Category: Nevin Manimala

Clin Endosc. 2023 Apr 17. doi: 10.5946/ce.2022.166. Online ahead of print.

ABSTRACT

BACKGROUND/AIMS: Colonic volvulus (CV), a common cause of bowel obstruction, often requires intervention. We aimed to identify hospitalization trends and CV outcomes in the United States.

METHODS: We used the National Inpatient Sample to identify all adult CV hospitalizations in the United States from 2007 to 2017. Patient demographics, comorbidities, and inpatient outcomes were highlighted. Outcomes of endoscopic and surgical management were compared.

RESULTS: Between 2007 and 2017, there were 220,666 CV hospitalizations. CV-related hospitalizations increased from 17,888 in 2007 to 21,715 in 2017 (p=0.001). However, inpatient mortality decreased from 7.6% in 2007 to 6.2% in 2017 (p<0.001). Of all CV-related hospitalizations, 13,745 underwent endoscopic intervention, and 77,157 underwent surgery. Although the endoscopic cohort had patients with a higher Charlson comorbidity index, we noted lower inpatient mortality (6.1% vs. 7.0%, p<0.001), mean length of stay (8.3 vs. 11.8 days, p<0.001), and mean total healthcare charge ($68,126 vs. $106,703, p<0.001) compared to the surgical cohort. Male sex, increased Charlson comorbidity index scores, acute kidney injury, and malnutrition were associated with higher odds of inpatient mortality in patients with CV who underwent endoscopic management.

CONCLUSIONS: Endoscopic intervention has lower inpatient mortality and is an excellent alternative to surgery for appropriately selected CV hospitalizations.

PMID:37070205 | DOI:10.5946/ce.2022.166

Nucleic Acids Res. 2023 Apr 18:gkad284. doi: 10.1093/nar/gkad284. Online ahead of print.

ABSTRACT

Over the last decade, Haplogrep has become a standard tool for haplogroup classification in the field of human mitochondrial DNA and is widely used by medical, forensic, and evolutionary researchers. Haplogrep scales well for thousands of samples, supports many file formats and provides an intuitive graphical web interface. Nevertheless, the currently available version has limitations when applying it to large biobank-scale data. In this paper, we present a major upgrade to the software by adding (a) haplogroup summary statistics and variant annotations from various publicly available genome databases, (b) an interface to connect new phylogenetic trees, (c) a new state-of-the-art web framework managing large scale data, (d) algorithmic adaptions to improve FASTA classification using BWA-specific alignment rules and (e) a pre-classification quality control step for VCF samples. These improvements will give researchers the opportunity to classify thousands of samples as usual but providing additional ways to investigate the dataset directly in the browser. The web service and its documentation can be accessed freely without any registration at https://haplogrep.i-med.ac.at.

PMID:37070190 | DOI:10.1093/nar/gkad284

Ann Cardiol Angeiol (Paris). 2023 Apr 15;72(3):101598. doi: 10.1016/j.ancard.2023.101598. Online ahead of print.

ABSTRACT

BACKGROUND: French health authorities recommend implementing a strong coordination between general practitioners and office-based cardiologists for the care and management of patients with chronic heart failure. The aim of this study was to describe the characteristics of patients with chronic heart failure who were infrequently referred to an office-based cardiologist (either first time referral or last visit more than 12 months before study inclusion) by a general practitioner or other healthcare professional versus those who were regularly followed by a general practitioner and an office-based cardiologist (at least one visit to an office-based cardiologist in the last 12 months).

METHODS: This was a non-interventional, cross-sectional study, conducted among office-based cardiologists in France during a single study visit. Descriptive statistics were performed.

