Search

BACKGROUND Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. OBJECTIVE The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients’ profiles for personalized service selection purposes. METHODS Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. RESULTS Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients’ profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). CONCLUSIONS The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation.

ObjectivesTo investigate whether the effect of chronological age on the risk of developing critical illness in COVID-19 hospitalized individuals is attenuated, and to which extent, when adjusting for increasingly exhaustive measures of the comorbidity burden.DesignRetrospective assessment of electronic health records.SettingAll public hospitals of the Catalan Institute of Health (Catalonia; North-East Spain; 7.7 million inhabitants), which account for 30% of all hospital admissions in Catalonia.ParticipantsWe included all individuals admitted to the hospital with COVID-19 as the main diagnosis between March 1, 2020, and January 31, 2022. Vaccinated individuals and those admitted within the first of the six COVID-19 epidemic waves were excluded from the primary analysis but were included in secondary analyses.Main outcome measuresThe primary composite outcome was critical illness, defined as the need for invasive mechanical ventilation, transfer to the intensive care unit (ICU), or in-hospital death (any of them). Explanatory variables included age, sex, and four summary measures of comorbidity burden on admission: the Charlson index (17 diagnostic group codes), the Elixhauser index and count (31 diagnostic group codes), and the Queralt DxS index (3,145 diagnostic group codes). All models were adjusted by wave and center. The proportion of the effect of age attributable to comorbidity burden was assessed using a causal mediation analysis.ResultsThe primary analysis included 10,090 hospitalizations due to COVID-19; of them, 3,524 experienced a critical illness. The frequency of critical illness increased with age and comorbidity burden on admission, irrespective of the measure used. In multivariate analyses, the effect size of age decreased with the number of diagnoses considered to estimate comorbidity burden. When adjusting for the Queralt DxS index, age showed a minimal contribution to critical illness; according to the causal mediation analysis, comorbidity burden on admission explained the 95.3% (95% CI 82.1% −112.7%) of the observed effect of age on critical illness.ConclusionsWhen measured exhaustively, comorbidity burden rather than chronological age explains the increased risk of critical illness observed in patients hospitalized with COVID-19.Summary boxWhat is already known on this topicAge is broadly acknowledged as a critical risk factor for developing critical illness in individuals hospitalized due to COVID-19.When adjusting for other underlying factors, such as comorbidities, the effect size of age for predicting critical illness decreases; nevertheless, most studies have suggested that age remains independently associated with COVID-19 outcomes.What this study addsThe observed contribution of chronological age to the risk of critical illness in hospitalized COVID-19 patients decreases with the exhaustivity of the measure of comorbidity burden.When adjusting for a comprehensive comorbidity index that considers all possible clinical conditions from a weighted list of 3,145 possible diagnostic groups, age has little or no relevant effect on the risk of critical illness.Mediation analyses confirm that the effect of chronological age on COVID-19 outcomes can be explained by comorbidity burden.

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.

Resumen Introduccion y objetivos Las alteraciones de la potasemia son frecuentes en las enfermedades cardiovasculares cronicas. El objetivo del estudio es evaluar las asociaciones de la hiperpotasemia y la hipopotasemia con eventos clinicos y costes sanitarios en pacientes con insuficiencia cardiaca, enfermedad renal cronica, diabetes mellitus, hipertension y cardiopatia isquemica. Metodos Estudio longitudinal que incluyo a 36.269 pacientes de un Area de Salud que tuvieran al menos una de las afecciones mencionadas. Se utilizaron bases de datos administrativas, hospitalarias y de atencion primaria. Se siguio a los participantes entre 2015 y 2017; estos tenian 55 o mas anos y al menos 1 medicion de potasio. Se utilizaron 4 disenos analiticos para evaluar la prevalencia y la incidencia y el uso de inhibidores del sistema renina-angiotensina-aldosterona. Resultados La hiperpotasemia fue 2 veces mas frecuente que la hipopotasemia. En los analisis ajustados, la hiperpotasemia se asocio de manera significativa con un mayor riesgo de muerte por todas las causas (HR de los modelos de regresion de Cox entre 1,31 y 1,68) y con un aumento de las probabilidades de que los gastos anuales de atencion sanitaria superen el 85% (OR entre 1,21 y 1,29). Las asociaciones fueron aun mayores en los pacientes hipopotasemicos (HR para la muerte por todas las causas, 1,92-2,60; OR para los gastos de atencion sanitaria > percentil 85, 1,81-1,85). Conclusiones Se necesitarian estudios experimentales para confirmar si la prevencion de los trastornos del potasio reduce la mortalidad y los gastos sanitarios en estas enfermedades cronicas. Hasta entonces, nuestros hallazgos proporcionan conclusiones observacionales sobre la importancia de mantener normales las concentraciones de potasio.

