Profile of Patients Hospitalized for Heart Failure in Tertiary Care
            Hospital

Poffo, Milton Ricardo; Assis, Amberson Vieira de; Fracasso, Maíra; Filho, Ozir Miguel Londero; Alves, Sulyane Matos de Menezes; Bald, Ana Paula; Schmitt, Camila Bussolo; Filho, Nilton Rogério Alves

doi:10.5935/2359-4802.20170044

Cited by 22 publications

(42 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this context, electronic health records (EHRs, also called medical records) can be considered a useful resource of information to unveil hidden and non-obvious correlations and relationships between patients' data, not only for research but also for clinical practice [13,14] and for debunking traditional myths on risk factors [15,16]. To this aim, several screening studies have been conducted in the last years, covering different conditions and demographics and with different data sources, to deepen the knowledge on the risk factors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

323

144

View full text Add to dashboard Cite

Background: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body. Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. Methods: In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Results: Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. Conclusions: This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Serum creatinine (x axis) range: [0.50, 9.40] mg/dL. Ejection fraction (y axis) range:[14,80]%. We manually drew a black straight line to highlight the discrimination between alive and dead patients Barplot of the survival percentage for each follow-up month.…”

mentioning

confidence: 99%

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

323

144

View full text Add to dashboard Cite

show abstract

“…La población incluida en este estudio refleja el perfil actual de los pacientes con IC atendidos en los servicios de Medicina Interna de toda España [18][19][20] . Nuestra población exhibió un nivel bastante bajo de mantenimiento del autocuidado debido a la falta de ejercicio físico, al olvido en la toma de la medicación y a llevar una dieta inadecuada.…”

Section: Discussionunclassified

Investigando el autocuidado en una muestra de pacientes con insuficiencia cardiaca descompensada: un estudio transversal

Juárez‐Vela

Sarabia‐Cobo

Antón-Solanas

et al. 2019

Revista Clínica Española

View full text Add to dashboard Cite

show abstract

“…Also influencing, for its higher prevalence, advances in the treatment of Acute Myocardial Infarction (AMI), Systemic Arterial Hypertension (SAH) and even HF, which increase survival and, consequently, promote an increase in its prevalence. The presence of comorbidities associated with HF only worsen the prognosis, such as Atrial Fibrillation (AF) which affects 20 to 30% of patients with HF, Renal Insufficiency (IR) which reaches 29.6% and lung diseases, increase mortality [4].…”

Section: Introductionmentioning

confidence: 99%

Nursing Diagnoses in Heart Failure: Integrative Review

Sardinha

Assis

Amoedo

et al. 2020

View full text Add to dashboard Cite

Heart Failure is characterized by a complex clinical syndrome in which the myocardium has an inability to adequately pump blood to meet tissue metabolic needs, or can do so only at high filling pressures. Thus, the aim of this study is to describe the Nursing Diagnostics in patients with Cardiac Failure described in the scientific literature. It is a descriptive, exploratory study, with a qualitative approach, in the modality Integrative Literature Review. In Latin American and Caribbean Literature in Health Sciences and PubMed databases, including original articles, case studies and systematic review, in Portuguese, English and Spanish, published from 2014. Tabulation was used for data collection and analysis. Seven articles were searched and the following nursing diagnoses were highlighted: Harmful Comfort; Acute Pain; Disposition to Improve Comfort; Decreased Cardiac Output; Excessive Liquid Volume; Ineffective Respiratory Pattern; Intolerance to Activity; Fatigue; Anxiety; Sexual Dysfunction; Disabled Knowledge; Ineffective Cardiopulmonary Tissue Perfusion. It was concluded that heart failure is a complex pathology and that the use of the systematization of nursing care only enhances the quality of service and benefits the patient, because from the nursing diagnostcs it is possible to prescribe evidence-based interventions.

show abstract

Profile of Patients Hospitalized for Heart Failure in Tertiary Care Hospital

Cited by 22 publications

References 0 publications

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Investigando el autocuidado en una muestra de pacientes con insuficiencia cardiaca descompensada: un estudio transversal

Nursing Diagnoses in Heart Failure: Integrative Review

Contact Info

Product

Resources

About