Post-cardiac injury syndrome and pericardial effusion recurrence after pericardial effusion drainage in chronic idiopathic pericardial effusion.

INTRODUCTION: The management of even large pericardial effusions in asymptomatic patients is still a matter of debate. Aim of the present study is to explore, in a multicenter setting, the rate of post-cardiac injury syndromes (PCIS) and pericardial effusion recurrence after pericardial effusion drainage procedure. MATERIAL AND METHODS: This is a multicenter international retrospective study including a consecutive cohort of patients diagnosed with large, chronic and idiopathic pericardial effusions, prospectively evaluated from January 2003 to December 2021 who underwent a clinically indicated pericardial drainage procedure. Two separate end-points were recorded: 1) recurrence of pericardial effusion after drainage without any sign of pericardial inflammation 2) occurrence of PCIS, defined as the new onset of pericarditis 1 to 6 weeks after pericardial intervention. RESULTS: 124 patients were enrolled (50 % female, mean age 64 years old). A mean follow-up of 29.6 ± 25.6 months was obtained in 110 patients (88 %). 110 patients were treated with pericardiocentesis (89 %), 25 with pleuro-pericardial windows (20 %), and 1 with pericardiectomy (1 %). PCIS occurred in 21 out of 124 patients followed for at least 6 weeks (16.9%). Recurrence of pericardial effusion after drainage without any sign of pericardial inflammation occurred in 68 out of 110 patients at a longer follow-up (61.8 %). At multivariate analysis only inflammatory cells in pericardial fluid was associated with PCIS and pericardiocentesis with pericardial effusion recurrency. CONCLUSION: Our data support the need of caution with the use of pericardiocentesis in asymptomatic patients with large pericardial effusion as it is often associated with pericardial effusion recurrence. Of interest the presence of inflammatory cells in the pericardial fluid is associated with PCIS after pericardial drainage procedures.

Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.

OBJECTIVE: We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems. METHODS: A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death. RESULTS: The final population comprised 743 patients (mean age 65  ±â€¯ 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores. CONCLUSION: Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time. ADVANCES IN KNOWLEDGE: Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.

The Contemporary Role of Cardiac Computed Tomography and Cardiac Magnetic Resonance Imaging in the Diagnosis and Management of Pericardial Diseases.

Pericardial syndromes encompass different clinical conditions from acute pericarditis to idiopathic chronic pericardial effusion. Transthoracic echocardiography is the first and most important initial diagnostic imaging modality in most patients affected by pericardial disease. However, cardiac computed tomography (CCT) and cardiac magnetic resonance imaging (CMR) have recently gained a pivotal role in cardiology, and recent reports have supported the role of both of these advanced techniques in the evaluation and guiding therapy of pericardial disease. Most promising is the capability of CMR to identify the presence of pericardial inflammation, carrying both diagnostic and prognostic value in the setting of recurrent and chronic pericarditis. In addition, CCT permits accurate evaluation of the presence and extension of pericardial calcification, providing important information in confirming the diagnosis of constrictive pericarditis and during the preprocedural planning for patients undergoing pericardiectomy. Both CCT and CMR require specific expertise, especially for the evaluation of pericardial disease. The aim of the present review is to provide physicians an updated overview of CCT and CMR in pericardial disease, focusing on technical issues, recent research findings, and potential clinical applications.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks.

Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 ± 0.07 ; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 ± 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95-0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.

Cardiac MRI after first episode of acute pericarditis: A pilot study for better identification of high risk patients.

BACKGROUND: Cardiac magnetic resonance (CMR) was proposed as an accurate non-invasive tool to evaluate pericardial inflammation. Aim of the present study was to evaluate the role of CMR early in the course of the first episode of acute pericarditis. MATERIAL AND METHODS: A clinical registry of consecutive patients who underwent clinical indicated CMR due to pericardial disease from January 2014 to January 2020 was screened. We analyzed patients with the clinical diagnosis of first episode of acute pericarditis needing hospitalization less than 7 days before CMR. Outcome measures were obtained using a single combined end-point, defined as pericardial event, including all the following: recurrent pericarditis, chronic constrictive pericarditis, surgery for pericardial disease. RESULTS: Twenty-six patients meet the study criteria and were enrolled. A mean follow-up of 34 ± 7 months was obtained and a second episode of pericardial event were recorded in 9 patients. At multivariate analysis adjusted for propensity score, based on clinical significative variable (younger age and higher CRP) the association between pericardial inflammation identified by CMR (positive late gadolinium enhancement on pericardium) and recurrence of pericardial events was confirmed [OR (95%CI) 8.94 (1.74-45.80), p = 0.008]. CONCLUSION: Pericardial inflammation identified by CMR, with LGE images, has a prognostic value independently from clinical and bio-humoral variables.