Background The aims of the present analysis are to estimate the prevalence of five key chronic cardiovascular, metabolic and renal conditions at the population level, the prevalence of renin-angiotensin-aldosterone system inhibitor (RAASI) medication use and the magnitude of potassium (K+) derangements among RAASI users. Methods and Results We used data from more than 375,000 individuals, 55 years of age or older, included in the population-based healthcare database of the Catalan Institute of Health between 2015 and 2017. The conditions of interest were chronic heart failure (CHF), chronic kidney disease (CKD), diabetes mellitus, ischemic heart disease and hypertension. RAASI medications included angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, mineralocorticoid receptor antagonists (MRAs) and renin inhibitors. Hyperkalemia was defined as K+ levels >5.0 mEq/L and hypokalemia as K+

Background Accurate risk adjustment is crucial for healthcare management and benchmarking. Purpose We aimed to compare the performance of classic comorbidity functions (Charlson's and Elixhauser's), of the All Patients Refined Diagnosis Related Groups (APR-DRG), and of the Queralt Indices, a family of novel, comprehensive comorbidity indices for the prediction of key clinical outcomes in hospitalized patients. Material and Methods We conducted an observational, retrospective cohort study using administrative healthcare data from 156,459 hospital discharges in Catalonia (Spain) during 2018. Study outcomes were in-hospital death, long hospital stay, and intensive care unit (ICU) stay. We evaluated the performance of the following indices: Charlson's and Elixhauser's functions, Queralt's Index for secondary hospital discharge diagnoses (Queralt DxS), the overall Queralt's Index, which includes pre-existing comorbidities, in-hospital complications, and principal discharge diagnosis (Queralt Dx), and the APR-DRG. Discriminative ability was evaluated using the area under the curve (AUC), and measures of goodness of fit were also computed. Subgroup analyses were conducted by principal discharge diagnosis, by age, and type of admission. Results Queralt DxS provided relevant risk adjustment information in a larger number of patients compared to Charlson's and Elixhauser's functions, and outperformed both for the prediction of the 3 study outcomes. Queralt Dx also outperformed Charlson's and Elixhauser's indices, and yielded superior predictive ability and goodness of fit compared to APR-DRG (AUC for in-hospital death 0.95 for Queralt Dx, 0.77-0.93 for all other indices; for ICU stay 0.84 for Queralt Dx, 0.73-0.83 for all other indices). The performance of Queralt DxS was at least as good as that of the APR-DRG in most principal discharge diagnosis subgroups. Conclusion Our findings suggest that risk adjustment should go beyond pre-existing comorbidities and include principal discharge diagnoses and in-hospital complications. Validation of comprehensive risk adjustment tools such as the Queralt indices in other settings is needed.

BackgroundComorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients’ complexity.PurposeTo evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity.Patients and methodsWe analyzed data from all COVID-19 hospitalizations reported in eight public hospitals of Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors included age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiving operating characteristics curve (AUC).ResultsOur analysis included 4,607 hospitalized COVID-19 patients. Of them, 1,315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. The AUC for prediction of critical illness was 0.641 (95% CI 0.624-0.660) for the Charlson index, 0.665 (0.645-0.681) for the Elixhauser index, and 0.787 (0.773-0.801) for Queralt DxS. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices.ConclusionIn our analysis, the ability of comorbidity indices to predict hospital outcomes in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting.

The shortage of recently approved vaccines against the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has highlighted the need for evidence-based tools to prioritize healthcare resources for people at higher risk of severe coronavirus disease 2019 (COVID-19). Current evidence indicates that age is far from accurate in identifying the risk of severe illness; furthermore, the count of individual risk factors has limited applicability to population-based “stratify-and-shield” strategies. We developed a COVID-19 risk stratification system that allows allocating people into four mutually-exclusive risk categories based on multivariate models for hospital admissions, transfer to intensive care unit (ICU), and mortality among the general population. The model was developed using clinical, hospital, and epidemiological data from the entire population of Catalonia (North-East Spain; 7.5 million people) and validated using an independent dataset of 218,329 individuals with PCR-confirmed COVID-19, who were infected after developing the model. This showed high discrimination capacity, with an area under the curve of the receiving operating characteristics of 0.85 (95% CI 0.85–0.85) for hospital admissions, 0.86 (0.86–0.97) for ICU transfers, and 0.96 (0.96–0.96) for deaths. Our results provide clinicians and policymakers with an evidence-based tool for prioritizing COVID-19 healthcare resources other population groups aside from those with higher exposure to SARS-CoV-2 and frontline workers.

