Category: Nevin Manimala

Obstet Gynecol. 2023 Aug 1;142(2):339-349. doi: 10.1097/AOG.0000000000005227. Epub 2023 Jul 5.

ABSTRACT

OBJECTIVE: To examine whether access to treatment for women with opioid use disorder (OUD) varied by race and ethnicity, community characteristics, and pregnancy status.

METHODS: We conducted a secondary data analysis of a simulated patient caller study of buprenorphine-waivered prescribers and opioid-treatment programs in 10 U.S. states. We conducted multivariable analyses, accounting for potential confounders, to evaluate factors associated with likelihood of successfully securing an appointment. Descriptive statistics and significance testing examined 1) caller characteristics and call outcome by assigned race and ethnicity and clinic type (combined, opioid-treatment programs, and buprenorphine-waivered prescribers) and 2) clinic and community characteristics and call outcome by community race and ethnicity distribution (majority White vs majority Black, Hispanic, Asian, American Indian, Alaska Native, Native Hawaiian, or Pacific Islander) and clinic type. A multiple logistic regression model was fitted to assess the likelihood of obtaining an appointment by callers’ race and ethnicity and pregnancy status with the exposure of interest being majority Black, Hispanic, Asian, American Indian, Alaska Native, Native Hawaiian, or Pacific Islander community distribution.

RESULTS: In total, 3,547 calls reached clinics to schedule appointments. Buprenorphine-waivered prescribers were more likely to be in communities that were more than 50% White (88.9% vs 77.3%, P<.001), and opioid-treatment programs were more likely to be in communities that were less than 50% White (11.1% vs 22.7%, P<.001). Callers were more likely to be granted appointments in majority Black, Hispanic, Asian, American Indian, Alaska Native, Native Hawaiian, or Pacific Islander communities (adjusted odds ratio [aOR] 1.06, 95% CI 1.02-1.10 per 10% Black, Hispanic, Asian, American Indian, Alaska Native, Native Hawaiian, or Pacific Islander community population) and at opioid-treatment programs (aOR 4.94, 95% CI 3.52-6.92) and if they were not pregnant (aOR 1.79, 95% CI 1.53-2.09).

CONCLUSION: Clinic distribution and likelihood of acceptance for treatment varied by community race and ethnicity distribution. Access to treatment for OUD remains challenging for pregnant people and in many historically marginalized U.S. communities.

PMID:37473410 | DOI:10.1097/AOG.0000000000005227

Obstet Gynecol. 2023 Aug 1;142(2):239-241. doi: 10.1097/AOG.0000000000005239. Epub 2023 Jul 5.

ABSTRACT

The ARRIVE (A Randomized Trial of Induction Versus Expectant Management) trial demonstrated lower rates of hypertensive disorders of pregnancy (HDP) among low-risk nulliparous patients undergoing labor induction at 39 weeks of gestation. We conducted a population-based cohort study in which we evaluated the association between the routinization of 39-week induction and the rate of HDP by comparing rates before and after the ARRIVE trial publication, using the National Vital Statistics System. Logistic regression models were used to project what the HDP rate would have been based on trends seen pre-ARRIVE. Despite an overall increase in the rate of HDP from pre-ARRIVE to post-ARRIVE (4.9% pre vs 6.3% post, adjusted odds ratio [aOR] 1.26, 95% CI 1.24-1.27), the HDP rate was significantly lower in the post-ARRIVE group among patients undergoing induction at 39 weeks of gestation (14.7% pre vs 14.1% post, aOR 0.91, 95% CI 0.90-0.93), decreasing by 12.0% per year (P<.001). The rate of HDP among all other delivering patients was higher in the post-ARRIVE group (4.1% pre vs 5.5% post, aOR1.32, 95% CI 1.30-1.34). Our findings may suggest that, as the overall HDP rate rises, the relative advantage of 39-week induction will rise similarly.

PMID:37473407 | DOI:10.1097/AOG.0000000000005239

Chic Med Exam. 1866 Mar;7(3):144-147.

NO ABSTRACT

PMID:37473327 | PMC:PMC9994901

Chic Med Exam. 1867 Jun;8(6):342-343.

NO ABSTRACT

PMID:37473311 | PMC:PMC9999863

Chic Med Exam. 1867 Oct;8(10):603-616.

NO ABSTRACT

PMID:37473240 | PMC:PMC9999752

Chic Med Exam. 1866 Jun;7(6):366.

NO ABSTRACT

PMID:37473025 | PMC:PMC9994912

Chic Med Exam. 1870 Aug;11(8):520-521.

NO ABSTRACT

PMID:37472084 | PMC:PMC10023041

Chic Med Exam. 1870 May;11(5):257-266.

NO ABSTRACT

PMID:37471998 | PMC:PMC10022927

Catheter Cardiovasc Interv. 2023 Jul 20. doi: 10.1002/ccd.30767. Online ahead of print.

ABSTRACT

BACKGROUND: Coronary artery disease (CAD) is a common finding in patients undergoing transcatheter aortic valve implantation (TAVI). However, its prognostic significance and its management remains controversial.

AIMS: This study sought to determine whether the presence of CAD, its complexity, and angiography-guided percutaneous coronary intervention (PCI) are associated with outcomes after TAVI.

METHODS: All patients undergoing TAVI at a tertiary referral center between 2008 and 2018 were included in a prospective observational study. Baseline SYNTAX (Synergy between PCI with Taxus and Cardiac Surgery) score (SS) and a residual SS after PCI were calculated. The endpoints on the 5 year follow-up were all-cause mortality and a composite of mayor cardiovascular adverse events (MACE).

RESULTS: In 379 patients, the presence of CAD and its complexity were not significantly associated with worse 5-year survival after TAVI, with a mortality for SS0 of 45%; for SS 1-22 of 36.5% (HR 0.77; 95% CI 0.53-1.11, p = 0.15) and for SS > 22 of 42.1% (HR 1.24; 95% CI 0.59-2.63, p = 0.57). Regarding the combined event of MACE, there were also no statistically significant differences between patients with CAD and without CAD (56.8% in patients without CAD and 54.9% in patients with CAD; HR 1.06; 95% CI 0.79-1.43, p = 0.7). Angiography-guided PCI or completeness of revascularization was not associated with different outcomes.

CONCLUSIONS: In the present analysis, neither the presence nor the extent of CAD, nor the degree of revascularization, was associated with a prognostic impact in patients undergoing TAVI at 5-year follow-up.

PMID:37471716 | DOI:10.1002/ccd.30767

Hum Brain Mapp. 2023 Jul 20. doi: 10.1002/hbm.26422. Online ahead of print.

ABSTRACT

Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high-powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site-related image variation. However, most statistical approaches may over-correct for technical, scanning-related, variation as they cannot distinguish between confounded image-acquisition based variability and site-related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition-based variability. To overcome this limitation, we consider site-related magnetic resonance (MR) imaging harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labels a priori. We trained our model using data from five large-scale multisite datasets with varied demographics. Results demonstrated that our style-encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain-age estimates, and case-control effect sizes before and after the harmonization. We showed that our harmonization removed the site-related variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising tool for ongoing collaborative studies. Source code is released in USC-IGC/style_transfer_harmonization (github.com).

PMID:37471702 | DOI:10.1002/hbm.26422