Tag: nevin manimala

Eur J Cardiothorac Surg. 2023 Nov 11:ezad385. doi: 10.1093/ejcts/ezad385. Online ahead of print.

ABSTRACT

Prognostic models are widely used across medicine and within cardiothoracic surgery, where predictive tools such as EuroSCORE are commonplace. Such models are a useful component of clinical assessment but may be misapplied. In this article, we demonstrate some of the major issues with risk scores by using the popular BBC television programme Strictly Come Dancing (SCD; known as Dancing with the Stars in many other countries) as an example. We generated a multivariable prognostic model using data from the then completed 19 series of SCD to predict prospectively the results of the 20th series. The initial model based solely on demographic data was limited in its predictive value (0.25, 0.22; R2 and Spearman’s Rank Correlation respectively) but was substantially improved following introduction of early judges’ scores deemed representative of whether contestants could actually dance (0.40, 0.30). Finally, we utilise our model to discuss the difficulties and pitfalls in using and interpreting prognostic models in cardiothoracic surgery and beyond, particularly where these do not adequately capture potentially important prognostic information.

PMID:37952190 | DOI:10.1093/ejcts/ezad385

Circulation. 2023 Nov 12. doi: 10.1161/CIRCULATIONAHA.123.067512. Online ahead of print.

ABSTRACT

Background: Device-detected atrial fibrillation (AF) (also known as subclinical AF or atrial high-rate episodes) is a common finding in patients with an implanted cardiac rhythm device and is associated with an increased risk of ischemic stroke. Whether oral anticoagulation is effective and safe in this patient population is unclear. Methods: We performed a systematic review of MEDLINE and Embase for randomized trials comparing oral anticoagulation to antiplatelet or no antithrombotic therapy in adults with device-detected AF recorded by a pacemaker, implantable cardioverter-defibrillator, cardiac resynchronization therapy device or implanted cardiac monitor. We used random-effects models for meta-analysis and rated the quality of evidence using the GRADE framework. The review was pre-registered (PROSPERO CRD42023463212). Results: From 785 unique citations, we identified two randomized trials with relevant clinical outcome data; NOAH-AFNET 6 (2,536 participants) evaluated edoxaban and ARTESiA (4,012 participants) evaluated apixaban. Meta-analysis demonstrated that oral anticoagulation with these agents reduced ischemic stroke (relative risk [RR] 0.68, 95% confidence interval [CI] 0.50-0.92; high-quality evidence). The results from the two trials were consistent (I² statistic for heterogeneity=0%). Oral anticoagulation also reduced a composite of cardiovascular death, all-cause stroke, peripheral arterial embolism, myocardial infarction or pulmonary embolism (RR 0.85, 95% CI 0.73-1.00, I²=0%; moderate-quality evidence). There was no reduction in cardiovascular death (RR 0.95, 95% CI 0.76-1.17, I²=0%; moderate-quality evidence) or all-cause mortality (RR 1.08, 95% CI 0.96-1.21 I²=0%; moderate-quality evidence). Oral anticoagulation increased major bleeding (RR 1.62, 95% CI 1.05-2.5 I^²=61%; high-quality evidence). Conclusions: The results of the NOAH-AFNET 6 and ARTESiA trials are consistent with each other. Meta-analysis of these two large randomized trials provides high-quality evidence that oral anticoagulation with edoxaban or apixaban reduces the risk of stroke in patients with device-detected AF and increases the risk of major bleeding.

PMID:37952187 | DOI:10.1161/CIRCULATIONAHA.123.067512

Gerontol Geriatr Educ. 2023 Nov 12:1-12. doi: 10.1080/02701960.2023.2280061. Online ahead of print.

ABSTRACT

Researchers conducted a four-week pilot sexual health education intervention to investigate changes among older adults’ attitudes about and comfort with sexuality. Six adults between the ages of 74-83 participated in a pilot intervention on sexual health and were administered a pretest and posttest on comfort with and attitudes about sexuality. The intervention included information on personal history of sexuality, sex education, masturbation, sexuality and bodily change in older adulthood, safe sex, healthy relationships, and talking to health care providers. Descriptive statistics for average responses in the pretest and posttest before and after the intervention were used to highlight differences among the participants. Implications for gerontologists, geriatric providers, and education and research regarding older adults are shared.

PMID:37952185 | DOI:10.1080/02701960.2023.2280061

Bioinformatics. 2023 Nov 11:btad683. doi: 10.1093/bioinformatics/btad683. Online ahead of print.

ABSTRACT

MOTIVATION: The LINCS L1000 project has collected gene expression profiles for thousands of compounds across a wide array of concentrations, cell lines, and time points. However, conventional analysis methods often fall short in capturing the rich information encapsulated within the L1000 transcriptional dose-response data.