Resumen: 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. Abstract: 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. Palabras clave: Agrupador de morbilidad, Multimorbilidad, Validación clínica, Keywords: Morbidity grouper, Multimorbidity, Clinical Validation

BackgroundMultimorbidity measures are useful for resource planning, patient selection and prioritization, and factor adjustment in clinical practice, research, and benchmarking. We aimed to compare the explanatory performance of the adjusted morbidity group (GMA) index in predicting relevant healthcare outcomes with that of other quantitative measures of multimorbidity.MethodsThe performance of multimorbidity measures was retrospectively assessed on anonymized records of the entire adult population of Catalonia (North-East Spain). Five quantitative measures of multimorbidity were added to a baseline model based on age, gender, and socioeconomic status: the Charlson index score, the count of chronic diseases according to three different proposals (i.e., the QOF, HCUP, and Karolinska institute), and the multimorbidity index score of the GMA tool. Outcomes included all-cause death, total and non-scheduled hospitalization, primary care and ER visits, medication use, admission to a skilled nursing facility for intermediate care, and high expenditure (time frame 2017). The analysis was performed on 10 subpopulations: all adults (i.e., aged > 17 years), people aged > 64 years, people aged > 64 years and institutionalized in a nursing home for long-term care, and people with specific diagnoses (e.g., ischemic heart disease, cirrhosis, dementia, diabetes mellitus, heart failure, chronic kidney disease, and chronic obstructive pulmonary disease). The explanatory performance was assessed using the area under the receiving operating curves (AUC-ROC) (main analysis) and three additional statistics (secondary analysis).ResultsThe adult population included 6,224,316 individuals. The addition of any of the multimorbidity measures to the baseline model increased the explanatory performance for all outcomes and subpopulations. All measurements performed better in the general adult population. The GMA index had higher performance and consistency across subpopulations than the rest of multimorbidity measures. The Charlson index stood out on explaining mortality, whereas measures based on exhaustive definitions of chronic diagnostic (e.g., HCUP and GMA) performed better than those using predefined lists of diagnostics (e.g., QOF or the Karolinska proposal).ConclusionsThe addition of multimorbidity measures to models for explaining healthcare outcomes increase the performance. The GMA index has high performance in explaining relevant healthcare outcomes and may be useful for clinical practice, resource planning, and public health research.

OBJECTIVETo identify the different subpopulations that are susceptible for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and hospitalisation or death due to coronavirus disease 2019 (COVID-19) in Catalonia, Spain.DESIGNCross-sectional study.SETTINGData collected from the Catalan Health Surveillance System (CatSalut) in Catalonia, a region of Spain.PARTICIPANTSUsing data collected between 1 March and 1 June 2020, we conducted the following comparative analyses: people infected by SARS-CoV-2 (328 892) vs Catalonia’s entire population (7 699 568); COVID-19 cases who required hospitalisation (37 638) vs cases who did not require hospitalisation (291 254); and COVID-19 cases who died during the study period vs cases who did not die during the study period (12 287).MAIN OUTCOME MEASURESThree clinical outcomes related to COVID-19 (infection, hospitalisation, or death). We analysed sociodemographic and environment variables (such as residing in a nursing home) and the presence of previous comorbidities.RESULTSA total of 328 892 cases were considered to be infected with SARS-CoV-2 (4.27% of total population). The main risk factors for the diagnostic were: female gender (risk ratio [RR] =1.49; 95% confidence interval [95% CI] =1.48-1.50), age (4564 years old; RR=1.02; 95% CI=1.01-1.03), high comorbidity burden (GMA index) (RR=3.03; 95% CI=2.97-3.09), reside in a nursing home (RR=11.82; 95% CI=11.66-11.99), and smoking (RR=1.06; 95% CI=1.05-1.07). During the study period, there were 37 638 (11.4 %) hospitalisations due to COVID-19, and the risk factors were: male gender (RR=1.45; 95% CI=1.43-1.48), age > 65 (RR=2.38; 95% CI=2.28-2.48), very low individual income (RR=1.03; 95% CI=0.97-1.08), and high burden of comorbidities (GMA index) (RR=5.15; 95% CI=4.89-5.42). The individual comorbidities with higher burden were obesity (RR=1.23; 95% CI=1.20-1.25), chronic obstructive pulmonary disease (RR=1.19; 95% CI=1.15-1.22), heart failure (RR=1.19; 95% CI=1.16-1.22), diabetes mellitus (RR=1.07; 95% CI=1.04-1.10), and neuropsychiatric comorbidities (RR=1.06; 95% CI=1.03-1.10). A total of 12 287 deaths (3.73%) were attributed to COVID-19, and the main risk factors were: male gender (RR=1.73; 95% CI=1.67-1.81), age > 65 (RR=37.45; 95% CI=29.23-47.93), residing in a nursing home (RR=9.22; 95% CI=8.81-9.65), and high burden of comorbidities (GMA index) (RR=5.25; 95% CI=4.60-6.00). The individual comorbidities with higher burden were: heart failure (RR=1.21; 95% CI=1.16-1.22), chronic kidney disease (RR=1.17; 95% CI=1.13-1.22), and diabetes mellitus (RR=1.10; 95% CI=1.06-1.14). These results did not change significantly when we considered only PCR-positive patients.CONCLUSIONSFemale gender, age between 45 to 64 years old, high burden of comorbidities, and factors related to environment (nursing home) play a relevant role in SARS-CoV-2 infection and transmission. In addition, we found risk factors for hospitalisation and death due to COVID-19 that had not been described to date, including comorbidity burden, neuro-psychiatric disorders, and very low individual income. This study supports interventions for transmission control beyond stratify-and-shield strategies focused only on protecting those at risk of death. Future COVID-19 studies should examine the role of gender, the burden of comorbidities, and socioeconomic status in disease transmission, and should determine its relationship to workplaces, especially healthcare centres and nursing homes.

