Kevin Duarte

Introduction: Although poorly researched, unplanned out‐of‐hospital deliveries are considered to carry a high risk of maternal and neonatal complications, with the primary suspected risk factor being the home‐to‐hospital travel time. The objective of this study was to describe the characteristics of unplanned out‐of‐hospital deliveries and to identify factors associated with maternal and neonatal complications. Material and Methods: Data were collected from the French unplanned out‐of‐hospital deliveries registry (79 of 402 mobile intensive care units). Analysis included 3473 deliveries between October 2011 and June 2024. Results: Mean women maternal age was 32.0 ± 5.4 years (range 15–49). Median parity was 2.0 (IQR 2.0–3.0). Gestational age was 39.1 ± 2.3 weeks. Most pregnancies were low‐risk (84%). Mean home‐to‐hospital travel time was 20.7 ± 13.6 min (range 0–200), with more than 45 min for only 3% of women (110/3221) and over 1 h for 1% (23/3221). Maternal complications accounted in 8.4% of women (290/3472), placental hemorrhage in 4.5% (156/3471) and maternal death in 0.1% (2/3471). Perinatal complications occurred in 4.4% (149/3393) of all deliveries, with neonatal deaths accounting for 1.1% (38/3456). Among unplanned out‐of‐hospital deliveries, factors associated with maternal and/or neonatal complications included birth at 32 + 0–33 + 6 weeks of gestation (adjusted OR 2.86, 95% CI 1.69 to 4.83; p < 0.0001), birth before 32 + 0 weeks of gestation (adjusted OR 25.21, 95% CI 12.13 to 52.40; p < 0.0001), and neonatal hypothermia upon hospital admission (adjusted OR 1.74, 95% CI 1.10 to 2.76; p = 0.019). Hypothermia affected 63% of newborns (1256/2002). Unplanned out‐of‐hospital deliveries prior to mobile intensive care unit arrival were significantly associated with neonatal hypothermia on‐site (adjusted OR 2.02, 95% CI 1.57 to 2.61; p < 0.0001) and at hospital admission (adjusted OR 1.56, 95% CI 1.21 to 2.00; p = 0.0005). Conclusions: Severe outcomes of unplanned out‐of‐hospital deliveries were uncommon but did occur. Complications were associated with prematurity and hypothermia, but not with home‐to‐hospital travel time. These findings highlight the importance of universal prehospital preparedness, with particular attention to early prevention of neonatal hypothermia and initial hemorrhage management.

Background: Guideline‐directed medical therapy (GDMT), including angiotensin receptor‐neprilysin inhibitor, angiotensin‐converting enzyme inhibitors, beta blockers, mineralocorticoid receptor antagonists, and sodium‐glucose cotransporter‐2 inhibitors, has significantly improved outcomes in patients with heart failure with reduced ejection fraction (HFrEF). We examined whether modern GDMT alters the historically observed parity in mortality between HF and cancer. Methods: From the TriNetX Global Collaborative Research Network (2015–2024), we identified 32 125 patients with HFrEF on GDMT (angiotensin receptor‐neprilysin inhibitor or angiotensin‐converting enzyme inhibitors, mineralocorticoid receptor antagonists, beta blockers, and sodium‐glucose cotransporter‐2 inhibitors), 403 389 not on full GDMT (not receiving all 4 drugs concurrently), and 2 481 106 with malignant cancers. All‐cause mortality was compared between HFrEF and cancer overall and across the most common cancers in men (prostate, colorectal, lung) and women (breast, lung, colorectal) using propensity score matching. Results: Patients with HFrEF on GDMT with angiotensin receptor‐neprilysin inhibitors had significantly better survival than patients with cancer (hazard ratio [HR], 0.44 [95% CI, 0.41–0.47]; P P P <0.001). Among the most common sex‐specific cancers, only patients with prostate and breast cancer had better survival than those with HFrEF on GDMT, whereas patients not on GDMT showed worse outcomes across most cancers, except lung cancer. Conclusions: In this large real‐world analysis, patients with HFrEF on GDMT showed better survival than those with cancer overall and across most sex‐specific cancers. Patients not on GDMT experienced worse outcomes, emphasizing the need to optimize treatment in HFrEF.

