Development and Validation of a Prediction Model and Score for Transthyretin Cardiac Amyloidosis Diagnosis: T-Amylo.

BACKGROUND: Although transthyretin cardiac amyloidosis (ATTR-CA) is often underdiagnosed, clinical suspicion is essential for early diagnosis. OBJECTIVES: The aim of this study was to develop and validate a feasible prediction model and score to facilitate the diagnosis of ATTR-CA. METHODS: This retrospective multicenter study enrolled consecutive patients who underwent 99mTc-DPD scintigraphy for suspected ATTR-CA. ATTR-CA was diagnosed if Grade 2 or 3 cardiac uptake was evidenced on 99mTc-DPD scintigraphy in the absence of a detectable monoclonal component or by demonstration of amyloid by biopsy. A prediction model for ATTR-CA diagnosis was developed in a derivation sample of 227 patients from 2 centers using multivariable logistic regression with clinical, electrocardiography, analytical, and transthoracic echocardiography variables. A simplified score was also created. Both of them were validated in an external cohort (n = 895) from 11 centers. RESULTS: The obtained prediction model combined age, gender, carpal tunnel syndrome, interventricular septum in diastole thickness, and low QRS interval voltages, with an area under the curve (AUC) of 0.92. The score had an AUC of 0.86. Both the T-Amylo prediction model and the score showed a good performance in the validation sample (ie, AUC: 0.84 and 0.82, respectively). They were tested in 3 clinical scenarios of the validation cohort: 1) hypertensive cardiomyopathy (n = 327); 2) severe aortic stenosis (n = 105); and 3) heart failure with preserved ejection fraction (n = 604), all with good diagnostic accuracy. CONCLUSIONS: The T-Amylo is a simple prediction model that improves the prediction of ATTR-CA diagnosis in patients with suspected ATTR-CA.

Prediction and Analysis of Heart Failure Decompensation Events Based on Telemonitored Data and Artificial Intelligence Methods.

Cardiovascular diseases are the leading cause of death globally, taking an estimated 17.9 million lives each year. Heart failure (HF) occurs when the heart is not able to pump enough blood to satisfy metabolic needs. People diagnosed with chronic HF may suffer from cardiac decompensation events (CDEs), which cause patients' worsening. Being able to intervene before decompensation occurs is the major challenge addressed in this study. The aim of this study is to exploit available patient data to develop an artificial intelligence (AI) model capable of predicting the risk of CDEs timely and accurately. Materials and Methods: The vital variables of patients (n = 488) diagnosed with chronic heart failure were monitored between 2014 and 2022. Several supervised classification models were trained with these monitoring data to predict CDEs, using clinicians' annotations as the gold standard. Feature extraction methods were applied to identify significant variables. Results: The XGBoost classifier achieved an AUC of 0.72 in the cross-validation process and 0.69 in the testing set. The most predictive physiological variables for CAE decompensations are weight gain, oxygen saturation in the final days, and heart rate. Additionally, the answers to questionnaires on wellbeing, orthopnoea, and ankles are strongly significant predictors.

Factors related to early readmissions after acute heart failure: a nested case-control study.

AIMS: To describe the main characteristics of patients who were readmitted to hospital within 1 month after an index episode for acute decompensated heart failure (ADHF). METHODS AND RESULTS: This is a nested case-control study in the ReIC cohort, cases being consecutive patients readmitted after hospitalization for an episode of ADHF and matched controls selected from those who were not readmitted. We collected clinical data and also patient-reported outcome measures, including dyspnea, Minnesota Living with Heart Failure Questionnaire (MLHFQ), Tilburg Frailty Indicator (TFI) and Hospital Anxiety and Depression Scale scores, as well as symptoms during a transition period of 1 month after discharge. We created a multivariable conditional logistic regression model. Despite cases consulted more than controls, there were no statistically significant differences in changes in treatment during this first month. Patients with chronic decompensated heart failure were 2.25 [1.25, 4.05] more likely to be readmitted than de novo patients. Previous diagnosis of arrhythmia and time since diagnosis ≥ 3 years, worsening in dyspnea, and changes in MLWHF and TFI scores were significant in the final model. CONCLUSION: We present a model with explanatory variables for readmission in the short term for ADHF. Our study shows that in addition to variables classically related to readmission, there are others related to the presence of residual congestion, quality of life and frailty that are determining factors for readmission for heart failure in the first month after discharge. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03300791. First registration: 03/10/2017.

Aproximación diagnóstica a la amiloidosis cardiaca por transtiretina en un área no endémica. ¿Qué variables no invasivas orientan a su diagnóstico? / Diagnostic approach to transthyretin amyloidosis in a non-endemic area. Which non-invasive variables could guide the diagnosis?

Al editor: Clásicamente se ha considerado la amiloidosis cardiaca como una afección rara, con un amplio espectro de síntomas que requiere un alto índice de sospecha. Sin embargo, los estudios han demostrado que la amiloidosis cardiaca por transtiretina (TTR) es más común de lo que previamente se creía1,2. Las características clínicas que se han asociado a la amiloidosis cardiaca por TTR son el sexo masculino, la edad avanzada, la hipertrofia concéntrica y la función ventricular izquierda preservada1. Se realizó un análisis descriptivo retrospectivo de las gammagrafías solicitadas en nuestro centro para descartar amiloidosis cardiaca por TTR desde septiembre de 2016 hasta noviembre de 2019. En dicho periodo se realizaron 39 gammagrafías, con una tendencia al alza en los últimos meses. Los objetivos fueron evaluar las gammagrafías solicitadas y conocer el porcentaje de gammagrafías diagnósticas de amiloidosis por TTR, establecer qué características son más frecuentes en los pacientes con amiloidosis por TTR en nuestra población de referencia y analizar las características diferenciales de las distintas posibilidades diagnósticas. Del total de las pruebas, 22 (56.4% de la muestra) mostraron una captación de grado 2-3 de Perugini, diagnóstica de amiloidosis por TTR. De acuerdo con las recomendaciones de diagnóstico no invasivo de amiloidosis cardiaca por TTR3, se descartó la presencia de pico monoclonal. Únicamente se realizó estudio genético a 10 pacientes, en dos de los cuales se detectó una mutación patogénica (Val50Met y variante patogénica c.290C>A en heterocigosis); los ocho restantes no mostraron mutaciones en el estudio molecular del gen TTR.

Protrombosis en tiempos de la COVID-19 / Prothrombosis in times of COVID-19

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Furosemida subcutánea como tratamiento para pacientes con insuficiencia cardiaca refractaria / Subcutaneous furosemide in patients with refractory heart failure

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