Category: Statistics

Blood. 2023 Dec 14:blood.2023021231. doi: 10.1182/blood.2023021231. Online ahead of print.

ABSTRACT

Extreme disease phenotypes can provide key insights into the pathophysiology of common conditions, but studying these patients is challenging due to their rarity and the limited statistical power of existing methods. Herein, we used a novel approach to pathway-based mutational burden testing, the rare variant trend test (RVTT), to investigate genetic risk factors for an extreme form of sepsis-induced coagulopathy, infectious purpura fulminans (PF). In addition to prospective patient sample collection, we electronically screened over 10.4 million medical records from four large hospital systems and identified historical cases of PF for which archived specimens were available to perform germline whole exome sequencing. We found a significantly increased burden of rare, putatively function-altering variants in the complement system in patients with PF compared to unselected patients with sepsis (p=0.01). A multivariable logistic regression analysis found that the number of complement system variants per patient was independently associated with PF after controlling for age, sex, and disease acuity (p=0.01). Functional characterization of PF-associated variants in the immunomodulatory complement receptors CR3 and CR4 revealed that they result in partial or complete loss of anti-inflammatory CR3 function and/or gain of pro-inflammatory CR4 function. Taken together, these findings suggest that inherited defects in CR3 and CR4 predispose to the maladaptive hyperinflammation that characterizes severe sepsis with coagulopathy.

PMID:38096369 | DOI:10.1182/blood.2023021231

PLoS One. 2023 Dec 14;18(12):e0296017. doi: 10.1371/journal.pone.0296017. eCollection 2023.

ABSTRACT

BACKGROUND: Circulating cytokines have been associated with colorectal cancer (CRC). However, their causal correlation remains undetermined. This investigation uses genetic data to evaluate the mechanism that links circulating cytokines and CRC via Mendelian Randomization (MR).

METHODS: A two-sample MR evaluation was carried out to investigate the mechanism associating circulating cytokines and CRC in individuals of European ancestry. The Genome-wide association studies statistics, which are publically accessible, were used. Eligible instrumental SNPs that were significantly related to the circulating cytokines were selected. Multiple MR analysis approaches were carried out, including Simple Mode, inverse variance weighted (IVW), MR-Egger, Weighted Mode, Weighted Median, and MR pleiotropy residual sum and outlier (MR-PRESSO) methods.

RESULTS: The evidence supporting the association of genetically predicted circulating levels with the increased risk of CRC was revealed; these included vascular endothelial growth factor (OR = 1.352, 95% CI: 1.019-1.315, P = 0.024), interleukin-12p70 (OR = 1.273, 95% CI: 1.133-1.430, P = 4.68×10-5), interleukin-13 (OR = 1.149, 95% CI: 1.012-1.299, P = 0.028), interleukin-10 (OR = 1.230, 95% CI: 1.013-1.493, P = 0.037), and interleukin-7 (OR = 1.191, 95% CI: 1.023-1.386 P = 0.024). Additionally, MR analysis negative causal association between macrophage colony stimulating factor and CRC (OR = 0.854, 95% CI: 0.764-0.955, P = 0.005). The data from Simple Mode, Weighted Median, MR-Egger, and Weighted Mode analyses were consistent with the IVW estimates. Furthermore, the sensitivity analysis indicated that the presence of no horizontal pleiotropy to bias the causal estimates.

CONCLUSION: This investigation identified a causal association between circulating cytokines levels risk of CRC and may provide a deeper understanding of the pathogenesis of CRC, as well as offer promising leads for the development of novel therapeutic targets for CRC.

PMID:38096329 | DOI:10.1371/journal.pone.0296017

PLoS One. 2023 Dec 14;18(12):e0289880. doi: 10.1371/journal.pone.0289880. eCollection 2023.

