TY - JOUR A1 - Dellepiane, Sergio A1 - Vaid, Akhil A1 - Jaladanki, Suraj K. A1 - Coca, Steven A1 - Fayad, Zahi A. A1 - Charney, Alexander W. A1 - Böttinger, Erwin A1 - He, John Cijiang A1 - Glicksberg, Benjamin S. A1 - Chan, Lili A1 - Nadkarni, Girish T1 - Acute kidney injury in patients hospitalized with COVID-19 in New York City BT - Temporal Trends From March 2020 to April 2021 JF - Kidney medicine Y1 - 2021 U6 - https://doi.org/10.1016/j.xkme.2021.06.008 SN - 2590-0595 VL - 3 IS - 5 SP - 877 EP - 879 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Datta, Suparno A1 - Sachs, Jan Philipp A1 - Freitas da Cruz, Harry A1 - Martensen, Tom A1 - Bode, Philipp A1 - Morassi Sasso, Ariane A1 - Glicksberg, Benjamin S. A1 - Böttinger, Erwin T1 - FIBER BT - enabling flexible retrieval of electronic health records data for clinical predictive modeling JF - JAMIA open N2 - Objectives: The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. Materials and Methods: FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. Results: Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case. Conclusion: FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process. KW - databases KW - factual KW - electronic health records KW - information storage and KW - retrieval KW - workflow KW - software/instrumentation Y1 - 2021 U6 - https://doi.org/10.1093/jamiaopen/ooab048 SN - 2574-2531 VL - 4 IS - 3 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Cope, Justin L. A1 - Baukmann, Hannes A. A1 - Klinger, Jörn E. A1 - Ravarani, Charles N. J. A1 - Böttinger, Erwin A1 - Konigorski, Stefan A1 - Schmidt, Marco F. T1 - Interaction-based feature selection algorithm outperforms polygenic risk score in predicting Parkinson’s Disease status JF - Frontiers in genetics N2 - Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem. KW - epistasis KW - machine learning KW - feature selection KW - parkinson's disease KW - PPMI (parkinson's progression markers initiative) Y1 - 2021 U6 - https://doi.org/10.3389/fgene.2021.744557 SN - 1664-8021 VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Zenner, Alexander M. A1 - Böttinger, Erwin A1 - Konigorski, Stefan T1 - StudyMe BT - a new mobile app for user-centric N-of-1 trials JF - Trials N2 - N-of-1 trials are multi-crossover self-experiments that allow individuals to systematically evaluate the effect of interventions on their personal health goals. Although several tools for N-of-1 trials exist, there is a gap in supporting non-experts in conducting their own user-centric trials. In this study, we present StudyMe, an open-source mobile application that is freely available from https://play.google.com/store/apps/details?id=health.studyu.me and offers users flexibility and guidance in configuring every component of their trials. We also present research that informed the development of StudyMe, focusing on trial creation. Through an initial survey with 272 participants, we learned that individuals are interested in a variety of personal health aspects and have unique ideas on how to improve them. In an iterative, user-centered development process with intermediate user tests, we developed StudyMe that features an educational part to communicate N-of-1 trial concepts. A final empirical evaluation of StudyMe showed that all participants were able to create their own trials successfully using StudyMe and the app achieved a very good usability rating. Our findings suggest that StudyMe provides a significant step towards enabling individuals to apply a systematic science-oriented approach to personalize health-related interventions and behavior modifications in their everyday lives. Y1 - 2022 U6 - https://doi.org/10.1186/s13063-022-06893-7 SN - 1745-6215 VL - 23 PB - BioMed Central CY - London ER - TY - JOUR A1 - Lewkowicz, Daniel A1 - Böttinger, Erwin A1 - Siegel, Martin T1 - Economic evaluation of digital therapeutic care apps for unsupervised treatment of low back pain BT - Monte Carlo Simulation JF - JMIR mhealth