidad, valor predictivo positivo y negativo y estadístico-F. Para los problemas de regresión se calcula el coeficiente de correlación, la raíz del error cuadrático medio y los errores porcentuales medio y mediano. Resultados El sistema de identificación de pacientes crónicos complejos es capaz de detectar satisfactoriamente los casos de complejidad clínica, social y de dependencia. La predicción de costes alcanza valores altos para costes totales, pero menores en farmacia. La estimación de ocupación se realiza con un sistema sencillo con un elevado nivel de correlación y poco error. Se ha podido configurar un sistema con un elevado valor predictivo positivo para el caso de los reingresos. Conclusiones Los modelos predictivos basados en aprendizaje automático permiten aprovechar los registros de los sistemas de información, ofreciendo herramientas de ayuda para la gestión con resultados fiables.[EN] Introduction Demographic and social changes are causing a transformation of the assistance model in the National Health System, which is witnessing a progressive increase in the demand for services. In order to guarantee the viability of the system, it is necessary to propose a new paradigm, focused on chronicity and optimization of resources. Objective Design predictive models based on machine learning to support some of the most common management problems. Methodology Four current problems of clinical and hospital management are considered for this study: identification of complex chronic patients, prediction of costs, estimation of hospital occupancy and prediction of readmissions. Different data sets are prepared for each of the problems, including clinical, economic, resource consumption, demographic and social variables. A state-of-the-art automatic learning algorithm (logistic regression, random forest or neural network) is designed as a solution to each problem and implemented in Matlab. Classification problems are evaluated with the area under the ROC curve value, accuracy, sensitivity, specificity, positive and negative predictive value and F-score. For regression problems, the correlation coefficient, the root of the quadratic mean error and the mean and median percentage errors are calculated. Results The complex chronic patient identification system is capable of properly detecting the cases of clinical, social and dependency complexity. The cost prediction reaches high values for total costs, but lower in pharmacy. Occupancy estimation is done with a simple system with a high level of correlation and low error. It has been possible to configure a system with a high positive predictive value in the case of readmissions. Conclusions Predictive models based on machine learning allow to take advantage of the records of the information systems, offering tools to help management with reliable results.Díaz Carnicero, J. (2019). Desarrollo de modelos predictivos basados en aprendizaje automático como solución a problemas en la gestión clínica y hospitalaria. http://hdl.handle.net/10251/129241TFG

Desarrollo de una herramienta para la identificación de pacientes crónicos complejos utilizado métodos estadísticos y algoritmos de aprendizaje automático

