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The role of the complement system in schizophrenia (Sz) is inconclusive due to heterogeneity of the disease and study designs. Here, we assessed the levels of complement activation products and functionality of the classical pathway in acutely ill unmedicated Sz patients at baseline and after 6 weeks of treatment versus matched controls. The study included analyses of the terminal complement complex (sTCC) and C5a in plasma from 96 patients and 96 controls by enzyme-linked immunosorbent assay. Sub-group analysis of serum was conducted for measurement of C4 component and activity of the classical pathway (28 and 24 cases per cohort, respectively). We found no differences in levels of C5a, C4 and classical pathway function in patients versus controls. Plasma sTCC was significantly higher in patients [486 (392–659) ng/mL, n = 96] compared to controls [389 (304–612) ng/mL, n = 96] (p = 0.027, δ = 0.185), but not associated with clinical symptom ratings or treatment. The differences in sTCC between Sz and controls were confirmed using an Aligned Rank Transformation model considering the covariates age and sex (p = 0.040). Additional analysis showed that sTCC was significantly associated with C-reactive protein (CRP; p = 0.006). These findings suggest that sTCC plays a role in Sz as a trait marker of non-specific chronic immune activation, as previously described for CRP. Future longitudinal analyses with more sampling time points from early recognition centres for psychoses may be helpful to better understand the temporal dynamics of innate immune system changes during psychosis development. [ABSTRACT FROM AUTHOR]

Aim: It is essential for health researchers to have a systematic understanding of third‐party variables that influence both the exposure and outcome under investigation, as shown by a directed acyclic graph (DAG). The traditional construction of DAGs through literature review and expert knowledge often needs to be more systematic and consistent, leading to potential biases. We try to introduce an automatic approach to building network linking variables of interest. Methods: Large‐scale text mining from medical literature was utilized to construct a conceptual network based on the Semantic MEDLINE Database (SemMedDB). SemMedDB is a PubMed‐scale repository of the "concept‐relation‐concept" triple format. Relations between concepts are categorized as Excitatory, Inhibitory, or General. Results: To facilitate the use of large‐scale triple sets in SemMedDB, we have developed a computable biomedical knowledge (CBK) system (https://cbk.bjmu.edu.cn/), a website that enables direct retrieval of related publications and their corresponding triples without the necessity of writing SQL statements. Three case studies were elaborated to demonstrate the applications of the CBK system. Conclusions: The CBK system is openly available and user‐friendly for rapidly capturing a set of influencing factors for a phenotype and building candidate DAGs between exposure‐outcome variables. It could be a valuable tool to reduce the exploration time in considering relationships between variables, and constructing a DAG. A reliable and standardized DAG could significantly improve the design and interpretation of observational health research. [ABSTRACT FROM AUTHOR]

The diagnostic approach to hypopituitarism involves many disciplines. Clinical symptoms rarely are specific. Imaging techniques are helpful but cannot prove the specific functional defects. Therefore, the definitive diagnosis of pituitary insufficiency is largely based on laboratory tests. However, also laboratory methods come with inherent limitations, and it is essential for the clinician to know and recognize typical pitfalls. Most factors potentially impairing the quality of hormone measurements are introduced in the preanalytical phase, i.e. before the hormones are measured by the laboratory. For example, the timing of blood drawing with respect to circadian rhythm, stress, and medication can have an influence on hormone concentrations. During the actual analysis of the hormones, cross-reactions with molecules present in the sample presenting the same or similar epitopes than the intended analyte may affect immunoassays. Interference can also come from heterophilic or human anti-animal antibodies. Unexpected problems can also be due to popular nutritional supplements which interfere with the measurement procedures. An important example in this respect is the interference from biotin. It became only clinically visible when the use of this vitamin became popular among patients. The extreme serum concentrations reached when patients take it as a supplement can lead to incorrect measurements in immunoassays employing the biotin-streptavidin system. To some extent, hormone analyses using liquid chromatography mass spectrometry (LCMS) can overcome problems, although availability and cost-effectiveness of this method still imposes restrictions. In the post-analytical phase, appropriateness of reference intervals and cut-offs with respect to the specific analytical method used is of outmost importance. Furthermore, for interpretation, additional biological and pharmacological factors like BMI, age and concomitant diseases must be considered to avoid misinterpretation of the measured concentrations. It is important for the clinician and the laboratory to recognize when one or more laboratory values do not match the clinical picture. In an interdisciplinary approach, the search for the underlying cause should be initiated. [ABSTRACT FROM AUTHOR]

Summary: Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visualizing such confounding factors. We propose DiffChest, a self-conditioned diffusion model trained on 515,704 chest radiographs from 194,956 patients across the US and Europe. DiffChest provides patient-specific explanations and visualizes confounding factors that might mislead the model. The high inter-reader agreement, with Fleiss’ kappa values of 0.8 or higher, validates its capability to identify treatment-related confounders. Confounders are accurately detected with 10%–100% prevalence rates. The pretraining process optimizes the model for relevant imaging information, resulting in excellent diagnostic accuracy for 11 chest conditions, including pleural effusion and heart insufficiency. Our findings highlight the potential of diffusion models in medical image classification, providing insights into confounding factors and enhancing model robustness and reliability.