ABSTRACT

This study aims to investigate socioeconomic disparities in nutritional status among ever-married women in Bangladesh and to break down urban-rural differences in the underlying causes of undernutrition. We utilized data from the Bangladesh Demographic and Health Survey 2017-18, a sample size of 18328 ever-married women, including 5170 from urban residences, and 13159 from rural residences. To explore socioeconomic inequality, we employed a concentration indexing measure, while a multiple binary logistic regression model was carried out to identify the determinants associated with the outcome variable. A Blinder-Oaxaca decomposition analysis was performed to decompose the urban-rural gap in women’s nutritional status using associated factors. The prevalence of undernutrition among ever-married women in Bangladesh was 12 percent. Notably, this percentage varied by region, with urban residents accounting for 8.6% and rural residents accounting for 13.3%. Our findings confirmed that undernutrition was more prevalent among women with lower wealth indexes in Bangladesh, as indicated by the concentration index (CIX = -0.26). The multivariable analysis investigating the determinants of undernutrition status among ever-married women, with a focus on residence revealed significant associations with respondent age, education, marital status, mass media access, wealth status, and division. According to the Blinder-Oaxaca decomposition and its extension, the prevalence was significantly higher in rural residences of Bangladesh than in urban residences, and the endowment effect explained 86 percent of the total urban-rural difference in undernutrition prevalence. The results of this study indicate that the factors that influence women’s nutritional status in rural areas play a significant role in the gap, and the majority of the gap is caused by education and economic position. In order to effectively promote maternal health policies in Bangladesh, intervention techniques should be created that are aimed at the population, that is, the poorest and least educated.

PMID:38096318 | DOI:10.1371/journal.pone.0289880

PLoS One. 2023 Dec 14;18(12):e0296039. doi: 10.1371/journal.pone.0296039. eCollection 2023.

ABSTRACT

BACKGROUND: The global population of individuals with gender diversity or LGBTQ+ people is on the rise. However, negative attitudes towards LGBTQ+ individuals persist, even among healthcare professionals, creating barriers to healthcare access. These attitudes are influenced by cultural variations worldwide and necessitate investigation across diverse cultures and settings.

OBJECTIVES: This study aimed to evaluate the attitudes towards LGBTQ+ people and describe associated factors with being LGBTQ+ among Thai medical students.

METHODS: During the 2021 academic year, a survey was conducted at a medical school in Bangkok, Thailand, collecting demographic data and attitudes measured by a standardised Thai questionnaire. Descriptive statistics as well as bivariate and multivariable logistic regression analyses were used to describe characteristics and association.

RESULTS: A total of 806 medical students participated, with a neutral attitude being the most prevalent (72.2%), followed by a positive attitude (27.2%), and a minority reporting a negative attitude (0.6%). Bivariate and multivariable logistic regression analyses revealed significant associations between positive attitudes and female sexual identity (aOR 2.02, 95%CI 1.45-2.81, p-value < 0.001), having LGBTQ+ family members (aOR 3.57, 95%CI 1.23-10.34, p-value = 0.019), having LGBTQ+ friend (aOR 1.46, 95%CI 1.02-2.11, p-value = 0.040), and coming from areas outside of Bangkok (aOR 1.41, 95%CI 1.01-1.97, p-value = 0.043).

CONCLUSION: Positive attitude towards the LGBTQ+ community are essential for physicians, emphasising the need to study factors that contribute to positive attitudes in order to foster an LGBTQ+-friendly environment for both patients and medical students.

PMID:38096311 | DOI:10.1371/journal.pone.0296039

PLoS One. 2023 Dec 14;18(12):e0290498. doi: 10.1371/journal.pone.0290498. eCollection 2023.

ABSTRACT

In epidemiologic studies, association estimates of an exposure with disease outcomes are often biased when the uncertainties of exposure are ignored. Consequently, corresponding confidence intervals (CIs) will not have correct coverage. This issue is particularly problematic when exposures must be reconstructed from physical measurements, for example, for environmental or occupational radiation doses that were received by a study population for which radiation doses cannot be measured directly. To incorporate complex uncertainties in reconstructed exposures, the two-dimensional Monte Carlo (2DMC) dose estimation method has been proposed and used in various dose reconstruction efforts. The 2DMC method generates multiple exposure realizations from dosimetry models that incorporate various sources of errors to reflect the uncertainty of the dose distribution as well as the uncertainties in individual doses in the exposed population. Traditional measurement-error model approaches, typically based on using mean doses in the dose-exposure analysis, do not fully account exposure uncertainties. A recently developed statistical approach that overcomes many of these limitations by analyzing multiple exposure realizations in relation to disease risk is Bayesian model averaging (BMA). The analytic advantage of the BMA is its ability to better accommodate complex exposure uncertainty in the risk estimation, but a practical. Drawback is its significant computational complexity. In this present paper, we propose a novel frequentist model averaging (FMA) approach which has all the analytical advantages of the BMA method but is much simpler to implement and computationally faster. We show in simulations that, like BMA, FMA yields 95% confidence intervals for association parameters that close to 95% coverage rate. In simulations, the FMA has shorter length of CIs than those of another frequentist approach, the corrected information matrix (CIM) method. We illustrate the similarities in performance of BMA and FMA from a study of exposures from radioactive fallout in Kazakhstan.