Background Multimorbidity is highly relevant for both service commissioning and clinical decision-making. Optimization of variables assessing multimorbidity in order to enhance chronic care management is an unmet need. To this end, we have explored the contribution of multimorbidity to predict use of healthcare resources at community level by comparing the predictive power of four different multimorbidity measures. Methods A population health study including all citizens ≥18 years (n = 6,102,595) living in Catalonia (ES) on 31 December 2014 was done using registry data. Primary care service utilization during 2015 was evaluated through four outcome variables: A) Frequent attendants, B) Home care users, C) Social worker users, and, D) Polypharmacy. Prediction of the four outcome variables (A to D) was carried out with and without multimorbidity assessment. We compared the contributions to model fitting of the following multimorbidity measures: i) Charlson index; ii) Number of chronic diseases; iii) Clinical Risk Groups (CRG); and iv) Adjusted Morbidity Groups (GMA). Results The discrimination of the models (AUC) increased by including multimorbidity as covariate into the models, namely: A) Frequent attendants (0.771 vs 0.853), B) Home care users (0.862 vs 0.890), C) Social worker users (0.809 vs 0.872), and, D) Polypharmacy (0.835 vs 0.912). GMA showed the highest predictive power for all outcomes except for polypharmacy where it was slightly below than CRG. Conclusions We confirmed that multimorbidity assessment enhanced prediction of use of healthcare resources at community level. The Catalan population-based risk assessment tool based on GMA presented the best combination of predictive power and applicability.

ResumenSe 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.AbstractThe 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.

Background: Population risk stratification is relevant for implementation of integrated care. It has been presented as an useful tool to (i) help to identify high-risk patients (ii) facilitate services design and (iii) ensure appropriate coverage of prevention and care interventions. Risk stratification (RS) tools are predictive models applied to foresee undesired events. They are algorithms that relate some parameters (demographic, clinical, health services utilization or living conditions) and some predicted outcomes (A&E visits, readmissions, death or expenditure).They are used to stratify a population according to the selected metric. However, lacklustre results have been presented and few stratification tools have been scaled up in Europe. Problems have been linked with scope, data, ICT, clinicians´ involvement, implementation barriers or impactability. The workshop will analyze recent experiences in Spain and UK. It will build from ASSEHS project (http://assehs.eu/). Catalonia has developed a new tool based on Adjusted Morbidity Groups (GMA). 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. Differences with other tools will be analyzed and concordance between clinical judgement and tool predictions will be discussed. Basque Population Risk stratification was deployed in 2011 covering the whole Basque population. Identified problems were time delays, clinical acceptance and impactability. We will discuss updates including new variables and data process and analysis. Swansea University will discuss Emergency Admission Risk Stratification (EARS). PRISMATIC examined effectiveness of an EARS tool introduced in 32 general practices in Wales, using data from 235,000 patients. We consider the surprising findings. We also surveyed over 70% of UK health boards and commissioning groups on EARS use. We identified widespread implementation and a host of tools. Many areas had made service changes to support EARS – but why were few evaluating it? Between 2015 and 2018, NHS England made major investments in a New Models of Care programme which supported local areas (vanguards) to prototype new ways of delivering integrated care. RS and multidisciplinary teams was a common feature of integrated care vanguards. The national evaluation team did in-depth studies of how five vanguards were implementing risk stratification. We discuss findings, challenges and lessons. Aims and Objectives: Risk stratification tools update Concordance between clinical judgment and tools predictions. Pitfalls in implementation. Impact on performance and outcomes Future use in European health services Format (90m) Introduction: Population Risk stratification, main challenges. Esteban Manuel.Keenoy10m Catalonian experience: Adjusted Morbidity Group. David Monterde. 10m Developments in the Basque Population Risk Stratification. Eduardo Millan. 10m Learnings from PRISMATIC and EARP projects: Alison Porter, Mark Kingston, 10m Risk stratification - learnings from New Care Models vanguards Charles Tallack 10m Q&A and discussion 40m What are the main issues? Are there any common lessons? Who will be using RS in 5 years time (what? why?) Target Audience: Professionals and managers interested in implementing and/or improving integrated care programs. RS researchers and tools developers. Learnings/take away: Recent findings on risk stratification tools development, implementation and impact in real world settings.

