Acute kidney injury (AKI) is a common and serious complication in hospitalized patients, which continues to pose a clinical challenge for treating physicians. The most recent Kidney Disease Improving Global Outcomes practice guidelines for AKI have restated the importance of earliest possible detection of AKI and adjusting treatment accordingly. Since the emergence of initial studies examining the use of neutrophil gelatinase-associated lipocalin (NGAL) and cycle arrest biomarkers, tissue inhibitor metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein (IGFBP7), for early diagnosis of AKI, a vast number of studies have investigated the accuracy and additional clinical benefits of these biomarkers. As proposed by the Acute Dialysis Quality Initiative, new AKI diagnostic criteria should equally utilize glomerular function and tubular injury markers for AKI diagnosis.In addition to refining our capabilities in kidney risk prediction with kidney injury biomarkers, structural disorder phenotypes referred to as “preclinical-” and “subclinical AKI” have been described and are increasingly recognized. Additionally, positive biomarker test findings were found to provide prognostic information regardless of an acute decline in renal function (positive serum creatinine criteria). We summarize and discuss the recent findings focusing on two of the most promising and clinically available kidney injury biomarkers, NGAL and cell cycle arrest markers, in the context of AKI phenotypes. Finally, we draw conclusions regarding the clinical implications for kidney risk prediction.

Background: Neutrophil gelatinase-associated lipocalin (NGAL) and hepcidin-25 are involved in catalytic iron-related kidney injury after cardiac surgery with cardiopulmonary bypass. We explored the predictive value of plasma NGAL, plasma hepcidin-25, and the plasma NGAL:hepcidin-25 ratio for major adverse kidney events (MAKE) after cardiac surgery. Methods: We compared the predictive value of plasma NGAL, hepcidin-25, and plasma NGAL:hepcidin-25 with that of serum creatinine (Cr) and urinary output and protein for primary-endpoint MAKE (acute kidney injury [AKI] stages 2 and 3, persistent AKI > 48 hours, acute dialysis, and in-hospital mortality) and secondary-endpoint AKI in 100 cardiac surgery patients at intensive care unit (ICU) admission. We performed ROC curve, logistic regression, and reclassification analyses. Results: At ICU admission, plasma NGAL, plasma NGAL:hepcidin-25, plasma interleukin-6, and Cr predicted MAKE (area under the ROC curve [AUC]: 0.77, 0.79, 0.74, and 0.74, respectively) and AKI (0.73, 0.89, 0.70, and 0.69). For AKI prediction, plasma NGAL:hepcidin-25 had a higher discriminatory power than Cr (AUC difference 0.26 [95% CI 0.00–0.53]). Urinary output and protein, plasma lactate, C-reactive protein, creatine kinase myocardial band, and brain natriuretic peptide did not predict MAKE or AKI (AUC < 0.70). Only plasma NGAL:hepcidin-25 correctly reclassified patients according to their MAKE and AKI status (category-free net reclassification improvement: 0.82 [95% CI 0.12–1.52], 1.03 [0.29–1.77]). After adjustment to the Cleveland risk score, plasma NGAL:hepcidin-25 ≥ 0.9 independently predicted MAKE (adjusted odds ratio 16.34 [95% CI 1.77–150.49], P = 0.014). Conclusions Plasma NGAL:hepcidin-25 is a promising marker for predicting postoperative MAKE.

BACKGROUND: The ability of urinary biomarkers to complement established clinical risk prediction models for postoperative adverse kidney events is unclear. We assessed the effect of urinary biomarkers linked to suspected pathogenesis of cardiac surgery-induced acute kidney injury (AKI) on the performance of the Cleveland Score, a risk assessment model for postoperative adverse kidney events. METHODS: This pilot study included 100 patients who underwent open-heart surgery. We determined improvements to the Cleveland Score when adding urinary biomarkers measured using clinical laboratory platforms (neutrophil gelatinase-associated lipocalin [NGAL], interleukin-6) and those in the preclinical stage (hepcidin-25, midkine, alpha-1 microglobulin), all sampled immediately post-surgery. The primary endpoint was major adverse kidney events (MAKE), and the secondary endpoint was AKI. We performed ROC curve analysis, assessed baseline model performance (odds ratios [OR], 95% CI), and carried out statistical reclassification analyses to assess model improvement. RESULTS: NGAL (OR [95% CI] per 20 concentration-units wherever applicable): (1.07 [1.01–1.14]), Interleukin-6 (1.51 [1.01–2.26]), midkine (1.01 [1.00–1.02]), 1-hepcidin-25 (1.08 [1.00–1.17]), and NGAL/hepcidin-ratio (2.91 [1.30–6.49]) were independent predictors of MAKE and AKI (1.38 [1.03–1.85], 1.08 [1.01–1.15], 1.01 [1.00–1.02], 1.09 [1.01–1.18], and 3.45 [1.54–7.72]). Category-free net reclassification improvement identified interleukin-6 as a model-improving biomarker for MAKE and NGAL for AKI. However, only NGAL/hepcidin-25 improved model performance for event- and event-free patients for MAKE and AKI. CONCLUSIONS NGAL and interleukin-6 measured immediately post cardiac surgery may complement the Cleveland Score. The combination of biomarkers with hepcidin-25 may further improve diagnostic discrimination.

