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We investigated whether type 2 diabetes (T2DM) and the presence of cognitive impairment are associated with altered cerebral blood flow (CBF). Forty-one participants with and thirty-nine without T2DM underwent 3-Tesla MRI, including a quantitative technique measuring (macrovascular) blood flow in the internal carotid artery and an arterial spin labeling technique measuring (microvascular) perfusion in the grey matter (GM). Three analysis methods were used to quantify the CBF: a region of interest analysis, a voxel-based statistical parametric mapping technique, and a ‘distributed deviating voxels’ method. Participants with T2DM exhibited significantly more tissue with low CBF values in the cerebral cortex and the subcortical GM (3.8-fold increase). The latter was the only region where the hypoperfusion remained after correcting for atrophy, indicating that the effect of T2DM on CBF, independent of atrophy, is small. Subcortical CBF was associated with depression. No associations were observed for CBF in other regions with diabetes status, for carotid blood flow with diabetes status, or for CBF or flow in relation with cognitive function. To conclude, a novel method that tallies total ‘distributed deviating voxels’ demonstrates T2DM-associated hypoperfusion in the subcortical GM, not associated with cognitive performance. Whether a vascular mechanism underlies cognitive decrements remains inconclusive.

Flow-mediated dilation is aimed at normalization of local wall shear stress under varying blood flow conditions. Blood flow velocity and vessel diameter are continuous and opposing influences that modulate wall shear stress. We derived an index FMDv to quantify wall shear stress normalization performance by flow-mediated dilation in the brachial artery. In 22 fasting presumed healthy men, we first assessed intra- and inter-session reproducibilities of two indices pFMDv and mFMDv, which consider the relative peak and relative mean hyperemic change in flow velocity, respectively. Second, utilizing oral glucose loading, we evaluated the tracking performance of both FMDv indices, in comparison with existing indices [i.e., the relative peak diameter increase (%FMD), the peak to baseline diameter ratio (Dpeak/Dbase), and the relative peak diameter increase normalized to the full area under the curve of blood flow velocity with hyperemia (FMD/shearAUC) or with area integrated to peak hyperemia (FMD/shearAUC_peak)]. Inter-session and intra-session reproducibilities for pFMDv, mFMDv and %FMD were comparable (intra-class correlation coefficients within 0.521-0.677 range). Both pFMDv and mFMDv showed more clearly a reduction after glucose loading (reduction of ~45%, p≤0.001) than the other indices (% given are relative reductions): %FMD (~11%, p≥0.074); Dpeak/Dbase (~11%, p≥0.074); FMD/shearAUC_peak (~20%, p≥0.016) and FMD/shearAUC (~38%, p≤0.038). Further analysis indicated that wall shear stress normalization under normal (fasting) conditions is already far from ideal (FMDv << 1), which (therefore) does not materially change with glucose loading. Our approach might be useful in intervention studies to detect intrinsic changes in shear stress normalization performance in conduit arteries.

Type 2 diabetes mellitus is associated with accelerated cognitive decline and various cerebral abnormalities visible on MRI. The exact pathophysiological mechanisms underlying cognitive decline in diabetes still remain to be elucidated. In addition to conventional images, MRI offers a versatile set of novel contrasts, including blood perfusion, neuronal function, white matter microstructure, and metabolic function. These more-advanced multiparametric MRI contrasts and the pertaining parameters are able to reveal abnormalities in type 2 diabetes, which may be related to cognitive decline. To further elucidate the nature of the link between diabetes, cognitive decline, and brain abnormalities, and changes over time thereof, biomarkers are needed which can be provided by advanced MRI techniques. This review summarizes to what extent MRI, especially advanced multiparametric techniques, can elucidate the underlying neuronal substrate that reflects the cognitive decline in type 2 diabetes.

