The impact of religion on changes in end-of-life practices in European intensive care units: a comparative analysis over 16 years.

PURPOSE: Religious beliefs affect end-of-life practices in intensive care units (ICUs). Changes over time in end-of-life practices were not investigated regarding religions. METHODS: Twenty-two European ICUs (3 regions: Northern, Central, and Southern Europe) participated in both Ethicus-1 (years 1999-2000) and Ethicus-2 studies (years 2015-2016). Data of ICU patients who died or had limitations of life-sustaining therapy were analysed regarding changes in end-of-life practices and patient/physician religious affiliations. Frequencies, timing of decision-making, and religious affiliations of physicians/patients were compared using the same definitions. RESULTS: In total, 4592 adult ICU patients (n = 2807 Ethicus-1, n = 1785 Ethicus-2) were analysed. In both studies, patient and physician religious affiliations were mostly Catholic, Greek Orthodox, Jewish, Protestant, or unknown. Treating physicians (but not patients) commonly reported no religious affiliation (18%). Distribution of end-of-life practices with respect to religion and geographical regions were comparable between the two studies. Withholding [n = 1143 (40.7%) Ethicus-1 and n = 892 (50%) Ethicus-2] and withdrawing [n = 695 (24.8%) Ethicus-1 and n = 692 (38.8%) Ethicus-2] were most commonly decided. No significant changes in end-of-life practices were observed for any religion over 16 years. The number of end-of-life discussions with patients/ families/ physicians increased, while mortality and time until first decision decreased. CONCLUSIONS: Changes in end-of-life practices observed over 16 years appear unrelated to religious affiliations of ICU patients or their treating physicians, but the effects of religiosity and/or culture could not be assessed. Shorter time until decision in the ICU and increased numbers of patient and family discussions may indicate increased awareness of the importance of end-of-life decision-making in the ICU.

Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.

PURPOSE: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated measurements with central retinal subfield thickness (CSFT) and fluid volume values. METHODS: We utilized an automated deep learning approach to quantify macular fluid in SD-OCT volumes (Cirrus, Spectralis, Topcon) from patients of HAWK and HARRIER Studies. Three-dimensional volumes for IRF and SRF were measured at baseline and under therapy in the central millimeter and compared to fluid gradings, CSFT and foveal centerpoint thickness (CPT) values measured by the Vienna Reading Center. RESULTS: 41.906 SD-OCT volume scans were included into the analysis. Concordance between human expert grading and automated algorithm performance reached AUC values of 0.93/0.85 for IRF and 0.87 for SRF in HARRIER/HAWK in the central millimeter. IRF volumes showed a moderate correlation with CSFT at baseline (HAWK: r = 0.54; HARRIER: r = 0.62) and weaker correlation under therapy (HAWK: r = 0.44; HARRIER: r = 0.34). SRF and CSFT correlations were low at baseline (HAWK: r = 0.29; HARRIER: r = 0.22) and under therapy (HAWK: r = 0.38; HARRIER: r = 0.45). The residual standard error (IRF: 75.90 µm; SRF: 95.26 µm) and marginal residual standard deviations (IRF: 46.35 µm; SRF: 44.19 µm) of fluid volume were high compared to the range of CSFT values. CONCLUSION: Deep learning-based segmentation of retinal fluid performs reliably on OCT images. CSFT values are weak indicators for fluid activity in nAMD. Automated quantification of fluid types, highlight the potential of deep learning-based approaches to objectively monitor anti-VEGF therapy.

Nationwide analysis of hospital admissions and outcomes of patients with SARS-CoV-2 infection in Austria in 2020 and 2021.

This retrospective study evaluated temporal and regional trends of patient admissions to hospitals, intensive care units (ICU), and intermediate care units (IMCU) as well as outcomes during the COVID-19 pandemic in Austria. We analysed anonymous data from patients admitted to Austrian hospitals with COVID-19 between January 1st, 2020 and December 31st, 2021. We performed descriptive analyses and logistic regression analyses for in-hospital mortality, IMCU or ICU admission, and in-hospital mortality following ICU admission. 68,193 patients were included, 8304 (12.3%) were primarily admitted to ICU, 3592 (5.3%) to IMCU. Hospital mortality was 17.3%; risk factors were male sex (OR 1.67, 95% CI 1.60-1.75, p < 0.001) and high age (OR 7.86, 95% CI 7.07-8.74, p < 0.001 for 90+ vs. 60-64 years). Mortality was higher in the first half of 2020 (OR 1.15, 95% CI 1.04-1.27, p = 0.01) and the second half of 2021 (OR 1.11, 95% CI 1.05-1.17, p < 0.001) compared to the second half of 2020 and differed regionally. ICU or IMCU admission was most likely between 55 and 74 years, and less likely in younger and older age groups. We find mortality in Austrian COVID-19-patients to be almost linearly associated with age, ICU admission to be less likely in older individuals, and outcomes to differ between regions and over time.

VALIDATION OF AN AUTOMATED FLUID ALGORITHM ON REAL-WORLD DATA OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION OVER FIVE YEARS.