PMID:38096309 | DOI:10.1371/journal.pone.0290498

JMIR Form Res. 2023 Dec 14;7:e45979. doi: 10.2196/45979.

ABSTRACT

BACKGROUND: Acute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.

OBJECTIVE: The primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.

METHODS: We developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.

RESULTS: The developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm’s predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.

CONCLUSIONS: NAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.

PMID:38096015 | DOI:10.2196/45979

J Med Internet Res. 2023 Dec 14;25:e49771. doi: 10.2196/49771.

ABSTRACT

BACKGROUND: The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has necessitated reliable and authoritative information for public guidance. The World Health Organization (WHO) has been a primary source of such information, disseminating it through a question and answer format on its official website. Concurrently, ChatGPT 3.5 and 4.0, a deep learning-based natural language generation system, has shown potential in generating diverse text types based on user input.

OBJECTIVE: This study evaluates the accuracy of COVID-19 information generated by ChatGPT 3.5 and 4.0, assessing its potential as a supplementary public information source during the pandemic.

METHODS: We extracted 487 COVID-19-related questions from the WHO’s official website and used ChatGPT 3.5 and 4.0 to generate corresponding answers. These generated answers were then compared against the official WHO responses for evaluation. Two clinical experts scored the generated answers on a scale of 0-5 across 4 dimensions-accuracy, comprehensiveness, relevance, and clarity-with higher scores indicating better performance in each dimension. The WHO responses served as the reference for this assessment. Additionally, we used the BERT (Bidirectional Encoder Representations from Transformers) model to generate similarity scores (0-1) between the generated and official answers, providing a dual validation mechanism.

RESULTS: The mean (SD) scores for ChatGPT 3.5-generated answers were 3.47 (0.725) for accuracy, 3.89 (0.719) for comprehensiveness, 4.09 (0.787) for relevance, and 3.49 (0.809) for clarity. For ChatGPT 4.0, the mean (SD) scores were 4.15 (0.780), 4.47 (0.641), 4.56 (0.600), and 4.09 (0.698), respectively. All differences were statistically significant (P<.001), with ChatGPT 4.0 outperforming ChatGPT 3.5. The BERT model verification showed mean (SD) similarity scores of 0.83 (0.07) for ChatGPT 3.5 and 0.85 (0.07) for ChatGPT 4.0 compared with the official WHO answers.

CONCLUSIONS: ChatGPT 3.5 and 4.0 can generate accurate and relevant COVID-19 information to a certain extent. However, compared with official WHO responses, gaps and deficiencies exist. Thus, users of ChatGPT 3.5 and 4.0 should also reference other reliable information sources to mitigate potential misinformation risks. Notably, ChatGPT 4.0 outperformed ChatGPT 3.5 across all evaluated dimensions, a finding corroborated by BERT model validation.

PMID:38096014 | DOI:10.2196/49771

JMIR Form Res. 2023 Dec 14;7:e48351. doi: 10.2196/48351.

ABSTRACT

BACKGROUND: Severe periodontitis affects 26% of Thai adults and 11.2% of adults globally and is characterized by the loss of alveolar bone height. Full-mouth examination by periodontal probing is the gold standard for diagnosis but is time- and resource-intensive. A screening model to identify those at high risk of severe periodontitis would offer a targeted approach and aid in reducing the workload for dentists. While statistical modelling by a logistic regression is commonly applied, optimal performance depends on feature selections and engineering. Machine learning has been recently gaining favor given its potential discriminatory power and ability to deal with multiway interactions without the requirements of linear assumptions.

OBJECTIVE: We aim to compare the performance of screening models developed using statistical and machine learning approaches for the risk prediction of severe periodontitis.