[ES] Introducción El Sistema Nacional de Salud se está viendo afectado por el aumento de la cronicidad y el envejecimiento de la población, que se traduce en una mayor demanda de servicios. Los estudios y estratificación de la población según morbilidad se hacen necesarios para mejorar la gestión de los recursos. Los Pacientes Crónicos Complejos son un pequeño porcentaje de la población caracterizada por unas necesidades especialmente elevadas y una dificultad en su gestión. Su correcta identificación permitiría mejorar la atención que se les presta y su calidad de vida. Objetivo Desarrollo de una herramienta para la identificación de pacientes crónicos complejos utilizando métodos estadísticos y algoritmos de aprendizaje automático. Metodología El estudio se realiza sobre la población asignada de un departamento de salud, clasificada como crónico en el sistema Clinical Risk Groups (CRG) (estado de salud nivel 4 de CRG o superior) en el periodo de 2015 (98.465 pacientes). Se dispone de variables de tipo demográfico (edad y sexo), clínico (clasificación ACRG3, número de contactos ambulatorios, número de urgencias, número de ingresos, número de problemas relacionados con los medicamentos e importe de gasto farmacéutico) y socio-económico (zona básica de salud, situación de empadronamiento, indicador de nacionalidad , cobertura sanitaria, situación de residencia, migraciones, actividad laboral, grupos de aseguramiento, conjunto geopolítico, unidad de residencia, régimen de aportación de farmacia e índice de exclusión social). Para cada paciente se incluye una etiqueta de identificación de paciente crónico complejo marcada por profesionales sanitarios. Se plantea un análisis descriptivo de las variables, creación de modelos supervisados con tres sistemas de clasificación (random forest, regresión logística y red neuronal) y evaluación de los mismos con medida de exactitud, precisión, sensibilidad, especificidad, estadístico-F, coeficiente de correlación de Matthews y área bajo la curva ROC. Finalmente, se ha implementado el modelo obtenido con mejores resultados en una herramienta de visualización desarrollada con MATLAB. Resultados Las variables clínicas y de consumo de recursos están estrechamente ligadas al estado de salud y el nivel de gravedad de los pacientes. Entre las variables sociales, las más relevantes son el índice de exclusión social y la zona básica de salud. Tanto el algoritmo random forest como la red neuronal consiguen muy buenos resultados de clasificación con variables clínicas, pero el primero es el que ofrece mejor rendimiento al incorporar las variables socio-económicas. Este modelo se programa en una interfaz que permite obtener la probabilidad de un paciente de ser o no crónico complejo al introducir las variables. Conclusiones Los algoritmos de aprendizaje automático pueden ser usados para crear sistemas de clasificación de acuerdo con los criterios de los profesionales sanitarios. Las variables clínicas y socio-económicas permiten una correcta identificación de los pacientes crónicos complejos.[CA] Introducció El Sistema Nacional de Salut s'està veient afectat per l'augment de la cronicitat i l'envelliment de la població, que es tradueix en una major demanda de serveis. Els estudis i estratificació de la població segons morbiditat es fan necessaris per a millorar la gestió dels recursos. Els Pacients Crònics Complexos són un petit percentatge de la població caracteritzada per unes necessitats especialment elevades i una dificultat en la seua gestió. La seua correcta identificació permetria millorar l'atenció que se'ls presta i la seua qualitat de vida. Objectiu Desenvolupament d'una eina per a la identificació de pacients crònics complexos utilitzant mètodes estadístics i algorismes d'aprenentatge automàtic. Metodologia L'estudi es realitza sobre la població assignada d'un departament de salut, classificats com a crònics en el sistema Clinical Risk Groups (CRG) (estat de salut nivell 4 de CRG o superior) en el període de 2015 (98.465 pacients). Es disposa de variables de tipus demogràfic (edat i sexe), clínic (classificació ACRG3, nombre de contactes ambulatoris, nombre d'urgències, nombre d'ingressos, nombre de problemes relacionats amb els medicaments i import de despesa farmacèutica) i soci-econòmic (zona bàsica de salut, situació d'empadronament, indicador de nacionalitat , cobertura sanitària, situació de residència, migracions, activitat laboral, grups d'assegurament, conjunt geopolític, unitat de residència, règim d'aportació de farmàcia i índex d'exclusió social). Per a cada pacient s'inclou una etiqueta d'identificació de pacient crònic complex marcada per professionals sanitaris. Es planteja una anàlisi descriptiva de les variables, creació de models supervisats amb tres sistemes de classificació (random forest, regressió logística i xarxa neuronal) i avaluació dels mateixos amb mesura d'exactitud, precisió, sensibilitat, especificitat, estadístic-F, coeficient de correlació de Matthews i àrea sota la corba ROC. Finalment, s'ha implementat el model obtingut amb millors resultats en una eina de visualització desenvolupada amb MATLAB. Resultats Les variables clíniques i de consum de recursos estan estretament lligades a l'estat de salut i el nivell de gravetat dels pacients. Entre les variables socials, les més rellevants són l'índex d'exclusió social i la zona bàsica de salut. Tant l'algorisme random forest com la xarxa neuronal aconsegueixen molt bons resultats de classificació amb variables clíniques, però el primer és el que ofereix millor rendiment en incorporar les variables soci-econòmiques. Aquest model es programa en una interfície que permet obtenir la probabilitat d'un pacient de ser o no crònic complex en introduir les variables. Conclusions Els algorismes d'aprenentatge automàtic poden ser usats per a crear sistemes de classificació d'acord amb els criteris dels professionals sanitaris. Les variables clíniques i soci-econòmiques permeten una correcta identificació dels pacients crònics complexos.[EN] Introduction The National Health Service is affected nowadays by a raise in chronicity and ageing of the population, which turns into a higher demand of services. The studies and stratification of the population based on the morbidity condition have become necessary to improve the resource management. The complex chronic patients are a small percentage of the population, characterized by especially profound necessities and difficulties in their management. Their correct identification would improve the health care attention that is provided and also their life quality. Objective Development of a tool for complex chronic patients’ identification employing statistical methods and machine learning algorithms. Methodology The study was conducted with the assigned population of a health district, which is classified as chronic in the Clinical Risk Group (CRG) system (CRG equal or greater than 4) in 2015 (98,465 patients). The variables available are demographic (age and sex), clinical (ACRG3 classification, number of ambulatory events, number of visits to emergency departments, number of hospital admissions, pharmaceutical problems and ambulatory prescription costs) and socio-economical (basic health area, registration of residency, nationality, health coverage, residency situation, migrations, labour activity, assurance groups, geopolitical cluster, residency unit, pharmaceutical tax level and social exclusion index). There is also a complex chronic patient identification label for each patient, marked by sanitary professionals. A descriptive analysis of the variables is developed, and supervised learning models are created employing three classification algorithms (random forest, logistic regression and neural network). The evaluation is made through the measurement of accuracy, precision, recall, specificity, F-statistical, Matthews’ correlation coefficient and area under curve ROC. Finally, the model presenting the best performance is implemented in a visualization tool developed in MATLAB. Results The clinical variables and the information about resources consumption are highly related to the state of health and severity level of the patients. Among the social variables, the social exclusion index and the basic health areas prove to be the most significant ones. Both the random forest algorithm and the neural network offer great results, but the first one performs better when adding the socio-economic variables. This model is programmed in a user interface, to obtain the probability for a patient to be considered a complex chronic one by entering the different variables. Conclusions Machine learning algorithms can be used to develop classification systems, according to the professional criteria. The clinical and socio-economical variables provide a correct method to identify the complex chronic patients.Díaz Carnicero, J. (2018). Desarrollo de una herramienta para la identificación de pacientes crónicos complejos utilizado métodos estadísticos y algoritmos de aprendizaje automático. http://hdl.handle.net/10251/106595TFG