Background Comprehensive assessment of integrated care deployment constitutes a major challenge to ensure quality, sustainability and transferability of both healthcare policies and services in the transition toward a coordinated service delivery scenario. To this end, the manuscript articulates four different protocols aiming at assessing large-scale implementation of integrated care, which are being developed within the umbrella of the regional project Nextcare (2016–2019), undertaken to foster innovation in technologically-supported services for chronic multimorbid patients in Catalonia (ES) (7.5 M inhabitants). Whereas one of the assessment protocols is designed to evaluate population-based deployment of care coordination at regional level during the period 2011–2017, the other three are service-based protocols addressing: i) Home hospitalization; ii) Prehabilitation for major surgery; and, iii) Community-based interventions for frail elderly chronic patients. All three services have demonstrated efficacy and potential for health value generation. They reflect different implementation maturity levels. While full coverage of the entire urban health district of Barcelona-Esquerra (520 k inhabitants) is the main aim of home hospitalization, demonstration of sustainability at Hospital Clinic of Barcelona constitutes the core goal of the prehabilitation service. Likewise, full coverage of integrated care services addressed to frail chronic patients is aimed at the city of Badalona (216 k inhabitants). Methods The population-based analysis, as well as the three service-based protocols, follow observational and experimental study designs using a non-randomized intervention group (integrated care) compared with a control group (usual care) with a propensity score matching method. Evaluation of cost-effectiveness of the interventions using a Quadruple aim approach is a central outcome in all protocols. Moreover, multi-criteria decision analysis is explored as an innovative method for health delivery assessment. The following additional dimensions will also be addressed: i) Determinants of sustainability and scalability of the services; ii) Assessment of the technological support; iii) Enhanced health risk assessment; and, iv) Factors modulating service transferability. Discussion The current study offers a unique opportunity to undertake a comprehensive assessment of integrated care fostering deployment of services at regional level. The study outcomes will contribute refining service workflows, improving health risk assessment and generating recommendations for service selection. Trials registration NCT03130283 (date released 04/06/2018), NCT03768050 (date released 12/05/2018), NCT03767387 (date released 12/05/2018). Electronic supplementary material The online version of this article (10.1186/s12913-019-4174-2) contains supplementary material, which is available to authorized users.

Background There is a high prevalence of work-related musculoskeletal disorders among health care professionals. Posture is an essential point to be addressed for health care professionals with musculoskeletal disorders. Cervical pain can result from several conditions. Treatment should include posture modification and home exercise. Objective This study aims to compare a new postural garment (Posture Plus Force; Medi, Bayreuth, Germany) with exercises for women with nonspecific cervical pain. The investigators focus on nurses and allied health professionals due to the importance of posture in work-related musculoskeletal disorders. Methods This randomized crossover clinical trial has a 3-month treatment sequence and a 3-month washout period. Participants will include nurses and allied health professionals 21 to 55 years of age with cervical pain. Participants are allocated at random to two intervention groups: a postural garment (Posture Plus Force) to be worn for 2 to 4 hours per day for 90 days (P+ group) and five physiotherapy sessions (20 minutes each) to learn stretching and strengthening exercises with instructions to continue at home on a daily basis for 90 days (Ex group). The participants in each group will crossover interventions after a 3-month washout period. The primary outcomes are postural control and pain intensity. A static posturography will be performed with a scan (SpinalMouse; Idiag AG, Fehraltorf, Switzerland). The visual analogue scale is a psychometric measuring instrument designed to document cervical pain severity in individual participants. The secondary outcomes are cervical pain-related disability, catastrophizing, the global perceived effect of treatment, and the evaluation of garment comfort. Physical activity is assessed with the International Physical Activity Questionnaire. Assessment of primary and secondary outcomes is performed at T0 (pre-intervention), T1 (immediately after garment fitting for P+ group), T30, T60, and T90. The same measurements are recorded after the washout period and during the second intervention following the same sequence. All patients are provided with a logbook for compliance recording, over the counter drug use, pain evaluation, and sick leave. Statistical analysis is conducted following intention-to-treat principles and the treatment effects calculated using linear mixed models. Results The study design has been approved by the Ethics Commission of Hospital N Sra de Meritxell, Andorra in March 2017. A total of 32 participants are already enrolled in the study. An extension of the study is planned in a Spanish university hospital to achieve a larger sample. Study results are expected to be published during 2020. Conclusions The Postural garment is expected to improve cervical pain by enhancing posture. Trial Registration ClinicalTrials.gov NCT03560492; https://clinicaltrials.gov/ct2/show/NCT03560492 International Registered Report Identifier (IRRID) DERR1-10.2196/14807