BACKGROUND: The sex-related prognosis of patients with cardiogenic shock undergoing venoarterial extracorporeal membrane oxygenation remains unclear. Our analyses aim to assess sex-specific outcomes in patients with cardiogenic shock receiving venoarterial extracorporeal membrane oxygenation and explore whether the effect of moderate hypothermia on clinical outcomes was modified by sex. METHODS: In this post hoc analysis of the HYPO-ECMO trial (Hypothermia During ECMO), clinical outcomes were compared by sex. The primary outcome was 30-day all-cause mortality. Key secondary outcomes included all-cause mortality and a composite outcome of all-cause mortality, heart transplant, escalation to left ventricular assist device implantation, or stroke at 30, 60, and 180 days. RESULTS: Among the 334 patients enrolled in the trial, 81 (24%) were female. At 30 days, 45.7% of female and 46.6% of male patients experienced the primary outcome, with no sex differences (adjusted odds ratio, 1.01 [0.57–1.78]; P =0.98 and log-rank test; P =0.93). No significant sex differences were observed in all-cause mortality at 60 and 180 days (adjusted odds ratio, 0.87 [0.49–1.52]; P =0.61 and 0.83 [0.47–1.46]; P =0.51, respectively) or in the composite outcome up to 180 days ( P >0.2 for all). The effect of moderate hypothermia on the primary outcome (adjusted odds ratio, 0.73 [0.43–1.25]; P =0.25 and 0.67 [0.26–1.76]; P =0.41, in male and female, respectively, interaction P =0.88) and secondary outcomes (interaction P >0.3 for all) was not modified by sex. CONCLUSIONS: In this post hoc analysis of the HYPO-ECMO trial, male and female patients experienced similar outcomes in cardiogenic shock treated with venoarterial extracorporeal membrane oxygenation. Sex did not significantly modify the effect of moderate hypothermia on outcomes. REGISTRATION: URL: https://clinicaltrials.gov/study/NCT02754193 ; Unique identifier: NCT02754193.

Background: Heart failure with preserved ejection fraction (HFpEF) is a multifaceted syndrome, likely stemming from comorbidity‐induced inflammation resulting in endothelial dysfunction. Endothelial glycocalyx degradation's role in the development and prognosis of HFpEF remains largely unexplored. Our study aimed at exploring the association between glycocalyx degradation and diastolic dysfunction and determining whether glycocalyx degradation can predict clinical outcomes in patients with HFpEF. Methods: Perlecan and thrombomodulin concentrations were assessed in individuals deemed healthy (STANISLAS [Suivi Temporaire Annuel Non‐Invasif de la Santé des Lorrains Assurés Sociaux (Annual Noninvasive Temporary Monitoring of the Health of Insured Lorrainers)] cohort, n=1705) and patients with HFpEF (MEDIA‐DHF [Metabolic Road to Diastolic Heart Failure], n=460 and BIOSTAT‐CHF [Biology Study to Tailored Treatment in Chronic Heart Failure], n=556) to evaluate endothelial glycocalyx degradation. Results: In patients with HFpEF, perlecan but not thrombomodulin was increased compared with controls ( P P =0.73). In adjusted analysis, perlecan was associated with peak early mitral inflow velocity/peak early diastolic mitral annular velocity ratio and thrombomodulin with peak early diastolic mitral annular velocity in control individuals, whereas perlecan and thrombomodulin were associated with peak early mitral inflow velocity/peak early diastolic mitral annular velocity and left atrial volume index in patients with HFpEF (all P P =0.026) and BIOSTAT‐CHF cohorts (adjusted HR, 2.12 [95% CI, 1.49–3.03]; P P =0.004) but not in MEDIA‐DHF. Conclusions: Higher circulating levels of the endothelial glycocalyx degradation biomarkers like perlecan and, to a lesser extent, thrombomodulin are associated with features of diastolic dysfunction in population and HFpEF settings and predict poor outcome in patients with HFpEF. These results suggest that glycocalyx degradation may be an early step in the pathological processes leading to HFpEF and gain further prognostic value in later stages (ie, overt HFpEF). Registration: URL: https://clinicaltrials.gov/ ; Unique identifiers: NCT01391442, https://clinicaltrials.gov/study/NCT01391442?cond=stanislas&rank=1 ; NCT02446327; URL: https://cordis.europa.eu ; BIOSTAT‐CHF ID: 242209.

