Lung ultrasound in congestion assessment of patients with advanced heart failure referred for heart transplant: Correlations with right heart catheterization findings.

Background: In advanced heart failure (HF), diagnostic performance of physical exam may be poor. Physical examination associated with lung ultrasound (LUS) may be an important tool to facilitate congestion screening. Objective: To evaluate performance of LUS for congestion screening in advanced HF referred for transplant, as compared to findings of right heart catheterization (RHC). Methods: Prospective study of 23 subjects with advanced HF referred for RHC. LUS was performed in association with clinical congestion score (CCS), analogue-visual dyspnea scale (AVDS) and presence of trepopnea/bendopnea prior to catheterization. Congestion was assessed by the number of B-lines in the LUS, and by findings of physical examination as well as by NT-proBNP serum values. Results: Congestion was present in 43.4 % of patients by LUS (B-lines ≥ 15), as compared to 21.7 % by CCS (score greater than or equal to 5), 56.5 % by NT-proBNP (>1000 pg/ml), and 60.8 % by pulmonary capillary wedge pressure (PCWP) (>15 mm Hg). The number of B-lines was correlated to cardiac index (CI) (rho = -0.619; p 0.002), but not with PCWP (rho 0.190; p 0.386), RAP (rho -0.244; p 0.262), CCS (rho 0.198; p 0.36) and neither with NT-proBNP (rho 0.282; p 0.193). Otherwise, NT-proBNP was correlated with PCWP (rho = 0.636; p = 0.001) and with CI (rho -0.667 p 0.001). Conclusions: In advanced HF patients referred for transplant, number of B-lines in LUS was not correlated with PCWP or RAP. Advanced HF patients seem to have increased filling pressures, but no interstitial pulmonary congestion that LUS could detect.

Effect of adding hydrochlorothiazide to usual treatment of patients with acute decompensated heart failure: a randomized clinical trial.

Acute decompensated heart failure (ADHF) is the leading cause of hospitalization in patients aged 65 years or older, and most of them present with congestion. The use of hydrochlorothiazide (HCTZ) may increase the response to loop diuretics. To evaluate the effect of adding HCTZ to furosemide on congestion and symptoms in patients with ADHF. This randomized clinical trial compared HCTZ 50 mg versus placebo for 3 days in patients with ADHF and signs of congestion. The primary outcome of the study was daily weight reduction. Secondary outcomes were change in creatinine, need for vasoactive drugs, change in natriuretic peptides, congestion score, dyspnea, thirst, and length of stay. Fifty-one patients were randomized-26 to the HCTZ group and 25 to the placebo group. There was an increment of 0.73 kg/day towards additional weight reduction in the HCTZ group (HCTZ: - 1.78 ± 1.08 kg/day vs placebo: - 1.05 ± 1.51 kg/day; p = 0.062). In post hoc analysis, the HCTZ group demonstrated significant weight reduction for every 40 mg of intravenous furosemide (HCTZ: - 0.74 ± 0.47 kg/40 mg vs placebo: - 0.33 ± 0.80 kg/40 mg; p = 0.032). There was a trend to increase in creatinine in the HCTZ group (HCTZ: 0.50 ± 0.37 vs placebo: 0.27 ± 0.40; p = 0.05) but no significant difference in onset of acute renal failure (HCTZ: 58% vs placebo: 41%; p = 0.38). No differences were found in the remaining outcomes. Adding hydrochlorothiazide to usual treatment of patients with acute decompensated heart failure did not cause significant difference in daily body weight reduction compared to placebo. In analysis adjusted to the dose of intravenous furosemide, adding HCTZ 50 mg to furosemide resulted in a significant synergistic effect on weight loss.Trial registration: The Brazilian Clinical Trials Registry (ReBEC), a publically accessible primary register that participates in the World Health Organization International Clinical Trial Registry Platform; number RBR-5qkn8h. Registered in 23/07/2019 (retrospectively), http://www.ensaiosclinicos.gov.br/rg/RBR-5qkn8h/ .

A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data

Abstract Objectives Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. Methods This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. Results A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7 × 103 mm-3, LDH >273 U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77-0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75-0.90). Conclusions Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations.

A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data.

