Nevin Manimala

Stat Methods Med Res. 2024 Mar 19:9622802241236954. doi: 10.1177/09622802241236954. Online ahead of print.

ABSTRACT

Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history. It is of great interest for clinicians and statisticians to identify an optimal dynamic treatment regime that can produce the best expected clinical outcome for each individual and thus maximize the treatment benefit over the population. Observational data impose various challenges for using statistical tools to estimate optimal dynamic treatment regimes. Notably, the task becomes more sophisticated when the clinical outcome of primary interest is time-to-event. Here, we propose a matching-based machine learning method to identify the optimal dynamic treatment regime with time-to-event outcomes subject to right-censoring using electronic health record data. In contrast to the established inverse probability weighting-based dynamic treatment regime methods, our proposed approach provides better protection against model misspecification and extreme weights in the context of treatment sequences, effectively addressing a prevalent challenge in the longitudinal analysis of electronic health record data. In simulations, the proposed method demonstrates robust performance across a range of scenarios. In addition, we illustrate the method with an application to estimate optimal dynamic treatment regimes for patients with advanced non-small cell lung cancer using a real-world, nationwide electronic health record database from Flatiron Health.

PMID:38502008 | DOI:10.1177/09622802241236954

Aliment Pharmacol Ther. 2024 Mar 19. doi: 10.1111/apt.17950. Online ahead of print.

ABSTRACT

BACKGROUND: Results of studies evaluating the effect of viral eradication following direct-acting antiviral (DDA) therapy on skeletal muscle mass of patients with chronic hepatitis C (CHC) are scarce.

AIM: To assess the components of sarcopenia (low muscle mass, low muscle strength and low physical performance) in a cohort of CHC individuals before and after DAA therapy.

METHODS: We performed a longitudinal study of patients with CHC who underwent body composition assessment before (T0), and at 12 (T1) and 48 (T2) weeks after DDA therapy. Bioelectrical Impedance Analysis was used to assess skeletal mass muscle (SM) and phase angle (PhA). SM index (SMI) was calculated by dividing the SM by squared height. Muscle function was evaluated by hand grip strength (HGS) and timed up-and-go (TUG) test. Mixed-effects linear regression models were fitted to SMI, HGS and physical performance and were used to test the effect of HCV eradication by DAA.

RESULTS: 62 outpatients (mean age, 58.6 ± 10.8 years; 58% with compensated cirrhosis) were included. Significant decreases in liver fibrosis markers and an increase of 0.20 and 0.22 kg/m² in the SMI were observed at T1 and T2. Following DAA therapy, an increase of one unit of PhA was associated with a reduction of 0.38 min in TUG.

CONCLUSION: HCV eradication with DAA therapy was associated with a dynamic reduction of non-invasive markers of liver fibrosis and increased muscle mass in 62 patients with CHC who had an undetectable HCV load at 12 weeks after completion of antiviral treatment.

PMID:38501893 | DOI:10.1111/apt.17950

Microsc Res Tech. 2024 Mar 19. doi: 10.1002/jemt.24550. Online ahead of print.

ABSTRACT

This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD-PCSANN-DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD-PCSANN-DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD-CNN), breast cancer classification from thermal images utilizing Grunwald-Letnikov assisted dragonfly algorithm-based deep feature selection (BCD-VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD-SqueezeNet), respectively. RESEARCH HIGHLIGHTS: The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD-PCSANN-DBO method is implemented using Python.

PMID:38501825 | DOI:10.1002/jemt.24550

Microbiol Spectr. 2024 Mar 19:e0365023. doi: 10.1128/spectrum.03650-23. Online ahead of print.