and uhealth N2 - Background: Digital therapeutic care (DTC) programs are unsupervised app-based treatments that provide video exercises and educational material to patients with nonspecific low back pain during episodes of pain and functional disability. German statutory health insurance can reimburse DTC programs since 2019, but evidence on efficacy and reasonable pricing remains scarce. This paper presents a probabilistic sensitivity analysis (PSA) to evaluate the efficacy and cost-utility of a DTC app against treatment as usual (TAU) in Germany. Objective: The aim of this study was to perform a PSA in the form of a Monte Carlo simulation based on the deterministic base case analysis to account for model assumptions and parameter uncertainty. We also intend to explore to what extent the results in this probabilistic analysis differ from the results in the base case analysis and to what extent a shortage of outcome data concerning quality-of-life (QoL) metrics impacts the overall results. Methods: The PSA builds upon a state-transition Markov chain with a 4-week cycle length over a model time horizon of 3 years from a recently published deterministic cost-utility analysis. A Monte Carlo simulation with 10,000 iterations and a cohort size of 10,000 was employed to evaluate the cost-utility from a societal perspective. Quality-adjusted life years (QALYs) were derived from Veterans RAND 6-Dimension (VR-6D) and Short-Form 6-Dimension (SF-6D) single utility scores. Finally, we also simulated reducing the price for a 3-month app prescription to analyze at which price threshold DTC would result in being the dominant strategy over TAU in Germany. Results: The Monte Carlo simulation yielded on average a euro135.97 (a currency exchange rate of EUR euro1=US $1.069 is applicable) incremental cost and 0.004 incremental QALYs per person and year for the unsupervised DTC app strategy compared to in-person physiotherapy in Germany. The corresponding incremental cost-utility ratio (ICUR) amounts to an additional euro34,315.19 per additional QALY. DTC yielded more QALYs in 54.96% of the iterations. DTC dominates TAU in 24.04% of the iterations for QALYs. Reducing the app price in the simulation from currently euro239.96 to euro164.61 for a 3-month prescription could yield a negative ICUR and thus make DTC the dominant strategy, even though the estimated probability of DTC being more effective than TAU is only 54.96%. Conclusions: Decision-makers should be cautious when considering the reimbursement of DTC apps since no significant treatment effect was found, and the probability of cost-effectiveness remains below 60% even for an infinite willingness-to-pay threshold. More app-based studies involving the utilization of QoL outcome parameters are urgently needed to account for the low and limited precision of the available QoL input parameters, which are crucial to making profound recommendations concerning the cost-utility of novel apps. KW - cost-utility analysis KW - cost KW - probabilistic sensitivity analysis KW - Monte Carlo simulation KW - low back pain KW - pain KW - economic KW - cost-effectiveness KW - Markov model KW - digital therapy KW - digital health app KW - mHealth KW - mobile health KW - health app KW - mobile app KW - orthopedic KW - QUALY KW - DALY KW - quality-adjusted life years KW - disability-adjusted life years KW - time horizon KW - veteran KW - statistics Y1 - 2023 U6 - https://doi.org/10.2196/44585 SN - 2291-5222 VL - 11 PB - JMIR Publications CY - Toronto ER - TY - JOUR A1 - Konigorski, Stefan A1 - Wernicke, Sarah A1 - Slosarek, Tamara A1 - Zenner, Alexander M. A1 - Strelow, Nils A1 - Ruether, Darius F. A1 - Henschel, Florian A1 - Manaswini, Manisha A1 - Pottbäcker, Fabian A1 - Edelman, Jonathan A. A1 - Owoyele, Babajide A1 - Danieletto, Matteo A1 - Golden, Eddye A1 - Zweig, Micol A1 - Nadkarni, Girish N. A1 - Böttinger, Erwin T1 - StudyU: a platform for designing and conducting innovative digital N-of-1 trials JF - Journal