Validación clínica de 2 agrupadores de morbilidad en el ámbito de atención primaria

Clèries, Montse; Monterde, David; Vela, Emili; Guarga, A; Eroles, Luís García; Sust, Pol Pérez; Oliva, Jordi; Genicio, Antonio Arévalo; Puente, Joan Barrot-de la; Agoües, Ander Burgaña; Jurado, Silvia; Rey, Joan Caselles i; Font, Jordi Casanovas; Cubells, Lourdes Cruz; Monfort, Rafael Cubí i; Vives, Silvia; Laredo, Xavier Gallego; Pastor, Marta García; Albas, Isabel Garcia; Lopez, Montenegro; Bellsolà, Montserrat Gavagnac; Valls, Pau; Martínez, Milagros Iglesias; Girado, Jordi Jiménez; Llordés, Montserrat Llordés; Lupión, Xavier López; Arpí, C. López; Pascual, Alfonso Martin; Ortega, Mireia Martínez; Vindel, Jaime; Salavert, José M.; Oliva, Ángels Moleiro; Guasch, Cristina Molina; Martí, T; Pérez, Sílvia Narejos; Gonzalez, Leonor; Sánchez, Daniel Panyart; Genar, M. Dolores Ribero; Ramos, Anna Ripoll; Soldevila, Marta Serrarols; Guerra, Mercè Soler; Pinsach, Laura Taberner i; Colet, M.; Pujal, Ramon Vilatimó; Hernández, Alicia Villanueva; Marco, Mugnaini

doi:10.1016/j.aprim.2018.09.016

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…However, the GMA provides additional outputs at the individual level, including the number of chronic diseases, the number of organ systems affected by a chronic disease, a clinical summary label, and the multimorbidity index (i.e., a weighted measure of all diagnostics, which allow quantitative health-risk stratification at a population level) [22]. The GMA tool has shown good clinical performance-comparable with the CRGs [23,24]-, adequate capacity to predict resource utilization in our area [25], and it has been validated in an external population using the ACG® and CRG® systems as a reference [16].…”

Section: Introductionmentioning

confidence: 99%

Performance of quantitative measures of multimorbidity: a population-based retrospective analysis

et al. 2021

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Background Multimorbidity 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. Methods The 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). Results The 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). Conclusions The 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.

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Section: Introductionmentioning

confidence: 99%

Performance of quantitative measures of multimorbidity: a population-based retrospective analysis

et al. 2021

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“…Llamó la atención que los sujetos de esta muestra con índice 0 eran portadores de patologías graves que no se encuentran tipificadas en el índice descrito por Charlson. Tal vez este índice debería adecuar sus criterios a las patologías emergentes o regionales como el caso del dengue o la neumonía asociada al virus H1N1 detectados en estos Servicios (36).…”

Section: Discussionunclassified

Charlson comorbidity index applied to Internal Medicine patients: a multicenter study

Torales

Giménez

Aquino

et al. 2019

Rev. virtual Soc. Parag. Med. Int

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Introducción: la comorbilidad es la presencia de enfermedades coexistentes o adicionales en relación al diagnóstico inicial o enfermedad guía. Es más frecuente en ancianos y contribuye a la aparición de eventos adversos de salud como mayor duración de la internación y mortalidad hospitalaria. Objetivo: determinar el efecto de las comorbilidades en la duración de la internación y en la mortalidad intrahospitalaria de los pacientes ingresados en el Servicio de Clínica Médica del Hospital Nacional (Itauguá, Paraguay) y del Hospital Militar Central (Asunción, Paraguay) entre marzo y julio del 2019. Materiales y métodos: se aplicó diseño de cohortes prospectivas en base al índice de comorbilidad de Charlson con punto de corte en 3 puntos. Ingresaron al estudio todos los varones y mujeres, mayores de 16 años, ingresados al Servicio de Clínica Médica del Hospital Nacional (Itauguá, Paraguay) y del Hospital Militar Central (Asunción, Paraguay) entre marzo y julio 2019. Se aplicó muestreo no probabilístico de casos consecutivos. Se midieron dos variables dependientes: duración de la hospitalización y mortalidad intrahospitalaria. Se calculó RR (IC 95%) y se consideró significativa toda p<0,05. El estudio respetó las normas éticas. Resultados: se incluyeron 543 sujetos, con predominio del sexo masculino (56%). La edad media fue 52±20 años. La mediana del índice de Charlson fue 2 puntos. No se detectó asociación estadísticamente significativa entre la severidad de este índice y la duración de la internación pero la mortalidad intrahospitalaria fue mayor con índice de Charlson ≥4: 6,5% vs 14,5% (p 0,005). Conclusiones: la mediana del índice de Charlson en dos Servicios de Clínica Médica fue 2. Se halló asociación estadísticamente significativa entre este índice y la mortalidad hospitalaria.