Investigation of health inequities tend to be examined, in human neurosciences, as biological factors at the level of the individual. In actuality, health inequities arise, due largely in part, to deep-seated structural factors. Structural inequality refers to the systemic disadvantage of one social group compared to others with whom they coexist. The term encompasses policy, law, governance, and culture and relates to race, ethnicity, gender or gender identity, class, sexual orientation, and other domains. These structural inequalities include but are not limited to social segregation, the intergenerational effects of colonialism and the consequent distribution of power and privilege. Principles to address inequities influenced by structural factors are increasingly prevalent in a subfield of the neurosciences, i.e., cultural neurosciences. Cultural neuroscience articulates the bidirectional relationship between biology and environmental contextual factors surrounding research participants. However, the operationalization of these principles may not have the intended spillover effect on the majority of human neurosciences: this limitation is the overarching focus of the present piece. Here, we provide our perspective that these principles are missing and very much needed in all human neuroscience subdisciplines to accelerate our understanding of the human brain. Furthermore, we provide an outline of two key tenets of a health equity lens necessary for achieving research equity in human neurosciences: the social determinants of health (SDoH) framework and how to deal with confounders using counterfactual thinking. We argue that these tenets should be prioritized across future human neuroscience research more generally, and doing so is a pathway to further gain an understanding of contextual background intertwined with the human brain, thus improving the rigor and inclusivity of human neuroscience research.

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Programs to protect biodiversity on private land are increasingly being used worldwide. To understand the efficacy of such programs, it is important to determine their impact: the difference between the program's outcome and what would have happened without the program. Typically, these programs are evaluated by estimating the average program‐level impact, which readily allows comparisons between programs or regions, but masks important heterogeneity in impact across the individual conservation interventions. We used synthetic control design, statistical matching, and time‐series data to estimate the impact of individual protected areas over time and combined individual‐level impacts to estimate program‐level impact with a meta‐analytic approach. We applied the method to private protected areas governed by conservation covenants (legally binding on‐title agreements to protect biodiversity) in the Goldfields region of Victoria, Australia using woody vegetation cover as our outcome variable. We compared our results with traditional approaches to estimating program‐level impact based on a subset of covenants that were the same age. Our results showed an overall program‐level impact of a 0.3–0.8% increase in woody vegetation cover per year. However, there was significant heterogeneity in the temporal pattern of impact for individual covenants, ranging from −4 to +7% change in woody vegetation cover per year. Results of our approach were consistent with results based on traditional approaches to estimating program‐level impact. Our study provides a transparent and robust workflow to estimate individual and program‐level impacts of private protected areas. [ABSTRACT FROM AUTHOR]

Non-destructive assessments are required for the quality control of tissue-engineered constructs and the optimization of the tissue culture process. Near-infrared (NIR) spectroscopy coupled with machine learning (ML) provides a promising approach for such assessment. However, due to its nonspecific nature, each spectrum incorporates information on both neotissue and non-neotissue constituents of the construct; the effect of these constituents on the NIR-based assessments of tissue-engineered constructs has been overlooked in previous studies. This study investigates the effect of scaffolds, growth factors, and buffers on NIR-based assessments of tissue-engineered constructs. To determine if these non-neotissue constituents have a measurable effect on the NIR spectra of the constructs that can introduce bias in their assessment, nine ML algorithms were evaluated in classifying the NIR spectra of engineered cartilage according to the scaffold used to prepare the constructs, the growth factors added to the culture media, and the buffers used for storing the constructs. The effect of controlling for these constituents was also evaluated using controlled and uncontrolled NIR-based ML models for predicting tissue maturity as an example of neotissue-related properties of interest. Samples used in this study were prepared using norbornene-modified hyaluronic acid scaffolds with or without the conjugation of an N-cadherin mimetic peptide. Selected samples were supplemented with transforming growth factor-beta1 or bone morphogenetic protein-9 growth factor. Some samples were frozen in cell lysis buffer, while the remaining samples were frozen in PBS until required for NIR analysis. The ML models for classifying the spectra of the constructs according to the four constituents exhibited high to fair performances, with F1 scores ranging from 0.9 to 0.52. Moreover, controlling for the four constituents significantly improved the performance of the models for predicting tissue maturity, with improvement in F1 scores ranging from 0.09 to 0.77. In conclusion, non-neotissue constituents have measurable effects on the NIR spectra of tissue-engineered constructs that can be detected by ML algorithms and introduce bias in the assessment of the constructs by NIR spectroscopy. Therefore, controlling for these constituents is necessary for reliable NIR-based assessments of tissue-engineered constructs.

