Current surgical risk assessment tools fall short of appreciating geriatric risk factors including cognitive deficits, depressive, and frailty symptoms that may worsen outcomes post-transcatheter ...aortic valve implantation (TAVI). This study hypothesized that a screening tool, SMARTIE, would improve detection of these risks pre-TAVI, and thus be predictive of postoperative delirium (POD) and 30-day mortality post-TAVI.
Prospective observational cohort study, using a historical cohort for comparison.
A total of 234 patients (age: 82.2±6.7 years, 59.4% male) were included. Half were screened using SMARTIE.
The SMARTIE cohort was assessed for cognitive deficits and depressive symptoms using the Mini-Cog test and PHQ-2, respectively. Measures of frailty included activities of daily living inventory, the Timed Up and Go test and grip strength. For the pre-SMARTIE cohort, we extracted cognitive deficits, depression and frailty symptoms from clinic charts. The incidence of POD and 30-day mortality were recorded. Bivariate chi-square analysis or
-tests were used to report associations between SMARTIE and pre-SMARTIE groups. Multivariable logistic regression models were employed to identify independent predictors of POD and 30-day mortality.
More patients were identified with cognitive deficits (χ
=11.73,
=0.001), depressive symptoms (χ
=8.15,
=0.004), and physical frailty (χ
=5.73,
=0.017) using SMARTIE. Cognitive deficits were an independent predictor of POD (OR: 8.4,
<0.01) and 30-day mortality (OR: 4.04,
=0.03).
This study emphasized the value of screening for geriatric risk factors prior to TAVI by demonstrating that screening increased identification of at-risk patients. It also confirmed findings that cognitive deficits are predictive of POD and mortality following TAVI.
In the course of the ICOS (Integrated Carbon Observation System) Demonstration Experiment a feasibility study on the usefulness of a travelling comparison instrument (TCI) was conducted in order to ...evaluate continuous atmospheric CO2 and CH4 measurements at two European stations. The aim of the TCI is to independently measure ambient air in parallel to the standard station instrumentation, thus providing a comprehensive comparison that includes the sample intake system, the instrument itself as well as its calibration and data evaluation. Observed differences between the TCI and a gas chromatographic system, which acted as a reference for the TCI, were -0.02 ± 0.08 μmol mol-1 for CO2 and -0.3 ± 2.3 nmol mol-1 for CH4 . Over a period of two weeks each, the continuous CO2 and CH4 measurements at two ICOS field stations, Cabauw (CBW), the Netherlands and Houdelaincourt (Observatoire Pérenne de l'Environnement, OPE), France, were compared to co-located TCI measurements. At Cabauw mean differences of 0.21 ± 0.06 μmol mol-1 for CO2 and 0.41 ± 0.50 nmol mol-1 for CH4 were found. For OPE the mean differences were 0.13 ± 0.07 μmol mol-1 for CO2 and 0.44 ± 0.36 nmol mol-1 for CH4 . Offsets arising from differences in the working standard calibrations or leakages/contaminations in the drying systems are too small to explain the observed differences. Hence the most likely causes of these observed differences are leakages or contaminations in the intake lines and/or their flushing pumps. For the Cabauw instrument an additional error contribution originates from insufficient flushing of standard gases. Although the TCI is an extensive quality control approach it cannot replace other quality control systems. Thus, a comprehensive quality management strategy for atmospheric monitoring networks is proposed as well.
To evaluate purified honey bee (
) venom (HBV) biotherapy for the treatment of osteoarthritis (OA) knee pain and physical function.
Five hundred and thirty-eight patients with Kellgren/Lawrence grade ...1-3 radiographic knee OA and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score ≥2 were randomized 1:2 to either control ("histamine") or HBV in this double-blind study.
After a dose escalation period, patients received 12 weekly dermal injections of control ("histamine") or HBV. At each of the 12 weekly visits, a set of 15 dermal injections (each containing 2.75 μg histamine or 100 μg HBV) were administered at prespecified acupuncture points (5 on each knee: knee top, eye-1 medial, eye-2 lateral, ST 34, BL 40 and 5 near the spinous processes: BL 19, 21, 23, 25, and 27).
Assessments included WOMAC pain and physical function subscales, visual analog scale (VAS), patient global assessment (PGA), and physician global assessment (PhGA). Rescue medication use (acetaminophen) and routine safety parameters were monitored.
HBV biotherapy demonstrated a highly significant improvement over control in WOMAC pain score after 12 weeks (1.1 U mean difference; confidence interval 95% CI: 0.3-2.0; analysis of covariance ANCOVA
= 0.0010 with baseline as covariate) that was also sustained 4 weeks post-treatment. Furthermore, WOMAC physical function was significantly improved over control with HBV (3.1 U mean difference; 95% CI: 0.3-5.9; ANCOVA
= 0.0046), and sustained 4 weeks post-treatment. VAS scores were significantly improved with HBV versus control, as well as PGA and PhGA evaluations, which showed that patients responded more favorably ("very good/good") to their overall OA condition (82.0% vs. 62.4%
= 0.0001 and 82.1% vs. 54.9%
= 0.0015, respectively). Use of rescue acetaminophen was similar between the groups (77%-78% of patients). HBV was associated with higher incidence of injection site reactions (<5%); however, the overall safety profiles were comparable between the treatment groups.