Background and Aims: Outpatient cardiology follow-up is the cornerstone of heart failure (HF) management, requiring adaptation based on patient severity. However, risk stratification using administrative data is scarce, and the association between follow-up and prognosis according to patient risk has yet to be described at a population level. This study aimed to describe prognosis and management across different strata using simple criteria, including diuretic use and prior HF hospitalization (HFH). Methods: This nationwide cohort included all French patients reported as having HF in the previous 5 years and alive on 1 January 2020. Patients were categorized into four groups: (i) HFH within the past year (HFH ≤ 1y), (ii) HFH 1–5 years ago (HFH > 1y), (iii) not hospitalized using loop diuretics (NoHFH/LD+), and (iv) not hospitalized without loop diuretics (NoHFH/LD−). Between-group associations, all-cause mortality (ACM), and cardiology follow-up were analysed using survival models. Results: The study included 655 919 patients [80 years (70–87), 48% female]. One-year ACM risk was 15.9%, ranging from 8.0% (NoHFH/LD−) to 25.0% (HFH ≤ 1y). Mortality risk was 1.61-fold higher for NoHFH/LD+, 1.83-fold for HFH > 1y, and 2.32-fold for HFH ≤ 1y compared to NoHFH/LD− (P < .0001). During the first year of follow-up (2020), cardiology consultation rates were similar across groups, with 40% of patients lacking an annual visit. Compared to no consultation, a single cardiology visit in the previous year (2019) was associated with a 6%–9% absolute reduction in 1-year ACM during the following year (2020) across all groups. The number needed to consult (NNC) to prevent one modelled death was 11–16. Additional visits showed greater benefit with increasing HF severity, with NNC ranging from 55 (NoHFH/LD−) to 20 (HFH ≤ 1y). The optimal follow-up to minimize the number of deaths without increasing the total number of consultations was 1 annual visit for NoHFH/LD−, 2–3 visits for NoHFH/LD+ and HFH > 1y, and 4 visits for HFH ≤ 1y patients. Conclusions: Despite having a HF diagnosis, 40% of patients do not see a cardiologist annually, regardless of disease severity. Simple stratification based on hospitalization history and diuretic use effectively predicts outcomes. Tailoring the annual number of HF consultations according to this stratification could optimize resource use and reduce avoidable modelled deaths.

Lung ultrasound (LUS) is an effective tool for diagnosing acute heart failure (AHF). However, several imaging protocols currently exist and how to best use LUS remains undefined. We aimed at developing a lung ultrasound-based model for AHF diagnosis using machine learning. Random forest and decision trees were generated using the LUS data (via an 8-zone scanning protocol) in patients with acute dyspnea admitted to the Emergency Department (PLUME study, N = 117) and subsequently validated in an external dataset (80 controls from the REMI study, 50 cases from the Nancy AHF cohort). Using the random forest model, total B-line sum (i.e., in both hemithoraces) was the most significant variable for identifying AHF, followed by the difference in B-line sum between the superior and inferior lung areas. The decision tree algorithm had a good diagnostic accuracy [area under the curve (AUC) = 0.865] and identified three risk groups (i.e., low 24%, high 70%, and very high-risk 96%) for AHF. The very high-risk group was defined by the presence of 14 or more B-lines in both hemithoraces while the high-risk group was described as having either B-lines mostly localized in superior points or in the right hemithorax. Accuracy in the validation cohort was excellent (AUC = 0.906). Importantly, adding the algorithm on top of a validated clinical score and classical definition of positive LUS scanning for AHF resulted in a significant improvement in diagnostic accuracy (continuous net reclassification improvement = 1.21, P < 0.001). Our simple lung ultrasound-based machine learning algorithm features an excellent performance and may constitute a validated strategy to diagnose AHF.