BackgroundClinical management of patients with chronic obstructive pulmonary disease (COPD) shows potential for improvement provided that patients’ heterogeneities are better understood. The study addresses the impact of comorbidities and its role in health risk assessment.ObjectiveTo explore the potential of health registry information to enhance clinical risk assessment and stratification.DesignFixed cohort study including all registered patients with COPD in Catalonia (Spain) (7.5 million citizens) at 31 December 2014 with 1-year (2015) follow-up.MethodsA total of 264 830 patients with COPD diagnosis, based on the International Classification of Diseases (Ninth Revision) coding, were assessed. Performance of multiple logistic regression models for the six main dependent variables of the study: mortality, hospitalisations (patients with one or more admissions; all cases and COPD-related), multiple hospitalisations (patients with at least two admissions; all causes and COPD-related) and users with high healthcare costs. Neither clinical nor forced spirometry data were available.ResultsMultimorbidity, assessed with the adjusted morbidity grouper, was the covariate with the highest impact in the predictive models, which in turn showed high performance measured by the C-statistics: (1) mortality (0.83), (2 and 3) hospitalisations (all causes: 0.77; COPD-related: 0.81), (4 and 5) multiple hospitalisations (all causes: 0.80; COPD-related: 0.87) and (6) users with high healthcare costs (0.76). Fifteen per cent of individuals with highest healthcare costs to year ratio represented 59% of the overall costs of patients with COPD.ConclusionsThe results stress the impact of assessing multimorbidity with the adjusted morbidity grouper on considered health indicators, which has implications for enhanced COPD staging and clinical management.Trial registration numberNCT02956395.

Rationale: Despite substantial progresses on standard of care recommendations, management of Chronic Obstructive Pulmonary (COPD) patients show potential for improvement provided that patients’ heterogeneities are better understood. Objectives: To perform a population-based analysis of the COPD patients in Catalonia (ES) (7,5 M citizens) aiming at exploring the potential of the health registry information to enhance risk assessment and stratification in the clinical arena. Methods: The characteristics of 264,830 COPD cases registered in 2014 in the Health Surveillance System were assessed. Multiple logistic regression analysis was used to elaborate predictive models for four health indicators: (i) mortality, (ii) unplanned hospitalizations (all and COPD-related hospitalizations), (iii) multiple exacerbators, and, (iv) high users of healthcare resources. No clinical, nor forced spirometry data were available. Main Results: Multimorbidity, expressed by GMA (adjusted morbidity groups) scoring, was the covariate with highest impact in the four predictive models. This score explained a substantial percentage of interindividual variability (AUC): (i) mortality (0.763); (ii) unplanned hospitalizations (0.829); (iii) multiple exacerbators (0.766); and, (iv) higher use of healthcare resources (0.803). Patients above the 85 percentile in terms of healthcare costs per year represented 59% of the overall costs of COPD patients. Conclusions: The predictive models stress the explanatory role of the covariate multimorbidity. The results highly encourage further developments fostering interoperability between health registries and electronic health records to enhance clinical risk prediction.

Resumen Objetivo Comparar el rendimiento referente a la bondad de ajuste y el poder explicativo de 2 agrupadores 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.

Background: Health risk assessment and stratification have proven highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. Aim: The principal objective was to analyse five health-risk assessment strategies used in the five regions participating in the Advancing Care Coordination and TeleHealth Deployment (ACT) program (www.act-programme.eu): Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). A second aim was to explore the potential of population-based health risk predictive tools to contribute to risk prediction in the clinical setting. Method: We characterized and compared risk assessment strategies among ACT regions by analysing operational risk predictive modelling tools for population-based stratification, as well as available health indicators. The risk assessment tool deployed in Catalonia (GMA, Adjusted Morbidity Groups) was used as an example to explore how population-based analysis can contribute to clinical risk prediction. Results: The study indicated a high degree of conceptual agreement among the five ACT regions on the relevant role of population-based health risk assessment for regional deployment of integrated care. The entire ACT consortium shared its usefulness for service commission, case finding and screening. There was also consensus on the use of a population health approach as the optimal strategy for population-based risk assessment. However, the health risk predictive modelling tools in place displayed marked heterogeneities that precluded comparability of the risk pyramid distributions across regions. We identified a clear need for evolving toward risk predictive modelling tools allowing proper quantification of the estimations. Likewise, different well-identified problems mostly associated to data reporting precluded appropriate comparisons of the recommended health indicators. The current study identified transferability across regions and potential for evolving, that is flexibility, as two key requirements for any population-based health risk assessment tool. Factors such as: i) license binding constraints, ii) insufficient public availability; iii) lack of availability for inspection; and/or, iv) rigidity of some computational algorithms (i.e. due to inclusion of expert-based criteria in some morbidity groupers) are currently limiting transferability. These factors might also preclude adaptation of the current risk prediction tools toward evolving requirements such as: i) integration between healthcare and social services; and, ii) implementation of synergies between population-based and clinically oriented risk predictive modelling, as described in the study. We acknowledge some intrinsic limitations of population-based predictive modelling in terms of strength of estimations. However, their potential for allocation of individuals into the risk stratification pyramid facilitates both design and implementation of preventive strategies that have shown high potential to generate healthcare value. Moreover, the study reports on the conceptual steps required for development of innovative strategies for clinical risk predictive modelling with potential to enhance its supporting role for decision making in the clinical scenario. Conclusions : We acknowledge that further efforts should be devoted to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. The novel strategy for enhanced clinical risk assessment requires prospective evaluation.