Background: Semi-automated software is essential for planning and prosthesis selection prior transcatheter aortic valve replacement (TAVR). Reliable data on the usability of software programs for planning a TAVR is missing. The aim of this study was to compare software programs ‘Valve Assist 2’ (GE Healthcare) and 3mensio ‘Structural Heart’ (Pie Medical Imaging) regarding usability and accuracy of prosthesis size selection in program-inexperienced users. Methods: Thirty-one participants (n = 31) were recruited and divided into program-inexperienced users (beginners) (n = 22) and experts (n = 9). After software training, beginners evaluated 3 patient cases in 129 measurements (n = 129) using either Valve Assist 2 (n = 11) or Structural Heart (n = 11) on 2 test days (T1, T2). System Usability Scale (SUS) and ISONORM 9241/110-S (ISONORM) questionnaire were used after the test. The valve size selected by each begin‑ ner was compared with the valve size selected from expert group. Results: Valve Assist 2 had higher SUS Score: median 78.75 (25th, 75th percentile: 67.50, 85.00) compared to Structural Heart: median 65.00 (25th, 75th percentile: 47.50, 73.75), (p < 0,001, r = 0.557). Also, Valve Assist 2 showed a higher ISONORM score: median 1.05 (25th, 75th percentile: − 0.19, 1.71) compared to Structural Heart with a median 0.05 (25th, 75th percentile: − 0.49, 0.13), (p = 0.036, r = 0.454). Correctly selected valve sizes were stable over time using Valve Assist 2: 72.73% to 69.70% compared to Structural Heart program: 93.94% to 40% (χ 2 (1) = 21.10, p < 0.001, φ = 0.579). Conclusion The study shows significant better usability scores for Valve Assist 2 compared to 3mensio Structural Heart in program-inexperienced users.

Background: Semi-automated software is essential for planning and prosthesis selection prior transcatheter aortic valve replacement (TAVR). Reliable data on the usability of software programs for planning a TAVR is missing. The aim of this study was to compare software programs ‘Valve Assist 2’ (GE Healthcare) and 3mensio ‘Structural Heart’ (Pie Medical Imaging) regarding usability and accuracy of prosthesis size selection in program-inexperienced users. Methods: Thirty-one participants (n = 31) were recruited and divided into program-inexperienced users (beginners) (n = 22) and experts (n = 9). After software training, beginners evaluated 3 patient cases in 129 measurements (n = 129) using either Valve Assist 2 (n = 11) or Structural Heart (n = 11) on 2 test days (T1, T2). System Usability Scale (SUS) and ISONORM 9241/110-S (ISONORM) questionnaire were used after the test. The valve size selected by each begin‑ ner was compared with the valve size selected from expert group. Results: Valve Assist 2 had higher SUS Score: median 78.75 (25th, 75th percentile: 67.50, 85.00) compared to Structural Heart: median 65.00 (25th, 75th percentile: 47.50, 73.75), (p < 0,001, r = 0.557). Also, Valve Assist 2 showed a higher ISONORM score: median 1.05 (25th, 75th percentile: − 0.19, 1.71) compared to Structural Heart with a median 0.05 (25th, 75th percentile: − 0.49, 0.13), (p = 0.036, r = 0.454). Correctly selected valve sizes were stable over time using Valve Assist 2: 72.73% to 69.70% compared to Structural Heart program: 93.94% to 40% (χ 2 (1) = 21.10, p < 0.001, φ = 0.579). Conclusion The study shows significant better usability scores for Valve Assist 2 compared to 3mensio Structural Heart in program-inexperienced users.

