Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.

AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients. METHODS AND RESULTS: We used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies. CONCLUSIONS: Our study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.

Applying the UTAUT2 framework to patients' attitudes toward healthcare task shifting with artificial intelligence.

BACKGROUND: Increasing patient loads, healthcare inflation and ageing population have put pressure on the healthcare system. Artificial intelligence and machine learning innovations can aid in task shifting to help healthcare systems remain efficient and cost effective. To gain an understanding of patients' acceptance toward such task shifting with the aid of AI, this study adapted the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), looking at performance and effort expectancy, facilitating conditions, social influence, hedonic motivation and behavioural intention. METHODS: This was a cross-sectional study which took place between September 2021 to June 2022 at the National Heart Centre, Singapore. One hundred patients, aged ≥ 21 years with at least one heart failure symptom (pedal oedema, New York Heart Association II-III effort limitation, orthopnoea, breathlessness), who presented to the cardiac imaging laboratory for physician-ordered clinical echocardiogram, underwent both echocardiogram by skilled sonographers and the experience of echocardiogram by a novice guided by AI technologies. They were then given a survey which looked at the above-mentioned constructs using the UTAUT2 framework. RESULTS: Significant, direct, and positive effects of all constructs on the behavioral intention of accepting the AI-novice combination were found. Facilitating conditions, hedonic motivation and performance expectancy were the top 3 constructs. The analysis of the moderating variables, age, gender and education levels, found no impact on behavioral intention. CONCLUSIONS: These results are important for stakeholders and changemakers such as policymakers, governments, physicians, and insurance companies, as they design adoption strategies to ensure successful patient engagement by focusing on factors affecting the facilitating conditions, hedonic motivation and performance expectancy for AI technologies used in healthcare task shifting.

Nurse-led home-based detection of cardiac dysfunction by ultrasound: results of the CUMIN pilot study.

Aims: Access to echocardiography is a significant barrier to heart failure (HF) care in many low- and middle-income countries. In this study, we hypothesized that an artificial intelligence (AI)-enhanced point-of-care ultrasound (POCUS) device could enable the detection of cardiac dysfunction by nurses in Tunisia. Methods and results: This CUMIN study was a prospective feasibility pilot assessing the diagnostic accuracy of home-based AI-POCUS for HF conducted by novice nurses compared with conventional clinic-based transthoracic echocardiography (TTE). Seven nurses underwent a one-day training program in AI-POCUS. A total of 94 patients without a previous HF diagnosis received home-based AI-POCUS, POC N-terminal pro-B-type natriuretic peptide (NT-proBNP) testing, and clinic-based TTE. The primary outcome was the sensitivity of AI-POCUS in detecting a left ventricular ejection fraction (LVEF) <50% or left atrial volume index (LAVI) >34 mL/m2, using clinic-based TTE as the reference. Out of seven nurses, five achieved a minimum standard to participate in the study. Out of the 94 patients (60% women, median age 67), 16 (17%) had an LVEF < 50% or LAVI > 34 mL/m2. AI-POCUS provided an interpretable LVEF in 75 (80%) patients and LAVI in 64 (68%). The only significant predictor of an interpretable LVEF or LAVI proportion was the nurse operator. The sensitivity for the primary outcome was 92% [95% confidence interval (CI): 62-99] for AI-POCUS compared with 87% (95% CI: 60-98) for NT-proBNP > 125 pg/mL, with AI-POCUS having a significantly higher area under the curve (P = 0.040). Conclusion: The study demonstrated the feasibility of novice nurse-led home-based detection of cardiac dysfunction using AI-POCUS in HF patients, which could alleviate the burden on under-resourced healthcare systems.

External validation of a deep learning algorithm for automated echocardiographic strain measurements.

Aims: Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging. Methods and results: We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson's correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean ± SD): -18.9 ± 4.5% vs. -18.2 ± 4.4%, respectively, bias 0.68 ± 2.52%, MAD 2.0 ± 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and -15.4 ± 4.1% vs. -15.9 ± 3.6%, respectively, bias -0.65 ± 2.71%, MAD 2.19 ± 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80. Conclusion: DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally.

Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography.

