Retrospective analysis of hospitalization costs using two payment systems: the diagnosis related groups (DRG) and the Queralt system, a newly developed case-mix tool for hospitalized patients.

BACKGROUND: Hospital services are typically reimbursed using case-mix tools that group patients according to diagnoses and procedures. We recently developed a case-mix tool (i.e., the Queralt system) aimed at supporting clinicians in patient management. In this study, we compared the performance of a broadly used tool (i.e., the APR-DRG) with the Queralt system. METHODS: Retrospective analysis of all admissions occurred in any of the eight hospitals of the Catalan Institute of Health (i.e., approximately, 30% of all hospitalizations in Catalonia) during 2019. Costs were retrieved from a full cost accounting. Electronic health records were used to calculate the APR-DRG group and the Queralt index, and its different sub-indices for diagnoses (main diagnosis, comorbidities on admission, andcomplications occurred during hospital stay) and procedures (main and secondary procedures). The primary objective was the predictive capacity of the tools; we also investigated efficiency and within-group homogeneity. RESULTS: The analysis included 166,837 hospitalization episodes, with a mean cost of € 4,935 (median 2,616; interquartile range 1,011-5,543). The components of the Queralt system had higher efficiency (i.e., the percentage of costs and hospitalizations covered by increasing percentages of groups from each case-mix tool) and lower heterogeneity. The logistic model for predicting costs at pre-stablished thresholds (i.e., 80th, 90th, and 95th percentiles) showed better performance for the Queralt system, particularly when combining diagnoses and procedures (DP): the area under the receiver operating characteristics curve for the 80th, 90th, 95th cost percentiles were 0.904, 0.882, and 0.863 for the APR-DRG, and 0.958, 0.945, and 0.928 for the Queralt DP; the corresponding values of area under the precision-recall curve were 0.522, 0.604, and 0.699 for the APR-DRG, and 0.748, 0.7966, and 0.834 for the Queralt DP. Likewise, the linear model for predicting the actual cost fitted better in the case of the Queralt system. CONCLUSIONS: The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.

Toward Adoption of Health Risk Assessment in Population-Based and Clinical Scenarios: Lessons From JADECARE.

Introduction: Health risk assessment (HRA) strategies are cornerstone for health systems transformation toward value-based patient-centred care. However, steps for HRA adoption are undefined. This article analyses the process of transference of the Adjusted Morbidity Groups (AMG) algorithm from the Catalan Good Practice to the Marche region (IT) and to Viljandi Hospital (EE), within the JADECARE initiative (2020-2023). Description: The implementation research approach involved a twelve-month pre-implementation period to assess feasibility and define the local action plans, followed by a sixteen-month implementation phase. During the two periods, a well-defined combination of experience-based co-design and quality improvement methodologies were applied. Discussion: The evolution of the Catalan HRA strategy (2010-2023) illustrates its potential for health systems transformation, as well as its transferability. The main barriers and facilitators for HRA adoption were identified. The report proposes a set of key steps to facilitate site customized deployment of HRA contributing to define a roadmap to foster large-scale adoption across Europe. Conclusions: Successful adoption of the AMG algorithm was achieved in the two sites confirming transferability. Marche identified the key requirements for a population-based HRA strategy, whereas Viljandi Hospital proved its potential for clinical use paving the way toward value-based healthcare strategies.

Serum potassium abnormalities, renin-angiotensin-aldosterone system inhibitor discontinuation, and clinical outcomes in patients with chronic cardiovascular, metabolic, and renal conditions: A population-based analysis.

BACKGROUND: Renin-angiotensin-aldosterone system inhibitors (RAASIs) play a crucial role in the treatment of several chronic cardiovascular conditions. Nonetheless, hyperkalemia, a frequent side effect, often leads to the discontinuation of RAASIs. The implications of hyperkalemia-driven changes in RAASI medications are poorly understood. METHODS: Population-based, observational, retrospective cohort study. Two large healthcare databases were utilized to identify 77,089 individuals aged 55 years and older with chronic conditions who were prescribed RAASIs between 2015 and 2017 in Southern Barcelona, Spain. We assessed the interplay between serum potassium abnormalities, RAASI management, and their associations with clinical outcomes, adjusting for potential confounders including socioeconomic factors, medical conditions, and potassium levels. RESULTS: The one-year prevalence of hyperkalemia (defined as serum potassium, K+ >5.0 mmol/L) was 17.8 %. RAASI were down-titrated in 16.1 % of these 13,673 patients with K+ levels. Factors linked to a higher likelihood of reducing/discontinuing RAASI after developing hyperkalemia included older age, impaired kidney function, higher potassium levels, and previous hospitalizations. Dose reduction/discontinuation of RAASI after developing hyperkalemia was associated with an increased risk of hospitalization (adjusted hazard ratio [HR] 1.16, 95 % confidence interval [CI] 1.10-1.21) and with increased mortality (HR 1.60, 95 % CI 1.56-1.84). CONCLUSION: In this large, observational study, hyperkalemia was linked to a greater likelihood of discontinuing RAASIs. Down-titration of RAASI was independently associated with unfavorable clinical outcomes such as hospitalization and specially mortality. Although the observational nature of the study, these findings underscore the importance of preventing circumstances that may lead to RAASI down-titration, such as hyperkalemia, as well as preventing hospitalizations and mortality, to ensure RAASI benefits.