Cardiac Care of Non-COVID-19 Patients During the SARS-CoV-2 Pandemic: The Pivotal Role of CCTA.

Aim: The aim of this study is to evaluate the potential use of coronary CT angiography (CCTA) as the sole available non-invasive diagnostic technique for suspected coronary artery disease (CAD) during the coronavirus disease 2019 (COVID-19) pandemic causing limited access to the hospital facilities. Methods and Results: A consecutive cohort of patients with suspected stable CAD and clinical indication to non-invasive test was enrolled in a hub hospital in Milan, Italy, from March 9 to April 30, 2020. Outcome measures were obtained as follows: cardiac death, ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), and unstable angina. All the changes in medical therapy following the result of CCTA were annotated. A total of 58 patients with a mean age of 64 ± 11 years (36 men and 22 women) were enrolled. CCTA showed no CAD in 14 patients (24.1%), non-obstructive CAD in 30 (51.7%) patients, and obstructive CAD in 14 (24.1%) patients. Invasive coronary angiography (ICA) was considered deferrable in 48 (82.8%) patients. No clinical events were recorded after a mean follow-up of 376.4 ± 32.1 days. Changes in the medical therapy were significantly more prevalent in patients with vs. those without CAD at CCTA. Conclusion: The results of the study confirm the capability of CCTA to safely defer ICA in the majority of symptomatic patients and to correctly identify those with critical coronary stenoses necessitating coronary revascularization. This characteristic could be really helpful especially when the hospital resources are limited.

Prevalence and prognosis of pericardial effusion in patients affected by pectus excavatum: A case-control study.

BACKGROUND: The presence of pectus excavatum(PEX) has been occasionally associated with pericardial effusion. Aim of the present study was to compare incidence and prognosis of pericardial effusion in a group of unselected patients with PEX vs a control group. METHODS: From a prospective registry of consecutive patients who underwent chest CT for cardiovascular disease, subjects with a radiological diagnosis of PEX were retrospectively identified (cases); from the same registry patients (controls) without rib cage abnormalities were randomly selected, until a 1:2 ratio was reached. The presence of pericardial effusion at CT was quantified. Follow-up was obtained for a composite end-point: cardiac tamponade, need for pericardiocentesis, need for cardiac surgery for relapsing pericardial effusion. RESULTS: A total of 43 patients with PEX (20 females) and a control group of 86 cases (31 females) without rib cage abnormalities were identified. Pericardial effusion evaluated at CT was significatively more prevalent in patients with PEX vs control group, 37.2% vs 13.9% (p < 0.001), respectively; four patients with PEX (9.3%) had at least moderate pericardial effusion vs no subjects among the controls (p = 0.004). PEX diagnosis was significantly associated to pericardial effusion at multi-variate analysis (OR95%CI 10.91[3.47-34.29], p < 0.001). At a mean follow-up of 6.5 ± 3.4 years no pericardial events were recorded. CONCLUSION: Our findings support the higher prevalence of pericardial effusion in patients with PEX when compared to a control group. The absence of adverse pericardial events at follow-up suggest the good prognosis of these effusions, that in the appropriate clinical setting might not be considered "idiopathic".

