Immunoglobulin Restores Immune Responses to BTH1677 in Patients With Low Levels of Antibodies to Beta-glucan.

BACKGROUND: BTH1677 is a beta-glucan pathogen-associated molecular pattern (PAMP) being evaluated as a novel immunotherapy of cancer. We previously described that the presence of antibodies against beta-glucan (ABA) in serum is necessary for BTH1677 antitumoral activity. We hypothesized that infusion of immunoglobulin can reinstate responses to BTH1677 in individuals with low ABA levels. PATIENTS AND METHODS: We report two single-patient studies: one in a patient with metastatic colorectal cancer who received BTH1677, combined with tumor targeting antibody cetuximab; and a second in a patient with metastatic neuroendocrine tumor who received BTH1677 combined with immune checkpoint inhibitor pembrolizumab. RESULTS: The patients had low serum titers of ABA and low innate immune effector functionality induced by BTH1677. Addition of intravenous immunoglobulins restored innate immune activity of BTH1677 and induced clinically meaningful anti-tumoral activity, with long-term disease control. CONCLUSION: Infusion of immunoglobulin can restore activity of BTH1677 in individuals with low serum ABA level.

Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data.

Background: Patient health information is collected routinely in electronic health records (EHRs) and used for research purposes, however, many health conditions are known to be under-diagnosed or under-recorded in EHRs. In research, missing diagnoses result in under-ascertainment of true cases, which attenuates estimated associations between variables and results in a bias toward the null. Bayesian approaches allow the specification of prior information to the model, such as the likely rates of missingness in the data. This paper describes a Bayesian analysis approach which aimed to reduce attenuation of associations in EHR studies focussed on conditions characterized by under-diagnosis. Methods: Study 1: We created synthetic data, produced to mimic structured EHR data where diagnoses were under-recorded. We fitted logistic regression (LR) models with and without Bayesian priors representing rates of misclassification in the data. We examined the LR parameters estimated by models with and without priors. Study 2: We used EHR data from UK primary care in a case-control design with dementia as the outcome. We fitted LR models examining risk factors for dementia, with and without generic prior information on misclassification rates. We examined LR parameters estimated by models with and without the priors, and estimated classification accuracy using Area Under the Receiver Operating Characteristic. Results: Study 1: In synthetic data, estimates of LR parameters were much closer to the true parameter values when Bayesian priors were added to the model; with no priors, parameters were substantially attenuated by under-diagnosis. Study 2: The Bayesian approach ran well on real life clinic data from UK primary care, with the addition of prior information increasing LR parameter values in all cases. In multivariate regression models, Bayesian methods showed no improvement in classification accuracy over traditional LR. Conclusions: The Bayesian approach showed promise but had implementation challenges in real clinical data: prior information on rates of misclassification was difficult to find. Our simple model made a number of assumptions, such as diagnoses being missing at random. Further development is needed to integrate the method into studies using real-life EHR data. Our findings nevertheless highlight the importance of developing methods to address missing diagnoses in EHR data.

Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches.

BACKGROUND: Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. METHODS: We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. RESULTS: The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. CONCLUSIONS: Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time.

Creating longitudinal datasets and cleaning existing data identifiers in a cystic fibrosis registry using a novel Bayesian probabilistic approach from astronomy.

Patient registry data are commonly collected as annual snapshots that need to be amalgamated to understand the longitudinal progress of each patient. However, patient identifiers can either change or may not be available for legal reasons when longitudinal data are collated from patients living in different countries. Here, we apply astronomical statistical matching techniques to link individual patient records that can be used where identifiers are absent or to validate uncertain identifiers. We adopt a Bayesian model framework used for probabilistically linking records in astronomy. We adapt this and validate it across blinded, annually collected data. This is a high-quality (Danish) sub-set of data held in the European Cystic Fibrosis Society Patient Registry (ECFSPR). Our initial experiments achieved a precision of 0.990 at a recall value of 0.987. However, detailed investigation of the discrepancies uncovered typing errors in 27 of the identifiers in the original Danish sub-set. After fixing these errors to create a new gold standard our algorithm correctly linked individual records across years achieving a precision of 0.997 at a recall value of 0.987 without recourse to identifiers. Our Bayesian framework provides the probability of whether a pair of records belong to the same patient. Unlike other record linkage approaches, our algorithm can also use physical models, such as body mass index curves, as prior information for record linkage. We have shown our framework can create longitudinal samples where none existed and validate pre-existing patient identifiers. We have demonstrated that in this specific case this automated approach is better than the existing identifiers.

