TY  - JOUR
A1  - Jobs, Alexander
A1  - Vonthein, Reinhard
A1  - König, Inke R.
A1  - Schäfer, Jane
A1  - Nauck, Matthias
A1  - Haag, Svenja
A1  - Fichera, Carlo Federico
A1  - Stiermaier, Thomas
A1  - Ledwoch, Jakob
A1  - Schneider, Alisa
A1  - Valentova, Miroslava
A1  - von Haehling, Stephan
A1  - Störk, Stefan
A1  - Westermann, Dirk
A1  - Lenz, Tobias
A1  - Arnold, Natalie
A1  - Edelmann, Frank
A1  - Seppelt, Philipp
A1  - Felix, Stephan
A1  - Lutz, Matthias
A1  - Hedwig, Felix
A1  - Borggrefe, Martin
A1  - Scherer, Clemens
A1  - Desch, Steffen
A1  - Thiele, Holger
T1  - Inferior vena cava ultrasound in acute decompensated heart failure: design rationale of the CAVA‐ADHF‐DZHK10 trial
JF  - ESC Heart Failure
N2  - Aims
Treating patients with acute decompensated heart failure (ADHF) presenting with volume overload is a common task. However, optimal guidance of decongesting therapy and treatment targets are not well defined. The inferior vena cava (IVC) diameter and its collapsibility can be used to estimate right atrial pressure, which is a measure of right‐sided haemodynamic congestion. The CAVA‐ADHF‐DZHK10 trial is designed to test the hypothesis that ultrasound assessment of the IVC in addition to clinical assessment improves decongestion as compared with clinical assessment alone.

Methods and results
CAVA‐ADHF‐DZHK10 is a randomized, controlled, patient‐blinded, multicentre, parallel‐group trial randomly assigning 388 patients with ADHF to either decongesting therapy guided by ultrasound assessment of the IVC in addition to clinical assessment or clinical assessment alone. IVC ultrasound will be performed daily between baseline and hospital discharge in all patients. However, ultrasound results will only be reported to treating physicians in the intervention group. Treatment target is relief of congestion‐related signs and symptoms in both groups with the additional goal to reduce the IVC diameter ≤21 mm and increase IVC collapsibility >50% in the intervention group. The primary endpoint is change in N‐terminal pro‐brain natriuretic peptide from baseline to hospital discharge. Secondary endpoints evaluate feasibility, efficacy of decongestion on other scales, and the impact of the intervention on clinical endpoints.

Conclusions
CAVA‐ADHF‐DZHK10 will investigate whether IVC ultrasound supplementing clinical assessment improves decongestion in patients admitted for ADHF.
KW  - acute decompensated heart failure
KW  - inferior vena cava
KW  - congestion
KW  - NT‐proBNP
KW  - ultrasound
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-212692
VL  - 7
IS  - 3
SP  - 973
EP  - 983
ER  - 
TY  - JOUR
A1  - Pennig, Lenhard
A1  - Hoyer, Ulrike Cornelia Isabel
A1  - Krauskopf, Alexandra
A1  - Shahzad, Rahil
A1  - Jünger, Stephanie T.
A1  - Thiele, Frank
A1  - Laukamp, Kai Roman
A1  - Grunz, Jan-Peter
A1  - Perkuhn, Michael
A1  - Schlamann, Marc
A1  - Kabbasch, Christoph
A1  - Borggrefe, Jan
A1  - Goertz, Lukas
T1  - Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage
JF  - Neuroradiology
N2  - Purpose
To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH).

Methods
Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm3, 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared.

Results
In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM).

Conclusion
Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.
KW  - aneurysms
KW  - aneurysmal subarachnoid hemorrhage
KW  - CT angiography
KW  - deep learning
KW  - convolutional neural networks
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-308117
SN  - 0028-3940
SN  - 1432-1920
VL  - 63
IS  - 12
ER  -