RESULTS: 1460 patients were included in the study with 37.1% in the group infrequently referred to an office-based cardiologist and 62.9% in the regularly followed group. The patients who were infrequently referred to an office-based cardiologist had relatively less heart failure with reduced ejection fraction (29.2% versus 36.6%), less prior chronic heart failure hospitalization (15.9% versus 31.4%), and less atrial fibrillation and ischemic heart failure as comorbidities (40.2% versus 50.5% and 39.3% versus 50.1%, respectively) than patients who were regularly followed by an office-based cardiologist and a general practitioner. They also received less clinical exams (25.5% versus 97.4%) and pharmacological (89.3% versus 98.4%) and non-pharmacological (17.3% versus 27.1%) heart failure treatments before the study visit.

CONCLUSIONS: This study suggested that patients regularly followed by a general practitioner and an office-based cardiologist had globally a more severe chronic heart failure and a better medical monitoring and follow-up than other patients.

PMID:37068350 | DOI:10.1016/j.ancard.2023.101598

Ann Intern Med. 2023 Apr 18. doi: 10.7326/M22-1628. Online ahead of print.

ABSTRACT

BACKGROUND: More evidence is needed to substantiate current recommendations about removing ovaries during hysterectomy for benign conditions.

OBJECTIVE: To compare long-term outcomes in women with and without bilateral salpingo-oophorectomy (BSO) during hysterectomy for benign conditions.

DESIGN: Emulated target trial using data from a population-based cohort.

SETTING: Women in Denmark aged 20 years or older during 1977 to 2017.

PARTICIPANTS: 142 985 women with hysterectomy for a benign condition, 22 974 with BSO and 120 011 without.

INTERVENTION: Benign hysterectomy with or without BSO.

MEASUREMENTS: The primary outcomes were overall hospitalization for cardiovascular disease (CVD), overall cancer incidence, and all-cause mortality through December 2018.

RESULTS: Compared with women without BSO, women with BSO who were younger than 45 years at surgery had a higher 10-year cumulative risk for hospitalization for CVD (risk difference [RD], 1.19 percentage points [95% CI, 0.09 to 2.43 percentage points]). Women with BSO had a higher 10-year cumulative risk for cancer for ages 45 to 54 years (RD, 0.73 percentage point [CI, 0.05 to 1.38 percentage points]), 55 to 64 years (RD, 1.92 percentage points [CI, 0.69 to 3.25 percentage points]), and 65 years or older (RD, 2.54 percentage points [CI, 0.91 to 4.25 percentage points]). Women with BSO had higher 10-year mortality in all age groups, although the differences were statistically significant only for ages 45 to 54 years (RD, 0.79 percentage point [CI, 0.27 to 1.30 percentage points]). The mortality at 20 years was inconsistent with that at 10 years in women aged 65 years or older.

LIMITATION: Age was a proxy for menopausal status.

CONCLUSION: The authors find that these results support current recommendations for conserving ovaries in premenopausal women without a high risk for ovarian cancer and suggest a cautious approach in postmenopausal women.

PRIMARY FUNDING SOURCE: The Danish Cancer Society’s Scientific Committee and the Mermaid Project.

PMID:37068275 | DOI:10.7326/M22-1628

Biom J. 2023 Apr 17:e2100368. doi: 10.1002/bimj.202100368. Online ahead of print.

ABSTRACT

We propose a semiparametric mean residual life mixture cure model for right-censored survival data with a cured fraction. The model employs the proportional mean residual life model to describe the effects of covariates on the mean residual time of uncured subjects and the logistic regression model to describe the effects of covariates on the cure rate. We develop estimating equations to estimate the proposed cure model for the right-censored data with and without length-biased sampling, the latter is often found in prevalent cohort studies. In particular, we propose two estimating equations to estimate the effects of covariates in the cure rate and a method to combine them to improve the estimation efficiency. The consistency and asymptotic normality of the proposed estimates are established. The finite sample performance of the estimates is confirmed with simulations. The proposed estimation methods are applied to a clinical trial study on melanoma and a prevalent cohort study on early-onset type 2 diabetes mellitus.

PMID:37068192 | DOI:10.1002/bimj.202100368

Biom J. 2023 Apr 17:e2200153. doi: 10.1002/bimj.202200153. Online ahead of print.

