DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders.

Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimating and rigorously inferring mediation effects is challenging, primarily due to the intertwined nature of the mediator selection issue. Despite recent developments, the existing methods are inadequate for addressing the complex relationships introduced by confounders. To tackle these challenges, we propose a novel approach called DP2LM (Deep neural network-based Penalized Partially Linear Mediation). This approach incorporates deep neural network techniques to account for nonlinear effects in confounders and utilizes the penalized partially linear model to accommodate high dimensionality. Unlike most existing works that concentrate on mediator selection, our method prioritizes estimation and inference on mediation effects. Specifically, we develop test procedures for testing the direct and indirect mediation effects. Theoretical analysis shows that the tests maintain the Type-I error rate. In simulation studies, DP2LM demonstrates its superior performance as a modeling tool for complex data, outperforming existing approaches in a wide range of settings and providing reliable estimation and inference in scenarios involving a considerable number of mediators. Further, we apply DP2LM to investigate the mediation effect of DNA methylation on cortisol stress reactivity in individuals who experienced childhood trauma, uncovering new insights through a comprehensive analysis.

Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer.

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

Plasma Instead of Serum Avoids Critical Confounding of Clinical Metabolomics Studies by Platelets.

Metabolomics is an emerging and powerful bioanalytical method supporting clinical investigations. Serum and plasma are commonly used without rational prioritization. Serum is collected after blood coagulation, a complex biochemical process involving active platelet metabolism. This may affect the metabolome and increase the variance, as platelet counts and function may vary substantially in individuals. A multiomics approach systematically investigating the suitability of serum and plasma for clinical studies demonstrated that metabolites correlated well (n = 461, R2 = 0.991), whereas lipid mediators (n = 83, R2 = 0.906) and proteins (n = 322, R2 = 0.860) differed substantially between specimen. Independently, analysis of platelet releasates identified most biomolecules significantly enriched in serum compared to plasma. A prospective, randomized, controlled parallel group metabolomics trial with acetylsalicylic acid administered for 7 days demonstrated that the apparent drug effects significantly differ depending on the analyzed specimen. Only serum analyses of healthy individuals suggested a significant downregulation of TXB2 and 12-HETE, which were specifically formed during coagulation in vitro. Plasma analyses reliably identified acetylsalicylic acid effects on metabolites and lipids occurring in vivo such as an increase in serotonin, 15-deoxy-PGJ2 and sphingosine-1-phosphate and a decrease in polyunsaturated fatty acids. The present data suggest that plasma should be preferred above serum for clinical metabolomics studies as the serum metabolome may be substantially confounded by platelets.

Pitfalls in the lab assessment of hypopituitarism.

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.

Soluble terminal complement complex blood levels are elevated in schizophrenia.

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.

Supplementary value and diagnostic performance of computed tomography scout view in the detection of thoracolumbar spine injuries.

PURPOSE: Assessing the diagnostic performance and supplementary value of whole-body computed tomography scout view (SV) images in the detection of thoracolumbar spine injuries in early resuscitation phase and identifying frequent image quality confounders. METHODS: In this retrospective database analysis at a tertiary emergency center, three blinded senior experts independently assessed SV to detect thoracolumbar spine injuries. The findings were categorized according to the AO Spine classification system. Confounders impacting SV image quality were identified. The suspected injury level and severity, along with the confidence level, were indicated. Diagnostic performance was estimated using the caret package in R programming language. RESULTS: We assessed images of 199 patients, encompassing 1592 vertebrae (T10-L5), and identified 56 spinal injuries (3.5%). Among the 199 cases, 39 (19.6%) exhibited at least one injury in the thoracolumbar spine, with 12 (6.0%) of them displaying multiple spinal injuries. The pooled sensitivity, specificity, and accuracy were 47%, 99%, and 97%, respectively. All experts correctly identified the most severe injury of AO type C. The most common image confounders were medical equipment (44.6%), hand position (37.6%), and bowel gas (37.5%). CONCLUSION: SV examination holds potential as a valuable supplementary tool for thoracolumbar spinal injury detection when CT reconstructions are not yet available. Our data show high specificity and accuracy but moderate sensitivity. While not sufficient for standalone screening, reviewing SV images expedites spinal screening in mass casualty incidents. Addressing modifiable factors like medical equipment or hand positioning can enhance SV image quality and assessment.

Discussion on "Instrumental variable estimation of the causal hazard ratio," by Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, and Stijn Vansteelandt.

We propose and study an augmented variant of the estimator proposed by Wang, Tchetgen Tchetgen, Martinussen, and Vansteelandt.

Sensitivity analysis of G-estimators to invalid instrumental variables.

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.

Evaluating the impact of private land conservation with synthetic control design.