This phase 3 trial demonstrated that HBV biotherapy resulted in significant improvements in knee OA pain and physical function.
Abstract
Background
Large-scale analyses of imaging in Crohn's disease (CD) hold promise for advancing research on the inflammatory burden in the bowel as well as developing predictive models of ...disease progression. However, the lack of structured human annotations in these datasets limits the ability use these for research. This study from epi-IIRN nationwide cohort, aims to develop and evaluate Natural Language Processing (NLP) algorithms for extracting structured information from unstructured radiology reports on CT and MR enterography.
Methods
We identified radiology reports of all patients diagnosed with inflammatory Bowel Disease (IBD) in the nationwide epi-IIRN cohort, which includes data from the four Israeli HMOs, covering 98% of the population. We developed an in-house NLP platform using a publicly available Hebrew pretrained BERT model. After annotating a small portion of the reports for validation, we fine-tuned the model on our dataset through a masked language task, followed by a few-shot approach based on the Next Sentence Prediction (NSP) pretraining objective for classification model fine-tuning. The platform extracts radiological indicators related to inflammation, stenosis, and location, including wall thickening, enhancement, lumen narrowing, and dilation in the following locations: jejunum, ileum, colon, sigmoid, and rectum. We validated our models using a 5-fold cross-validation experimental setup, employing accuracy, PPV, NPV, F1 score and Cohen’s kappa score as the evaluation metrics.
Results
We extracted 9,704 radiology reports (6,299 MRE, 2,405 CTE) of 7,062 IBD patients (5,972 were diagnosed with CD, and 1,076 with ulcerative colitis). The mean age on the first imaging study was 36.8±17.1 years and 52% were male. We selected 500 studies for being annotated for the radiological indicators. The most common label was wall thickening in the ileum (215 positive patients vs.285 negative) while the least common was lumen narrowing in the jejunum (1 positive patient vs. 499 negative). Table 1 summarizes the results and label distributions. The mean 95% CI accuracy/PPV/NPV/F1/Cohen's kappa score averaged over all labels was 0.98 0.95,1/0.99 0.96,1/0.83 0.56,1/0.86 0.6,1/0.63 0.16,1. The labels with the highest F1/Cohen's kappa score were wall thickening, enhancement, and narrowing in the Ileum while the label with the lowest F1/Cohen's kappa score were dilation in the Colon and the Jejunum.
Conclusion
NLP methods can extract structured information from radiology reports with high accuracy. Few-shot approaches based on the Next Sentence Prediction can alleviate the need for large scale data annotation for training. NLP offers exciting possibilities for large-scale studies utilizing imaging data in CD.
The Integrated Carbon Observation System Atmosphere Thematic Centre (ICOS ATC) automatically processes atmospheric greenhouse gases mole fractions of data coming from sites of the ICOS network. Daily ...transferred raw data files are automatically processed and archived. Data are stored in the ICOS atmospheric database, the backbone of the system, which has been developed with an emphasis on the traceability of the data processing. Many data products, updated daily, explore the data through different angles to support the quality control of the dataset performed by the principal operators in charge of the instruments. The automatic processing includes calibration and water vapor corrections as described in the paper. The mole fractions calculated in near-real time (NRT) are automatically revaluated as soon as a new instrument calibration is processed or when the station supervisors perform quality control. By analyzing data from 11 sites, we determined that the average calibration corrections are equal to 1.7 ± 0.3µmolmol-1 for CO2 and 2.8 ± 3nmolmol-1 for CH4. These biases are important to correct to avoid artificial gradients between stations that could lead to error in flux estimates when using atmospheric inversion techniques. We also calculated that the average drift between two successive calibrations separated by 15 days amounts to ±0.05µmolmol-1 and ±0.7nmolmol-1 for CO2 and CH4, respectively. Outliers are generally due to errors in the instrument configuration and can be readily detected thanks to the data products provided by the ATC. Several developments are still ongoing to improve the processing, including automated spike detection and calculation of time-varying uncertainties.
The identification of spikes (i.e., short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using ...continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, inversion experiments devoted to the top-down quantification of GHG surface–atmosphere fluxes, the characterization of local emissions, or the quality control of GHG measurements. In this work, we analyzed the results provided by two automatic spike identification methods (i.e., the standard deviation of the background (SD) and the robust extraction of baseline signal (REBS)) for a 2-year dataset of 1 min in situ observations of CO2, CH4 and CO at 10 different atmospheric sites spanning different environmental conditions (remote, continental, urban).The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification by the site principal investigators (PIs).The study showed that, for CO2 and CH4, REBS identified a larger number of spikes than SD and it was less “site-sensitive” than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO2 and CH4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events, and the temporal variabilities in CO2, CH4 and CO were more consistent with those of the original datasets. Further activities are needed for better consolidating the fitness for purpose of the two proposed methods and to compare them with other spike detection techniques.
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