Healthcare risk stratification model for emergency departments based on drugs, income and comorbidities: the DICER-score.

BACKGROUND: During the last decade, the progressive increase in age and associated chronic comorbidities and polypharmacy. However, assessments of the risk of emergency department (ED) revisiting published to date often neglect patients' pharmacotherapy plans, thus overseeing the Drug-related problems (DRP) risks associated with the therapy burden. The aim of this study is to develop a predictive model for ED revisit, hospital admission, and mortality based on patient's characteristics and pharmacotherapy. METHODS: Retrospective cohort study including adult patients visited in the ED (triage 1, 2, or 3) of multiple hospitals in Catalonia (Spain) during 2019. The primary endpoint was a composite of ED visits, hospital admission, or mortality 30 days after ED discharge. The study population was randomly split into a model development (60%) and validation (40%) datasets. The model included age, sex, income level, comorbidity burden, measured with the Adjusted Morbidity Groups (GMA), and number of medications. Forty-four medication groups, associated with medication-related health problems, were assessed using ATC codes. To assess the performance of the different variables, logistic regression was used to build multivariate models for ED revisits. The models were created using a "stepwise-forward" approach based on the Bayesian Information Criterion (BIC). Area under the curve of the receiving operating characteristics (AUCROC) curve for the primary endpoint was calculated. RESULTS: 851.649 patients were included; 134.560 (15.8%) revisited the ED within 30 days from discharge, 15.2% were hospitalized and 9.1% died within 30 days from discharge. Four factors (sex, age, GMA, and income level) and 30 ATC groups were identified as risk factors and combined into a final score. The model showed an AUCROC values of 0.720 (95%CI:0.718-0.721) in the development cohort and 0.719 (95%CI.0.717-0.721) in the validation cohort. Three risk categories were generated, with the following scores and estimated risks: low risk: 18.3%; intermediate risk: 40.0%; and high risk: 62.6%. CONCLUSION: The DICER score allows identifying patients at high risk for ED revisit within 30 days based on sociodemographic, clinical, and pharmacotherapeutic characteristics, being a valuable tool to prioritize interventions on discharge.

Impacto clínico y en costes sanitarios de alteraciones de potasio en pacientes con enfermedades cardiovasculares, metabólicas y renales crónicas / Impact on clinical outcomes and health costs of deranged potassium levels in patients with chronic cardiovascular, metabolic, and renal conditions

Introducción y objetivos Las alteraciones de la potasemia son frecuentes en las enfermedades cardiovasculares crónicas. El objetivo del estudio es evaluar las asociaciones de la hiperpotasemia y la hipopotasemia con eventos clínicos y costes sanitarios en pacientes con insuficiencia cardiaca, enfermedad renal crónica, diabetes mellitus, hipertensión y cardiopatía isquémica. Métodos Estudio longitudinal que incluyó a 36.269 pacientes de un Área de Salud que tuvieran al menos una de las afecciones mencionadas. Se utilizaron bases de datos administrativas, hospitalarias y de atención primaria. Se siguió a los participantes entre 2015 y 2017; estos tenían 55 o más años y al menos 1 medición de potasio. Se utilizaron 4 diseños analíticos para evaluar la prevalencia y la incidencia y el uso de inhibidores del sistema renina-angiotensina-aldosterona. Resultados La hiperpotasemia fue 2 veces más frecuente que la hipopotasemia. En los análisis ajustados, la hiperpotasemia se asoció de manera significativa con un mayor riesgo de muerte por todas las causas (HR de los modelos de regresión de Cox entre 1,31 y 1,68) y con un aumento de las probabilidades de que los gastos anuales de atención sanitaria superen el 85% (OR entre 1,21 y 1,29). Las asociaciones fueron aún mayores en los pacientes hipopotasémicos (HR para la muerte por todas las causas, 1,92-2,60; OR para los gastos de atención sanitaria> percentil 85, 1,81-1,85). Conclusiones Se necesitarían estudios experimentales para confirmar si la prevención de los trastornos del potasio reduce la mortalidad y los gastos sanitarios en estas enfermedades crónicas. Hasta entonces, nuestros hallazgos proporcionan conclusiones observacionales sobre la importancia de mantener normales las concentraciones de potasio. (AU)

