Remote dielectric sensing detects pulmonary congestion in emergency patients with dyspnoea

Background

Immediate diagnosis of acute decompensated heart failure (ADHF) is essential in patients with dyspnoea. Remote Dielectric Sensing (ReDS), an electromagnetic non-invasive technology, estimates lung fluid content fast and observer-independently. In previous studies, ReDS discriminated congested heart failure patients from normal subjects with high accuracy. But not all ADHF patients have pulmonary interstitial congestion in the real world, and it is unknown if ReDS detects ADHF in consecutive patients with acute dyspnoea.

Purpose

To examine if ReDS can detect ADHF in consecutive dyspnoeic emergency patients and to compare ReDS with other diagnostic methods.

Method

This prospective observational study included consecutive patients with dyspnoea from the emergency departments. The exclusion criteria were age below 50 years, acute coronary syndrome, conditions prohibiting a supine CT scan, and no informed consent. We examined all patients immediately with ReDS, low-dose chest CT, echocardiogram, lung ultrasound (LUS), NT-proBNP, and Boston score. The Boston score used chest X-ray and clinical signs such as orthopnoea, jugular venous elevation, lung crackles and pedal oedema, and a score ≥8 equalled definite ADHF. A “LUS-score” ≥3 with at least 3 B-lines in one zone bilaterally equalled ADHF. ReDS values >35% lung fluid content were positive for pulmonary congestion, according to previous studies.

According to ESC guidelines, an expert panel adjudicated the ADHF diagnosis based on clinical signs, chest X-ray image, NT-proBNP, echocardiographic cardiac dysfunction (HFvhd, HFrEF, HFmrEF, HFpEF), and elevated LV filling pressure. Importantly, the panel was blinded to the ReDS values. For sub-analyses, we divided ADHF patients into a “CT-congested” ADHF subgroup if an independent chest CT showed interstitial congestion. We classified ADHF patients without congestion on CT, as the “mildly-congested” subgroup.

Results

97 included patients were examined within a median of 4.8 hours from admittance: 39 (40%) had ADHF, and 25 (26%) were ReDS-positive. ADHF patients had median LVEF 48%, NT-proBNP 347 pmol/l, and 85% had echocardiographic elevated LV filling pressure. ReDS detected ADHF with 46% sensitivity, 88% specificity, and 71% accuracy. The AUC for ReDS to detect ADHF (Figure 1), on a continuous scale, was similar to the Boston score (p=0.88) and the LUS score (p=0.74), but lower than NT-proBNP (p=0.02). The 21 (22%) CT-congested ADHF patients had higher ReDS values than the 18 (19%) mildly-congested ADHF patients (Figure 2, median 38% vs 30%, p<0.001). Furthermore, the mildly-congested ADHF patients had ReDS values similar to non-ADHF patients (median 30% vs 28%, p=0.36).

Conclusion

ReDS detects ADHF similarly to the Boston score and lung ultrasound but is inferior to NT-proBNP. This study suggests that ReDS primarily identifies CT-congested ADHF patients, but not the ADHF patients without interstitial congestion.

Funding Acknowledgement

Type of funding sources: Public hospital(s). Main funding source(s): This work was supported by the research fund of Bispebjerg University Hospital and Holger & Ruth Hesse's Mindefond. Sensible Medical Ltd made the ReDS device available for free and provided an unrestricted grant to specifically collect the ReDS measurements. The sponsors did not affect the statistical analyses, study design, data collection, or writing of the paper.

Value of a chest X-ray and CT to diagnose acute heart failure in acute patients with dyspnoea: a prospective comparative diagnostic study in the emergency department

Background and purpose

Diagnosing heart failure (HF) remains difficult in the acute setting where multiple diagnoses are in play. Objective evidence of pulmonary congestion by chest X-ray (CXR) is one criteria for the recent universal definition of heart failure (UniHF). But, since CXR is known to have a low diagnostic value, we hypothesized that a chest CT (CT) would outdo the CXR to diagnose decompensated HF in acute breathless patients. This study's primary objective was to examine if the CT has higher accuracy than the CXR to diagnose HF in the acute setting; and, secondly, to identify what pre-test characteristics would predict a false negative CXR or CT.