Aims To assess coronary mortality and morbidity secular trends in Spain. Methods and results Acute coronary events occurring in both sexes at ages 35-74 years between 1985 and 1997, were monitored in a geographical area of Catalonia, through a population-based registry. Information was collected from annual discharge lists of 78 hospitals and from death certificates, and validated following the methods and quality control of the World Health Organization MONItoring Trends and Determinants in CArdiovascular Disease Project (MONICA). Registration included 19 119 valid events (14 221 in men, 4898 in women) of which 30% were fatal and 41% were definite acute myocardial infarctions. Average attack rates were 315 per 100 000 (95% CI 300-329) and 80 (75-86) in men and women, respectively. Incidence (first-ever event) rates were 209 (194-224) and 56 (52-60) per 100 000. Attack rates increased annually by 2.1% (0.3-4.1) and 1.8% (-0.9 to +4.6). Average 28-day case fatality was 46% (44-47) in men decreasing significantly by 1.4 and 53% (51-55) in women with no change. Fatal trends remained stable. Nationwide morbidity statistics showed similar trends. Conclusion Acute coronary syndromes are rising in Spanish men.

Introduction : In the last decade , the focus of the health care have slowly shifted from the acute care to the care of the chronic conditions . Several documents from the WHO indicated the path to a new paradigm of care . Significantly in 2008 the WHO Europe , has published “ How can chronic disease management programmes operate a cross care settings and providers? “In this document the mental Health problems , addiction condicions intellectual disabilities , brain injuries and mental health problems beguining at childhood ages have been taken in account. For decades , the mental health sector have been dealing with mental chronic conditions and developed programs and resources to manage the evolution of the mentioned conditions. The concept of Severe Mental illness has , actually , been loosing the significance to identify the complexity of the people who is attended in the Mental Health System , because the increase of comorbidities : physical chronic conditions , use and abuse of substances , mental health problems beguinning at childhood , intellectual disabilities , behaviour diseases ,.. , so a wide concept of SMD is need to be developed . Objective : To aligne the Mental Health chronic conditions inside the same stratification method for the physical chronic conditions ; we set a criteria to define the Chronic Mental Health Patient ( CMHP ) , a wide criteria for SMD , and Complex Chronic Mental Health Patient ( CCMHP ) : also we develop a 3 Clinical Risk Groups ( groupers ) for the Mental Health conditions , named GMA ( Adjusted Morbidity Groups ) , and we test the consistency and the prediction of needs of the identified people into these criteria. Taking in account social needs and involving the Primary care , welfare network , Hospitals , mental health and addictions network of care at the management and therapeutic plan. Practice implemented : The Catalan Department of Health , through the Mental Health and Addictions Master Plan ( PDSMiA ) and the Chronicity Prevention and Care Program ( PPAC ) has established the use of the Criteria of Chronic Patient ( broader concept of SMD ) and the Criteria for Complex Chronic Patient ; This last concept includes physical comorbities and some social conditions and needs ; after the results obtained by the Analysis Division of the Demand and Activity. Catalan Health Service have accorded to use this taxonomy in the national register system ; At, the moment, following the personal health card for the all Catalan population , we got data from 10.121.939 registered persons of wich 7.610.154 milions cards are from living people ( the Catalan population ) . The robust health registration catalan system is the key to make the results reliables and feasible.

Resumen Objetivo Este articulo presenta los resultados del analisis del peso que cada dominio competencial del instrumento COM-VA © tiene en funcion del area asistencial hospitalaria en la que el profesional de enfermeria presta sus cuidados. Metodo El formulario de recogida de datos para la variable “peso” contenia una matriz con los dominios competenciales del instrumento COM-VA © y las diferentes areas de cuidados. Los participantes debian asignar un peso a cada dominio, repartiendo el porcentaje total en las diferentes competencias. El analisis de los datos incluyo la descriptiva de las variables demograficas. Las medidas de tendencia central fueron empleadas para el analisis de los pesos competenciales. Las diferencias se consideraron significativas si p Resultados Se obtuvieron un total del 78,7% de respuestas correctamente cumplimentadas. Los resultados de la ponderacion media de cada una de las competencias varian ostensiblemente en funcion del area de cuidados. Las diferencias de peso competencial por area de cuidados son estadisticamente significativas, excepto para la competencia 1: “Cuidar” y 6: “Trabajar en equipo y adaptarse a un entorno cambiante”. Conclusiones Es recomendable incluir en los sistemas de evaluacion de competencias la variable ponderacion por area, para garantizar una evaluacion de la ejecucion competencial mas ajustada y equitativa.