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in Coronavirus disease 2019 (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (LSTM) networks. Utilizing the expert annotations, model training was performed using 5-fold cross-validation to segment ground-glass opacity and high opacity (including consolidation and pleural effusion). The performance of the method was evaluated on CT data sets from 197 patients with positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score coefficient of 0.876 ± 0.005; excellent correlations of 0.978 and 0.981 for ground-glass opacity and high opacity volumes. In the external validation set of 67 patients, there was dice score coefficient of 0.767 ± 0.009 as well as excellent correlations of 0.989 and 0.996 for ground-glass opacity and high opacity volumes. Computations for a CT scan comprising 120 slices were performed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX graphics processing unit. Therefore, our deep learning-based method allows rapid fully-automated quantitative measurement of pneumonia burden from CT and may generate the big data with an accuracy similar to the expert readers.

Quantitative Evaluation of COVID-19 Pneumonia Lung Extension by Specific Software and Correlation with Patient Clinical Outcome.

Lung infection named as COVID-19 is an infectious disease caused by the most recently discovered coronavirus 2 (SARS-CoV-2). CT (computed tomography) has been shown to have good sensitivity in comparison with RT-PCR, particularly in early stages. However, CT findings appear to not always be related to a certain clinical severity. The aim of this study is to evaluate a correlation between the percentage of lung parenchyma volume involved with COVID-19 infection (compared to the total lung volume) at baseline diagnosis and correlated to the patient's clinical course (need for ventilator assistance and or death). All patients with suspected COVID-19 lung disease referred to our imaging department for Chest CT from 24 February to 6 April 2020were included in the study. Specific CT features were assessed including the amount of high attenuation areas (HAA) related to lung infection. HAA, defined as the percentage of lung parenchyma above a predefined threshold of -650 (HAA%, HAA/total lung volume), was automatically calculated using a dedicated segmentation software. Lung volumes and CT findings were correlated with patient's clinical course. Logistic regressions were performed to assess the predictive value of clinical, inflammatory and CT parameters for the defined outcome. In the overall population we found an average infected lung volume of 31.4 ± 26.3% while in the subgroup of patients who needed ventilator assistance and who died as well as the patients who died without receiving ventilator assistance the volume of infected lung was significantly higher 41.4 ± 28.5 and 72.7 ± 36.2 (p < 0.001). In logistic regression analysis best predictors for ventilation and death were the presence of air bronchogram (p = 0.006), crazy paving (p = 0.007), peripheral distribution (p < 0.001), age (p = 0.002), fever at admission (p = 0.007), dyspnea (p = 0.002) and cardiovascular comorbidities (p < 0.001). In multivariable analysis, quantitative CT parameters and features added incremental predictive value beyond a model with only clinical parameters (area under the curve, 0.78 vs. 0.74, p = 0.02). Our study demonstrates that quantitative evaluation of lung volume involved by COVID-19 pneumonia helps to predict patient's clinical course.

Role of computed tomography in COVID-19.

Coronavirus disease 2019 (COVID-19) has become a rapid worldwide pandemic. While COVID-19 primarily manifests as an interstitial pneumonia and severe acute respiratory distress syndrome, severe involvement of other organs has been documented. In this article, we will review the role of non-contrast chest computed tomography in the diagnosis, follow-up and prognosis of patients affected by COVID-19 pneumonia with a detailed description of the imaging findings that may be encountered. Given that patients with COVID-19 may also suffer from coagulopathy, we will discuss the role of CT pulmonary angiography in the detection of acute pulmonary embolism. Finally, we will describe more advanced applications of CT in the differential diagnosis of myocardial injury with an emphasis on ruling out acute coronary syndrome and myocarditis.

Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19.