METHODS: This study used data from the prospective Electricity Generating Authority of Thailand cohort. Dental examinations were performed for the 2008 and 2013 surveys. Oral examinations (ie, number of teeth and oral hygiene index and plaque scores), periodontal pocket depth, and gingival recession were performed by dentists. The outcome of interest was severe periodontitis diagnosed by the Centre for Disease Control-American Academy of Periodontology, defined as 2 or more interproximal sites with a clinical attachment level ≥6 mm (on different teeth) and 1 or more interproximal sites with a periodontal pocket depth ≥5 mm. Risk prediction models were developed using mixed-effects logistic regression (MELR), recurrent neural network, mixed-effects support vector machine, and mixed-effects decision tree models. A total of 21 features were considered as predictive features, including 4 demographic characteristics, 2 physical examinations, 4 underlying diseases, 1 medication, 2 risk behaviors, 2 oral features, and 6 laboratory features.

RESULTS: A total of 3883 observations from 2086 participants were split into development (n=3112, 80.1%) and validation (n=771, 19.9%) sets with prevalences of periodontitis of 34.4% (n=1070) and 34.1% (n=263), respectively. The final MELR model contained 6 features (gender, education, smoking, diabetes mellitus, number of teeth, and plaque score) with an area under the curve (AUC) of 0.983 (95% CI 0.977-0.989) and positive likelihood ratio (LR+) of 11.9 (95% CI 8.8-16.3). Machine learning yielded lower performance than the MELR model, with AUC (95% CI) and LR+ (95% CI) values of 0.712 (0.669-0.754) and 2.1 (1.8-2.6), respectively, for the recurrent neural network model; 0.698 (0.681-0.734) and 2.1 (1.7-2.6), respectively, for the mixed-effects support vector machine model; and 0.662 (0.621-0.702) and 2.4 (1.9-3.0), respectively, for the mixed-effects decision tree model.

CONCLUSIONS: The MELR model might be more useful than machine learning for large-scale screening to identify those at high risk of severe periodontitis for periodontal evaluation. External validation using data from other centers is required to evaluate the generalizability of the model.

PMID:38096008 | DOI:10.2196/48351

Personal Disord. 2023 Dec 14. doi: 10.1037/per0000648. Online ahead of print.

ABSTRACT

Personality impairment is a core feature of personality disorders in both current (i.e., Diagnostic and Statistical Manual of Mental Disorders, fifth edition [DSM-5] personality disorders, International Classification of Diseases,11th revision personality disorders) and emerging (i.e., DSM-5′s alternative model of personality disorders) models of psychopathology. Yet, despite its importance within clinical nosology, attempts to identify its optimal lower-order structure have yielded inconsistent findings. Given its presence in diagnostic models, it is important to better understand its empirical structure across a variety of instantiations. To the degree that impairment is multifaceted, various factors may have different nomological networks and varied implications for assessment, diagnosis, and treatment. Therefore, participants were recruited from two large public universities in the present preregistered study (N = 574) to explore the construct’s structure with exploratory “bass-ackward” factor analyses at the item level. Participants completed over 250 items from six commonly used measures of personality dysfunction. Criterion variables in its nomological network were also collected (e.g., general and pathological personality traits, internalizing/externalizing behavior, and personality disorders) using both self- and informant-reports. These factor analyses identified four lower-order facets of impairment (i.e., negative self-regard, disagreeableness, intimacy problems, and lack of direction), all of which showed moderate to strong overlap with traits from both general and pathological models of personality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

PMID:38095995 | DOI:10.1037/per0000648

Psychol Methods. 2023 Dec 14. doi: 10.1037/met0000611. Online ahead of print.

ABSTRACT

The planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand simultaneous tuning of multiple design parameter dimensions and can only be addressed using Monte Carlo simulation if no analytical approach is available. In addition, cost considerations, for example, in terms of monetary costs, are a relevant target for optimization. In this context, optimal design parameters can imply a desired level of power at minimum cost or maximum power at a cost threshold. We introduce a surrogate modeling framework based on machine learning predictions to solve these optimization tasks. In a simulation study, we demonstrate the efficiency for a wide range of hypothesis testing scenarios with single- and multidimensional design parameters, including t tests, analysis of variance, item response theory models, multilevel models, and multiple imputations. Our framework provides an algorithmic solution for optimizing study designs when no analytic power analysis is available, handling multiple design dimensions and cost considerations. Our implementation is publicly available in the R package mlpwr. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

PMID:38095992 | DOI:10.1037/met0000611