A Challenging Case Of Pulmonary Arterial Hypertension: Never Judge By Appearances

Abstract We report the case of a patient with symptomatic pulmonary hypertension (PH) associated with diffuse systemic sclerosis (SSc) whose initial assessment suggested a group 3 (clinical classification) PH. The patient had a history of drugs/toxins consumption, which contributed to the development of intrinsic pulmonary vascular disease. This changed the panorama towards the diagnosis of pulmonary arterial hypertension (PAH), with important therapeutic and prognostic implications. In fact, the excellent clinical, laboratory and hemodynamic response to therapy confirmed the hypothesis of a case of drug-associated PAH (DPAH) in a patient with diffuse SSc and lung disease. Considering the presence of DPAH, it was deemed necessary to assess acute vasoreactivity during right heart catheterization (RHC). If criteria were met, the clinical scenario may change towards a favorable and sustained clinical and hemodynamic response with oral calcium channel blockers. However, the response to inhaled nitric oxide was negative in our patient and the therapeutic strategy with dual oral combination therapy with tadalafil and ambrisentan was continued. After six-months of therapy the patient significantly improved, from a high to a low risk of one-year mortality.

Machine learning prediction of mortality in Acute Myocardial Infarction.

BACKGROUND: Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. METHODS: Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. RESULTS: Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. CONCLUSIONS: Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.

Decision-making support systems on extended hospital length of stay: Validation and recalibration of a model for patients with AMI.

Background: Cardiovascular diseases are still a significant cause of death and hospitalization. In 2019, circulatory diseases were responsible for 29.9% of deaths in Portugal. These diseases have a significant impact on the hospital length of stay. Length of stay predictive models is an efficient way to aid decision-making in health. This study aimed to validate a predictive model on the extended length of stay in patients with acute myocardial infarction at the time of admission. Methods: An analysis was conducted to test and recalibrate a previously developed model in the prediction of prolonged length of stay, for a new set of population. The study was conducted based on administrative and laboratory data of patients admitted for acute myocardial infarction events from a public hospital in Portugal from 2013 to 2015. Results: Comparable performance measures were observed upon the validation and recalibration of the predictive model of extended length of stay. Comorbidities such as shock, diabetes with complications, dysrhythmia, pulmonary edema, and respiratory infections were the common variables found between the previous model and the validated and recalibrated model for acute myocardial infarction. Conclusion: Predictive models for the extended length of stay can be applied in clinical practice since they are recalibrated and modeled to the relevant population characteristics.

Molecular Changes In Cardiac Tissue As A New Marker To Predict Cardiac Dysfunction Induced By Radiotherapy.

The contribution of radiotherapy, per se, to late cardiotoxicity remains controversial. To clarify its impact on the development of early cardiac dysfunction, we developed an experimental model in which the hearts of rats were exposed, in a fractionated plan, to clinically relevant doses of ionizing radiation for oncological patients that undergo thoracic radiotherapy. Rat hearts were exposed to daily doses of 0.04, 0.3, and 1.2 Gy for 23 days, achieving cumulative doses of 0.92, 6.9, and 27.6 Gy, respectively. We demonstrate that myocardial deformation, assessed by global longitudinal strain, was impaired (a relative percentage reduction of >15% from baseline) in a dose-dependent manner at 18 months. Moreover, by scanning electron microscopy, the microvascular density in the cardiac apex was significantly decreased exclusively at 27.6 Gy dosage. Before GLS impairment detection, several tools (qRT-PCR, mass spectrometry, and western blot) were used to assess molecular changes in the cardiac tissue. The number/expression of several genes, proteins, and KEGG pathways, related to inflammation, fibrosis, and cardiac muscle contraction, were differently expressed in the cardiac tissue according to the cumulative dose. Subclinical cardiac dysfunction occurs in a dose-dependent manner as detected by molecular changes in cardiac tissue, a predictor of the severity of global longitudinal strain impairment. Moreover, there was no dose threshold below which no myocardial deformation impairment was detected. Our findings i) contribute to developing new markers and exploring non-invasive magnetic resonance imaging to assess cardiac tissue changes as an early predictor of cardiac dysfunction; ii) should raise red flags, since there is no dose threshold below which no myocardial deformation impairment was detected and should be considered in radiation-based imaging and -guided therapeutic cardiac procedures; and iii) highlights the need for personalized clinical approaches.

Prognostic stratification in pulmonary hypertension: A multi-biomarker approach. / Estratificação prognóstica na hipertensão pulmonar: valor acrescido da abordagem multibiomarcadores.