Although frozen section pathology (FSP) is commonly performed during surgery for glioma-suspicious lesions, confounders of accuracy are largely unknown. FSP and final diagnosis were compared in 398 surgeries for glioma-suspicious lesions. Diagnostic accuracy, risk factors for diagnostic shift from neoplastic to non-neoplastic tissue and vice versa according to the final diagnosis, and the impact on intraoperative and postoperative decision-making were analyzed. Diagnostic shift occurred in 70 cases (18%), and sensitivity, specificity, and the positive (PPV) and negative (NPV) predictive value of FSP were 82.5%, 77.8%, 99.4%, and 9.3%, respectively. No correlations between shift and patients' age and sex, sample fluorescence or volume, tumor location, correct information on the pathology form, final high- or low-grade histology, or molecular alterations were found (p >.05, each). Shift was more common after irradiation (25% vs 15%; p =.025) or chemotherapy (26% vs 15%; p =.022) than in treatment naïve cases and correlated with the type of surgery (p =.002). FSP altered intraoperative decision-making in 25 cases (6%). Postoperative shift led to repeated surgery in 12 patients (3%). In 45 cases, in which FSP and final diagnosis based on the same tissue, shift occurred in only 5 patients (11%), and sensitivity, specificity, PPV, and NPV for FSP were 77.4%, 78.6%, 88.9%, and 61.1%, respectively. No correlations between diagnostic shift and any of the analyzed variables were found (p >.05, each). Although accuracy of FSP during glioma surgery is sufficient, moderate NPV should be considered during intraoperative decision-making. While confounders are sparse, accuracy might be increased by repeated sampling. Diagnostic shift rarely alters postoperative treatment strategy. [ABSTRACT FROM AUTHOR]

Instrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effect of a certain exposure in the presence of unmeasured confounders. A valid instrumental variable is a variable that is associated with the exposure, affects the outcome only through the exposure (exclusion), and is not confounded with the outcome (exogeneity). Unlike the first assumption, the other two are generally untestable and rely on subject‐matter knowledge. Therefore, a sensitivity analysis is desirable to assess the impact of assumptions' violation on the estimated parameters. In this paper, we propose and demonstrate a new method of sensitivity analysis for G‐estimators in causal linear and non‐linear models. We introduce two novel aspects of sensitivity analysis in instrumental variables studies. The first is a single sensitivity parameter that captures violations of exclusion and exogeneity assumptions. The second is an application of the method to non‐linear models. The introduced framework is theoretically justified and is illustrated via a simulation study. Finally, we illustrate the method by application to real‐world data and provide guidelines on conducting sensitivity analysis. [ABSTRACT FROM AUTHOR]

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women's Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings. [ABSTRACT FROM AUTHOR]

We review recent literature that proposes to adapt ideas from classical model based optimal design of experiments to problems of data selection of large datasets. Special attention is given to bias reduction and to protection against confounders. Some new results are presented. Theoretical and computational comparisons are made. [ABSTRACT FROM AUTHOR]

A parallel process growth model is often limited to the developments of two main trends due to model complexity. Two major parallel processes are only modeled to examine how the changes in two longitudinal processes might be related without considering other growth models possibly related to the two main processes. However, the exclusion of a third LGM risks introducing bias into key associations while capturing the relations between only two growth processes, since they are not controlled. This study presents a method to address the effect of spurious relationship while minimizing convergence issues when other processes are not considered. The suggested method utilizes the factor score estimates for other relevant growth models to control the spurious effect. Results revealed the superiority of the parallel process model using the factor scores over the typical parallel process model and illustrated the importance of considering growth models relevant to the parallel process model. [ABSTRACT FROM AUTHOR]

To date, many studies in dermatology exploring the gut-skin axis including ours are small, observational studies that aim to associate any perturbation in the gut microbiome with a specific skin disease. Keywords: acne; confounders; experiment; gut microbiome; Prevotella copri; race EN acne confounders experiment gut microbiome Prevotella copri race 1864 1866 3 10/13/23 20231001 NES 231001 There is increasing evidence that the gut microbiome may play an important role in the pathogenesis of inflammatory skin diseases including acne, rosacea and hidradenitis suppurativa.[1] However, studies to date profiling the gut microbiome in patients with dermatologic conditions have been challenging with multiple limitations including small sample sizes, different demographic and lifestyle factors, and various stool sampling, DNA extraction, and sequencing techniques. [Extracted from the article]