Cost-Effectiveness Mathematical Model to Evaluate the Impact of Improved Cardiac Ablation Strategies for Atrial Fibrillation Treatment

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia. Despite the frequency of the disease, the treatment strategies for AF are inefficient. We developed a cost-effectiveness model to evaluate potential improvements in the application of cardiac ablations to treat AF. These are surgical procedures to terminate the arrhythmia and restore Sinus Rhythm. A Markov Model with a time horizon of five years was built to represent the management of patients in AF. A Montecarlo simulation was developed as a sensitivity analysis when the effectiveness increases the estimate of the potential impact of an improvement on the efficacy of cardiac ablation. The result of the analysis showed 44% of patients were untreated in any way. The base case ends up with 45% of patients having sinus rhythm restored after five years. The Montecarlo simulation estimates that in 58% of cases, the alternative of increasing ablation effectiveness by 25% would be cost-effective. If the number of performed ablations is doubled, the robustness increases to 86%. In conclusion, the model of management of AF highlights the importance of not only increasing effectiveness, but also treating more patients. Our study shows that investing in new screening technology to increase the effectiveness of ablations would be cost-effective

Kidney Transplant: Survival Analysis and Prognostic Factors after 10 Years of Follow-Up

The aim of this work is to analyse recipient and graft survival after kidney transplant in a three-year cohort and to identify predictive factors with up to 10 years of follow-up. Methods: retrospective consecutive cohort study of 250 kidney transplant recipients operated between 2010 and 2012. Multiorganic transplants and both dead-donor and living-donor transplants were included. Data were collected from electronic health records. A survival analysis was conducted using the Kaplan-Meier method and a Cox proportional-hazards multivariate model. Results: mean follow-up was 8.1 ± 3.2 years. Graft survival at 2, 5 and 10 years was 89.0%, 85.1% and 78.4% respectively. The multivariate model identified the following risk factors for graft loss: diabetic nephropathy (HR 3.2 CI95% [1.1–9.4]), delayed graft function (3.8 [2.0–7.4]), chronic kidney rejection (3.7 [1.2–11.4]), and early surgical complications (2.6 [1.4–5.1]). Conversely, combined transplant was found to be a protective factor for graft loss (0.1 [0.0–0.5]). Recipient patient survival was 94.3%, 90.0% and 76.6% at 2, 5 and 10 years respectively. The model identified the following mortality risk factors: older recipient age (1.1 [1.1–1.2]), combined transplant (7.6 [1.7–34.5]) and opportunistic infections (2.6 [1.3–5.0]). Conclusions: 10-year recipient and graft survival were 76.6% and 78.4% respectively. Main mortality risk factors were older recipient age, opportunistic infections and multiorganic transplant. Main graft loss risk factors were diabetic nephropathy, delayed graft function, chronic kidney rejection and early surgical complications