AIM: We sought to examine the association of epicardial adipose tissue (EAT) quantified on chest computed tomography (CT) with the extent of pneumonia and adverse outcomes in patients with coronavirus disease 2019 (COVID-19). METHODS: We performed a post-hoc analysis of a prospective international registry comprising 109 consecutive patients (age 64 ±â€¯16 years; 62% male) with laboratory-confirmed COVID-19 and noncontrast chest CT imaging. Using semi-automated software, we quantified the burden (%) of lung abnormalities associated with COVID-19 pneumonia. EAT volume (mL) and attenuation (Hounsfield units) were measured using deep learning software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. RESULTS: In multivariable linear regression analysis adjusted for patient comorbidities, the total burden of COVID-19 pneumonia was associated with EAT volume (ß = 10.6, p = 0.005) and EAT attenuation (ß = 5.2, p = 0.004). EAT volume correlated with serum levels of lactate dehydrogenase (r = 0.361, p = 0.001) and C-reactive protein (r = 0.450, p < 0.001). Clinical deterioration or death occurred in 23 (21.1%) patients at a median of 3 days (IQR 1-13 days) following the chest CT. In multivariable logistic regression analysis, EAT volume (OR 5.1 [95% CI 1.8-14.1] per doubling p = 0.011) and EAT attenuation (OR 3.4 [95% CI 1.5-7.5] per 5 Hounsfield unit increase, p = 0.003) were independent predictors of clinical deterioration or death, as was total pneumonia burden (OR 2.5, 95% CI 1.4-4.6, p = 0.002), chronic lung disease (OR 1.3 [95% CI 1.1-1.7], p = 0.011), and history of heart failure (OR 3.5 [95% 1.1-8.2], p = 0.037). CONCLUSIONS: EAT measures quantified from chest CT are independently associated with extent of pneumonia and adverse outcomes in patients with COVID-19, lending support to their use in clinical risk stratification.

Extent of lung involvement over severity of cardiac disease for the prediction of adverse outcome in COVID-19 patients with cardiovascular disease.

BACKGROUND: Aim of the present study was to assess if the presence of high cardiovascular risk, left ventricle systolic dysfunction or elevated BNP or Troponin are able to independently predict the outcome of patients with known cardiac disease and COVID-19 pneumonia. METHODS AND RESULTS: From March 7th to April 28th, forty consecutive patients with known cardiac disease (chronic coronary artery disease, n=38; atrial fibrillation, n = 7; valvular disease, n = 13) referred to our emergency department for symptoms of suspected COVID-19, laboratory diagnosis of COVID-19 and typical signs of viral pneumonia at chest CT were enrolled in the study. The only predictor of the composite end-point (all cause of death + invasive ventilation + thromboembolic event) was the lung involvement % at chest CT (OR: 1.06; 95%CI: 1.01-1.11, P = 0.02). In the multivariate analysis, the lung involvement % at chest CT was the only independent predictor of the composite end-point (OR: 1.06; 95%CI: 1.01-1.11, P = 0.034). CONCLUSIONS: The extent of lung involvement by COVID-19 is the only independent predictor of adverse outcome of patients and is predominant over the severity of cardiac disease.

Quantitative Burden of COVID-19 Pneumonia on Chest CT Predicts Adverse Outcomes: A Post-Hoc Analysis of a Prospective International Registry.

PURPOSE: To examine the independent and incremental value of CT-derived quantitative burden and attenuation of COVID-19 pneumonia for the prediction of clinical deterioration or death. METHODS: This was a retrospective analysis of a prospective international registry of consecutive patients with laboratory-confirmed COVID-19 and chest CT imaging, admitted to four centers between January 10 and May 6, 2020. Total burden (expressed as a percentage) and mean attenuation of ground glass opacities (GGO) and consolidation were quantified from CT using semi-automated research software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. Logistic regression was performed to assess the predictive value of clinical and CT parameters for the primary outcome. RESULTS: The final population comprised 120 patients (mean age 64 ± 16 years, 78 men), of whom 39 (32.5%) experienced clinical deterioration or death. In multivariable regression of clinical and CT parameters, consolidation burden (odds ratio [OR], 3.4; 95% confidence interval [CI]: 1.7, 6.9 per doubling; P = .001) and increasing GGO attenuation (OR, 3.2; 95% CI: 1.3, 8.3 per standard deviation, P = .02) were independent predictors of deterioration or death; as was C-reactive protein (OR, 2.1; 95% CI: 1.3, 3.4 per doubling; P = .004), history of heart failure (OR 1.3; 95% CI: 1.1, 1.6, P = .01), and chronic lung disease (OR, 1.3; 95% CI: 1.0, 1.6; P = .02). Quantitative CT measures added incremental predictive value beyond a model with only clinical parameters (area under the curve, 0.93 vs 0.82, P = .006). The optimal prognostic cutoffs for burden of COVID-19 pneumonia as determined by Youden's index were consolidation of greater than or equal to 1.8% and GGO of greater than or equal to 13.5%. CONCLUSIONS: Quantitative burden of consolidation or GGO on chest CT independently predict clinical deterioration or death in patients with COVID-19 pneumonia. CT-derived measures have incremental prognostic value over and above clinical parameters, and may be useful for risk stratifying patients with COVID-19.