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.

Evaluación del impacto del suelo privado de conservación con diseño de control sintético Resumen Los programas de protección de la biodiversidad en suelo privado se utilizan cada vez más en todo el mundo. Para entender la eficacia de estos programas, es importante determinar la diferencia entre el resultado del programa y lo que habría ocurrido sin él. Normalmente, estos programas se evalúan estimando el impacto medio a nivel de programa, lo que permite fácilmente las comparaciones entre programas o regiones, pero oculta una importante heterogeneidad en el impacto entre las intervenciones individuales de conservación. Utilizamos un diseño de control sintético, un emparejamiento estadístico y datos de series temporales para estimar el impacto de las áreas protegidas individuales a lo largo del tiempo y combinamos los impactos a nivel individual para estimar el impacto a nivel de programa con un enfoque meta-analítico. Aplicamos el método a áreas protegidas privadas regidas por acuerdos de conservación (acuerdos con vínculos jurídicos sobre la titularidad para proteger la biodiversidad) destinados a mejorar la cubierta vegetal leñosa en la región de Goldfields de Victoria, Australia. Comparamos nuestros resultados con los métodos tradicionales de estimación del impacto a nivel de programa basados en un subconjunto de pactos de la misma antigüedad. Nuestros resultados mostraron un impacto global a nivel de programa de un aumento del 0.3-0.8% de la cubierta vegetal leñosa al año. Sin embargo, hubo una heterogeneidad significativa en el patrón temporal del impacto para los pactos individuales, que osciló entre −4 y +7% de cambio en la cubierta vegetal leñosa por año. Los resultados de nuestra estrategia fueron consecuentes con los resultados basados en las estrategias tradicionales usadas para estimar el impacto a nivel de programa. Nuestro estudio proporciona un flujo de trabajo transparente y sólido para estimar el impacto individual a nivel de programa de las áreas protegidas privadas.

Potential pharmacological confounders in the setting of death determined by neurologic criteria: a narrative review.

Guidelines for the determination of death by neurologic criteria (DNC) require an absence of confounding factors if clinical examination alone is to be used. Drugs that depress the central nervous system suppress neurologic responses and spontaneous breathing and must be excluded or reversed prior to proceeding. If these confounding factors cannot be eliminated, ancillary testing is required. These drugs may be present after being administered as part of the treatment of critically ill patients. While measurement of serum drug concentrations can help guide the timing of assessments for DNC, they are not always available or feasible. In this article, we review sedative and opioid drugs that may confound DNC, along with pharmacokinetic factors that govern the duration of drug action. Pharmacokinetic parameters including a context-sensitive half-life of sedatives and opioids are highly variable in critically ill patients because of the multitude of clinical variables and conditions that can affect drug distribution and clearance. Patient-, disease-, and treatment-related factors that influence the distribution and clearance of these drugs are discussed including end organ function, age, obesity, hyperdynamic states, augmented renal clearance, fluid balance, hypothermia, and the role of prolonged drug infusions in critically ill patients. In these contexts, it is often difficult to predict how long after drug discontinuation the confounding effects will take to dissipate. We propose a conservative framework for evaluating when or if DNC can be determined by clinical criteria alone. When pharmacologic confounders cannot be reversed, or doing so is not feasible, ancillary testing to confirm the absence of brain blood flow should be obtained.

RéSUMé: Les lignes directrices pour la détermination du décès selon des critères neurologiques (DCN) exigent une absence de facteurs confondants si l'examen clinique seul doit être utilisé. Les médicaments qui dépriment le système nerveux central suppriment les réponses neurologiques et la respiration spontanée et doivent être exclus ou neutralisés avant de procéder. Si ces facteurs confondants ne peuvent être éliminés, un examen auxiliaire est nécessaire. Ces médicaments peuvent être présents après avoir été administrés dans le cadre du traitement de patients en état critique. Bien que la mesure des concentrations sériques de médicaments puisse guider l'horaire des évaluations pour un DCN, ces mesures ne sont pas toujours disponibles ou réalisables. Dans cet article, nous passons en revue les médicaments sédatifs et opioïdes qui peuvent confondre un DCN, ainsi que les facteurs pharmacocinétiques qui régissent la durée d'action de ces médicaments. Les paramètres pharmacocinétiques, y compris une demi-vie des sédatifs et des opioïdes sensible au contexte, sont très variables chez les patients gravement malades en raison de la multitude de variables cliniques et de conditions qui peuvent affecter la diffusion et l'élimination des médicaments. Les facteurs liés au patient, à la maladie et au traitement qui influencent la diffusion et l'élimination de ces médicaments sont discutés, notamment la fonction des organes cibles, l'âge, l'obésité, les états hyperdynamiques, l'augmentation de la clairance rénale, l'équilibre liquidien, l'hypothermie et le rôle des perfusions prolongées de médicaments chez les patients gravement malades. Dans ces contextes, il est souvent difficile de prédire combien de temps après l'arrêt du médicament les effets confusionnels prendront pour se dissiper. Nous proposons un cadre conservateur pour évaluer quand ou si un DCN peut être déterminé selon des critères cliniques uniquement. Lorsque les facteurs confondants pharmacologiques ne peuvent pas être neutralisés, ou que cela n'est pas possible, un examen auxiliaire pour confirmer l'absence de circulation sanguine cérébrale doit être réalisé.

