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- Jung, Christian, et al.
(författare)
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A comparison of very old patients admitted to intensive care unit after acute versus elective surgery or intervention
- 2019
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Ingår i: Journal of critical care. - : W B SAUNDERS CO-ELSEVIER INC. - 0883-9441 .- 1557-8615. ; 52, s. 141-148
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Tidskriftsartikel (refereegranskat)abstract
- Background: We aimed to evaluate differences in outcome between patients admitted to intensive care unit (ICU) after elective versus acute surgery in a multinational cohort of very old patients (80 years; VIP). Predictors of mortality, with special emphasis on frailty, were assessed.Methods: In total, 5063 VIPs were induded in this analysis, 922 were admitted after elective surgery or intervention, 4141 acutely, with 402 after acute surgery. Differences were calculated using Mann-Whitney-U test and Wilcoxon test. Univariate and multivariable logistic regression were used to assess associations with mortality.Results: Compared patients admitted after acute surgery, patients admitted after elective surgery suffered less often from frailty as defined as CFS (28% vs 46%; p < 0.001), evidenced lower SOFA scores (4 +/- 5 vs 7 +/- 7; p < 0.001). Presence of frailty (CFS >4) was associated with significantly increased mortality both in elective surgery patients (7% vs 12%; p = 0.01), in acute surgery (7% vs 12%; p = 0.02).Conclusions: VIPs admitted to ICU after elective surgery evidenced favorable outcome over patients after acute surgery even after correction for relevant confounders. Frailty might be used to guide clinicians in risk stratification in both patients admitted after elective and acute surgery.
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- Tobias, Deirdre K, et al.
(författare)
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Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine
- 2023
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Ingår i: Nature Medicine. - 1546-170X. ; 29:10, s. 2438-2457
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Forskningsöversikt (refereegranskat)abstract
- Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
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- Andersén, Christoffer, 1991-, et al.
(författare)
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Deep learning based digitisation of prostate brachytherapy needles in ultrasound images
- 2020
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Ingår i: Medical physics. - : Wiley-Blackwell Publishing Inc.. - 2473-4209 .- 0094-2405. ; 47:12, s. 6414-6420
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To develop, and evaluate the performance of, a deep learning based 3D convolutional neural network (CNN) artificial intelligence (AI) algorithm aimed at finding needles in ultrasound images used in prostate brachytherapy.METHODS: Transrectal ultrasound (TRUS) image volumes from 1102 treatments were used to create a clinical ground truth (CGT) including 24422 individual needles that had been manually digitised by medical physicists during brachytherapy procedures. A 3D CNN U-net with 128x128x128 TRUS image volumes as input was trained using 17215 needle examples. Predictions of voxels constituting a needle were combined to yield a 3D linear function describing the localisation of each needle in a TRUS volume. Manual and AI digitisations were compared in terms of the root-mean-square distance (RMSD) along each needle, expressed as median and interquartile range (IQR). The method was evaluated on a dataset including 7207 needle examples. A subgroup of the evaluation data set (n=188) was created, where the needles were digitised once more by a medical physicist (G1) trained in brachytherapy. The digitisation procedure was timed.RESULTS: The RMSD between the AI and CGT was 0.55 (IQR: 0.35-0.86) mm. In the smaller subset, the RMSD between AI and CGT was similar (0.52 [IQR: 0.33-0.79] mm) but significantly smaller (p<0.001) than the difference of 0.75 (IQR: 0.49-1.20) mm between AI and G1. The difference between CGT and G1 was 0.80 (IQR: 0.48-1.18) mm, implying that the AI performed as well as the CGT in relation to G1. The mean time needed for human digitisation was 10 min 11 sec, while the time needed for the AI was negligible.CONCLUSIONS: A 3D CNN can be trained to identify needles in TRUS images. The performance of the network was similar to that of a medical physicist trained in brachytherapy. Incorporating a CNN for needle identification can shorten brachytherapy treatment procedures substantially.
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- Martinsson, Johan, et al.
(författare)
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Impacts of Combustion Conditions and Photochemical Processing on the Light Absorption of Biomass Combustion Aerosol
- 2015
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Ingår i: Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 49:24, s. 14663-14671
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Tidskriftsartikel (refereegranskat)abstract
- The aim was to identify relationships between combustion conditions, particle characteristics, and optical properties of fresh and photochemically processed emissions from biomass combustion. The combustion conditions included nominal and high burn rate operation and individual combustion phases from a conventional wood stove. Low temperature pyrolysis upon fuel addition resulted in "tar-ball" type particles dominated by organic aerosol with an absorption Angstrom exponent (AAE) of 2.5-2.7 and estimated Brown Carbon contributions of 50-70% to absorption at the climate relevant aethalometer-wavelength (520 nm). High temperature combustion during the intermediate (flaming) phase was dominated by soot agglomerates with AAE 1.0-1.2 and 85-100% of absorption at 520 nm attributed to Black Carbon. Intense photochemical processing of high burn rate flaming combustion emissions in an oxidation flow reactor led to strong formation of Secondary Organic Aerosol, with no or weak absorption. PM1 mass emission factors (mg/kg) of fresh emissions were about an order of magnitude higher for low temperature pyrolysis compared to high temperature combustion. However, emission factors describing the absorption cross section emitted per kg of fuel consumed (m(2)/kg) were of similar magnitude at 520 nm for the diverse combustion conditions investigated in this study. These results provide a link between biomass combustion conditions, emitted particle types, and their optical properties in fresh and processed plumes which can be of value for source apportionment and balanced mitigation of biomass combustion emissions from a climate and health perspective.
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