Physiological insights of exercise hyperventilation in arterial and chronic thromboembolic pulmonary hypertension.

BACKGROUND: Pulmonary hypertension (PH) patients show, during exercise, an excessive increase in ventilation (VE) compared to carbon dioxide output (VCO2), determining a high VE/VCO2 slope. There are several possible causes, including an elevated dead space ventilation (VD), VE/perfusion (Q) mismatch and/or an enhanced peripheral or central chemoreceptor activity. We evaluated the causes of exercise hyperventilation in PH patients. METHODS: Eighteen group I and IV PH patients underwent cardiopulmonary exercise test with blood gas analysis at every minute. VE, alveolar ventilation (VA) and VD vs. VCO2 relationship were calculated. Resting chemoreceptor sensitivity was analyzed through hypoxia/hypercapnia tests. RESULTS: PeakVO2 and VE/VCO2 slopes were 1.06±0.24l/min and 39.1±9.0, respectively. Throughout the exercise, 30% of VE was due to VD. VE/VCO2 slope significantly correlated with VD/VCO2 slope (r=0.82, p<0.001) but not with VA/VCO2 slope (r=0.3, p=ns). Peak exercise end-tidal CO2 (PetCO2) correlated with VD/VCO2 slope (r=-0.79, p<0.001) and VE/VCO2 slope (r=-0.91, p<0.001). Dead space(DS)/Tidal volume and P(arterial-et)CO2 were elevated without arterial hypoxemia suggesting a high VE/Q mismatch. Chemoreceptor peripheral response to hypoxia and central CO2 response were both enhanced being peripheral responses to hypoxia and hypercapnia 0.416±0.402 (normal ref values=0.285±0.221) l/min/O2Sat and 0.076±0.047 (0.066±0.430) l/min/mmHg, respectively; central hypercapnic chemosensitivity was 4.475±3.99 (2.352±0.936) l/min/mmHg. CONCLUSIONS: Increased DS, VE/Q mismatch and chemorecptor response are among the main mechanisms involved in exercise hyperventilation in PH. ClinicalTrial.govNCT02892981.

Evaluation of coronary plaque characteristics with coronary computed tomography angiography in patients with non-obstructive coronary artery disease: a long-term follow-up study.

AIMS: Recent studies suggested that even non-obstructive coronary artery disease (CAD) increases major cardiovascular adverse events (MACE) rate. Aim of this study was to evaluate whether coronary computed tomography angiography (CCTA) may detect specific plaque characteristics that may affect prognosis in patients with non-obstructive CAD. METHODS: We enrolled 245 patients who underwent CCTA between April 2004 and April 2007 for suspected CAD and were found to have non-obstructive CAD. Positive remodelling index (PRI), low-attenuation plaque (LAP), plaque burden (PB), spotty calcification (SC), and napkin-ring sign (NRS) have been evaluated for each coronary plaque detected. Acute coronary syndrome, all-cause/cardiac death, and very late elective revascularization (vl-ER) were the endpoints of the study. RESULTS: A total of 28 events were recorded (2 STEMI, 4 NSTEMI, 6 UA, 2 cardiac deaths, 4 non-cardiac death, and 10 vl-ERs) at long-term follow-up (98 ± 20 months). When adjusted for significant clinical variables PRI > 1.4 (HR 3.31 CI 95% 1.11-9.91, P = 0.0392), LAP (HR 8.45 CI 95% 2.22-32.21, P = 0.0019), PB > 0.7 (HR 5.25 CI 95% 1.45-19.03, P = 0.0120), and NRS (HR 12.52 CI 95% 1.51-103.90, P = 0.0198) were still significantly associated with higher rate of hard cardiac events at follow-up. The Kaplan-Meyer curves confirmed lower cumulative hard cardiac events-free survival in patients presenting at least one coronary plaque with two or more high-risk characteristics when compared with patients with no lesion with more than one high-risk plaque characteristics (log-rank P < 0.0001). CONCLUSIONS: High-risk plaque characteristics at CCTA (PRI > 1.4, PB > 0.7, LAP, and NRS) seem to be promising for risk stratification of patients with non-obstructive CAD.