ABSTRACT

Buckley-James (BJ) model is a typical semiparametric accelerated failure time model, which is closely related to the ordinary least squares method and easy to be constructed. However, traditional BJ model built on linearity assumption only captures simple linear relationships, while it has difficulty in processing nonlinear problems. To overcome this difficulty, in this paper, we develop a novel regression model for right-censored survival data within the learning framework of BJ model, basing on random survival forests (RSF), extreme learning machine (ELM), and L₂ boosting algorithm. The proposed method, referred to as ELM-based BJ boosting model, employs RSF for covariates imputation first, then develops a new ensemble of ELMs-ELM-based boosting algorithm for regression by ensemble scheme of L₂ boosting, and finally, uses the output function of the proposed ELM-based boosting model to replace the linear combination of covariates in BJ model. Due to fitting the logarithm of survival time with covariates by the nonparametric ELM-based boosting method instead of the least square method, the ELM-based BJ boosting model can capture both linear covariate effects and nonlinear covariate effects. In both simulation studies and real data applications, in terms of concordance index and integrated Brier sore, the proposed ELM-based BJ boosting model can outperform traditional BJ model, two kinds of BJ boosting models proposed by Wang et al., RSF, and Cox proportional hazards model.

PMID:37068191 | DOI:10.1002/bimj.202200153

Biom J. 2023 Apr 17:e2200092. doi: 10.1002/bimj.202200092. Online ahead of print.

ABSTRACT

Quantifying drug potency, which requires an accurate estimation of dose-response relationship, is essential for drug development in biomedical research and life sciences. However, the standard estimation procedure of the median-effect equation to describe the dose-response curve is vulnerable to extreme observations in common experimental data. To facilitate appropriate statistical inference, many powerful estimation tools have been developed in R, including various dose-response packages based on the nonlinear least squares method with different optimization strategies. Recently, beta regression-based methods have also been introduced in estimation of the median-effect equation. In theory, they can overcome nonnormality, heteroscedasticity, and asymmetry and accommodate flexible robust frameworks and coefficients penalization. To identify a reliable estimation method(s) to estimate dose-response curves even with extreme observations, we conducted a comparative study to review 14 different tools in R and examine their robustness and efficiency via Monte Carlo simulation under a list of comprehensive scenarios. The simulation results demonstrate that penalized beta regression using the mgcv package outperforms other methods in terms of stable, accurate estimation, and reliable uncertainty quantification.

PMID:37068189 | DOI:10.1002/bimj.202200092

Biom J. 2023 Apr 17:e2200112. doi: 10.1002/bimj.202200112. Online ahead of print.

ABSTRACT

When observing data on a patient-reported outcome measure in, for example, clinical trials, the variables observed are often correlated and intended to measure a latent variable. In addition, such data are also often characterized by a hierarchical structure, meaning that the outcome is repeatedly measured within patients. To analyze such data, it is important to use an appropriate statistical model, such as structural equation modeling (SEM). However, researchers may rely on simpler statistical models that are applied to an aggregated data structure. For example, correlated variables are combined into one sum score that approximates a latent variable. This may have implications when, for example, the sum score consists of indicators that relate differently to the latent variable being measured. This study compares three models that can be applied to analyze such data: the multilevel multiple indicators multiple causes (ML-MIMIC) model, a univariate multilevel model, and a mixed analysis of variance (ANOVA) model. The focus is on the estimation of a cross-level interaction effect that presents the difference over time on the patient-reported outcome between two treatment groups. The ML-MIMIC model is an SEM-type model that considers the relationship between the indicators and the latent variable in a multilevel setting, whereas the univariate multilevel and mixed ANOVA model rely on sum scores to approximate the latent variable. In addition, the mixed ANOVA model uses aggregated second-level means as outcome. This study showed that the ML-MIMIC model produced unbiased cross-level interaction effect estimates when the relationships between the indicators and the latent variable being measured varied across indicators. In contrast, under similar conditions, the univariate multilevel and mixed ANOVA model underestimated the cross-level interaction effect.