BACKGROUND: Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time-consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment. METHODS: Two-dimensional and Doppler echocardiographic images from patients with normal aortic valves and all degrees of AS were analyzed by an artificial neural network (Us2.ai) with no human input to measure key variables in AS assessment. Trained echocardiographers blinded to AI data performed manual measurements of these variables, and correlation analyses were performed. RESULTS: Our cohort included 256 patients with an average age of 67.6 ± 9.5 years. Across all AS severities, AI closely matched human measurement of aortic valve peak velocity (r = 0.97, P < .001), mean pressure gradient (r = 0.94, P < .001), aortic valve area by continuity equation (r = 0.88, P < .001), stroke volume index (r = 0.79, P < .001), left ventricular outflow tract velocity-time integral (r = 0.89, P < .001), aortic valve velocity-time integral (r = 0.96, P < .001), and left ventricular outflow tract diameter (r = 0.76, P < .001). CONCLUSIONS: Artificial neural networks have the capacity to closely mimic human measurement of all relevant parameters in the adjudication of AS severity. Application of this AI technology may minimize interscan variability, improve interpretation and diagnosis of AS, and allow for precise and reproducible identification and management of patients with AS.

Predictors of adverse diastolic remodeling in non-diabetic patients presenting with ST-elevation myocardial infarction.

BACKGROUND: Adverse systolic remodeling after ST-elevation myocardial infarction (STEMI) is associated with poor clinical outcomes. However, little is known about diastolic remodeling. The purpose of this study was to identify the factors leading to diastolic remodeling. METHODS: Echocardiography was performed during hospitalization and at 4 months follow-up in 267 non-diabetic STEMI patients from the GIPS-III trial. As parameters of diastolic remodeling we used (1.) the E/e' at 4 months adjusted for the E/e' at hospitalization and (2.) the change in E/e' between hospitalization and 4 months. Multivariable regression models correcting for age and sex were constructed to identify possible association of clinical and angiographic variables as well as biomarkers with diastolic remodeling. RESULTS: Older age, female gender, hypertension, multi vessel disease, higher glucose and higher peak CK were independent predictors of higher E/e' at 4 months in a multivariable model (R2:0.20). After adjustment for E/e' during hospitalization only female gender, multivessel disease and higher glucose remained predictors of E/e' at four months (R2:0.40). Lower myocardial blush grade, AST and NT-proBNP were independent predictors of a higher increase of E/e' between hospitalization and at 4 months in a multivariable model (R2:0.08). CONCLUSIONS: Our data supports the hypothesis that female gender, multivessel coronary artery disease, and microvascular damage are important predictors of adverse diastolic remodeling after STEMI. In addition, our data suggests that older age and hypertension prior to STEMI may have contributed to worse pre-existing diastolic function. TRIAL REGISTRATION: NIH, NCT01217307. Prospectively registered on October 8th 2010, https://clinicaltrials.gov/ct2/show/NCT01217307 .

Optimal Timing of a Physical Exercise Intervention to Improve Cardiorespiratory Fitness: During or After Chemotherapy.

Background: Despite the widely acknowledged benefit of exercise for patients with cancer, little evidence on the optimal timing of exercise on adverse effects of cancer treatment is available. Objectives: The aim of this study was to determine whether an exercise intervention initiated during chemotherapy is superior to an intervention initiated after chemotherapy for improving long-term cardiorespiratory fitness (peak oxygen uptake [VO2peak]). Methods: In this prospective, randomized clinical trial, patients scheduled to receive curative chemotherapy were randomized to a 24-week exercise intervention, initiated either during chemotherapy (group A) or afterward (group B). The primary endpoint was VO2peak 1 year postintervention. Secondary endpoints were VO2peak postintervention, muscle strength, health-related quality of life (HRQoL), fatigue, physical activity, and self-efficacy. Between-group differences were calculated using intention-to-treat linear mixed-models analyses. Results: A total of 266 patients with breast (n = 139), testicular (n = 95), and colon cancer (n = 30) as well as lymphoma (n = 2) were included. VO2peak immediately postintervention and 1 year postintervention did not differ between the 2 groups. Immediately postchemotherapy, patients in group A exhibited significantly lower decreases in VO2peak (3.1 mL/kg/min; 95% CI: 2.2-4.0 mL/kg/min), HRQoL, and muscle strength and reported less fatigue and more physical activity than those in group B. Conclusions: Exercise can be safely performed during chemotherapy and prevents fatigue and decreases in VO2peak, muscle strength, and HRQoL, in addition to hastening the return of function after chemotherapy. Also, if exercise cannot be performed during chemotherapy, a program afterward can enable patients to regain the same level of function, measured 1 year after completion of the intervention. (Optimal Timing of Physical Activity in Cancer Treatment [ACT]; NCT01642680).