Delayed Anaerobic Threshold in Heart Failure Patients With Atrial Fibrillation.

PURPOSE: To assess whether atrial fibrillation (AF) in heart failure (HF) affects oxygen uptake at anaerobic threshold ((Equation is included in full-text article.)O2 AT) and heart rate (HR) kinetics. METHODS: A total of 15 patients with HF and AF and 18 with HF and sinus rhythm (SR) performed a maximal incremental and 2 constant workload cycle ergometer cardiopulmonary exercise tests (below and above AT, at 25% and 75% of maximal workload, respectively). At constant workload tests, kinetics of (Equation is included in full-text article.)O2 and HR were assessed by calculating time constant (τ). RESULTS: HF patients with AF showed a similar peak (Equation is included in full-text article.)O2 to those with SR (16.7 ± 4.5 mL/kg/min vs 16.6 ± 3.9 mL/kg/min). However, (Equation is included in full-text article.)O2 AT (11.3 ± 2.9 mL/kg/min vs 9.3 ± 2.8 mL/kg/min; P < .05), peak HR (149 ± 18.8 bpm vs 116.4 ± 20.4 bpm; P < .001), HR AT (125.3 ± 19.1 bpm vs 90.3 ± 15.5 bpm; P < .001), and HR increase during exercise were greater in HF patients with AF. Finally, τHR and τ(Equation is included in full-text article.)O2 below and above AT were not significantly different. CONCLUSIONS: In HF patients with AF, despite a similar peak (Equation is included in full-text article.)O2 compared with patients with HF and SR, (Equation is included in full-text article.)O2 AT is higher because of a higher HR and a greater HR increase during exercise. One postulated mechanism would be a greater cardiac output increase at the beginning of exercise in HF patients with AF. The delayed AT generates uncertainty about the meaning of a (Equation is included in full-text article.)O2 value at AT in HF patients with AF, because a higher AT is usually associated with better performance and a better prognosis.

Impact of chronic obstructive pulmonary disease on exercise ventilatory efficiency in heart failure.

BACKGROUND: Heart failure (HF) and chronic obstructive pulmonary disease (COPD) coexistence increases morbidity and mortality. The intercept of ventilation (VEint) on the VE vs. carbon dioxide production (VCO2) relationship during exercise has been found to vary in proportion with dead space (VD) in HF. Considering that increased VD is the key pathophysiological abnormality in COPD but a secondary finding in HF we hypothesized that a high VEint would be useful in suggesting COPD as HF co-morbidity. Our aim was to assess whether an elevated VEint suggests the presence of COPD in HF. METHODS: In a multicenter retrospective study, the VE-VCO2 relationship was analyzed both as slope and intercept in HF (n = 108), HF-COPD (n = 106) and COPD (n = 95). Patients with pulmonary arterial hypertension (PAH) (n = 85) and healthy subjects (HF) (n = 56) served as positive and negative controls relative to VE-VCO2 abnormalities, respectively. RESULTS: Slope and VEint varied in opposite directions in all groups (p < 0.05) being VE-VCO2 slope highest and lowest in PAH and healthy subjects, respectively. No slope differences were observed among HF, HF-COPD and COPD (32 ± 7, 31 ± 7, and 31 ± 6, respectively). VEint was higher in HF-COPD and COPD compared to HF, PAH and controls (4.8 ± 2.4 L/min, 5.9 ± 3.0 L/min, 3.0 ± 2.6L/min, 2.3 ± 3.3 L/min and 3.9 ± 2.5L/min, respectively; p < 0.01). A VEint ≥ 4.07 L/min identified patients with high probability of having COPD or HF-COPD (sensitivity of 71.6% and specificity of 72.0%). CONCLUSION: These data provide novel evidence that a high VEint (≥ 4.07 L/min) should be valued to suggest coexistent COPD in HF patients.