Confounders of intraoperative frozen section pathology during glioma surgery.

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.

Generation of all randomizations using circuits.

After a rich history in medicine, randomized control trials (RCTs), both simple and complex, are in increasing use in other areas, such as web-based A/B testing and planning and design of decisions. A main objective of RCTs is to be able to measure parameters, and contrasts in particular, while guarding against biases from hidden confounders. After careful definitions of classical entities such as contrasts, an algebraic method based on circuits is introduced which gives a wide choice of randomization schemes.

Risk for newly diagnosed diabetes after COVID-19: a systematic review and meta-analysis.

BACKGROUND: There is growing evidence that patients recovering after a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may have a variety of acute sequelae including newly diagnosed diabetes. However, the risk of diabetes in the post-acute phase is unclear. To solve this question, we aimed to determine if there was any association between status post-coronavirus disease (COVID-19) infection and a new diagnosis of diabetes. METHODS: We performed a systematic review and meta-analysis of cohort studies assessing new-onset diabetes after COVID-19. PubMed, Embase, Web of Science, and Cochrane databases were all searched from inception to June 10, 2022. Three evaluators independently extracted individual study data and assessed the risk of bias. Random-effects models estimated the pooled incidence and relative risk (RR) of diabetes compared to non-COVID-19 after COVID-19. RESULTS: Nine studies with nearly 40 million participants were included. Overall, the incidence of diabetes after COVID-19 was 15.53 (7.91-25.64) per 1000 person-years, and the relative risk of diabetes after COVID-19 infection was elevated (RR 1.62 [1.45-1.80]). The relative risk of type 1 diabetes was RR=1.48 (1.26-1.75) and type 2 diabetes was RR=1.70 (1.32-2.19), compared to non-COVID-19 patients. At all ages, there was a statistically significant positive association between infection with COVID-19 and the risk of diabetes: <18 years: RR=1.72 (1.19-2.49), ≥18 years: RR=1.63 (1.26-2.11), and >65 years: RR=1.68 (1.22-2.30). The relative risk of diabetes in different gender groups was about 2 (males: RR=2.08 [1.27-3.40]; females: RR=1.99 [1.47-2.80]). The risk of diabetes increased 1.17-fold (1.02-1.34) after COVID-19 infection compared to patients with general upper respiratory tract infections. Patients with severe COVID-19 were at higher risk (RR=1.67 [1.25-2.23]) of diabetes after COVID-19. The risk (RR=1.95 [1.85-2.06]) of diabetes was highest in the first 3 months after COVID-19. These results remained after taking confounding factors into account. CONCLUSIONS: After COVID-19, patients of all ages and genders had an elevated incidence and relative risk for a new diagnosis of diabetes. Particular attention should be paid during the first 3 months of follow-up after COVID-19 for new-onset diabetes.

Instrumental variable analysis to estimate treatment effects: a simulation study showing potential benefits of conditioning on hospital.

BACKGROUND: Instrumental variable (IV) analysis holds the potential to estimate treatment effects from observational data. IV analysis potentially circumvents unmeasured confounding but makes a number of assumptions, such as that the IV shares no common cause with the outcome. When using treatment preference as an instrument, a common cause, such as a preference regarding related treatments, may exist. We aimed to explore the validity and precision of a variant of IV analysis where we additionally adjust for the provider: adjusted IV analysis. METHODS: A treatment effect on an ordinal outcome was simulated (beta - 0.5 in logistic regression) for 15.000 patients, based on a large data set (the IMPACT data, n = 8799) using different scenarios including measured and unmeasured confounders, and a common cause of IV and outcome. We compared estimated treatment effects with patient-level adjustment for confounders, IV with treatment preference as the instrument, and adjusted IV, with hospital added as a fixed effect in the regression models. RESULTS: The use of patient-level adjustment resulted in biased estimates for all the analyses that included unmeasured confounders, IV analysis was less confounded, but also less reliable. With correlation between treatment preference and hospital characteristics (a common cause) estimates were skewed for regular IV analysis, but not for adjusted IV analysis. CONCLUSION: When using IV analysis for comparing hospitals, some limitations of regular IV analysis can be overcome by adjusting for a common cause. TRIAL REGISTRATION: We do not report the results of a health care intervention.