of medical internet research N2 - N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice. KW - digital interventions KW - N-of-1 trial KW - SCED KW - single-case experimental design KW - web application KW - mobile application KW - app KW - digital health Y1 - 2022 U6 - https://doi.org/10.2196/35884 SN - 1439-4456 SN - 1438-8871 VL - 24 IS - 7 PB - Healthcare World CY - Richmond, Va. ER - TY - JOUR A1 - Woutersen, Amber A1 - Jardine, Phillip E. A1 - Giovanni Bogota-Angel, Raul A1 - Zhang, Hong-Xiang A1 - Silvestro, Daniele A1 - Antonelli, Alexandre A1 - Gogna, Elena A1 - Erkens, Roy H. J. A1 - Gosling, William D. A1 - Dupont-Nivet, Guillaume A1 - Hoorn, Carina T1 - A novel approach to study the morphology and chemistry of pollen in a phylogenetic context, applied to the halophytic taxon Nitraria L.(Nitrariaceae) JF - PeerJ N2 - Nitraria is a halophytic taxon (i.e., adapted to saline environments) that belongs to the plant family Nitrariaceae and is distributed from the Mediterranean, across Asia into the south-eastern tip of Australia. This taxon is thought to have originated in Asia during the Paleogene (66-23 Ma), alongside the proto-Paratethys epicontinental sea. The evolutionary history of Nitraria might hold important clues on the links between climatic and biotic evolution but limited taxonomic documentation of this taxon has thus far hindered this line of research. Here we investigate if the pollen morphology and the chemical composition of the pollen wall are informative of the evolutionary history of Nitraria and could explain if origination along the proto-Paratethys and dispersal to the Tibetan Plateau was simultaneous or a secondary process. To answer these questions, we applied a novel approach consisting of a combination of Fourier Transform Infrared spectroscopy (FTIR), to determine the chemical composition of the pollen wall, and pollen morphological analyses using Light Microscopy (LM) and Scanning Electron Microscopy (SEM). We analysed our data using ordinations (principal components analysis and non-metric multidimensional scaling), and directly mapped it on the Nitrariaceae phylogeny to produce a phylomorphospace and a phylochemospace. Our LM, SEM and FTIR analyses show clear morphological and chemical differences between the sister groups Peganum and Nitraria. Differences in the morphological and chemical characteristics of highland species (Nitraria schoberi, N. sphaerocarpa, N. sibirica and N. tangutorum) and lowland species (Nitraria billardierei and N. retusa) are very subtle, with phylogenetic history appearing to be a more important control on Nitraria pollen than local environmental conditions. Our approach shows a compelling consistency between the chemical and morphological characteristics of the eight studied Nitrariaceae species, and these traits are in agreement with the phylogenetic tree. Taken together, this demonstrates how novel methods for studying fossil pollen can facilitate the evolutionary investigation of living and extinct taxa, and the environments they represent. KW - FTIR KW - LM KW - SEM KW - Paratethys KW - Tibet KW - Sporopollenin KW - Mediterranean KW - Steppe-desert KW - Australia KW - Palynology Y1 - 2018 U6 - https://doi.org/10.7717/peerj.5055 SN - 2167-8359 VL - 6 PB - PeerJ Inc. CY - London ER - TY - JOUR A1 - Vaid, Akhil A1 - Somani, Sulaiman A1 - Russak, Adam J. A1 - De Freitas, Jessica K. A1 - Chaudhry, Fayzan F. A1 - Paranjpe, Ishan A1 - Johnson, Kipp W. A1 - Lee, Samuel J. A1 - Miotto, Riccardo A1 - Richter, Felix A1 - Zhao, Shan A1 - Beckmann, Noam D. A1 - Naik, Nidhi A1 - Kia, Arash A1 - Timsina, Prem A1 - Lala, Anuradha A1 - Paranjpe, Manish A1 - Golden, Eddye A1 - Danieletto, Matteo A1 - Singh, Manbir A1 - Meyer, Dara A1 - O'Reilly, Paul F. A1 - Huckins, Laura A1 - Kovatch, Patricia A1 - Finkelstein, Joseph A1 - Freeman, Robert M. A1 - Argulian, Edgar A1 - Kasarskis, Andrew A1 - Percha, Bethany A1 - Aberg, Judith A. A1 - Bagiella, Emilia A1 - Horowitz, Carol