PMID:37068180 | DOI:10.1002/bimj.202200112

Kidney360. 2023 Apr 17. doi: 10.34067/KID.0000000000000129. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic kidney disease of a nontraditional etiology (CKDnt) is responsible for high mortality in Central America, although its causes remain unclear. Evidence of kidney dysfunction has been observed among youth, suggesting that early kidney damage contributing to CKDnt may initiate in childhood.

METHODS: Urine specimens of Nicaraguan adolescents 12-23 years without CKDnt (n=136) were analyzed by proton nuclear magnetic resonance (1H-NMR) spectroscopy for 50 metabolites associated with kidney dysfunction. Urinary metabolite levels were compared by CKiD U25 estimated glomerular filtration rate (eGFR), regional CKDnt prevalence, sex, age, and family history of CKDnt using supervised statistical methods and pathway analysis in MetaboAnalyst. Magnitude of associations and changes over time were assessed through multivariable linear regression.

RESULTS: In adjusted analyses, glycine concentrations were higher among youth from high-risk regions (β=0.82, (95% CI: 0.16, 1.85); p=0.01). Pyruvate concentrations were lower among youth with low eGFR (β= -0.36; (95% CI:-0.57, -0.04); p=0.03) and concentrations of other citric acid (TCA) cycle metabolites differed by key risk factors. Over four years, participants with low eGFR experienced greater declines in 1-methylnicotinamide and 2-oxoglutarate and greater increases in citrate and guanidinoacetate concentrations.

CONCLUSION: Urinary concentration of glycine, a molecule associated with thermoregulation and kidney function preservation, was higher among youth in high-risk CKDnt regions, suggestive of greater heat exposure or renal stress. Lower pyruvate concentrations were associated with low eGFR, and citric acid cycle metabolites like pyruvate likely relate to mitochondrial respiration rates in the kidneys. Participants with low eGFR experienced longitudinal declines in concentrations of 1-methylnicotinamide, an anti-inflammatory metabolite associated with anti-fibrosis in tubule cells. These findings merit further consideration in research on the origins of CKDnt.

PMID:37068179 | DOI:10.34067/KID.0000000000000129

Curr Protoc. 2023 Apr;3(4):e734. doi: 10.1002/cpz1.734.

ABSTRACT

Prior to the development of genome-wide arrays and whole genome sequencing technologies, heritability estimation mainly relied on the study of related individuals. Over the past decade, various approaches have been developed to estimate SNP-based narrow-sense heritability ( $h_{\mathrm{SNP}}^2$ ) in unrelated individuals. These latter approaches use either individual-level genetic variations or summary results from genome-wide association studies (GWAS). Recently, several studies compared these approaches using extensive simulations and empirical datasets. However, sparse information on hands-on training necessitates revisiting these approaches from the perspective of a stepwise guide for practical applications. Here, we provide an overview of the commonly used SNP-heritability estimation approaches utilizing genome-wide array, imputed or whole genome data from unrelated individuals, or summary results. We not only discuss these approaches based on their statistical concepts, utility, advantages, and limitations, but also provide step-by-step protocols to apply these approaches. For illustration purposes, we estimate $h_{\mathrm{SNP}}^2$ of height and BMI utilizing individual-level data from The Northern Finland Birth Cohort (NFBC) and summary results from the Genetic Investigation of ANthropometric Traits (GIANT;) consortium. We present this review as a template for the researchers who estimate and use heritability in their studies and as a reference for geneticists who develop or extend heritability estimation approaches. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: GREML (GCTA) Alternate Protocol 1: Stratified GREML Basic Protocol 2: LDAK Alternate Protocol 2: Stratified LDAK Basic Protocol 3: Threshold GREML Basic Protocol 4: LD score (LDSC) regression Basic Protocol 5: SumHer.

PMID:37068172 | DOI:10.1002/cpz1.734