Management of inadequate response and adverse effects to dupilumab in atopic dermatitis.

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.

The effect of adjustment to register-based and questionnaire-based covariates on the association between air pollution and cardiometabolic disease.

OBJECTIVE: Recent studies on air pollution and disease have been based on millions of participants within a region or country, relying entirely on register-based confounder adjustment. We aimed to investigate the effects of increasing adjustment for register- and questionnaire-based covariates on the association between air pollution and cardiometabolic diseases. METHODS: In a population-based cohort of 246,766 eligible participants randomly selected across Denmark in 2010 and 2013 and followed up until December 31, 2017, we identified 3,247 myocardial infarction (MI) cases, 4,166 stroke cases and 6,366 type 2 diabetes cases. Based on historical address-information, we calculated 5-year time-weighted exposure to PM2.5 and NO2 modelled using a validated air pollution model. We used Cox proportional hazards models to calculate hazard ratios (HR) with increasing adjustment for a number of individual- and area-level register-based covariates as well as lifestyle covariates assessed through questionnaires. RESULTS: We found that a 5 µg/m3 higher PM2.5 was associated with HRs (95% CI) for MI, stroke and diabetes, of respectively, 1.18 (0.91-1.52), 1.11 (0.88-1.40) and 1.24 (1.03-1.50) in the fully adjusted models. For all three diseases, adjustment for either individual-level, area-level or lifestyle covariates, or combinations of these resulted in higher HRs compared to HRs adjusted only for age, sex and calendar-year, most marked for MI and diabetes. Further adjustment for lifestyle in models with full register-based individual- and area-level adjustment resulted in only minor changes in HRs for all three diseases. CONCLUSIONS: Our findings suggest that in studies of air pollution and cardiometabolic disease, which use an adjustment strategy with a broad range of register-based socioeconomic variables, there is no effect on risk estimates from subsequent lifestyle adjustment.

The effect of prone positioning on surgical pleth index in patients undergoing spine surgery under general anesthesia - A prospective observational study.

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.

To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies.

Covariate adjustment is integral to the validity of observational studies assessing causal effects. It is common practice to adjust for as many variables as possible in observational studies in the hopes of reducing confounding by other variables. However, indiscriminate adjustment for variables using standard regression models may actually lead to biased estimates. In this paper, we differentiate between confounders, mediators, colliders, and effect modifiers. We will discuss that while confounders should be adjusted for in the analysis, one should be wary of adjusting for colliders. Mediators should not be adjusted for when examining the total effect of an exposure on an outcome. Automated statistical programs should not be used to decide which variables to include in causal models. Using a case scenario in cardiology, we will demonstrate how to identify confounders, colliders, mediators and effect modifiers and the implications of adjustment or non-adjustment for each of them.

A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic arthritis.

OBJECTIVE: To build a prediction model for uveitis in children with JIA for use in current clinical practice. METHODS: Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. RESULTS: JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). CONCLUSION: We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis.

Depression in Patients with Heart Diseases: Gender Differences and Association of Comorbidities, Optimism, and Spiritual Struggle.

BACKGROUND: Depression is a well-established comorbidity of heart disease (HD) and is more prevalent in women than in men. Few studies have examined the gender effect on depression in patients with advanced heart disease prior to open heart surgery (OHS), controlling for cardiac indices. Previous studies indicated the health benefit of optimism but often lacked adjustment for medical confounders. This interdisciplinary study investigated gender differences in pre-OHS depression and the role of medical comorbidities and strength factors. METHOD: Two waves of survey data were collected from 481 patients (mean age = 62, female 42%) along with medical indices in the Society of Thoracic Surgeon's (STS) national database used by all US cardiac surgeons. RESULTS: A t test showed significantly higher levels of pre-OHS depressive symptoms in women than in men. In multivariate analyses, the gender effect on depression vanished after entry of other socio-demographics, medical comorbidities, objectively assessed cardiac indices in the STS database, and psychosocial strength factors. Depressive symptoms linked inversely with dispositional optimism and positively with medical comorbidities and religious/spiritual struggle, but not with any cardiac indices. A mediation analysis supported the role of comorbidities in the gender difference. CONCLUSION: Women with heart disease were more depressed, but the gender difference may be partly explained by multiple comorbid conditions that could complicate disease burden. Reinforcing the literature, optimism, but not other strength factors, appeared to counteract depression after adjusting for health and cardiac conditions. The finding suggests that health providers should be more attentive to overall health of women with heart disease and to the positive expectations of OHS patients.