Investigation of health inequities tend to be examined, in human neurosciences, as biological factors at the level of the individual. In actuality, health inequities arise, due largely in part, to deep-seated structural factors. Structural inequality refers to the systemic disadvantage of one social group compared to others with whom they coexist. The term encompasses policy, law, governance, and culture and relates to race, ethnicity, gender or gender identity, class, sexual orientation, and other domains. These structural inequalities include but are not limited to social segregation, the intergenerational effects of colonialism and the consequent distribution of power and privilege. Principles to address inequities influenced by structural factors are increasingly prevalent in a subfield of the neurosciences, i.e., cultural neurosciences. Cultural neuroscience articulates the bidirectional relationship between biology and environmental contextual factors surrounding research participants. However, the operationalization of these principles may not have the intended spillover effect on the majority of human neurosciences: this limitation is the overarching focus of the present piece. Here, we provide our perspective that these principles are missing and very much needed in all human neuroscience subdisciplines to accelerate our understanding of the human brain. Furthermore, we provide an outline of two key tenets of a health equity lens necessary for achieving research equity in human neurosciences: the social determinants of health (SDoH) framework and how to deal with confounders using counterfactual thinking. We argue that these tenets should be prioritized across future human neuroscience research more generally, and doing so is a pathway to further gain an understanding of contextual background intertwined with the human brain, thus improving the rigor and inclusivity of human neuroscience research.

Background: Establishing a large study network to conduct influenza vaccine effectiveness (IVE) studies while collecting appropriate variables to account for potential bias is important; the most relevant variables should be prioritized. We explored the impact of potential confounders on IVE in the DRIVE multi‐country network of sites conducting test‐negative design (TND) studies. Methods: We constructed a directed acyclic graph (DAG) to map the relationship between influenza vaccination, medically attended influenza infection, confounders, and other variables. Additionally, we used the Development of Robust and Innovative Vaccines Effectiveness (DRIVE) data from the 2018/2019 and 2019/2020 seasons to explore the effect of covariate adjustment on IVE estimates. The reference model was adjusted for age, sex, calendar time, and season. The covariates studied were presence of at least one, two, or three chronic diseases; presence of six specific chronic diseases; and prior healthcare use. Analyses were conducted by site and subsequently pooled. Results: The following variables were included in the DAG: age, sex, time within influenza season and year, health status and comorbidities, study site, health‐care‐seeking behavior, contact patterns and social precautionary behavior, socioeconomic status, and pre‐existing immunity. Across all age groups and settings, only adjustment for lung disease in older adults in the primary care setting resulted in a relative change of the IVE point estimate >10%. Conclusion: Our study supports a parsimonious approach to confounder adjustment in TND studies, limited to adjusting for age, sex, and calendar time. Practical implications are that necessitating fewer variables lowers the threshold for enrollment of sites in IVE studies and simplifies the pooling of data from different IVE studies or study networks. [ABSTRACT FROM AUTHOR]