Cardiovascular Complications of Hematopoietic Stem Cell Transplantation.

OPINION STATEMENT: Survivors of hematopoietic stem cell transplant (HSCT) are at significant risk for cardiac disease and cardiac complications. While there may be cardiac complications during the acute period of HSCT, long-term survivors remain at risk for cardiovascular disease at a rate at least fourfold higher than the general population. Aggressive screening for cardiac risk factors such as diabetes, hypertension, and arrhythmias is warranted pretransplant. For those with risk factors, particularly a history of cardiovascular disease or atrial fibrillation, cardiology consultation is warranted in the pretransplantation period. Aggressive screening for cardiac risk factors such as diabetes, hypertension, and hyperlipidemia is warranted in HSCT survivors as well; early and aggressive treatment of left ventricular dysfunction is warranted. Collaboration between hematology/oncology and cardiology through a cardio-oncology clinic is an optimal way to help manage these patients.

The TrxG Complex Mediates Cytokine Induced De Novo Enhancer Formation in Islets.

To better understand how ß-cells respond to proinflammatory cytokines we mapped the locations of histone 3 lysine 4 monomethylation (H3K4me1), a post-translational histone modification enriched at active and poised cis-regulatory regions, in IFNγ, Il-1ß, and TNFα treated pancreatic islets. We identified 96,721 putative cis-regulatory loci, of which 3,590 were generated de novo, 3,204 had increased H3K4me1, and 5,354 had decreased H3K4me1 in IFNγ, Il-1ß, and TNFα exposed islets. Roughly 10% of the de novo and increased regions were enriched for the repressive histone modification histone 3 lysine 27 trimethylation (H3K27me3) in untreated cells, and these were frequently associated with chemokine genes. We show that IFNγ, Il-1ß, and TNFα exposure overcomes this repression and induces chemokine gene activation in as little as three hours, and that this expression persists for days in absence of continued IFNγ, Il-1ß, and TNFα exposure. We implicate trithorax group (TrxG) complexes as likely players in the conversion of these repressed loci to an active state. To block the activity of these complexes, we suppressed Wdr5, a core component of the TrxG complexes, and used the H3K27me3 demethylase inhibitor GSK-J4. We show that GSK-J4 is particularly effective in blunting IFNγ, Il-1ß, and TNFα-induced chemokine gene expression in ß-cells; however, it induced significant islet-cell apoptosis and ß-cell dysfunction. Wdr5 suppression also reduced IFNγ, Il-1ß, and TNFα induced chemokine gene expression in ß-cells without affecting islet-cell survival or ß-cell function after 48hrs, but did begin to increase islet-cell apoptosis and ß-cell dysfunction after four days of treatment. Taken together these data suggest that the TrxG complex is potentially a viable target for preventing cytokine induced chemokine gene expression in ß-cells.

Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI.

Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community. Researchers and clinicians are also increasingly interested in the study of functional-connectivity patterns of brains at rest and how these relations might change in conditions like Alzheimer's disease or clinical depression. In this study we investigate the efficacy of a specific multivariate statistical machine learning technique to perform patient stratification from functional-connectivity patterns of brains at rest. Whilst the majority of previous approaches to this problem have employed support vector machines (SVMs) we investigate the performance of Bayesian Gaussian process logistic regression (GP-LR) models with linear and non-linear covariance functions. GP-LR models can be interpreted as a Bayesian probabilistic analogue to kernel SVM classifiers. However, GP-LR methods confer a number of benefits over kernel SVMs. Whilst SVMs only return a binary class label prediction, GP-LR, being a probabilistic model, provides a principled estimate of the probability of class membership. Class probability estimates are a measure of the confidence the model has in its predictions, such a confidence score may be extremely useful in the clinical setting. Additionally, if miss-classification costs are not symmetric, thresholds can be set to achieve either strong specificity or sensitivity scores. Since GP-LR models are Bayesian, computationally expensive cross-validation hyper-parameter grid-search methods can be avoided. We apply these methods to a sample of 77 subjects; 27 with a diagnosis of probable AD, 50 with a diagnosis of a-MCI and a control sample of 39. All subjects underwent a MRI examination at 3T to obtain a 7minute and 20second resting state scan. Our results support the hypothesis that GP-LR models can be effective at performing patient stratification: the implemented model achieves 75% accuracy disambiguating healthy subjects from subjects with amnesic mild cognitive impairment and 97% accuracy disambiguating amnesic mild cognitive impairment subjects from those with Alzheimer's disease, accuracies are estimated using a held-out test set. Both results are significant at the 1% level.

Hematopoietic stem cell transplantation in patients with systolic dysfunction: can it be done?

Hematopoietic cell transplantation (HCT) is a potential cure for certain hematologic malignancies. However, because of risks of complications and mortality, this treatment option is limited to patients with minimal comorbidities. We performed a retrospective cohort study evaluating the impact of pre-HCT systolic dysfunction on outcomes. We identified 49 subjects with systolic dysfunction, defined as left ventricular ejection fraction (LVEF) < 50% and 49 controls (matched by age, gender, conditioning regimen, and HCT donor number; all with LVEF ≥ 50%) undergoing HCT at the University of Minnesota between 2002 and 2012. Treatment complications, use of beta-blockers and angiotensin-converting enzyme inhibitors, and overall survival (OS) after HCT out to 24 months were analyzed. The median LVEF was 45% (range, 27.5% to 49%) for the study group and 60% (range, 50% to 69%) for controls. The majority of patients in both groups (81.6%) received reduced-intensity conditioning (RIC). Treatment-related mortality (TRM) at day 100 was identical, with a cumulative incidence of 14% in the study (95% confidence interval [CI], 5% to 24%) versus 14% in controls (95% CI, 5% to 24%) (P = .89). Two-year OS was similar in the study group (53%; 95% CI, 38% to 66%) versus controls (61%; 95% CI, 46% to 73%) (P = .34). LVEF ≥ 43% was associated with improved OS at 1 year (hazard ratio [HR], .36; 95% CI, .15 to .87; P = .02). There was no significant difference in the incidence of non-life-threatening cardiac complications (12.2% in cases versus 8.2% in controls, P = .50) or serious (life-threatening or fatal) cardiac complications (4.1% in cases versus 2.0% in controls, P = .56). Pre-existing coronary artery disease was associated with increased TRM at 100 days (HR, 4.35; 95% CI, 1.24 to 15.32; P = .02). Cardiac medication use had no effect on TRM. Our study demonstrates that patients with asymptomatic borderline systolic dysfunction can safely undergo HCT with RIC. Coronary artery disease remains a risk factor for increased TRM. Patients with borderline systolic dysfunction can safely undergo HCT, but may need particular vigilance for potential hemodynamic or ischemic cardiac complications.

An approach for estimating exposure to ambient concentrations.

The degree of certainty in epidemiological studies is probably limited more by estimates of exposure than by any other component. We present a methodology for computing daily pollutant concentration fields that reduces exposure uncertainty and bias by taking account of spatial variation in air quality. This approach, using elliptical influence functions, involves the optimum blending of observations from a monitoring network with gridded pollution fields predicted by the complex air quality model TAPM. Such fields allow more information to be incorporated in the exposure fields used in epidemiological studies, rather than having to assume that ambient exposure is the same across a whole city and/or that individuals remain at the one location for the duration of a study.