Introduction and objectives Potassium derangements are frequent among patients with chronic cardiovascular conditions. Studies on the associations between potassium derangements and clinical outcomes have yielded mixed findings, and the implications for health care expenditure are unknown. We assessed the population-based associations between hyperkalemia, hypokalemia and clinical outcomes and health care costs, in patients with chronic heart failure, chronic kidney disease, diabetes mellitus, hypertension, and ischemic heart disease. Methods Population-based, longitudinal study including up to 36 269 patients from a health care area with at least one of the above-mentioned conditions. We used administrative, hospital and primary care databases. Participants were followed up between 2015 and 2017, were aged ≥ 55 years and had at least 1 potassium measurement. Four analytic designs were used to evaluate prevalent and incident cases and the use of renin-angiotensin-aldosterone system inhibitors. Results Hyperkalemia was twice as frequent as hypokalemia. On multivariable-adjusted analyses, hyperkalemia was robustly and significantly associated with an increased risk of all-cause death (HR from Cox regression models ranging from 1.31–1.68) and with an increased odds of a yearly health care expenditure >85th percentile (OR, 1.21–1.29). Associations were even stronger in hypokalemic patients (HR for all-cause death, 1.92–2.60; OR for health care expenditure> percentile 85th, 1.81–1.85). Conclusions Experimental studies are needed to confirm whether the prevention of potassium derangements reduces mortality and health care expenditure in these chronic conditions. Until then, our findings provide observational evidence on the potential importance of maintaining normal potassium levels. (AU)

Validación clínica de 2 agrupadores de morbilidad en el ámbito de atención primaria / Clinical validation of 2 morbidity groups in the primary care setting

INTRODUCCIÓN: Los grupos de morbilidad ajustados (GMA) y los clinical risk groups (CRG) son herramientas de estratificación poblacional basada en la morbilidad que permiten clasificar a los pacientes en categorías mutuamente excluyentes. OBJETIVO: Comparar la estratificación, según niveles de complejidad, proporcionada por los GMA con la de los CRG y con la realizada ad-hoc por los evaluadores. DISEÑO: Muestra aleatoria por estratos de riesgo de morbilidad. Emplazamiento: Cataluña. PARTICIPANTES: Cuarenta médicos de atención primaria emparejados 2 a 2. INTERVENCIONES: Cada pareja de evaluadores tuvo que validar 25 historias. Mediciones principales: Se evaluó la concordancia entre evaluadores, y entre los evaluadores y los resultados obtenidos por los 2 agrupadores de morbilidad con el índice kappa, la sensibilidad, la especificidad, el valor predictivo positivo y el negativo. RESULTADOS: La concordancia entre evaluadores se situó alrededor del valor kappa 0,75 (valor medio = 0,67), entre los GMA y los evaluadores fue similar (valor medio = 0,63), y más elevada que para los CRG (valor medio = 0,35). Los profesionales otorgaron una puntuación de 7,5 a la bondad de ambos agrupadores, aunque para los estratos de mayor complejidad, según asignación de los profesionales, los GMA obtuvieron mejores puntuaciones que los CRG. Los profesionales prefirieron mayoritariamente los GMA frente a los CRG. Estas diferencias se incrementaron con el aumento de la complejidad de los pacientes según el criterio clínico. Globalmente, se encontró menos de un 2% de errores graves de clasificación proporcionada por ambos agrupadores. CONCLUSIÓN: Los profesionales consideraron que ambos agrupadores clasificaban adecuadamente a la población, aunque los GMA tienen un mejor comportamiento en los estratos que los profesionales identifican como de mayor complejidad. Además, en la mayoría de los casos, los evaluadores clínicos prefirieron los GMA

INTRODUCTION: Adjusted Morbidity Groups (GMAs) and the Clinical Risk Groups (CRGs) are population morbidity based stratification tools which classify patients into mutually exclusive categories. Objetive: To compare the stratification provided by the GMAs, CRGs and that carried out by the evaluators according to the levels of complexity. DESIGN: Random sample stratified by morbidity risk. LOCATION: Catalonia. PARTICIPANTS: Forty paired general practitioners in the primary care, matched pairs. INTERVENTIONS: Each pair of evaluators had to review 25 clinical records. Main outputs: The concordance by evaluators, and between the evaluators and the results obtained by the 2 morbidity tools were evaluated according to the kappa index, sensitivity, specificity, and positive and negative predicted values. RESULTS: The concordance between general practitioners pairs was around the kappa value 0.75 (mean value = 0.67), between the GMA and the evaluators was similar (mean value = 0.63), and higher than for the CRG (mean value = 0.35). The general practitioners gave a score of 7.5 over 10 to both tools, although for the most complex strata, according to the professionals' assignment, the GMA obtained better scores than the CRGs. The professionals preferred the GMAs over the CRGs. These differences increased with the complexity level of the patients according to clinical criteria. Overall, less than 2% of serious classification errors were found by both groupers. CONCLUSION: The evaluators considered that both grouping systems classified the studied population satisfactorily, although the GMAs showed a better performance for more complex strata. In addition, the clinical raters preferred the GMAs in most cases