Epicardial Adipose Tissue and Invasive Hemodynamics in Heart Failure With Preserved Ejection Fraction.

OBJECTIVES: This study examined associations between epicardial adipose tissue (EAT), invasive hemodynamics, and exercise capacity in patients with heart failure with preserved ejection fraction (HFpEF). BACKGROUND: EAT is increased in patients with HFpEF and may play a role in the pathophysiology of this disorder. METHODS: Patients with heart failure and a left ventricular ejection fraction >45% who underwent right and left heart catheterization with simultaneous echocardiography were included. Pulmonary capillary wedge pressure (PCWP), left ventricular end-diastolic pressure (LVEDP), right ventricular end-diastolic pressure (RVEDP), and pulmonary vascular resistance (PVR) were invasively measured. Obesity was defined as body mass index (BMI) ≥30 kg/m2. EAT thickness alongside the right ventricle was measured on echocardiographic long- and short-axis views. Cardiopulmonary exercise testing was performed to obtain maximal oxygen uptake (VO2-max). RESULTS: This study examined 75 patients, mean age 74 ± 9 years; 68% were women, mean BMI was 29 ± 6 kg/m2, and 36% were obese. Higher BMI was strongly associated with increased EAT (r = 0.74; p < 0.001). Increased EAT was associated with higher RVEDP, independent of PVR (odds ratio [OR]: 1.16; 95% confidence interval [CI]: 1.02 to 1.34; p = 0.03), but not independent of obesity (p = 0.10). Increased EAT and higher RVEDP were both associated with lower VO2-max (r = -0.43; p < 0.001 and r = -0.43; p = 0.001, respectively). Increased EAT remained associated with lower VO2-max after adjustment for PVR (OR: 0.64; 95% CI: 0.49 to 0.84; p = 0.002) and obesity (OR: 0.69; 95% CI: 0.53 to 0.92; p = 0.01). EAT thickness was not associated with left-sided filling pressures (i.e., PCWP and LVEDP). CONCLUSIONS: In HFpEF, obesity and increased EAT were associated with higher right-sided filling pressures and with reduced exercise capacity.

Long-term survivors of early breast cancer treated with chemotherapy are characterized by a pro-inflammatory biomarker profile compared to matched controls.

BACKGROUND: Chemo- and radiotherapy for breast cancer (BC) can lead to cardiotoxicity even years after the initial treatment. The pathophysiology behind these late cardiac effects is poorly understood. Therefore, we studied a large panel of biomarkers from different pathophysiological domains in long-term BC survivors, and compared these to matched controls. METHODS AND RESULTS: In total 91 biomarkers were measured in 688 subjects: 342 BC survivors stratified either to treatment with chemotherapy ± radiotherapy (n = 170) or radiotherapy alone (n = 172) and matched controls. Mean age was 59 ± 9 years and 65 ± 8 years for women treated with chemotherapy ± radiotherapy and radiotherapy alone, respectively, with a mean time since treatment of 11 ± 5.5 years. No biomarkers were differentially expressed in survivors treated with radiotherapy alone vs. controls (P for all >0.1). In sharp contrast, a total of 19 biomarkers were elevated, relative to controls, in BC survivors treated with chemotherapy ± radiotherapy after correction for multiple comparisons (P <0.05 for all). Network analysis revealed upregulation of pathways relating to collagen degradation and activation of matrix metalloproteinases. Furthermore, several inflammatory biomarkers including growth differentiation factor 15, monocyte chemoattractant protein 1, chemokine (C-X-C motif) ligand 16, tumour necrosis factor super family member 13b and proprotein convertase subtilisin/kexin type 9, elevated in survivors treated with chemotherapy, showed an independent association with lower left ventricular ejection fraction. CONCLUSION: Breast cancer survivors treated with chemotherapy ± radiotherapy show a distinct biomarker profile associated with mild cardiac dysfunction even 10 years after treatment. These results suggest that an ongoing pro-inflammatory state and activation of matrix metalloproteinases following initial treatment with chemotherapy might play a role in the observed cardiac dysfunction in late BC survivors.

Feasibility of cardiac output measurements in critically ill patients by medical students.

