Submaximal Decision Theory and Health Resource Conservation: The Example of Congestive Heart Failure

The challenge of eliminating disparities in chronic illness in the United States is hampered by the diversity of the epidemiology of the chronic conditions themselves, and by the individuality of the communities and patients affected by them. This article outlines some of the ways in which the complexity of chronic illness in underserved communities in the United States limits the data and the strategies available to clinicians and patients. We then present the example of chronic heart failure (CHF) to illustrate a possible solution that we are developing for supporting underserved patients’ selfmanagement of chronic illness: individualized health care (through “personal normals” derived from the patient’s own clinical history combined with population-based data), and distributed health care (point of care through wireless biosensors and community health workers). We present some of the possible barriers to the implementation of the model. Conclusion: we believe that this approach is a pathway to empowering CHF patients in underserved communities. Further research is necessary to test the clinical viability of the model and the acceptability of the model for patients, physicians, and families

A multi-layer monitoring system for clinical management of Congestive Heart Failure

BACKGROUND: Congestive Heart Failure (CHF) is a serious cardiac condition that brings high risks of urgent hospitalization and death. Remote monitoring systems are well-suited to managing patients suffering from CHF, and can reduce deaths and re-hospitalizations, as shown by the literature, including multiple systematic reviews. METHODS: The monitoring system proposed in this paper aims at helping CHF stakeholders make appropriate decisions in managing the disease and preventing cardiac events, such as decompensation, which can lead to hospitalization or death. Monitoring activities are stratified into three layers: scheduled visits to a hospital following up on a cardiac event, home monitoring visits by nurses, and patient's self-monitoring performed at home using specialized equipment. Appropriate hardware, desktop and mobile software applications were developed to enable a patient's monitoring by all stakeholders. For the first two layers, we designed and implemented a Decision Support System (DSS) using machine learning (Random Forest algorithm) to predict the number of decompensations per year and to assess the heart failure severity based on a variety of clinical data. For the third layer, custom-designed sensors (the Blue Scale system) for electrocardiogram (EKG), pulse transit times, bio-impedance and weight allowed frequent collection of CHF-related data in the comfort of the patient's home. We also performed a short-term Heart Rate Variability (HRV) analysis on electrocardiograms self-acquired by 15 healthy volunteers and compared the obtained parameters with those of 15 CHF patients from PhysioNet's PhysioBank archives. RESULTS: We report numerical performances of the DSS, calculated as multiclass accuracy, sensitivity and specificity in a 10-fold cross-validation. The obtained average accuracies are: 71.9% in predicting the number of decompensations and 81.3% in severity assessment. The most serious class in severity assessment is detected with good sensitivity and specificity (0.87 / 0.95), while, in predicting decompensation, high specificity combined with good sensitivity prevents false alarms. The HRV parameters extracted from the self-measured EKG using the Blue Scale system of sensors are comparable with those reported in the literature about healthy people. CONCLUSIONS: The performance of DSSs trained with new patients confirmed the results of previous work, and emphasizes the strong correlation between some CHF markers, such as brain natriuretic peptide (BNP) and ejection fraction (EF), with the outputs of interest. Comparing HRV parameters from healthy volunteers with HRV parameters obtained from PhysioBank archives, we confirm the literature that considers the HRV a promising method for distinguishing healthy from CHF patients

12-h clock regulation of genetic information flow by XBP1s

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Pan, Y., Ballance, H., Meng, H., Gonzalez, N., Kim, S., Abdurehman, L., York, B., Chen, X., Schnytzer, Y., Levy, O., Dacso, C. C., McClung, C. A., O'Malley, B. W., Liu, S., & Zhu, B. 12-h clock regulation of genetic information flow by XBP1s. Plos Biology, 18(1), (2020): e3000580, doi:10.1371/journal.pbio.3000580.Our group recently characterized a cell-autonomous mammalian 12-h clock independent from the circadian clock, but its function and mechanism of regulation remain poorly understood. Here, we show that in mouse liver, transcriptional regulation significantly contributes to the establishment of 12-h rhythms of mRNA expression in a manner dependent on Spliced Form of X-box Binding Protein 1 (XBP1s). Mechanistically, the motif stringency of XBP1s promoter binding sites dictates XBP1s’s ability to drive 12-h rhythms of nascent mRNA transcription at dawn and dusk, which are enriched for basal transcription regulation, mRNA processing and export, ribosome biogenesis, translation initiation, and protein processing/sorting in the Endoplasmic Reticulum (ER)-Golgi in a temporal order consistent with the progressive molecular processing sequence described by the central dogma information flow (CEDIF). We further identified GA-binding proteins (GABPs) as putative novel transcriptional regulators driving 12-h rhythms of gene expression with more diverse phases. These 12-h rhythms of gene expression are cell autonomous and evolutionarily conserved in marine animals possessing a circatidal clock. Our results demonstrate an evolutionarily conserved, intricate network of transcriptional control of the mammalian 12-h clock that mediates diverse biological pathways. We speculate that the 12-h clock is coopted to accommodate elevated gene expression and processing in mammals at the two rush hours, with the particular genes processed at each rush hour regulated by the circadian and/or tissue-specific pathways.This study was supported by the American Diabetes Association junior faculty development award 1-18-JDF-025 to B.Z., by funding from National Institute of Health HD07879 and 1P01DK113954 to B.W.O, by funding from National Science Foundation award 1703170 to C.C.D. and B.Z., and by funding from Brockman Foundation to C.C.D and B.W.O. This work was further supported by the UPMC Genome Center with funding from UPMC’s Immunotherapy and Transplant Center. This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. Research reported in this publication was further supported by the National Institute of Diabetes And Digestive And Kidney Diseases of the National Institutes of Health under award number P30DK120531 to Pittsburgh Liver Research Center, in which both S.L. and B.Z. are members. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty

Abstract Background Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. Aims We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Methods Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. Results We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. Conclusions The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.</p

A novel mathematical method for disclosing oscillations in gene transcription: A comparative study.

Circadian rhythmicity, the 24-hour cycle responsive to light and dark, is determined by periodic oscillations in gene transcription. This phenomenon has broad ramifications in physiologic function. Recent work has disclosed more cycles in gene transcription, and to the uncovering of these we apply a novel signal processing methodology known as the pencil method and compare it to conventional parametric, nonparametric, and statistical methods. METHODS:In order to assess periodicity of gene expression over time, we analyzed a database derived from livers of mice entrained to a 12-hour light/12-hour dark cycle. We also analyzed artificially generated signals to identify differences between the pencil decomposition and other alternative methods. RESULTS:The pencil decomposition revealed hitherto-unsuspected oscillations in gene transcription with 12-hour periodicity. The pencil method was robust in detecting the 24-hour circadian cycle that was known to exist, as well as confirming the existence of shorter-period oscillations. A key consequence of this approach is that orthogonality of the different oscillatory components can be demonstrated. thus indicating a biological independence of these oscillations, that has been subsequently confirmed empirically by knocking out the gene responsible for the 24-hour clock. CONCLUSION:System identification techniques can be applied to biological systems and can uncover important characteristics that may elude visual inspection of the data. SIGNIFICANCE:The pencil method provides new insights on the essence of gene expression and discloses a wide variety of oscillations in addition to the well-studied circadian pattern. This insight opens the door to the study of novel mechanisms by which oscillatory gene expression signals exert their regulatory effect on cells to influence human diseases