RESULTS: We present DOSE-L1000, a database that unravels the potency and efficacy of compound-gene pairs and the intricate landscape of compound-induced transcriptional changes. Our study employs the fitting of over 140 million generalized additive models and robust linear models, spanning the complete spectrum of compounds and landmark genes within the LINCS L1000 database. This systematic approach provides quantitative insights into differential gene expression and the potency and efficacy of compound-gene pairs across diverse cellular contexts. Through examples, we showcase the application of DOSE-L1000 in tasks such as cell line and compound comparisons, along with clustering analyses and predictions of drug-target interactions. DOSE-L1000 fosters applications in drug discovery, accelerating the transition to omics-driven drug development.

AVAILABILITY: DOSE-L1000 is publicly available at https://doi.org/10.5281/zenodo.8286375.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37952162 | DOI:10.1093/bioinformatics/btad683

Environ Sci Technol. 2023 Nov 12. doi: 10.1021/acs.est.3c01259. Online ahead of print.

ABSTRACT

Water-soluble organic matter (WSOM) formed through aqueous processes contributes substantially to total atmospheric aerosol, however, the impact of water evaporation on particle concentrations is highly uncertain. Herein, we present a novel approach to predict the amount of evaporated organic mass induced by sample drying using multivariate polynomial regression and random forest (RF) machine learning models. The impact of particle drying on fine WSOM was monitored during three consecutive summers in Baltimore, MD (2015, 2016, and 2017). The amount of evaporated organic mass was dependent on relative humidity (RH), WSOM concentrations, isoprene concentrations, and NO_x/isoprene ratios. Different models corresponding to each class were fitted (trained and tested) to data from the summers of 2015 and 2016 while model validation was performed using summer 2017 data. Using the coefficient of determination (R²) and the root-mean-square error (RMSE), it was concluded that an RF model with 100 decision trees had the best performance (R² of 0.81) and the lowest normalized mean error (NME < 1%) leading to low model uncertainties. The relative feature importance for the RF model was calculated to be 0.55, 0.2, 0.15, and 0.1 for WSOM concentrations, RH levels, isoprene concentrations, and NO_x/isoprene ratios, respectively. The machine learning model was thus used to predict summertime concentrations of evaporated organics in Yorkville, Georgia, and Centerville, Alabama in 2016 and 2013, respectively. Results presented herein have implications for measurements that rely on sample drying using a machine learning approach for the analysis and interpretation of atmospheric data sets to elucidate their complex behavior.

PMID:37952161 | DOI:10.1021/acs.est.3c01259

J Hand Surg Am. 2023 Nov 10:S0363-5023(23)00502-6. doi: 10.1016/j.jhsa.2023.09.010. Online ahead of print.

ABSTRACT

PURPOSE: Ulnar variance (UV) is a radiographic measurement relating the articular surface heights of the distal radius and ulna. Abnormal UV increases the risk for wrist pathology; however, it only provides a static measurement of an inherently dynamic bony relationship that changes with wrist position and loading. The purpose of this study was to investigate how full-body weight-bearing affects UV using weight-bearing computed tomography (WBCT).

METHODS: Ten gymnasts completed two 45-second scans inside a WBCT machine while performing a handstand on a flat platform (H) and parallettes (P). A non-weight-bearing CT scan was collected to match clinical practice (N). Differences in UV between weight-bearing conditions were evaluated separately for dominant and nondominant sides, and then, UV was compared between weight-bearing conditions on pooled dominant/nondominant data.

RESULTS: Pooled analyses comparing weight-bearing conditions revealed a significant increase in UV for H versus N (0.58 mm) and P versus N (1.00 mm), but no significant change in UV for H versus P (0.43 mm). Significant differences in UV were detected for H versus N, P versus N, and H versus P for dominant and nondominant extremities. The change from N to H was significantly greater in the dominant versus nondominant side, but greater in the nondominant side from N to P.

CONCLUSIONS: Ulnar variance changed with the application of load and position of the wrist. Differences in UV were found between dominant and nondominant extremities.

CLINICAL RELEVANCE: Upper extremity loading patterns are affected by hand dominance as defined by a cartwheel and suggest skeletal consequences from repetitive load on a dominantly used wrist. Although statistically significant, subtle changes detected in this investigational study do not necessarily bear clinical significance. Future WBCT research can lead to improved diagnostic measures for wrist pathologies affected by active loading and rotational wrist behavior.