BACKGROUND: Critical care ultrasonography (CCUS) is increasingly applied also in the intensive care unit (ICU) and performed by non-experts, including even medical students. There is limited data on the training efforts necessary for novices to attain images of sufficient quality. There is no data on medical students performing CCUS for the measurement of cardiac output (CO), a hemodynamic variable of importance for daily critical care. OBJECTIVE: The aim of this study was to explore the agreement of cardiac output measurements as well as the quality of images obtained by medical students in critically ill patients compared to the measurements obtained by experts in these images. METHODS: In a prospective observational cohort study, all acutely admitted adults with an expected ICU stay over 24 h were included. CCUS was performed by students within 24 h of admission. CCUS included the images required to measure the CO, i.e., the left ventricular outflow tract (LVOT) diameter and the velocity time integral (VTI) in the LVOT. Echocardiography experts were involved in the evaluation of the quality of images obtained and the quality of the CO measurements. RESULTS: There was an opportunity for a CCUS attempt in 1155 of the 1212 eligible patients (95%) and in 1075 of the 1212 patients (89%) CCUS examination was performed by medical students. In 871 out of 1075 patients (81%) medical students measured CO. Experts measured CO in 783 patients (73%). In 760 patients (71%) CO was measured by both which allowed for comparison; bias of CO was 0.0 L min-1 with limits of agreement of - 2.6 L min-1 to 2.7 L min-1. The percentage error was 50%, reflecting poor agreement of the CO measurement by students compared with the experts CO measurement. CONCLUSIONS: Medical students seem capable of obtaining sufficient quality CCUS images for CO measurement in the majority of critically ill patients. Measurements of CO by medical students, however, had poor agreement with expert measurements. Experts remain indispensable for reliable CO measurements. Trial registration Clinicaltrials.gov; http://www.clinicaltrials.gov; registration number NCT02912624.

Concentric vs. eccentric remodelling in heart failure with reduced ejection fraction: clinical characteristics, pathophysiology and response to treatment.

AIMS: Heart failure is traditionally classified by left ventricular ejection fraction (LVEF), rather than by left ventricular (LV) geometry, with guideline-recommended therapies in heart failure with reduced ejection fraction (HFrEF) but not heart failure with preserved ejection fraction (HFpEF). Most patients with HFrEF have eccentric LV hypertrophy, but some have concentric LV hypertrophy. We aimed to compare clinical characteristics, biomarker patterns, and response to treatment of patients with HFrEF and eccentric vs. concentric LV hypertrophy. METHODS AND RESULTS: We performed a retrospective post-hoc analysis including 1015 patients with HFrEF (LVEF <40%) from the multinational observational BIOSTAT-CHF study. LV geometry was classified using two-dimensional echocardiography. Network analysis of 92 biomarkers was used to investigate pathophysiologic pathways. Concentric LV hypertrophy was present in 142 (14%) patients, who were on average older and more likely hypertensive compared to those with eccentric LV hypertrophy. Network analysis revealed that N-terminal pro-B-type natriuretic peptide was an important hub in eccentric hypertrophy, whereas in concentric hypertrophy, tumour necrosis factor receptor 1, urokinase plasminogen activator surface receptor, paraoxonase and P-selectin were central hubs. Up-titration of beta-blockers was associated with a mortality benefit in HFrEF with eccentric but not concentric LV hypertrophy (P-value for interaction ≤0.001). For angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, the hazard ratio for mortality was higher in concentric hypertrophy, but the interaction was not significant. CONCLUSION: Patients with HFrEF with concentric hypertrophy have a clinical and biomarker phenotype that is distinctly different from those with eccentric hypertrophy. Patients with concentric hypertrophy may not experience similar benefit from up.-titration of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers and beta-blockers compared to patients with eccentric hypertrophy.

Myocardial dysfunction in long-term breast cancer survivors treated at ages 40-50 years.