Fetal alcohol spectrum disorder (FASD) is a major problem worldwide and dysmorphic facial features may be a prenatal biomarker for FASD. Deviations from normal facial development cannot be explored before establishing the normal variation in a specific population, since ethnic differences may exist. Objectives: Main objective: to establish reference standards for 23 facial measurements on 3D ultrasound volumes obtained between days 196 and 224 of gestation in healthy unexposed South African fetuses from an area with historically high alcohol consumption prevalence and a population group with no existing normative values. Secondary objective: to assess the confounding effect of maternal and fetal characteristics. Design: This study involves 97 women (including 43 smokers) who had been enrolled in the Safe Passage Study (SPS), a large prospective multinational cohort study assessing the effects of prenatal alcohol exposure. They had adequate 3 D ultrasound volumes of the fetal face acquired at 28+0–31+6 weeks in singleton pregnancies without comorbidities, congenital abnormalities or exposure to alcohol, marijuana, or methamphetamines from 4 weeks before conception. Participants, materials, setting, methods: The participants were recruited from two residential areas of low socioeconomic status in Cape Town. Meticulous information was collected on maternal and pregnancy characteristics, including alcohol use at different time points. Gestational age (GA) was based on ultrasound biometry before 24 weeks, and 3D ultrasound volumes were acquired trans-abdominally from a sagittal and axial plane of the fetal face. Volumes were independently assessed offline by two observers and the image with the best landmark definition was used for 23 facial measurements, representing features previously described in children with FASD. The relation to the exact GA was assessed by regression analysis, the expected mean value and standard error of the estimate (SEE) was determined to transform all raw measurements into z-scores, and the effect of possible confounders on zscores was assessed by ANOVA. Results: Ten variables changed significantly with advancing GA (extraocular diameter, anteroposterior, medio-lateral and supero-inferior ocular diameter, ocular volume, interlens distance, prenasal thickness, nasal bone length, nose length and nose protrusion) and thirteen did not (interocular distance; interocular: extraocular diameter ratio, prenasal thickness: nasal bone length ratio, pronasal-subnasal distance, subnasal-mouth distance, philtrum length, upper vermillion thickness, nose-philtrum angle, maxillary angle, facial height, facial protrusion, frontomaxillary facial angle and maxilla-nasion-mandible angle). Reference values (expected mean and SEE) for the 23 measurements were established for each day. The z-scores of all facial measurements were not independently affected by maternal age, parity, gravidity, smoking or body mass index, but infant sex and birthweight z-score significantly influenced several z-scores (infant sex for extraocular, medio-lateral, and supero-inferior ocular diameter, ocular volume, prenasal thickness and nose protrusion; birthweight z-score for extraocular diameter, interocular and interlens distance, nose protrusion and maxillary angle). Limitations: GA was not always confirmed by first trimester ultrasound and some measurements could not be obtained in all cases due to suboptimal image quality. The cohort included few heavy smokers so an effect of heavy or continued smoking cannot be ruled out, and the effect of ethnicity was not assessed. Conclusions: These are the first local reference standards for fetal facial measurements and, to our knowledge, the first reference standards for the supero-inferior ocular diameter, face protrusion, upper vermillion thickness, maxillary angle, and nose-philtrum angle. They were broadly in keeping with published references, with small discrepancies explained by minor differences in technique. Even in this narrow GA window, the distribution of many variables changed over time and normal variation was significantly influenced by fetal sex and birthweight z-score. The possible confounding effect of these factors needs to be considered when assessing the impact of harmful exposures like alcohol on facial development. [ABSTRACT FROM AUTHOR]

Causal inference is a process of uncovering causal relationship between effect variable and disease outcome in epidemiologic research. When estimating causal effect in observational studies, confounders that influence both the effect variable and the outcome need to be adjusted for in the estimation process. In addition, missing data often arise in data collection procedure; working with complete cases often results in biased parameter estimates. We consider the causal effect estimation in the presence of missingness in the confounders under the missing at random assumption. We investigate how the double robust estimators perform when applying complete-case analysis or multiple imputations. Given the uncertainty of appropriate imputation model and computational challenge for many imputations, we propose an expectation-maximization (EM) algorithm to estimate the expected values of the missing confounder and utilize a weighting approach in the estimation of the average treatment effect. Simulation studies are conducted to see whether there is any gain in estimation efficiency using the proposed method, instead of the complete case analysis and multiple imputations. The results identified EM as the most efficient and accurate method for dealing with missingness in confounder. Our study result is applied in a B-aware trial, which is a multi-centre clinical trial, to estimate the effect of total intravenous anaesthetic on post-operative anxiety. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimer's disease (AD). The aim of this article is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimer's Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Agrarian land is being converted into a non-agricultural sector, which is a matter of significant concern. This study aims to determine the factors responsible for the agrarian land conversion to the non-agricultural sector in Pakistan. Both qualitative and quantitative data were collected for analysis. Quantitative data were collected from 608 farmers through a questionnaire survey, while qualitative data by in-depth interviews and focus group discussions. The study uses a logistic regression model and thematic analysis to determine the factors responsible for land conversion. Results show that farmers' education, total landholding, urbanization, and the provision of government facilities positively influence land conversion. The ratio of irrigated land and production per acre were negatively associated with such land conversion. The qualitative data identified the push factors such as the social network of farmers, institutional and economic, while the pull factors included commercial opportunities, industrialization, and future expectations. Farmers should be assisted in diversifying their farm operations to generate multiple income streams, reducing the financial pressure that might lead to their selling land. Strict policies are also necessary to control the conversion of agrarian land into use for non-agricultural activities. • The farmers' characteristics, land, geographical, and institutional factors played a role in agrarian land conversion. • Farmers' education, the price of land, urbanization, and government facilities positively influenced land conversion. • The lack of check & balance in government institutions triggered the problem. • The mixed study has provided an in-depth analysis of the factors influencing land conversion. • The regression model is checked for overfitting of the model, multicollinearity, and confounders. [ABSTRACT FROM AUTHOR]

