La escala Identification of Senior at Risk predice la mortalidad a los 30 días en los pacientes mayores con insuficiencia cardiaca aguda

Martín‐Sánchez, Francisco Javier; García, Guillermo; Harmand, Magali González‐Colaço; Pérez, Cristina Fernández; Castillo, Juan González del; Llorens, Pere; Herrero, Pablo; Jacob, Javier; Gil, Víctor; Rosselló, Xavier; Miró, Òscar; Adrada, Esther Rodríguez; Santos, Maria Carmen Lopes Ferreira Silva; Salgado, Lucía; Brizzi, Berenice Nayla; Docavo, María Luisa; Cadenas, María Suárez; Xipell, Carolina; Sánchez, Carolina; Aguiló, Sira; Gaytán, Josep María; Jerez, Alba Rivas; Pérez‐Durá, María José; Gil, Pablo Berrocal; López‐Grima, María Luisa; Valero, Amparo; Aguirre, Alfons; Pedragosa, María Àngels; Piñera, Pascual; Nicolás, José Andrés Sánchez; Rizzi, Miguel Alberto; Mateo, Sergio Herrera; Alquézar, Aitor; Roset, Álex; Ferrer, Carles; Llopis, Ferrán; Pérez, José María Álvarez; Díez, María Pilar López; Richard, Fernando; Fernández‐Cañadas, José María; Carratalá, José Manuel; Javaloyes, Patricia; Andueza, Juan Antonio; Fernández, J.A. Sevillano; Romero, Rodolfo; Loranc, Marta Merlo; Rodríguez, Virginia Álvarez; Lorca, María Teresa; Calderón, Luis; Ferrer, Ester Soy; Garrido, José M.; Mojarro, Enrique Martín

doi:10.1016/j.medin.2018.07.009

Medicina Intensiva

2020

DOI: 10.1016/j.medin.2018.07.009

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La escala Identification of Senior at Risk predice la mortalidad a los 30 días en los pacientes mayores con insuficiencia cardiaca aguda

Francisco Javier Martín‐Sánchez

Guillermo García

Magali González‐Colaço Harmand

et al.

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Cited by 8 publications

(1 citation statement)

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Cardiovascular diseases (CVDs) have become a major health problem because of the associated high morbidity and mortality among patients. [1][2][3] Over the recent years, the gut microbiota has been found to be linked with multiple diseases, such as obesity, 4 diabetes mellitus, 5 digestive system diseases 6,7 and cancers. 8 Emerging data indicate the relationship between gut microbiota and CVD.

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confidence: 99%

The role of intestinal microbiota in cardiovascular disease

Jin

Qian

Yin

et al. 2019

J Cellular Molecular Medi

187

113

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Accumulating evidence has indicated that intestinal microbiota is involved in the development of various human diseases, including cardiovascular diseases (CVDs). In the recent years, both human and animal experiments have revealed that alterations in the composition and function of intestinal flora, recognized as gut microflora dysbiosis, can accelerate the progression of CVDs. Moreover, intestinal flora metabolizes the diet ingested by the host into a series of metabolites, including trimethylamine N‐oxide, short chain fatty acids, secondary bile acid and indoxyl sulfate, which affects the host physiological processes by activation of numerous signalling pathways. The aim of this review was to summarize the role of gut microbiota in the pathogenesis of CVDs, including coronary artery disease, hypertension and heart failure, which may provide valuable insights into potential therapeutic strategies for CVD that involve interfering with the composition, function and metabolites of the intestinal flora.

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“…

…”

mentioning

confidence: 99%

The role of intestinal microbiota in cardiovascular disease

Jin

Qian

Yin

et al. 2019

J Cellular Molecular Medi

187

113

View full text Add to dashboard Cite

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Derivation of an electronic frailty index for predicting short‐term mortality in heart failure: a machine learning approach

Zhou

Lee

et al. 2021

ESC Heart Failure

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Aims Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure. Methods and resultsThis was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co-morbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five-fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively. Conclusions The electronic frailty index based on co-morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

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Gut microbiome associated dysbiosis: Limited regimens and expanding horizons of phage therapy

Singha,

Rawat,

Venkataraman

et al. 2023

Aspects of Molecular Medicine

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La escala Identification of Senior at Risk predice la mortalidad a los 30 días en los pacientes mayores con insuficiencia cardiaca aguda

Cited by 8 publications

References 26 publications

The role of intestinal microbiota in cardiovascular disease

The role of intestinal microbiota in cardiovascular disease

Derivation of an electronic frailty index for predicting short‐term mortality in heart failure: a machine learning approach

Gut microbiome associated dysbiosis: Limited regimens and expanding horizons of phage therapy

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