AIMS: Anthracyclines increase heart failure (HF) risk, but the long-term prevalence of myocardial dysfunction in young breast cancer (BC) survivors is unknown. Early measures of left ventricular myocardial dysfunction are needed to identify BC patients at risk of symptomatic HF. METHODS AND RESULTS: Within an established cohort, we studied markers for myocardial dysfunction among 569 women, who were 5-7 years (n = 277) or 10-12 years (n = 292) after BC treatment at ages 40-50 years. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) were assessed by echocardiography. N-terminal pro-brain natriuretic peptide (NT-proBNP) was measured in serum. Associations between patient-related and treatment-related risk factors and myocardial dysfunction were evaluated using linear and logistic regression. Median ages at BC diagnosis and cardiac assessment were 46.7 and 55.5 years, respectively. Anthracycline-treated patients (n = 313), compared to the no-anthracycline group (n = 256), more often had decreased LVEF (10% vs. 4%), impaired GLS (34% vs. 27%) and elevated NT-proBNP (23% vs. 8%). GLS and LVEF declined in a linear fashion with increasing cumulative anthracycline dose (GLS: +0.23 and LVEF: -0.40 per cycle of 60 mg/m2 ; P < 0.001) and GLS was worse for patients with left breast irradiation. The risk of NT-proBNP >125 ng/L was highest for patients who received 241-300 mg/m2 anthracycline dose compared to the no-anthracycline group (odds ratio: 3.30, 95% confidence interval: 1.83-5.96). CONCLUSION: Impaired GLS and increased NT-proBNP levels are present in a substantial proportion of young BC survivors treated with anthracyclines. Whether this will lead to future cardiac disease needs to be evaluated by longitudinal assessment.

Agreement of 2D transthoracic echocardiography with cardiovascular magnetic resonance imaging after ST-elevation myocardial infarction.

BACKGROUND: This study was designed to investigate the agreement of 2D transthoracic echocardiography (2D TTE) with cardiovascular magnetic resonance imaging (CMR) in a contemporary population of ST-elevation myocardial infarction (STEMI) patients. METHODS: In this subanalysis of the GIPS-III trial, a randomized controlled trial investigating the administration of metformin in STEMI patients to prevent reperfusion injury, we studied 259 patients who underwent same-day CMR and 2D TTE assessments four months after hospitalization for a first STEMI. Bland-Altman analyses were performed to assess agreement between LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV ejection fraction (LVEF), and LV mass measurements. Sensitivity and specificity of 2D TTE to detect categories of LVEF (≤35%, 35-50%, ≥50%) was determined. Linear regression of absolute differences in measurements between imaging modalities was used to investigate whether patient characteristics impact measurement bias. RESULTS: Pairwise difference (bias) and 95% limits of agreement between CMR and 2D TTE measurements were +84 (37, 147) ml for LVEDV, +39 (6, 85) ml for LVESV, -1.1 ± 13.5% for LVEF, and -75 (-154, -14) g for LV mass. Sensitivity and specificity of 2D TTE to detect subjects with moderately depressed LVEF (35-50%) as measured by CMR were 52% and 88% respectively. We observed a significant effect of enzymatic infarct size on bias between 2D TTE and CMR in measuring LVESV and LVEF (P = 0.029, P = 0.001 respectively), of age and sex on bias between 2D TTE and CMR in measuring LV mass (P = 0.027, P < 0.001) and LVEDV (P = 0.001, P = 0.039), and of heart rate on bias between 2D TTE and CMR in LV volume measurements (P = 0.004, P = 0.016). CONCLUSIONS: Wide limits of agreement, underestimation of LV volumes and overestimation of LV mass was observed when comparing 2D TTE to CMR. Enzymatic infarct size, age, sex, and heart rate are potential sources of bias between imaging modalities.

The diagnostic accuracy of clinical examination for estimating cardiac index in critically ill patients: the Simple Intensive Care Studies-I.

PURPOSE: Clinical examination is often the first step to diagnose shock and estimate cardiac index. In the Simple Intensive Care Studies-I, we assessed the association and diagnostic performance of clinical signs for estimation of cardiac index in critically ill patients. METHODS: In this prospective, single-centre cohort study, we included all acutely ill patients admitted to the ICU and expected to stay > 24 h. We conducted a protocolised clinical examination of 19 clinical signs followed by critical care ultrasonography for cardiac index measurement. Clinical signs were associated with cardiac index and a low cardiac index (< 2.2 L min-1 m2) in multivariable analyses. Diagnostic test accuracies were also assessed. RESULTS: We included 1075 patients, of whom 783 (73%) had a validated cardiac index measurement. In multivariable regression, respiratory rate, heart rate and rhythm, systolic and diastolic blood pressure, central-to-peripheral temperature difference, and capillary refill time were statistically independently associated with cardiac index, with an overall R2 of 0.30 (98.5% CI 0.25-0.35). A low cardiac index was observed in 280 (36%) patients. Sensitivities and positive and negative predictive values were below 90% for all signs. Specificities above 90% were observed only for 110/280 patients, who had atrial fibrillation, systolic blood pressures < 90 mmHg, altered consciousness, capillary refill times > 4.5 s, or skin mottling over the knee. CONCLUSIONS: Seven out of 19 clinical examination findings were independently associated with cardiac index. For estimation of cardiac index, clinical examination was found to be insufficient in multivariable analyses and in diagnostic accuracy tests. Additional measurements such as critical care ultrasonography remain necessary.

Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies.

Longitudinal evaluations of critically ill patients by combinations of clinical examination, biochemical analysis and critical care ultrasonography (CCUS) may detect adverse events of interventions such as fluid overload at an early stage. The Simple Intensive Care Studies (SICS) is a research line that focuses on the prognostic and diagnostic value of combinations of clinical variables. The SICS-I specifically focused on the use of clinical variables obtained within 24 h of acute admission for prediction of cardiac output (CO) and mortality. Its sequel, SICS-II, focuses on repeated evaluations during ICU admission. The first clinical examination by trained researchers is performed within 3 h after admission consisting of physical examination and educated guessing. The second clinical examination is performed within 24 h after admission and includes physical examination and educated guessing, biochemical analysis and CCUS assessments of heart, lungs, inferior vena cava (IVC) and kidney. This evaluation is repeated at days 3 and 5 after admission. CCUS images are validated by an independent expert, and all data is registered in an online secured database. Follow-up at 90 days includes registration of complications and survival status according to patient's medical charts and the municipal person registry. The primary focus of SICS-II is the association between venous congestion and organ dysfunction. The purpose of publishing this protocol is to provide details on the structure and methods of this on-going prospective observational cohort study allowing answering multiple research questions. The design of the data collection of combined clinical examination and CCUS assessments in critically ill patients are explicated. The SICS-II is open for other centers to participate and is open for other research questions that can be answered with our data.

Cardiac Function After Radiation Therapy for Breast Cancer.

PURPOSE: The main purpose of this study was to test the hypothesis that incidental cardiac irradiation is associated with changes in cardiac function in breast cancer (BC) survivors treated with radiation therapy (RT). METHODS AND MATERIALS: We conducted a cross-sectional study consisting of 109 BC survivors treated with RT between 2005 and 2011. The endpoint was cardiac function, assessed by echocardiography. Systolic function was assessed with the left ventricular ejection fraction (LVEF) (n = 107) and the global longitudinal strain (GLS) of the left ventricle (LV) (n = 52). LV diastolic dysfunction (n = 109) was defined by e' at the lateral and septal region, which represents the relaxation velocity of the myocardium. The individual calculated RT dose parameters of the LV and coronary arteries were collected from 3-dimensional computed tomography-based planning data. Univariable and multivariable analysis using forward selection was performed to identify the best predictors of cardiac function. Robustness of selection was assessed using bootstrapping. The resulting multivariable linear regression model was presented for the endpoints of systolic and diastolic function. RESULTS: The median time between BC diagnosis and echocardiography was 7 years. No relation between RT dose parameters and LVEF was found. In the multivariable analysis for the endpoint GLS of the LV, the maximum dose to the left main coronary artery was most often selected across bootstrap samples. For decreased diastolic function, the most often selected model across bootstrap samples included age at time of BC diagnosis and hypertension at baseline. Cardiac dose-volume histogram parameters were less frequently selected for this endpoint. CONCLUSIONS: This study shows an association between individual cardiac dose distributions and GLS of the LV after RT for BC. No relation between RT dose parameters and LVEF was found. Diastolic function was most associated with age and hypertension at time of BC diagnosis. Further research is needed to make definitive conclusions.

Correlation with invasive left ventricular filling pressures and prognostic relevance of the echocardiographic diastolic parameters used in the 2016 ESC heart failure guidelines and in the 2016 ASE/EACVI recommendations: a systematic review in patients with heart failure with preserved ejection fraction.