OBJECTIVES: Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. METHODS: This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. RESULTS: A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7×103mm-3, LDH >273U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77-0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75-0.90). CONCLUSIONS: Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations.

Analysis of Risk Scores to Predict Mortality in Patients Undergoing Cardiac Surgery for Endocarditis. / Análise de Escores de Risco para Predição de Mortalidade em Pacientes Submetidos à Cirurgia Cardíaca por Endocardite.

BACKGROUND: Risk scores are available for use in daily clinical practice, but knowing which one to choose is still fraught with uncertainty. OBJECTIVES: To assess the logistic EuroSCORE, EuroSCORE II, and the infective endocarditis (IE)-specific scores STS-IE, PALSUSE, AEPEI, EndoSCORE and RISK-E, as predictors of hospital mortality in patients undergoing cardiac surgery for active IE at a tertiary teaching hospital in Southern Brazil. METHODS: Retrospective cohort study including all patients aged ≥ 18 years who underwent cardiac surgery for active IE at the study facility from 2007-2016. The scores were assessed by calibration evaluation (observed/expected [O/E] mortality ratio) and discrimination (area under the ROC curve [AUC]). Comparison of AUC was performed by the DeLong test. A p < 0.05 was considered statistically significant. RESULTS: A total of 107 patients were included. Overall hospital mortality was 29.0% (95%CI: 20.4-37.6%). The best O/E mortality ratio was achieved by the PALSUSE score (1.01, 95%CI: 0.70-1.42), followed by the logistic EuroSCORE (1.3, 95%CI: 0.92-1.87). The logistic EuroSCORE had the highest discriminatory power (AUC 0.77), which was significantly superior to EuroSCORE II (p = 0.03), STS-IE (p = 0.03), PALSUSE (p = 0.03), AEPEI (p = 0.03), and RISK-E (p = 0.02). CONCLUSIONS: Despite the availability of recent IE-specific scores, and considering the trade-off between the indexes, the logistic EuroSCORE seemed to be the best predictor of mortality risk in our cohort, taking calibration (O/E mortality ratio: 1.3) and discrimination (AUC 0.77) into account. Local validation of IE-specific scores is needed to better assess preoperative surgical risk. (Arq Bras Cardiol. 2020; 114(3):518-524).

FUNDAMENTO: Escores de risco estão disponíveis para uso na prática clínica diária, mas saber qual deles escolher é ainda incerto. OBJETIVOS: Avaliar o EuroSCORE logístico, o EuroSCORE II e os escores específicos para endocardite infecciosa STS-IE, PALSUSE, AEPEI, EndoSCORE e RISK-E na predição de mortalidade hospitalar de pacientes submetidos à cirurgia cardíaca por endocardite ativa em um hospital terciário de ensino do sul do Brasil. MÉTODOS: Estudo de coorte retrospectivo incluindo todos os pacientes com idade ≥ 18 anos submetidos à cirurgia cardíaca por endocardite ativa no centro do estudo entre 2007 e 2016. Foram realizadas análises de calibração (razão de mortalidade observada/esperada, O/E) e de discriminação (área sob a curva ROC, ASC), sendo a comparação das ASC realizada pelo teste de DeLong. P < 0,05 foi considerado estatisticamente significativo. RESULTADOS: Foram incluídos 107 pacientes, sendo a mortalidade hospitalar de 29,0% (IC95%: 20.4-37.6%). A melhor razão de mortalidade O/E foi obtida pelo escore PALSUSE (1,01, IC95%: 0,70-1,42), seguido pelo EuroSCORE logístico (1,3, IC95%: 0,92-1,87). O EuroSCORE logístico apresentou o maior poder discriminatório (ASC 0,77), significativamente superior ao EuroSCORE II (p = 0,03), STS-IE (p = 0,03), PALSUSE (p = 0,03), AEPEI (p = 0,03) e RISK-E (p = 0,02). CONCLUSÕES: Apesar da disponibilidade dos recentes escores específicos, o EuroSCORE logístico foi o melhor preditor de mortalidade em nossa coorte, considerando-se análise de calibração (mortalidade O/E: 1,3) e de discriminação (ASC 0,77). A validação local dos escores específicos é necessária para uma melhor avaliação do risco cirúrgico. (Arq Bras Cardiol. 2020; 114(3):518-524).