Background and Aims: Surgical Pleth Index (SPI) provides an objective assessment of nociception - anti-nociception balance but is influenced by multiple confounders. The effect of change of position on SPI, has not been studied extensively. The aim of the study was to observe the effect of prone positioning on SPI and its correlation with hemodynamic variables, in patients undergoing lumbar and thoracic spine surgery. Material and Methods: This prospective observational pilot study included 14 patients. In addition to hemodynamic monitoring, SPI, entropy and pulse pressure variability (PPV) were monitored. Propofol and Fentanyl infusions were used for maintenance of anesthesia. The patients were made prone on bolsters and all the variables were recorded every 5 minutes in supine position and after making prone for 20 minutes, before and after incision, muscle splitting and laminectomy. Results: Comparing the last value of the variables in the supine position with those immediately after making prone, SPI increased by 16.36 units (P = 0.003), followed by gradual reduction over the next 20 minutes. Mean arterial pressure and heart rate increased transiently (Pvalue = 0.028 and 0.025, respectively) without any significant change in PPV. Surgical incision also led to a significant increase in SPI. Conclusion: Prone positioning leads to significant increase in SPI, probably due to increased sympathetic tone.

Abstract We would like to thank authors Reece and Hulse (2022) for their three-part article titled “Geotemporospatial and causal inferential epidemiological overview and survey of USA cannabis, cannabidiol and cannabinoid genotoxicity expressed in cancer incidence 2003-2017”, in which the authors infer that cannabis use has a causal role in the development of various cancer types. While the authors use reputable datasets and a well-established epidemiological methodology, the authors’ conclusion of a causal association is limited due to biases inherent in ecological epidemiological studies. Though the researchers attempt to overcome these biases through validation and statistical manipulations, their approaches are insufficient to create conditions suitable for causal inferencing upon examination. There are also concerns in the practical and conceptual application of the studies’ dataset that further question the validity of the authors’ inferences. Further research exploring the potential benefits and harm of cannabinoids in the context of cancer must be performed before a distinct relationship can be defined.

High-dimensional mediation analysis has been developed to study whether epigenetic phenotype in a high-dimensional data form would mediate the causal pathway of exposure to disease. However, most existing models are designed based on the assumption that there are no confounders between the exposure, the mediators, and the outcome. In practice, this assumption may not be feasible since high-dimensional mediation analysis (HIMA) tends to be observational where a randomized controlled trial (RCT) cannot be conducted for some economic or ethical reasons. Thus, to deal with the confounders in HIMA cases, we proposed three propensity score-related approaches named PSR (propensity score regression), PSW (propensity score weighting), and PSU (propensity score union) to adjust for the confounder bias in HIMA, and compared them with the traditional covariate regression method. The procedures mainly include four parts: calculating the propensity score, sure independence screening, MCP (minimax concave penalty) variable selection, and joint-significance testing. Simulation results show that the PSU model is the most recommended. Applying our models to the TCGA lung cancer dataset, we find that smoking may lead to lung disease through the mediation effect of some specific DNA-methylation sites, including site Cg24480765 in gene RP11-347H15.2 and site Cg22051776 in gene KLF3. [ABSTRACT FROM AUTHOR]

Background and Aims: Surgical Pleth Index (SPI) provides an objective assessment of nociception - anti-nociception balance but is influenced by multiple confounders. The effect of change of position on SPI, has not been studied extensively. The aim of the study was to observe the effect of prone positioning on SPI and its correlation with hemodynamic variables, in patients undergoing lumbar and thoracic spine surgery. Material and Methods: This prospective observational pilot study included 14 patients. In addition to hemodynamic monitoring, SPI, entropy and pulse pressure variability (PPV) were monitored. Propofol and Fentanyl infusions were used for maintenance of anesthesia. The patients were made prone on bolsters and all the variables were recorded every 5 minutes in supine position and after making prone for 20 minutes, before and after incision, muscle splitting and laminectomy. Results: Comparing the last value of the variables in the supine position with those immediately after making prone, SPI increased by 16.36 units (P = 0.003), followed by gradual reduction over the next 20 minutes. Mean arterial pressure and heart rate increased transiently (Pvalue = 0.028 and 0.025, respectively) without any significant change in PPV. Surgical incision also led to a significant increase in SPI. Conclusion: Prone positioning leads to significant increase in SPI, probably due to increased sympathetic tone. [ABSTRACT FROM AUTHOR]

Competing Interests: Conflict of interest: none declared.

Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders-external factors unrelated to the condition, e.g. batch effect or age-on clustering is often overlooked, introducing bias and spurious biological conclusions. In this work, we introduce four novel VAE-based deconfounding frameworks tailored for clustering multi-omics data. These frameworks effectively mitigate confounding effects while preserving genuine biological patterns. The deconfounding strategies employed include (i) removal of latent features correlated with confounders, (ii) a conditional VAE, (iii) adversarial training, and (iv) adding a regularization term to the loss function. Using real-life multi-omics data from The Cancer Genome Atlas, we simulated various confounding effects (linear, nonlinear, categorical, mixed) and assessed model performance across 50 repetitions based on reconstruction error, clustering stability, and deconfounding efficacy. Our results demonstrate that our novel models, particularly the conditional multi-omics VAE (cXVAE), successfully handle simulated confounding effects and recover biologically driven clustering structures. cXVAE accurately identifies patient labels and unveils meaningful pathological associations among cancer types, validating deconfounded representations. Furthermore, our study suggests that some of the proposed strategies, such as adversarial training, prove insufficient in confounder removal. In summary, our study contributes by proposing innovative frameworks for simultaneous multi-omics data integration, dimensionality reduction, and deconfounding in clustering. Benchmarking on open-access data offers guidance to end-users, facilitating meaningful patient stratification for optimized precision medicine., (© The Author(s) 2024. Published by Oxford University Press.)

Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visualizing such confounding factors. We propose DiffChest, a self-conditioned diffusion model trained on 515,704 chest radiographs from 194,956 patients across the US and Europe. DiffChest provides patient-specific explanations and visualizes confounding factors that might mislead the model. The high inter-reader agreement, with Fleiss' kappa values of 0.8 or higher, validates its capability to identify treatment-related confounders. Confounders are accurately detected with 10%-100% prevalence rates. The pretraining process optimizes the model for relevant imaging information, resulting in excellent diagnostic accuracy for 11 chest conditions, including pleural effusion and heart insufficiency. Our findings highlight the potential of diffusion models in medical image classification, providing insights into confounding factors and enhancing model robustness and reliability., Competing Interests: Declaration of interests J.N.K. declares consulting services for Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; and Scailyte, Basel, Switzerland. Furthermore, J.N.K. holds shares in Kather Consulting, Dresden, Germany and StratifAI GmbH, Dresden, Germany and has received honoraria for lectures and advisory board participation by AstraZeneca, Bayer, Eisai, MSD, BMS, Roche, Pfizer, and Fresenius. D.T. received honoraria for lectures by Bayer and holds shares in StratifAI GmbH, Germany., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Abstract Background To evaluate and determine the protective role of statins in COVID-19 patients. Methods This is a retrospective cohort study conducted across five hospitals in India. Patients diagnosed with COVID-19 and hospitalized with existing and valid medical documentation were included. Results This study comprised 3252 COVID-19 patients, of whom 1048 (32.2%) were on statins, with 52.4% being males. The comorbidity prevalence of hypertension was 75%, followed by diabetes 62.51% and coronary artery disease being 47.5%. At the time of hospitalization, statin users had a higher incidence of dyspnea, cough, and fatigue (95.8, 93.3, and 92.7%). The laboratory results revealed a lower mean of WBC count (7.8 × 103/μL), D-dimer (2.4 μg/mL), and C-reactive protein (103 mg/L) among statin users. They also had lower mortality rates (17.1%), a lesser requirement for mechanical ventilation (20%), and hemodialysis (5.4%). Conclusion This observation study elaborates on the beneficial effects of statins in COVID-19 patients. However, the inferences from this study should be viewed with caution due to the impending effect of confounding factors on its statistical results.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies. [ABSTRACT FROM AUTHOR]

Summary: To compare hospitals' hip fracture patient mortality in a quality of care registry, correction for patient characteristics is needed. This study evaluates in 39,374 patients which characteristics are associated with 30 and 90-day mortality, and showed how using these characteristics in a case mix-model changes hospital comparisons within the Netherlands. Purpose: Mortality rates after hip fracture surgery are considerable and may be influenced by patient characteristics. This study aims to evaluate hospital variation regarding patient demographics and disease burden, to develop a case-mix adjustment model to analyse differences in hip fracture patients' mortality to calculate case-mix adjusted hospital-specific mortality rates. Methods: Data were derived from 64 hospitals participating in the Dutch Hip Fracture Audit (DHFA). Adult hip fracture patients registered in 2017–2019 were included. Variation of case-mix factors between hospitals was analysed, and the association between case-mix factors and mortality at 30 and 90 days was determined through regression models. Results: There were 39,374 patients included. Significant variation in case-mix factors amongst hospitals was found for age ≥ 80 (range 25.8–72.1% p < 0.001), male gender (12.0–52.9% p < 0.001), nursing home residents (42.0–57.9% p < 0.001), pre-fracture mobility aid use (9.9–86.7% p < 0,001), daily living dependency (27.5–96.5% p < 0,001), ASA-class ≥ 3 (25.8–83.3% p < 0.001), dementia (3.6–28.6% p < 0.001), osteoporosis (0.0–57.1% p < 0.001), risk of malnutrition (0.0–29.2% p < 0.001) and fracture types (all p < 0.001). All factors were associated with 30- and 90-day mortality. Eight hospitals showed higher and six showed lower 30-day mortality than expected based on their case-mix. Six hospitals showed higher and seven lower 90-day mortality than expected. The specific outlier hospitals changed when correcting for case-mix factors. Conclusions: Dutch hospitals show significant case-mix variation regarding hip fracture patients. Case-mix adjustment is a prerequisite when comparing hospitals' 30-day and 90-day hip fracture patients' mortality. Adjusted mortality may serve as a starting point for improving hip fracture care. [ABSTRACT FROM AUTHOR]