AIMS: Five echocardiographic parameters-left atrial volume index, left ventricular mass index, tricuspid regurgitation velocity, myocardial tissue velocity, and the ratio of early mitral inflow to tissue velocity of the mitral annulus (E/e')-are recommended in both the current European Society of Cardiology heart failure guidelines and the American Society of Echocardiography/European Association of Cardiovascular Imaging recommendations for the evaluation of left ventricular diastolic function. We aimed to perform a systematic review of these echocardiographic parameters at resting conditions for their correlation with left ventricular filling pressures in patients with heart failure with preserved ejection fraction (HFpEF). In addition, the prognostic value of these parameters was assessed. METHODS AND RESULTS: Nine studies reported the correlation between echocardiography and invasive haemodynamics, and 18 papers reported on the prognostic value of echocardiography in HFpEF. Among the parameters, most data were reported for E/e'. The pooled correlation coefficient r was 0.56 for the relation between E/e' and invasively measured filling pressures. Combined weighted-mean meta-analysis of prognostic studies resulted in a hazard ratio of 1.05 (95% confidence interval 1.03-1.06) per unit increase in E/e' for the combined outcome of all-cause mortality and cardiovascular hospitalization. The other echocardiographic parameters, when taken individually, had similar or lower association with prognosis. CONCLUSION: Only a small number of studies validated the use of echocardiographic parameters at rest in patients with HFpEF. The best established parameter appears to be E/e', but the existing data only show modest correlations of E/e' with invasive filling pressures and outcomes in HFpEF.

Impact of Thoracoscopic Pulmonary Vein Isolation on Right Ventricular Function: A Pilot Study.

OBJECTIVE: Thoracoscopic surgical pulmonary vein isolation (sPVI) has been added to the treatment of atrial fibrillation (AF), showing excellent efficacy outcomes. However, data on right ventricular (RV) function following sPVI has never been studied. Our aim was to investigate RV function following sPVI and compare it to patients who underwent endocardial cryoballoon PVI. METHODS: 25 patients underwent sPVI and were pair-matched according to age, sex, and AF type with 21 patients who underwent cryoballoon PVI. RV function was measured using tricuspid annular plane systolic excursion (TAPSE) and RV strain with 2D speckle tracking. Echocardiography was performed at baseline and at median 6-month follow-up. RESULTS: Age was 54 ± 9 years and 84% were male; AF was paroxysmal in 92%. In the sPVI group, TAPSE was reduced with 31% at follow-up echocardiography (p < 0.001) and RV strain showed a 25% reduction compared to baseline (p = 0.018). In the control group, TAPSE and RV strain did not change significantly (-3% and +13%, p = 0.410 and p = 0.148). Change in TAPSE and RV strain was significantly different between groups (p ≤ 0.001 and p = 0.005). CONCLUSIONS: This study shows that RV function is significantly decreased following sPVI. This effect was not observed in the cryoballoon PVI control group.

Diabetes Mellitus and Right Ventricular Dysfunction in Heart Failure With Preserved Ejection Fraction.

Diabetes mellitus is associated with left-sided myocardial remodeling in heart failure with preserved ejection fraction (HFpEF). Little is known about the impact of diabetes mellitus on right ventricular (RV) function in HFpEF. We therefore studied the relation between diabetes mellitus and RV dysfunction in HFpEF. We have examined patients with HFpEF who underwent simultaneous right-sided cardiac catheterization and echocardiography. RV systolic function was assessed using multiple established echocardiographic parameters, and systolic dysfunction was present if ≥2 parameters were outside the normal range. RV diastolic function was assessed using the peak diastolic tissue velocity of the lateral tricuspid annulus (RV e') and was present if <8.0 cm/s. Diabetes mellitus was defined as a documented history of diabetes, a fasting glucose level of ≥7.0 mmol/L, a positive glucose intolerance test result, or a glycated hemoglobin level of ≥6.5%. A total of 91 patients were studied (mean age 74 ± 9 years, 69% women). A total of 37% had RV systolic dysfunction and 23% RV diastolic dysfunction. Thirty-seven percent of the patients had type 2 diabetes mellitus. These patients had higher pulmonary artery pressure (34 mm Hg vs 29 mm Hg, p = 0.004), more RV systolic dysfunction (57% vs 29%, p = 0.009), more RV diastolic dysfunction (46% vs 12%, p = 0.001), and lower RV e' (8.7 cm/s vs 11.5 cm/s, p = 0.006). The presence of diabetes mellitus was independently associated with RV systolic dysfunction (odds ratio 2.84, 95% confidence interval 1.09 to 7.40, p = 0.03) and with RV diastolic dysfunction (odds ratio 4.33, 95% confidence interval 1.25 to 15.07, p = 0.02), after adjustment for age, gender, and pulmonary pressures. In conclusion, diabetes mellitus is strongly associated with RV systolic and diastolic dysfunctions in patients with HFpEF, independent of RV afterload.