Validez de los grupos de morbilidad ajustados respecto a los clinical risk groups en el ámbito de la atención primaria / Validity of adjusted morbidity groups with respect to clinical risk groups in the field of primary care

Objetivo: Comparar el rendimiento referente a la bondad de ajuste y el poder explicativo de 2agrupadores de morbilidad en el ámbito de la atención primaria (AP): los grupos de morbilidad ajustados (GMA) y los clinical risk groups (CRG). Diseño: Estudio transversal. Emplazamiento: Ámbito de la AP del Instituto Catalán de la Salud (ICS), Cataluña, España. Participantes: Población asignada a centros de AP del ICS para el año 2014. Mediciones principales: Se analizan 3 indicadores de interés, como son el ingreso urgente, el número de visitas y el gasto en farmacia. Se aplica un análisis estratificado por centros ajustando modelos lineales generalizados a partir de las variables edad, sexo y agrupador de morbilidad para explicar cada una de las 3 variables de interés. Las medidas estadísticas para analizar el rendimiento de los distintos modelos aplicados son el índice de Akaike, el índice de Bayes y la seudovariabilidad explicada mediante cambio de deviance. Resultados: Los resultados muestran que en el ámbito de la AP del ICS el poder explicativo de los GMA es superior al ofrecido por los CRG, especialmente para el caso de las visitas y el gasto en farmacia. Conclusiones: El rendimiento de los GMA en el ámbito de la AP del ICS es superior al mostrado por los CRG

Objective: To compare the performance in terms of goodness of fit and explanatory power of 2 morbidity groupers in primary care (PC): adjusted morbidity groups (AMG) and clinical risk groups (CRG). Design: Cross-sectional study. Location: PC in the Catalan Institute for the Health (CIH), Catalonia, Spain. Participants: Population allocated in primary care centers of the CIH for the year 2014. Main measurements: Three indicators of interest are analyzed such as urgent hospitalization, number of visits and spending in pharmacy. A stratified analysis by centers is applied adjusting generalized lineal models from the variables age, sex and morbidity grouping to explain each one of the 3 variables of interest. The statistical measures to analyze the performance of the different models applied are the Akaike index, the Bayes index and the pseudo-variability explained by deviance change. Results: The results show that in the area of the primary care the explanatory power of the AMGs is higher to that offered by the CRGs, especially for the case of the visits and the pharmacy. Conclusions: The performance of GMAs in the area of the CIH PC is higher than that shown by the CRGs

Respuesta a la carta "Los grupos de morbilidad ajustados: un debate pendiente" / Author’s reply to letter «Adjusted morbidity groups: A pending debate»

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Los grupos de morbilidad ajustados: nuevo agrupador de morbilidad poblacional de utilidad en el ámbito de la atención primaria / Adjusted morbidity groups: a new multiple morbidity measurement of use in Primary Care

Se ha desarrollado un agrupador de morbilidad adaptado a nuestro entorno sanitario que permite clasificar a la población en 6 grupos de morbilidad, divididos a su vez en 5 niveles de complejidad, más un grupo de población sana; de este modo la población queda agrupada en 31 categorías mutuamente excluyentes. Se presentan resultados de la estratificación en Cataluña. Los grupos de morbilidad ajustados (GMA) son un agrupador de morbilidad comparable a otros existentes en el mercado, pero desarrollado con los datos de nuestro sistema sanitario. Permite generar una adecuada estratificación poblacional y es capaz de identificar a poblaciones diana. Muestra buenos resultados explicativos en indicadores de uso de recursos sanitarios. El Ministerio de Sanidad está impulsando la implantación del agrupador en el Sistema Nacional de Salud

The Adjusted Morbidity Groups (GMA) is a new morbidity measurement developed and adapted to the Spanish healthcare System. It enables the population to be classified into 6 morbidity groups, and in turn divided into 5 levels of complexity, along with one healthy population group. Consequently, the population is divided into 31 mutually exclusive categories. The results of the stratification in Catalonia are presented. GMA is a method for grouping morbidity that is comparable to others in the field, but has been developed with data from the Spanish health system. It can be used to stratify the population and to identify target populations. It has good explanatory and predictive results in the use of health resources indicators. The Spanish Ministry of Health is promoting the introduction of the GMA into the National Health System