Purpose: Pelvic trauma has increased risk of mortality in the elderly. Our study aimed to analyze the impact of the additional burden of pelvic fractures in severely injured elderly. Methods: This is a retrospective analysis of a prospectively maintained trauma registry from 2012 to 2018 at an American College of Surgeons (ACS) verified Level I Trauma Center. Trauma patients aged ≥ 65 years with ISS ≥ 16 and AIS severity score ≥ 3 in at least two body regions were divided in two groups: group I, consisted of elderly polytrauma patients without pelvic fractures, and group II elderly who had concomitant pelvic fractures. We used a double-adjustment method using propensity score matching (PSM) with subsequent covariate adjustment to minimize the effect of confounding factors, and give unbiased estimation of the impact of pelvic fractures. Balance assessment was conducted by computing absolute standardized mean differences (ASMDs) and ASMD < 0.10 reflects good balance between groups. Results: Of 12,774 patients admitted during this time, 411 (3.2%) elderly with a mean age of 77.75 ± 8.32 years met the inclusion criteria. Of this cohort, only 92 patients (22.4%) had pelvic fractures. Females outnumbered males (55 vs. 45%). Comparing characteristics of group I and group II using ASMDs, pelvic trauma patients were more likely to have higher systolic blood pressure (SBP), head injuries, lower extremity injuries, anticoagulant therapy, and cirrhosis. Fewer variables differed significantly after matching. We observed few instances of worse outcomes associated with pelvic trauma using PSM with and without covariate adjustment. Crude PSM without covariate adjustment, showed a significantly higher rate of deep vein thrombosis (DVT) for pelvic trauma (p < 0.001). Crude PSM also showed a significantly higher rate of ventilator-associated pneumonia (VAP) in group II (p = 0.006). PSM with covariate adjustment did not confirm differences on these outcomes. PSM both without and with covariate adjustment found lower ventilator days and ICU length of stay among patients with pelvic trauma. No significant differences were seen on 12 outcomes: death, acute kidney injury (AKI), acute respiratory distress syndrome (ARDS), cardiac arrest with cardiopulmonary resuscitation (CPR), myocardial infarction (MI), pulmonary embolism (PE), unplanned intubation, unplanned admission to intensive care unit (ICU), catheter-associated urinary tract infection (CAUTI), and hospital length of stay. Conclusions: At a Level I Trauma Center the additional burden of pelvic fractures in seriously injured elderly did not translate into higher mortality. PSM without covariate adjustment suggests worse rates among pelvic trauma patients for DVT and VAP but covariate adjustment removed statistical significance for both outcomes. Pelvic trauma patients had shorter time on ventilator and in the ICU. Whether similar analytic methods applied to patients from larger data sources would produce similar findings remains to be seen. [ABSTRACT FROM AUTHOR]

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

Atopic dermatitis (AD) is a chronic inflammatory skin disease characterized by pruritus, skin pain, and sleep disturbances. Currently, dupilumab is the only systemic therapy and biologic medication approved by the United States Food and Drug Administration for moderate-to-severe AD in adults and children. There is a sparsity of literature available on determining treatment failure with dupilumab and the next steps health care providers can take to treat AD. Individual goals and quality of life and not just body surface area should be considered when defining treatment failure. Possible confounding dermatoses also should be ruled out. Early identification of dupilumab-induced adverse events is important. For most patients, dupilumab can be continued while treatment for the adverse event is initiated. Adjusting the frequency of dupilumab dosing also may be considered in some circumstances. Adjuvant therapies, such as methotrexate, azathioprine, mycophenolate mofetil, cyclosporine, or phototherapy can be added but the safety and efficacy of these combination treatments are not known at this time. [ABSTRACT FROM AUTHOR]