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“…Since the complexity index is a continuous variable, putting this index into percentiles allows the stratification of each individual of the population, allocating them into four risk groups following the model of the Kaiser-Permanente pyramid [18] (high-risk patients with chronic conditions above the 95th percentile, medium-risk patients with chronic conditions between the 85th and 95th percentiles, patients with chronic conditions in the low risk level between the 50th and 85th percentiles and patients without relevant chronic conditions below the 50th percentile). These AMGs have been elaborated and subsequently analysed with data from the general population [12,13,[15][16][17]19] and specifically checked on different types of populations [20][21][22].…”

Section: Methodsmentioning

confidence: 99%

“…We explored by bivariate analysis which factors were associated with survival, and the independent variables with a level of significance lower than 0.05 were included in the Cox multivariate regression. A threshold of 5 or more concurrent chronic conditions was established instead of the cut-off point from two (qualitative) or the number of chronic conditions (quantitative) in the analysis because five is a threshold previously used in the literature to define more complex patients (9)(10)(11)(12)(13)(14)(15). The 95% confidence intervals were calculated for hazard ratios.…”

Section: Methodsmentioning

confidence: 99%

“…Although there is growing population-based evidence about the impact of AMG morbidity and complexity on survival in Catalonia, there is still limited evidence on the impact of the complexity index and the risk level by AMG on survival in specific cohorts of patients with chronic conditions in primary-care follow-up in other regions of Spain [12,[15][16][17], as stratification in different autonomous communities could not be comparable [13]. This work provides useful and novel information to better explain the AMG complexity index and risk levels in a clinical practice setting.…”

Section: Introductionmentioning

confidence: 99%

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Adjusted morbidity groups and survival: a retrospective cohort study of primary care patients with chronic conditions

et al. 2023

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Background Chronic conditions are one of the main determinants of frailty, functional disability, loss of quality of life and the number one cause of death worldwide. This study aimed to describe the survival of patients with chronic conditions who were followed up in primary care according to the level of risk by adjusted morbidity groups and to analyse the effects of sex, age, clinician and care factors on survival. Methods This was a longitudinal observational study of a retrospective cohort of patients with chronic conditions identified by the adjusted morbidity group stratifier of the electronic medical records in a primary health centre of the Region of Madrid, which has an assigned population of 18,107 inhabitants. The follow-up period was from June 2015 to June 2018. A description of survival according to the Kaplan–Meier method and Cox proportional hazards multivariate regression model was used to analyse the effects of sex, age, clinician and care factors. Results A total of 9,866 patients with chronic conditions were identified; 77.4% (7,638) had a low risk, 18.1% (1,784) had a medium risk, and 4.5% (444) had a high risk according to the adjusted morbidity groups. A total of 477 patients with chronic conditions died (4.8%). The median survival was 36 months. The factors associated with lower survival were age over 65 years (hazard ratio [HR] = 1.3; 95% confidence interval [CI] = 1.1–1.6), receiving palliative care (HR = 3.4; 95% CI = 2.6–4.5), high versus low risk level (HR = 2.4; 95% CI = 1.60–3.7), five chronic conditions or more (HR = 1.5; 95% CI = 1.2-2), complexity index (HR = 1.01; 95% CI = 1.02–1.04) and polymedication (HR = 2.6; 95% CI = 2.0-3.3). Conclusions There was a gradual and significant decrease in the survival of patients with chronic conditions according to their level of risk as defined by adjusted morbidity groups. Other factors, such as older age, receiving palliative care, high number of chronic conditions, complexity, and polymedication, had a negative effect on survival. The adjusted morbidity groups are useful in explaining survival outcomes and may be valuable for clinical practice, resource planning and public health research.

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Validación clínica de 2 agrupadores de morbilidad en el ámbito de atención primaria

Cited by 12 publications

References 29 publications

Performance of quantitative measures of multimorbidity: a population-based retrospective analysis

Performance of quantitative measures of multimorbidity: a population-based retrospective analysis

Charlson comorbidity index applied to Internal Medicine patients: a multicenter study

Adjusted morbidity groups and survival: a retrospective cohort study of primary care patients with chronic conditions

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