Background: Semi-automated software is essential for planning and prosthesis selection prior transcatheter aortic valve replacement (TAVR). Reliable data on the usability of software programs for planning a TAVR is missing. The aim of this study was to compare software programs ‘Valve Assist 2’ (GE Healthcare) and 3mensio ‘Structural Heart’ (Pie Medical Imaging) regarding usability and accuracy of prosthesis size selection in program-inexperienced users. Methods: Thirty-one participants (n = 31) were recruited and divided into program-inexperienced users (beginners) (n = 22) and experts (n = 9). After software training, beginners evaluated 3 patient cases in 129 measurements (n = 129) using either Valve Assist 2 (n = 11) or Structural Heart (n = 11) on 2 test days (T1, T2). System Usability Scale (SUS) and ISONORM 9241/110-S (ISONORM) questionnaire were used after the test. The valve size selected by each begin‑ ner was compared with the valve size selected from expert group. Results: Valve Assist 2 had higher SUS Score: median 78.75 (25th, 75th percentile: 67.50, 85.00) compared to Structural Heart: median 65.00 (25th, 75th percentile: 47.50, 73.75), (p < 0,001, r = 0.557). Also, Valve Assist 2 showed a higher ISONORM score: median 1.05 (25th, 75th percentile: − 0.19, 1.71) compared to Structural Heart with a median 0.05 (25th, 75th percentile: − 0.49, 0.13), (p = 0.036, r = 0.454). Correctly selected valve sizes were stable over time using Valve Assist 2: 72.73% to 69.70% compared to Structural Heart program: 93.94% to 40% (χ 2 (1) = 21.10, p < 0.001, φ = 0.579). Conclusion The study shows significant better usability scores for Valve Assist 2 compared to 3mensio Structural Heart in program-inexperienced users.

Background: Semi-automated software is essential for planning and prosthesis selection prior transcatheter aortic valve replacement (TAVR). Reliable data on the usability of software programs for planning a TAVR is missing. The aim of this study was to compare software programs ‘Valve Assist 2’ (GE Healthcare) and 3mensio ‘Structural Heart’ (Pie Medical Imaging) regarding usability and accuracy of prosthesis size selection in program-inexperienced users. Methods: Thirty-one participants (n = 31) were recruited and divided into program-inexperienced users (beginners) (n = 22) and experts (n = 9). After software training, beginners evaluated 3 patient cases in 129 measurements (n = 129) using either Valve Assist 2 (n = 11) or Structural Heart (n = 11) on 2 test days (T1, T2). System Usability Scale (SUS) and ISONORM 9241/110-S (ISONORM) questionnaire were used after the test. The valve size selected by each begin‑ ner was compared with the valve size selected from expert group. Results: Valve Assist 2 had higher SUS Score: median 78.75 (25th, 75th percentile: 67.50, 85.00) compared to Structural Heart: median 65.00 (25th, 75th percentile: 47.50, 73.75), (p < 0,001, r = 0.557). Also, Valve Assist 2 showed a higher ISONORM score: median 1.05 (25th, 75th percentile: − 0.19, 1.71) compared to Structural Heart with a median 0.05 (25th, 75th percentile: − 0.49, 0.13), (p = 0.036, r = 0.454). Correctly selected valve sizes were stable over time using Valve Assist 2: 72.73% to 69.70% compared to Structural Heart program: 93.94% to 40% (χ 2 (1) = 21.10, p < 0.001, φ = 0.579). Conclusion The study shows significant better usability scores for Valve Assist 2 compared to 3mensio Structural Heart in program-inexperienced users.

BACKGROUND: Transfemoral aortic valve replacement (TAVR) is the standard treatment for elderly patients with aortic valve stenosis. Although safe and well-established, there is a risk of intraprocedural hemodynamic instability and silent cerebral embolism, which can lead to a decline in neurocognitive function and dementia. In clinical practice, comprehensive cognitive testing is difficult to perform. AI-assisted digital applications may help to optimize diagnosis and monitoring. METHODS: Neurocognitive function was assessed by validated psychometric tests using "∆elta -App", which uses artificial intelligence and computational linguistic methods for extraction and analysis. Memory function was assessed using the 'Consortium to Establish a Registry for Alzheimer's Disease' (CERAD) word list and digit span task (DST) before TAVR and before hospital discharge. The study is registered in the German Register of Clinical Trials (https://drks.de/search/de/trial/DRKS00020813). RESULTS: From October 2020 until March 2022, 141 patients were enrolled at University Hospital Heart Centre Brandenburg. Mean age was 81 ± 6 years, 42.6% were women. Time between the pre- and post-interventional test was on average 6 ± 3 days. Memory function before TAVR was found to be below average in relation to age and educational level. The pre-post TAVR comparison showed significant improvements in the wordlist repeat, P < 0.001 and wordlist recall test of CERAD, P < 0.001. There were no changes in the digital span test. CONCLUSIONS Despite impaired preoperative memory function before TAVR, no global negative effect on memory function after TVAR was detected. The improvements shown in the word list test should be interpreted as usual learning effects in this task.