ABSTRACT

Anaerobic microbes play crucial roles in environmental processes, industry, and human health. Traditional methods for monitoring the growth of anaerobes, including plate counts or subsampling broth cultures for optical density measurements, are time and resource-intensive. The advent of microplate readers revolutionized bacterial growth studies by enabling high-throughput and real-time monitoring of microbial growth kinetics. Yet, their use in anaerobic microbiology has remained limited. Here, we present a workflow for using small-footprint microplate readers and the Growthcurver R package to analyze the kinetic growth metrics of anaerobic bacteria. We benchmarked the small-footprint Cerillo Stratus microplate reader against a BioTek Synergy HTX microplate reader in aerobic conditions using Escherichia coli DSM 28618 cultures. The growth rates and carrying capacities obtained from the two readers were statistically indistinguishable. However, the area under the logistic curve was significantly higher in cultures monitored by the Stratus reader. We used the Stratus to quantify the growth responses of anaerobically grown E. coli and Clostridium bolteae DSM 29485 to different doses of the toxin sodium arsenite. The growth of E. coli and C. bolteae was sensitive to arsenite doses of 1.3 µM and 0.4 µM, respectively. Complete inhibition of growth was achieved at 38 µM arsenite for C. bolteae and 338 µM in E. coli. These results show that the Stratus performs similarly to a leading brand of microplate reader and can be reliably used in anaerobic conditions. We discuss the advantages of the small format microplate readers and our experiences with the Stratus.

IMPORTANCE: We present a workflow that facilitates the production and analysis of growth curves for anaerobic microbes using small-footprint microplate readers and an R script. This workflow is a cost and space-effective solution to most high-throughput solutions for collecting growth data from anaerobic microbes. This technology can be used for applications where high throughput would advance discovery, including microbial isolation, bioprospecting, co-culturing, host-microbe interactions, and drug/toxin-microbial interactions.

PMID:38501820 | DOI:10.1128/spectrum.03650-23

Acta Oncol. 2024 Mar 19;63:83-94. doi: 10.2340/1651-226X.2024.24970.

ABSTRACT

BACKGROUND: Surveillance of incidence and survival of central nervous system tumors is essential to monitor disease burden and epidemiological changes, and to allocate health care resources. Here, we describe glioma incidence and survival trends by histopathology group, age, and sex in the Norwegian population.

MATERIAL AND METHODS: We included patients with a histologically verified glioma reported to the Cancer Registry of Norway from 2002 to 2021 (N = 7,048). Population size and expected mortality were obtained from Statistics Norway. Cases were followed from diagnosis until death, emigration, or 31 December 2022, whichever came first. We calculated age-standardized incidence rates (ASIR) per 100,000 person-years and age-standardized relative survival (RS). Results: The ASIR for histologically verified gliomas was 7.4 (95% CI: 7.3-7.6) and was higher for males (8.8; 95% CI: 8.5-9.1) than females (6.1; 95% CI: 5.9-6.4). Overall incidence was stable over time. Glioblastoma was the most frequent tumor entity (ASIR = 4.2; 95% CI: 4.1-4.4). Overall, glioma patients had a 1-year RS of 63.6% (95% CI: 62.5-64.8%), and a 5-year RS of 32.8% (95% CI: 31.6-33.9%). Females had slightly better survival than males. For most entities, 1- and 5-year RS improved over time (5-year RS for all gliomas 29.0% (2006) and 33.1% (2021), p < 0.001). Across all tumor types, the RS declined with increasing age at diagnosis.

INTERPRETATION: The incidence of gliomas has been stable while patient survival has increased over the past 20 years in Norway. As gliomas represent a heterogeneous group of primary CNS tumors, regular reporting from cancer registries at the histopathology group level is important to monitor disease burden and allocate health care resources in a population.

PMID:38501768 | DOI:10.2340/1651-226X.2024.24970

J Clin Hypertens (Greenwich). 2024 Mar 19. doi: 10.1111/jch.14793. Online ahead of print.

ABSTRACT

Previous work comparing safety and effectiveness outcomes for new initiators of angiotensin converting-enzyme inhibitors (ACEi) and thiazides demonstrated more favorable outcomes for thiazides, although cohort definitions allowed for addition of a second antihypertensive medication after a week of monotherapy. Here, we modify the monotherapy definition, imposing exit from cohorts upon addition of another antihypertensive medication. We determine hazard ratios (HR) for 55 safety and effectiveness outcomes over six databases and compare results to earlier findings. We find, for all primary outcomes, statistically significant differences in effectiveness between ACEi and thiazides were not replicated (HRs: 1.11, 1.06, 1.12 for acute myocardial infarction, hospitalization with heart failure and stroke, respectively). While statistical significance is similarly lost for several safety outcomes, the safety profile of thiazides remains more favorable. Our results indicate a less striking difference in effectiveness of thiazides compared to ACEi and reflect some sensitivity to the monotherapy cohort definition modification.