Resumen Objetivos Este articulo tiene un doble objetivo: presentar la segunda parte del marco conceptual empleado en el desarrollo del proyecto COM VA © y los resultados del proceso de definicion de un instrumento de competencias asistenciales del profesional de enfermeria en el ambito hospitalario, que enfatiza la participacion de los profesionales, la reflexion critica individual y grupal, y el compromiso profesional. El marco conceptual explora los significados de los terminos toma de decisiones y ambito de responsabilidad, competencias, estandares y normas de la practica, dominio competencial, elemento competencial y matriz de competencias. Material y metodo Para el proceso de definicion del marco competencial se empleo el modelo de investigacion-accion participativa. La muestra incluyo a 487 profesionales de enfermeria hospitalarios. Los resultados del analisis definen el ambito de competencias asistenciales en 6 dominios: 1) cuidar al enfermo; 2) valorar, diagnosticar y abordar situaciones clinicas cambiantes; 3) ayudar al enfermo a cumplir el tratamiento; 4) contribuir a garantizar la seguridad y el proceso asistencial; 5) facilitar el proceso de adaptacion y afrontamiento, y 6) trabajar en equipo y adaptarse a un entorno cambiante. Cada dominio queda finalmente definido por 5 elementos competenciales que explican su significado. Cada elemento competencial contiene una definicion que incluye las principales intervenciones y actuaciones enfermeras que lo configuran y las conductas que conforman los criterios de valoracion de la ejecucion competencial.

El objetivo de este articulo es presentar el marco teorico y conceptual empleado en el desarrollo del proyecto COM VA © de definicion y evaluacion de competencias asistenciales y de gestion asistencial del profesional de enfermeria en el ambito hospitalario. El marco teorico recoge principalmente los trabajos de la Dra. Benner y de los profesores Dreyfus en relacion con el modelo de adquisicion de competencia que define 5 niveles de pericia, de aprendiz a experto. El marco conceptual explora los significados y las interrelaciones de los terminos competencia, expertez, pericia, experiencia e inteligencia, entre otros. El articulo incluye tambien una revision sobre los principales aspectos relacionados con los instrumentos y los metodos de evaluacion de competencias.

Summary Background The control of gastric residual volume (GRV) is a common nursing intervention in intensive care; however the literature shows a wide variation in clinical practice regarding the management of GRV, potentially affecting patients’ clinical outcomes. The aim of this study is to determine the effect of returning or discarding GRV, on gastric emptying delays and feeding, electrolyte and comfort outcomes in critically ill patients. Method A randomised, prospective, clinical trial design was used to study 125 critically ill patients, assigned to the return or the discard group. Main outcome measure was delayed gastric emptying. Feeding outcomes were determined measuring intolerance indicators, feeding delays and feeding potential complications. Fluid and electrolyte measures included serum potassium, glycaemia control and fluid balance. Discomfort was identified by significant changes in vital signs. Results Patients in both groups presented similar mean GRV with no significant differences found (p = 0.111), but participants in the intervention arm showed a lower incidence and severity of delayed gastric emptying episodes (p = 0.001). No significant differences were found for the rest of outcome measurements, except for hyperglycaemia. Conclusions The results of this study support the recommendation to reintroduce gastric content aspirated to improve GRV management without increasing the risk for potential complications.

IntroductionComorbidities in patients with chronic obstructive pulmonary disease (COPD) generate a major burden on healthcare. Identification of cost-effective strategies aiming at preventing and enhancing management of comorbid conditions in patients with COPD requires deeper knowledge on epidemiological patterns and on shared biological pathways explaining co-occurrence of diseases.MethodsThe study assesses the co-occurrence of several chronic conditions in patients with COPD using two different datasets: Catalan Healthcare Surveillance System (CHSS) (ES, 1.4 million registries) and Medicare (USA, 13 million registries). Temporal order of disease diagnosis was analysed in the CHSS dataset.ResultsThe results demonstrate higher prevalence of most of the diseases, as comorbid conditions, in elderly (>65) patients with COPD compared with non-COPD subjects, an effect observed in both CHSS and Medicare datasets. Analysis of temporal order of disease diagnosis showed that comorbid conditions in elderly patients with COPD tend to appear after the diagnosis of the obstructive disease, rather than before it.ConclusionThe results provide a population health perspective of the comorbidity challenge in patients with COPD, indicating the increased risk of developing comorbid conditions in these patients. The research reinforces the need for novel approaches in the prevention and management of comorbidities in patients with COPD to effectively reduce the overall burden of the disease on these patients.