INTRODUCTION: Pulmonary hypertension (PH) covers a group of conditions characterized by an increase in pulmonary vascular resistance leading to right ventricular failure. Risk stratification is crucial for adequate prognostic and therapeutic assessment. However, the accuracy of conventional parameters is limited, especially biomarkers. OBJECTIVES: To determine the prognostic value of new biomarkers and their combination in a multi-biomarker approach to predict outcome in patients with PH. METHODS: In this prospective cohort study, PH patients underwent clinical, echocardiographic and laboratory assessment, including quantification of serum N-terminal pro-brain natriuretic peptide (NT-proBNP) and of the following new biomarkers: mid-regional pro-adrenomedullin (MR-proADM), copeptin, endothelin-1, mid-regional pro-atrial natriuretic peptide (MR-proANP) and soluble ST2 (sST2), the interleukin-33 receptor. The accuracy of the different parameters for predicting all-cause mortality and death or hospitalization of cardiac causes was determined. The prognostic value of a multi-biomarker score based on the tertile distribution of serum NT-proBNP, MR-proANP, renin and sST2 was compared to conventional markers. RESULTS: Forty-three patients (72.1% female, age 59±15 years) were included, most of whom (65.1%) had group 1 PH. During a median follow-up of 34 months, 26% of the patients died and 35% were hospitalized for cardiac causes. Atrial and ventricular dimensions and right ventricular fractional area change were prognostic predictors. Log NT-proBNP (HR: 31.14; 95% CI: 3.12-310.7; p=0.003) and renin (HR: 1.02; 95% CI: 1.005-1.038; p=0.009) were independent predictors of mortality. MR-proANP (HR: 1.008; 95% CI 1.004-1.011; p<0.001) and sST2 (HR: 1.005; 95% CI 1.001-1.009; p=0.04) were predictors of death or hospitalization. The prognostic value of the multi-biomarker score was higher than any of the conventional parameters, and enabled identification of risk groups (the high-risk group had three-year mortality of 77.8%). CONCLUSION: A multi-biomarker approach was superior for risk stratification to any single marker. A score that incorporates NT-proBNP, MR-proANP, renin and sST2 accurately identifies patients at low, intermediate and high risk.

Organization and implementation of a cardio-oncology program. / Organização e implementação de uma consulta de cardio-oncologia.

Considerable advances in cancer therapies in recent decades have reshaped the prognosis of cancer patients. There are now estimated to be over 20 million cancer survivors in the USA and Europe, numbers unimaginable a few years ago. However, this increase in survival, along with the aging of the patient population, has been accompanied by a rise in adverse cardiovascular effects, particularly when there is a previous history of heart disease. The incidence of cardiotoxicity continues to grow, which can compromise the effectiveness of cancer therapy. Cardiotoxicity associated with conventional therapies, especially anthracyclines and radiation, is well known, and usually leads to left ventricular dysfunction. However, heart failure represents only a fraction of the cardiotoxicity associated with newer therapies, which have diverse cardiovascular effects. There are few guidelines for early detection, prevention and treatment of cardiotoxicity of cancer treatments, and no well-established tools for screening these patients. Echocardiography is the method of choice for assessment of patients before, during and after cancer treatment. It therefore makes sense to adopt a multidisciplinary approach to these patients, involving cardiologists, oncologists and radiotherapists, collaborating in the development of new training modules, and performing clinical and translational research in a cardio-oncology program. Cardio-oncology is a new frontier in medicine and has emerged as a new medical subspecialty that concentrates knowledge, understanding, training and treatment of cardiovascular comorbidities, risks and complications in patients with cancer in a comprehensive approach to the patient rather than to the disease.

An exploratory panel of biomarkers for risk prediction in pulmonary hypertension: emerging role of CT-proET-1.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a rare, deadly condition. Although risk stratification is extremely important for assessment of prognosis and to guide therapy, there is lack of evidence concerning the role of novel biomarkers. In a pivotal study, we sought to comparatively investigate the predictive power of several new biomarkers in PAH. METHODS: Patients with prevalent PAH were enrolled in the study protocol, which included clinical, functional and echocardiographic assessment. Blood samples were collected at baseline for determination of NT-proBNP, CT-proET-1, MR-proANP, MR-proADM, copeptin and troponin I. Patients were clinically followed-up up to 12 months for first occurrence of hospital admission due to PAH-related clinical worsening, heart/lung transplantation or all-cause mortality. RESULTS: Among the 28 included patients the pre-specified end-point occurred in 8 (29% event rate). There were higher baseline levels of CT-proET-1, copeptin, MR-proANP, NT-proBNP and troponin I in patients who reached the composite end-point. They also had larger right atria. In multivariate Cox regression, CT-proET-1 was the only biomarker associated with increased hazard of reaching the primary composite end-point (hazard ratio per tertile increase = 10.1; 95% CI 2.0 to 50.6). CONCLUSIONS: CT-proET-1 provided prognostic information independent of other biomarkers. Importantly, we have provided the first evidence that CT-proET-1 may be superior to commonly used biomarkers.