Análise de Escores de Risco para Predição de Mortalidade em Pacientes Submetidos à Cirurgia Cardíaca por Endocardite / Analysis of Risk Scores to Predict Mortality in Patients Undergoing Cardiac Surgery for Endocarditis

Resumo Fundamento Escores de risco estão disponíveis para uso na prática clínica diária, mas saber qual deles escolher é ainda incerto. Objetivos Avaliar o EuroSCORE logístico, o EuroSCORE II e os escores específicos para endocardite infecciosa STS-IE, PALSUSE, AEPEI, EndoSCORE e RISK-E na predição de mortalidade hospitalar de pacientes submetidos à cirurgia cardíaca por endocardite ativa em um hospital terciário de ensino do sul do Brasil. Métodos Estudo de coorte retrospectivo incluindo todos os pacientes com idade ≥ 18 anos submetidos à cirurgia cardíaca por endocardite ativa no centro do estudo entre 2007 e 2016. Foram realizadas análises de calibração (razão de mortalidade observada/esperada, O/E) e de discriminação (área sob a curva ROC, ASC), sendo a comparação das ASC realizada pelo teste de DeLong. P < 0,05 foi considerado estatisticamente significativo Resultados Foram incluídos 107 pacientes, sendo a mortalidade hospitalar de 29,0% (IC95%: 20.4-37.6%). A melhor razão de mortalidade O/E foi obtida pelo escore PALSUSE (1,01, IC95%: 0,70-1,42), seguido pelo EuroSCORE logístico (1,3, IC95%: 0,92-1,87). O EuroSCORE logístico apresentou o maior poder discriminatório (ASC 0,77), significativamente superior ao EuroSCORE II (p = 0,03), STS-IE (p = 0,03), PALSUSE (p = 0,03), AEPEI (p = 0,03) e RISK-E (p = 0,02). Conclusões Apesar da disponibilidade dos recentes escores específicos, o EuroSCORE logístico foi o melhor preditor de mortalidade em nossa coorte, considerando-se análise de calibração (mortalidade O/E: 1,3) e de discriminação (ASC 0,77). A validação local dos escores específicos é necessária para uma melhor avaliação do risco cirúrgico. (Arq Bras Cardiol. 2020; 114(3):518-524)

Abstract Background Risk scores are available for use in daily clinical practice, but knowing which one to choose is still fraught with uncertainty. Objectives To assess the logistic EuroSCORE, EuroSCORE II, and the infective endocarditis (IE)-specific scores STS-IE, PALSUSE, AEPEI, EndoSCORE and RISK-E, as predictors of hospital mortality in patients undergoing cardiac surgery for active IE at a tertiary teaching hospital in Southern Brazil. Methods Retrospective cohort study including all patients aged ≥ 18 years who underwent cardiac surgery for active IE at the study facility from 2007-2016. The scores were assessed by calibration evaluation (observed/expected [O/E] mortality ratio) and discrimination (area under the ROC curve [AUC]). Comparison of AUC was performed by the DeLong test. A p < 0.05 was considered statistically significant. Results A total of 107 patients were included. Overall hospital mortality was 29.0% (95%CI: 20.4-37.6%). The best O/E mortality ratio was achieved by the PALSUSE score (1.01, 95%CI: 0.70-1.42), followed by the logistic EuroSCORE (1.3, 95%CI: 0.92-1.87). The logistic EuroSCORE had the highest discriminatory power (AUC 0.77), which was significantly superior to EuroSCORE II (p = 0.03), STS-IE (p = 0.03), PALSUSE (p = 0.03), AEPEI (p = 0.03), and RISK-E (p = 0.02). Conclusions Despite the availability of recent IE-specific scores, and considering the trade-off between the indexes, the logistic EuroSCORE seemed to be the best predictor of mortality risk in our cohort, taking calibration (O/E mortality ratio: 1.3) and discrimination (AUC 0.77) into account. Local validation of IE-specific scores is needed to better assess preoperative surgical risk. (Arq Bras Cardiol. 2020; 114(3):518-524)