Background: While exercise impairments are central to symptoms and diagnosis of heart failure with preserved ejection fraction (HFpEF), prior studies of HFpEF biomarkers have mostly focused on resting phenotypes. We combined precise exercise phenotypes with cardiovascular proteomics to identify protein signatures of HFpEF exercise responses and new potential therapeutic targets. Methods and Results: We analyzed 277 proteins (Olink) in 151 individuals (N=103 HFpEF, 48 controls; 62±11 years; 56% women) with cardiopulmonary exercise testing with invasive monitoring. Using ridge regression adjusted for age/sex, we defined proteomic signatures of 5 physiological variables involved in HFpEF: peak oxygen uptake, peak cardiac output, pulmonary capillary wedge pressure/cardiac output slope, peak pulmonary vascular resistance, and peak peripheral O ₂ extraction. Multiprotein signatures of each of the exercise phenotypes captured a significant proportion of variance in respective exercise phenotypes. Interrogating the importance (ridge coefficient magnitude) of specific proteins in each signature highlighted proteins with putative links to HFpEF pathophysiology (eg, inflammatory, profibrotic proteins), and novel proteins linked to distinct physiologies (eg, proteins involved in multiorgan [kidney, liver, muscle, adipose] health) were implicated in impaired O ₂ extraction. In a separate sample (N=522, 261 HF events), proteomic signatures of peak oxygen uptake and pulmonary capillary wedge pressure/cardiac output slope were associated with incident HFpEF (odds ratios, 0.67 [95% CI, 0.50–0.90] and 1.43 [95% CI, 1.11–1.85], respectively) with adjustment for clinical factors and B‐type natriuretic peptides. Conclusions: The cardiovascular proteome is associated with precision exercise phenotypes in HFpEF, suggesting novel mechanistic targets and potential methods for risk stratification to prevent HFpEF early in its pathogenesis.

Background Diagnosis of heart failure with preserved ejection fraction (HFpEF) remains challenging despite the use of scores/algorithms. This study intended to assess the diagnostic value of exercise lung ultrasound (LUS) for HFpEF diagnosis. Methods We studied two independent case–control studies of HFpEF patients and control subjects undergoing different exercise protocols: (i) submaximal exercise stress echocardiography (ESE) with LUS performed by expert cardiologists (N = 116, HFpEF = 65.5%), and (ii) maximal cycle ergometer test (CET) (N = 54, HFpEF = 50%) with LUS performed by unexperienced physicians shortly trained for the study. B-line kinetics (i.e. peak values and their changes from rest) were assessed. Results In the ESE cohort, the C-index (95% CI) of peak B-lines for HFpEF diagnosis was 0.985 (0.968–1.000), whereas the C-index of rest and exercise HFA-PEFF scores (i.e. including stress echo findings) were < 0.90 (CI 0.823–0.949), and that of H2FPEF score was < 0.70 (CI 0.558–0.764). The C-index increase of peak B-lines on top of the above-mentioned scores was significant (C-index increase > 0.090 and P-value < 0.001 for all). Similar results were observed for change B-lines. Peak B-lines > 5 (sensitivity = 93.4%, specificity = 97.5%) and change B-lines > 3 (sensitivity = 94.7%, specificity = 87.5%) were the best cutoffs for HFpEF diagnosis. Adding peak or change B-lines on top of HFpEF scores and BNP significantly improved diagnostic accuracy. Peak B-lines showed a good diagnostic accuracy in the LUS beginner-led CET cohort (C-index = 0.713, 0.588–0.838). Conclusions Exercise LUS showed excellent diagnostic value for HFpEF diagnosis regardless of different exercise protocols/level of expertise, with additive diagnostic accuracy on top of available scores and natriuretic peptides. Graphical abstract