PMID:38501749 | DOI:10.1111/jch.14793

Foot Ankle Int. 2024 Mar 19:10711007241237532. doi: 10.1177/10711007241237532. Online ahead of print.

ABSTRACT

BACKGROUND: Acquired adult flatfoot deformity (AAFD) results in a loss of the medial longitudinal arch of the foot and dysfunction of the posteromedial soft tissues. Hintermann osteotomy (H-O) is often used to treat stage II AAFD. The procedure is challenging because of variations in the subtalar facets and limited intraoperative visibility. We aimed to assess the impact of augmented reality (AR) guidance on surgical accuracy and the facet violation rate.

METHODS: Sixty AR-guided and 60 conventional osteotomies were performed on foot bone models. For AR osteotomies, the ideal osteotomy plane was uploaded to a Microsoft HoloLens 1 headset and carried out in strict accordance with the superimposed holographic plane. The conventional osteotomies were performed relying solely on the anatomy of the calcaneal lateral column. The rate and severity of facet joint violation was measured, as well as accuracy of entry and exit points. The results were compared across AR-guided and conventional osteotomies, and between experienced and inexperienced surgeons.

RESULTS: Experienced surgeons showed significantly greater accuracy for the osteotomy entry point using AR, with the mean deviation of 1.6 ± 0.9 mm (95% CI 1.26, 1.93) compared to 2.3 ± 1.3 mm (95% CI 1.87, 2.79) in the conventional method (P = .035). The inexperienced had improved accuracy, although not statistically significant (P = .064), with the mean deviation of 2.0 ± 1.5 mm (95% CI 1.47, 2.55) using AR compared with 2.7 ± 1.6 mm (95% CI 2.18, 3.32) in the conventional method. AR helped the experienced surgeons avoid full violation of the posterior facet (P = .011). Inexperienced surgeons had a higher rate of middle and posterior facet injury with both methods (P = .005 and .021).

CONCLUSION: Application of AR guidance during H-O was associated with improved accuracy for experienced surgeons, demonstrated by a better accuracy of the osteotomy entry point. More crucially, AR guidance prevented full violation of the posterior facet in the experienced group. Further research is needed to address limitations and test this technology on cadaver feet. Ultimately, the use of AR in surgery has the potential to improve patient and surgeon safety while minimizing radiation exposure.

CLINICAL RELEVANCE: Subtalar facet injury during lateral column lengthening osteotomy represents a real problem in clinical orthopaedic practice. Because of limited intraoperative visibility and variable anatomy, it is hard to resolve this issue with conventional means. This study suggests the potential of augmented reality to improve the osteotomy accuracy.

PMID:38501722 | DOI:10.1177/10711007241237532

Pediatr Pulmonol. 2024 Mar 19. doi: 10.1002/ppul.26975. Online ahead of print.

ABSTRACT

BACKGROUND: Fetal exposure to tobacco smoking throughout pregnancy is associated with wheezing in infancy. We investigated the influence of in utero smoking exposure on lung ventilation homogeneity and the relationship between lung ventilation inhomogeneity at 7 weeks of age and wheezing in the first year of life.

METHODS: Maternal smoking was defined as self-reported smoking of tobacco or validated by exhaled (e)CO > 6 ppm. Lung function data from healthy infants (age 5-9 weeks) born to asthmatic mothers and parent-reported respiratory questionnaire data aged 12 months were collected in the Breathing for Life Trial (BLT) birth cohort. Tidal breathing analysis and SF₆ -based Multiple Breath Washout testing were performed in quiet sleep. Descriptive statistics and regression analysis were used to assess associations.