Methods

We performed a single-centre, prospective observational study and included consecutive adult patients with dyspnoea in the emergency department. Patients underwent immediate clinical examination, blood tests, CXR, CT and an echocardiogram. Congestion on CXR and CT was defined as the congruent verdict by two expert thorax radiologists, blinded to each others reading and all other clinical data. The absence of congestion was defined as the congruent verdict of “no congestion”.

Congestion of CXR and CT was held up against UniHF ascertained by an expert panel of cardiologists where the pulmonary congestion component primarily was based on elevated filling pressures from the simultaneous comprehensive echocardiogram. Univariate- and multivariate logistic analyses identified factors associated with a false negative chest x-ray and CT.

Results

Of 228 patients with a mean age of 74,5 years, 129 (56,5%) were male, 98 (43%) had UniHF, and 139 (61.0%) had pulmonary disease. Congestion on the CXR diagnosed UniHF with a 54% sensitivity and 95% specificity, with almost similar figures for the CT with 54% and 99% respectively. A marginally better performance of the CT was shown by a significantly lower Akaike Information Criterion for pulmonary congestion by CT than for CXR. However, the net reclassification improvement by CT was 4% (p:0.5586). The CXR and CT were false negative for UniHF in 46% (45/98) for both modalities (Table 1). The only independent pre-test predictor of a false negative radiology examination in multivariable logistic regression analysis was NT-proBNP (CXR: OR 1.670 per log(BNP), p: <0.001) and CT: OR 1.693 per log(BNP), p: <0.001).

Conclusions

For the first time, CT has been directly compared with CXR to diagnose HF in consecutive breathless patients from the emergency department. The chest CT was marginally more specific than the CXR to diagnose HF, but with a similar sensitivity. Approximately half the patients obeying the universal definition of HF have no definite congestion on CXR nor CT, and these can only be identified by a high proBNP.

Funding Acknowledgement

Type of funding sources: None. Table 1

The cerebral metabolic ratio is not affected by oxygen availability during maximal exercise in humans

Intense exercise decreases the cerebral metabolic ratio of O(2) to carbohydrates (glucose + (1/2) lactate) and the cerebral lactate uptake depends on its arterial concentration, but whether these variables are influenced by O(2) availability is not known. In six males, maximal ergometer rowing increased the arterial lactate to 21.4 +/- 0.8 mm (mean +/- s.e.m.) and arterial-jugular venous (a-v) difference from -0.03 +/- 0.01 mm at rest to 2.52 +/- 0.03 mm (P < 0.05). Arterial glucose was raised to 8.5 +/- 0.5 mm and its a-v difference increased from 1.03 +/- 0.01 to 1.86 +/- 0.02 mm (P < 0.05) in the immediate recovery. During exercise, the cerebral metabolic ratio decreased from 5.67 +/- 0.52 at rest to 1.70 +/- 0.23 (P < 0.05) and remained low in the early recovery. Arterial haemoglobin O(2) saturation was 92.5 +/- 0.2% during exercise with room air, and it reached 87.6 +/- 1.0% and 98.9 +/- 0.2% during exercise with an inspired O(2) fraction of 0.17 and 0.30, respectively. Whilst the increase in a-v lactate difference was attenuated by manipulation of cerebral O(2) availability, the cerebral metabolic ratio was not affected significantly. During maximal rowing, the cerebral metabolic ratio reaches the lowest value with no effect by a moderate change in the arterial O(2) content. These findings suggest that intense whole body exercise is associated with marked imbalance in the cerebral metabolic substrate preferences independent of oxygen availability.