Background: We sought to identify protein biomarkers of new-onset heart failure (HF) in 3 independent cohorts (HOMAGE cohort [Heart Omics and Ageing], ARIC study [Atherosclerosis Risk in Communities], and FHS [Framingham Heart Study]) and assess if and to what extent they improve HF risk prediction compared to clinical risk factors alone. Methods: A nested case-control design was used with cases (incident HF) and controls (without HF) matched on age and sex within each cohort. Plasma concentrations of 276 proteins were measured at baseline in ARIC (250 cases/250 controls), FHS (191/191), and HOMAGE cohort (562/871). Results: In single protein analysis, after adjusting for matching variables and clinical risk factors (and correcting for multiple testing), 62 proteins were associated with incident HF in ARIC, 16 in FHS, and 116 in HOMAGE cohort. Proteins associated with incident HF in all cohorts were BNP (brain natriuretic peptide), NT-proBNP (N-terminal pro-B-type natriuretic peptide), eukaryotic translation initiation factor 4E-BP1 (4E-binding protein 1), hepatocyte growth factor (HGF), Gal-9 (galectin-9), TGF-alpha (transforming growth factor alpha), THBS2 (thrombospondin-2), and U-PAR (urokinase plasminogen activator surface receptor). The increment in C -index for incident HF based on a multiprotein biomarker approach, in addition to clinical risk factors and NT-proBNP, was 11.1% (7.5%–14.7%) in ARIC, 5.9% (2.6%–9.2%) in FHS, and 7.5% (5.4%–9.5%) in HOMAGE cohort, all P <0.001), each of which was a larger increase than that for NT-proBNP on top of clinical risk factors. Complex network analysis revealed a number of overrepresented pathways related to inflammation (eg, tumor necrosis factor and interleukin) and remodeling (eg, extracellular matrix and apoptosis). Conclusions: A multiprotein biomarker approach improves prediction of incident HF when added to natriuretic peptides and clinical risk factors.

A substantial proportion of patients with heart failure (HF) receive suboptimal guideline-recommended therapy. We aimed to identify the factors leading to suboptimal drug prescription in HF and according to HF phenotypes. This retrospective, single-centre observational cohort study included 702 patients admitted for worsening HF (HF with a reduced ejection fraction [HFrEF], n = 198; HF with a mildly reduced EF [HFmrEF], n = 122; and HF with a preserved EF [HFpEF], n = 382). A score based on the prescription and dose percentage of ACEi/ARBs, β-blockers, and MRAs at discharge was calculated (a total score ranging from zero to six). Approximately 70% of patients received ACEi/ARBs/ARNi, 80% of patients received β-blockers, and 20% received MRAs. The mean HF drug dose was approximately 50% of the recommended dose, irrespective of the HF phenotype. Ischaemic heart disease was associated with a higher prescription score (ranging from 0.4 to 1) compared to no history of ischaemic heart disease, irrespective of the left ventricular EF (LVEF) level. A lower prescription score was associated with older age and male sex in HFrEF and diabetes in HFmrEF. The overall ability of the models to predict the optimal drug dose, including key HF variables (including natriuretic peptides at admission), was poor (R² < 0.25). A higher prescription score was associated with a lower risk of re-hospitalization and death (HR: 0.75 (0.57–0.97), p = 0.03), irrespective of phenotype (p-interaction = 0.41). Despite very different HF management guidelines according to LVEF, the prescription pattern of HF drugs is poorly related to LVEF and clinical characteristics, thus suggesting that physician-driven factors may be involved in the setting of therapeutic inertia. It may also be related to drug intolerance or clinical stability that is not predicted by the patients’ profiles.