Data-driven decision support tools have been increasingly recognized to transform health care. However, such tools are often developed on predefined research datasets without adequate knowledge of the origin of this data and how it was selected. How a dataset is extracted from a clinical database can profoundly impact the validity, interpretability and interoperability of the dataset, and downstream analyses, yet is rarely reported. Therefore, we present a case study illustrating how a definitive patient list was extracted from a clinical source database and how this can be reported. A single-center observational study was performed at an academic hospital in the Netherlands to illustrate the impact of selecting a definitive patient list for research from a clinical source database, and the importance of documenting this process. All admissions from the critical care database admitted between January 1, 2013, and January 1, 2023, were used. An interdisciplinary team collaborated to identify and address potential sources of data insufficiency and uncertainty. We demonstrate a stepwise data preparation process, reducing the clinical source database of 54,218 admissions to a definitive patient list of 21,553 admissions. Transparent documentation of the data preparation process improves the quality of the definitive patient list before analysis of the corresponding patient data. This study generated seven important recommendations for preparing observational health-care data for research purposes. Documenting data preparation is essential for understanding a research dataset originating from a clinical source database before analyzing health-care data. The findings contribute to establishing data standards and offer insights into the complexities of preparing health-care data for scientific investigation. Meticulous data preparation and documentation thereof will improve research validity and advance critical care.

In mechanically ventilated COVID-19 patients, a higher degree of coronary artery calcification (CAC) has been associated with increased severity of multi-organ failure. Furthermore, non-survivors showed worse development of multi-organ failure over time compared to survivors with COVID-19. Nevertheless, it remains unclear whether more CAC is associated with worse long-term survival. Therefore, we studied the association between CAC and one-year survival. In a prospective cohort of 241 mechanically ventilated patients who underwent chest CT scans for clinical evaluation of critical disease, CAC was scored using a semi-quantitative 12-point grading system. Cox proportional hazards analyses were used to investigate the association between CAC score (continuous and tertiles) and one-year survival in crude models and models adjusted for risk factors. In the crude model, a 1-point higher CAC score was associated with a higher hazard ratio (HR) (with 95% confidence interval (CI)) of 1.13 (95%CI: 1.08;1.19, p-value: <0.001). Compared to the lowest tertile ( n  = 85), a higher mortality was shown for the medium ( n  = 81) and the highest ( n  = 75) tertiles, HR 1.21 (95%CI: 0.73;2.02, p-value:0.443) and HR 3.32 (95%CI: 2.10;5.27, p-value:<0.001), respectively. After adjustment for age, sex and APACHE-II score, and comorbidities, a higher CAC score was associated with statistically significant worse one-year survival HR 2.07 (95% CI: 1.18–3.63, p-value:0.012). More coronary artery calcifications (CAC) are associated with worse one-year survival in patients on mechanical ventilation for severe COVID-19.

Background Data on hyperglycemia and glucose variability in relation to diabetes mellitus, either known or unknown in ICU-setting in COVID-19, are scarce. We prospectively studied daily glucose variables and mortality in strata of diabetes mellitus and glycosylated hemoglobin among mechanically ventilated COVID-19 patients. Methods We used linear-mixed effect models in mechanically ventilated COVID-19 patients to investigate mean and maximum difference in glucose concentration per day over time. We compared ICU survivors and non-survivors and tested for effect-modification by pandemic wave 1 and 2, diabetes mellitus, and admission HbA1c. Results Among 232 mechanically ventilated COVID-19 patients, 21.1% had known diabetes mellitus, whereas 16.9% in wave 2 had unknown diabetes mellitus. Non-survivors had higher mean glucose concentrations (ß 0.62 mmol/l; 95%CI 0.20–1.06; ß 11.2 mg/dl; 95% CI 3.6–19.1; P  = 0.004) and higher maximum differences in glucose concentrations per day (ß 0.85 mmol/l; 95%CI 0.37–1.33; ß 15.3; 95%CI 6.7–23.9; P  = 0.001). Effect modification by wave, history of diabetes mellitus and admission HbA1c in associations between glucose and survival was not present. Effect of higher mean glucose concentrations was modified by pandemic wave (wave 1 (ß 0.74; 95% CI 0.24–1.23 mmol/l) ; (ß 13.3; 95%CI 4.3–22.1 mg/dl)) vs. (wave 2 (ß 0.37 (95%CI 0.25–0.98) mmol/l) (ß 6.7 (95% ci 4.5–17.6) mg/dl)). Conclusions Hyperglycemia and glucose variability are associated with mortality in mechanically ventilated COVID-19 patients irrespective of the presence of diabetes mellitus.