RESULTS: Data were collected on 423 participants. Infants born to women who self-reported smoking during pregnancy (n = 42) had higher lung clearance index (LCI) than those born to nonsmoking mothers (7.90 vs. 7.64; p = .030). Adjusted regression analyzes revealed interactions between self-reported smoking and LCI (RR: 1.98, 95% CI: 1.07-3.63, 0.028, for each unit increase in LCI) and between eCO > 6 ppm and LCI (RR: 2.25, 95% CI: 1.13-4.50, 0.022) for the risk of wheeze in the first year of life.

CONCLUSION: In utero tobacco smoke exposure induces lung ventilation inhomogeneities. Furthermore, an interaction between smoke exposure and lung ventilation inhomogeneities increases the risk of having a wheeze in the first year of life.

PMID:38501326 | DOI:10.1002/ppul.26975

Clin Transl Sci. 2024 Mar;17(3):e13772. doi: 10.1111/cts.13772.

ABSTRACT

Genetic variants affect drug responses, making pre-emptive genotyping crucial for averting serious adverse events (SAEs) and treatment failure. However, assessing the benefits of pre-emptive genotyping based on genetic distribution, drug exposure, and demographics is challenging. This study aimed to estimate the population-level benefits of pre-emptive genotyping in the Korean population using nationwide cohort data. We reviewed actionable gene-drug combinations recommended by both the Clinical Pharmacogenomics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) as of February 2022, identifying high-risk phenotypes. We collected reported risk reduction from genotyping and standardized it into population attributable risks. Healthcare reimbursement costs for SAEs and treatment failures were obtained from the Health Insurance Review and Assessment Service Statistics in 2021. The benefits of pre-emptive genotyping for a specific group were determined by multiplying drug exposure from nationwide cohort data by individual genotyping benefits. We identified 31 gene-drug-event pairs, with CYP2D6 and CYP2C19 demonstrating the greatest benefits for both male and female patients. Individuals aged 65-70 years had the highest individual benefit from pre-emptive genotyping, with $84.40 for men and $100.90 for women. Pre-emptive genotyping, particularly for CYP2D6 and CYP2C19, can provide substantial benefits.

PMID:38501281 | DOI:10.1111/cts.13772

Hypertension. 2024 Mar 19. doi: 10.1161/HYPERTENSIONAHA.123.22142. Online ahead of print.

ABSTRACT

BACKGROUND: Coarctation of the aorta (CoA) often leads to hypertension) posttreatment. Evidence is lacking for the current >20 mm Hg peak-to-peak blood pressure (BP) gradient (BPGpp) guideline, which can cause aortic thickening, stiffening, and dysfunction. This study sought to find the BPGpp severity and duration that avoid persistent dysfunction in a preclinical model and test if predictors translate to hypertension status in patients with CoA.

METHODS: Rabbits (n=75; 5-12/group) were exposed to mild, intermediate, or severe CoA (≤12, 13-19, ≥20 mm Hg BPGpp) for ≈1, 3, or 22 weeks using dissolvable and permanent sutures with thickening, stiffening, contraction, and endothelial function evaluated via multivariate regression. Relevance to patients with CoA (n=239; age, 0.01-46 years; median 3.7 months) was tested by retrospective review of predictors (preoperative BPGpp, surgical age, etc.) versus follow-up hypertension status.

RESULTS: CoA duration and severity were predictive of aortic remodeling and active dysfunction in rabbits, and hypertension in patients with CoA. Interaction between patient age and BPGpp at surgery contributed significantly to hypertension, similar to rabbits, suggesting preclinical findings translate to patients. Machine learning decision tree analysis uncovered that preoperative BPGpp and surgical age predict risk of hypertension along with residual postoperative BPGpp.

CONCLUSIONS: These findings suggest the current BPGpp threshold determined decades ago is likely too high to prevent adverse coarctation-induced aortic remodeling. The results and decision tree analysis provide a foundation for revising CoA treatment guidelines considering the interaction between CoA severity and duration to limit the risk of hypertension.

PMID:38501250 | DOI:10.1161/HYPERTENSIONAHA.123.22142