BACKGROUND/PURPOSE: To apply an automated deep learning automated fluid algorithm on data from real-world management of patients with neovascular age-related macular degeneration for quantification of intraretinal/subretinal fluid volumes in optical coherence tomography images. METHODS: Data from the Vienna Imaging Biomarker Eye Study (VIBES, 2007-2018) were analyzed. Databases were filtered for treatment-naive neovascular age-related macular degeneration with a baseline optical coherence tomography and at least one follow-up and 1,127 eyes included. Visual acuity and optical coherence tomography at baseline, Months 1 to 3/Years 1 to 5, age, sex, and treatment number were included. Artificial intelligence and certified manual grading were compared in a subanalysis of 20%. Main outcome measures were fluid volumes. RESULTS: Intraretinal/subretinal fluid volumes were maximum at baseline (intraretinal fluid: 21.5/76.6/107.1 nL; subretinal fluid 13.7/86/262.5 nL in the 1/3/6-mm area). Intraretinal fluid decreased to 5 nL at M1-M3 (1-mm) and increased to 11 nL (Y1) and 16 nL (Y5). Subretinal fluid decreased to a mean of 4 nL at M1-M3 (1-mm) and remained stable below 7 nL until Y5. Intraretinal fluid was the only variable that reflected VA change over time. Comparison with human expert readings confirmed an area under the curve of >0.9. CONCLUSION: The Vienna Fluid Monitor can precisely quantify fluid volumes in optical coherence tomography images from clinical routine over 5 years. Automated tools will introduce precision medicine based on fluid guidance into real-world management of exudative disease, improving clinical outcomes while saving resources.

SYSTEMATIC CORRELATION OF CENTRAL SUBFIELD THICKNESS WITH RETINAL FLUID VOLUMES QUANTIFIED BY DEEP LEARNING IN THE MAJOR EXUDATIVE MACULAR DISEASES.

PURPOSE: To investigate the correlation of volumetric measurements of intraretinal (IRF) and subretinal fluid obtained by deep learning and central retinal subfield thickness (CSFT) based on optical coherence tomography in retinal vein occlusion, diabetic macular edema, and neovascular age-related macular degeneration. METHODS: A previously validated deep learning-based approach was used for automated segmentation of IRF and subretinal fluid in spectral domain optical coherence tomography images. Optical coherence tomography volumes of 2.433 patients obtained from multicenter studies were analyzed. Fluid volumes were measured at baseline and under antivascular endothelial growth factor therapy in the central 1, 3, and 6 mm. RESULTS: Patients with neovascular age-related macular degeneration generally demonstrated the weakest association between CSFT and fluid volume measurements in the central 1 mm (0.107-0.569). In patients with diabetic macular edema, IRF correlated moderately with CSFT (0.668-0.797). In patients with retinal vein occlusion, IRF volumes showed a moderate correlation with CSFT (0.603-0.704). CONCLUSION: The correlation of CSFT and fluid volumes depends on the underlying pathology. Although the amount of central IRF seems to partly drive CSFT in diabetic macular edema and retinal vein occlusion, it has only a limited impact on patients with neovascular age-related macular degeneration. Our findings do not support the use of CSFT as a primary or secondary outcome measure for the quantification of exudative activity or treatment guidance.

Variations in end-of-life practices in intensive care units worldwide (Ethicus-2): a prospective observational study.

BACKGROUND: End-of-life practices vary among intensive care units (ICUs) worldwide. Differences can result in variable use of disproportionate or non-beneficial life-sustaining interventions across diverse world regions. This study investigated global disparities in end-of-life practices. METHODS: In this prospective, multinational, observational study, consecutive adult ICU patients who died or had a limitation of life-sustaining treatment (withholding or withdrawing life-sustaining therapy and active shortening of the dying process) during a 6-month period between Sept 1, 2015, and Sept 30, 2016, were recruited from 199 ICUs in 36 countries. The primary outcome was the end-of-life practice as defined by the end-of-life categories: withholding or withdrawing life-sustaining therapy, active shortening of the dying process, or failed cardiopulmonary resuscitation (CPR). Patients with brain death were included in a separate predefined end-of-life category. Data collection included patient characteristics, diagnoses, end-of-life decisions and their timing related to admission and discharge, or death, with comparisons across different regions. Patients were studied until death or 2 months from the first limitation decision. FINDINGS: Of 87 951 patients admitted to ICU, 12 850 (14·6%) were included in the study population. The number of patients categorised into each of the different end-of-life categories were significantly different for each region (p<0·001). Limitation of life-sustaining treatment occurred in 10 401 patients (11·8% of 87 951 ICU admissions and 80·9% of 12 850 in the study population). The most common limitation was withholding life-sustaining treatment (5661 [44·1%]), followed by withdrawing life-sustaining treatment (4680 [36·4%]). More treatment withdrawing was observed in Northern Europe (1217 [52·8%] of 2305) and Australia/New Zealand (247 [45·7%] of 541) than in Latin America (33 [5·8%] of 571) and Africa (21 [13·0%] of 162). Shortening of the dying process was uncommon across all regions (60 [0·5%]). One in five patients with treatment limitations survived hospitalisation. Death due to failed CPR occurred in 1799 (14%) of the study population, and brain death occurred in 650 (5·1%). Failure of CPR occurred less frequently in Northern Europe (85 [3·7%] of 2305), Australia/New Zealand (23 [4·3%] of 541), and North America (78 [8·5%] of 918) than in Africa (106 [65·4%] of 162), Latin America (160 [28·0%] of 571), and Southern Europe (590 [22·5%] of 2622). Factors associated with treatment limitations were region, age, and diagnoses (acute and chronic), and country end-of-life legislation. INTERPRETATION: Limitation of life-sustaining therapies is common worldwide with regional variability. Withholding treatment is more common than withdrawing treatment. Variations in type, frequency, and timing of end-of-life decisions were observed. Recognising regional differences and the reasons behind these differences might help improve end-of-life care worldwide. FUNDING: None.