Myocardial injury in COVID-19 is associated with in-hospital mortality. However, the development of myocardial injury over time and whether myocardial injury in patients with COVID-19 at the intensive care unit is associated with outcome is unclear. This study prospectively investigates myocardial injury with serial measurements over the full course of intensive care unit admission in mechanically ventilated patients with COVID-19. As part of the prospective Maastricht Intensive Care COVID cohort, predefined myocardial injury markers, including high-sensitivity cardiac troponin T (hs-cTnT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and electrocardiographic characteristics were serially collected in mechanically ventilated patients with COVID-19. Linear mixed-effects regression was used to compare survivors with nonsurvivors, adjusting for gender, age, APACHE-II score, daily creatinine concentration, hypertension, diabetes mellitus, and obesity. In 90 patients, 57 (63%) were survivors and 33 (37%) nonsurvivors, and a total of 628 serial electrocardiograms, 1,565 hs-cTnT, and 1,559 NT-proBNP concentrations were assessed. Log-hs-cTnT was lower in survivors compared with nonsurvivors at day 1 (β −0.93 [−1.37; −0.49], p <0.001) and did not change over time. Log-NT-proBNP did not differ at day 1 between both groups but decreased over time in the survivor group (β −0.08 [−0.11; −0.04] p <0.001) compared with nonsurvivors. Many electrocardiographic abnormalities were present in the whole population, without significant differences between both groups. In conclusion, baseline hs-cTnT and change in NT-proBNP were strongly associated with mortality. Two-thirds of patients with COVID-19 showed electrocardiographic abnormalities. Our serial assessment suggests that myocardial injury is common in mechanically ventilated patients with COVID-19 and is associated with outcome.

Patients with SARS-CoV-2 infection present with different lung compliance and progression of disease differs. Measures of lung mechanics in SARS-CoV-2 patients may unravel different pathophysiologic mechanisms during mechanical ventilation. The objective of this prospective observational study is to describe whether Electrical Impedance Tomography (EIT) guided positive end-expiratory pressure (PEEP) levels unravel changes in EIT-derived parameters over time and whether the changes differ between survivors and non-survivors. Serial EIT-measurements of alveolar overdistension, collapse, and compliance change in ventilated SARS-CoV-2 patients were analysed. In 80 out of 94 patients, we took 283 EIT measurements (93 from day 1–3 after intubation, 66 from day 4–6, and 124 from day 7 and beyond). Fifty-one patients (64%) survived the ICU. At admission mean PaO 2 /FiO 2 -ratio was 184.3 (SD 61.4) vs. 151.3 (SD 54.4) mmHg, ( p  = 0.017) and PEEP was 11.8 (SD 2.8) cmH 2 O vs. 11.3 (SD 3.4) cmH 2 O, ( p  = 0.475), for ICU survivors and non-survivors. At day 1–3, compliance was ~ 55 mL/cmH 2 O vs. ~ 45 mL/cmH 2 O in survivors vs. non-survivors. The intersection of overdistension and collapse curves appeared similar at a PEEP of ~ 12–13 cmH 2 O. At day 4–6 compliance changed to ~ 50 mL/cmH 2 O vs. ~ 38 mL/cmH 2 O. At day 7 and beyond, compliance was ~ 38 mL/cmH 2 O with the intersection at a PEEP of ~ 9 cmH 2 O vs. ~ 25 mL/cmH 2 O with overdistension intersecting at collapse curves at a PEEP of ~ 7 cmH 2 O. Surviving SARS-CoV-2 patients show more favourable EIT-derived parameters and a higher compliance compared to non-survivors over time. This knowledge is valuable for discovering the different groups.

We validated a Deep Embedded Clustering (DEC) model and its adaptation for integrating mixed datatypes (in this study, numerical and categorical variables). Deep Embedded Clustering (DEC) is a promising technique capable of managing extensive sets of variables and non-linear relationships. Nevertheless, DEC cannot adequately handle mixed datatypes. Therefore, we adapted DEC by replacing the autoencoder with an X-shaped variational autoencoder (XVAE) and optimising hyperparameters for cluster stability. We call this model “X-DEC”. We compared DEC and X-DEC by reproducing a previous study that used DEC to identify clusters in a population of intensive care patients. We assessed internal validity based on cluster stability on the development dataset. Since generalisability of clustering models has insufficiently been validated on external populations, we assessed external validity by investigating cluster generalisability onto an external validation dataset. We concluded that both DEC and X-DEC resulted in clinically recognisable and generalisable clusters, but X-DEC produced much more stable clusters.