PMID:37952145 | DOI:10.1016/j.jhsa.2023.09.010

Vet Ophthalmol. 2023 Nov 12. doi: 10.1111/vop.13161. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare intraocular pressure (IOP) measurements in dogs taken with the Reichert® Tono-Vera® Vet rebound tonometer with and without the automatic positioning system.

ANIMALS STUDIED: Measurements were taken on 49 eyes from 26 Beagle-derived dogs with variable genetics-four non-glaucomatous and 22 ADAMTS10-mutant dogs affected with different stages of open-angle glaucoma. Seventeen of the 26 dogs were measured 2-4 times on different days with variable intervals since IOP-lowering medications were administered.

PROCEDURES: In each dog, tonometry was performed with the Tono-Vera® Vet using three different methods in a randomized order: (Method 1) Average of three readings with an automatic positioning system; (Method 2) one reading with an automatic positioning system; and (Method 3) average of three readings obtained without the automatic positioning system. Statistical analyses included one-way ANOVA, Tukey pairwise comparisons, and Bland-Altman plots (MiniTab®).

RESULTS: With each of the three tonometry methods, 116 measurements were taken, resulting in 348 total IOP measurements with a range of 12.8-49.9 mmHg. The means and standard deviations for each method were 25.4 ± 6.9 mmHg (Method 1), 26.0 ± 7.2 mmHg (Method 2), and 26.9 ± 7.7 mmHg (Method 3), with no significant differences (p = .27). Mean IOP variances were also not significantly different between tonometry methods (p = .24 to .78).

CONCLUSIONS: Because mean IOPs and their standard deviations were not statistically different between the three tonometry methods, we conclude that Tono-Vera® Vet measurements conducted without the aid of the positioning system still provide reliable results.

PMID:37952123 | DOI:10.1111/vop.13161

J Am Med Inform Assoc. 2023 Nov 10:ocad214. doi: 10.1093/jamia/ocad214. Online ahead of print.

ABSTRACT

OBJECTIVE: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful.

MATERIALS AND METHODS: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research.

RESULTS: The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study.

DISCUSSION: This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time.

CONCLUSION: This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence.

PMID:37952118 | DOI:10.1093/jamia/ocad214

J Radiat Res. 2023 Nov 10:rrad080. doi: 10.1093/jrr/rrad080. Online ahead of print.

ABSTRACT

The objective of this study was to determine the outcomes of radical radiotherapy for early glottic squamous cell carcinoma (EGSCC) with the policy of increasing the fraction size during radiotherapy when the overall treatment time (OTT) was expected to be prolonged. Patients diagnosed with clinical T1-2N0M0 EGSCC, who were treated with radical radiotherapy between 2008 and 2019 at Hokkaido University Hospital, were included. Patients received 66 Gy in 33 fractions for T1 disease and 70 Gy in 35 fractions for T2 disease as our standard regimen (usual group [UG]). If the OTT was expected to extend for >1 week, the dose fraction size was increased from 2.0 to 2.5 Gy from the beginning or during radiotherapy (adjusted group [AG]). At this time, we performed a statistical analysis between UG and AG. In total, 116 patients were identified, and the treatment schedules of 29 patients were adjusted. The median follow-up was 60.9 months. In the T1 group, the cumulative 5-year local failure rate was 12.0% in the AG and 15.4% in the UG, and in the T2 group, the rate was 40.7% in the AG and 25.3% in the UG. There were no significant differences between the AG and UG. Similarly, no significant differences were observed for overall survival and progression-free survival rates. Our single-institutional retrospective analysis of EGSCC patients suggested that a method of adjusting the radiotherapy schedule to increase fraction size from the beginning or during the course may be effective in maintaining treatment outcomes.

PMID:37952082 | DOI:10.1093/jrr/rrad080

NPJ Breast Cancer. 2023 Nov 11;9(1):92. doi: 10.1038/s41523-023-00597-0.

ABSTRACT

Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained tumor slides could offer temporal and financial efficiency. We evaluated a data set of 704 1-mm tumor core H&E images (2-4 cores per case), corresponding to 202 participants (101 who recurred; 101 non-recurrent matched on age and follow-up time) from breast cancers diagnosed between 2008-2012 in the Carolina Breast Cancer Study. We leveraged deep learning to extract image information and trained a model to identify recurrence. Cross-validation accuracy for predicting recurrence was 62.4% [95% CI: 55.7, 69.1], similar to grade (65.8% [95% CI: 59.3, 72.3]) and ER status (66.3% [95% CI: 59.8, 72.8]). Interestingly, 70% (19/27) of early-recurrent low-intermediate grade tumors were identified by our image model. Relative to existing markers, image-based analyses provide complementary information for predicting early recurrence.

PMID:37952058 | DOI:10.1038/s41523-023-00597-0