R. A1 - Murphy, Barbara A1 - Nestler, Eric J. A1 - Schadt, Eric E. A1 - Cho, Judy H. A1 - Cordon-Cardo, Carlos A1 - Fuster, Valentin A1 - Charney, Dennis S. A1 - Reich, David L. A1 - Böttinger, Erwin A1 - Levin, Matthew A. A1 - Narula, Jagat A1 - Fayad, Zahi A. A1 - Just, Allan C. A1 - Charney, Alexander W. A1 - Nadkarni, Girish N. A1 - Glicksberg, Benjamin S. T1 - Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation JF - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR N2 - Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes. KW - machine learning KW - COVID-19 KW - electronic health record KW - TRIPOD KW - clinical KW - informatics KW - prediction KW - mortality KW - EHR KW - cohort KW - hospital KW - performance Y1 - 2020 U6 - https://doi.org/10.2196/24018 SN - 1439-4456 SN - 1438-8871 VL - 22 IS - 11 PB - Healthcare World CY - Richmond, Va. ER - TY - JOUR A1 - Döll, Stefanie A1 - Djalali Farahani-Kofoet, Roxana A1 - Zrenner, Rita A1 - Henze, Andrea A1 - Witzel, Katja T1 - Tissue-specific signatures of metabolites and proteins in asparagus roots and exudates JF - Horticulture research N2 - Comprehensive untargeted and targeted analysis of root exudate composition has advanced our understanding of rhizosphere processes. However, little is known about exudate spatial distribution and regulation. We studied the specific metabolite signatures of asparagus root exudates, root outer (epidermis and exodermis), and root inner tissues (cortex and vasculature). The greatest differences were found between exudates and root tissues. In total, 263 non-redundant metabolites were identified as significantly differentially abundant between the three root fractions, with the majority being enriched in the root exudate and/or outer tissue and annotated as 'lipids and lipid-like molecules' or 'phenylpropanoids and polyketides'. Spatial distribution was verified for three selected compounds using MALDI-TOF mass spectrometry imaging. Tissue-specific proteome analysis related root tissue-specific metabolite distributions and rhizodeposition with underlying biosynthetic pathways and transport mechanisms. The proteomes of root outer and inner tissues were spatially very distinct, in agreement with the fundamental differences between their functions and structures. According to KEGG pathway analysis, the outer tissue proteome was characterized by a high abundance of proteins related to 'lipid metabolism', 'biosynthesis of other secondary metabolites' and 'transport and catabolism', reflecting its main functions of providing a hydrophobic barrier, secreting secondary metabolites, and mediating water and nutrient uptake. Proteins more abundant in the inner tissue related to 'transcription', 'translation' and 'folding, sorting and degradation', in accord with the high activity of cortical and vasculature cell layers in growth- and development-related processes. In summary, asparagus root fractions accumulate specific metabolites. This expands our knowledge of tissue-specific plant cell function. Y1 - 2021 U6 - https://doi.org/10.1038/s41438-021-00510-5 SN - 2052-7276 SN - 2662-6810 VL - 8 IS - 1 PB - Nanjing Agricultural Univ. CY - Nanjing ER - TY - JOUR A1 - Kapidzic, Sanja A1 - Frey, Felix A1 - Neuberger, Christoph A1 - Stieglitz, Stefan A1 - Mirbabaie, Milad T1 - Crisis communication on Twitter BT - differences between user types in top tweets about the 2015 “refugee crisis” in Germany JF - International journal of communication N2 - The study explores differences between three user types in the top tweets about the 2015 “refugee crisis” in Germany and presents the results of a quantitative content analysis. All tweets with the keyword “Flüchtlinge” posted for a monthlong period following September 13, 2015, the day Germany decided to implement border controls, were collected (N = 763,752). The top 2,495 tweets according to number of retweets were selected for analysis. Differences between news media, public and private actor tweets in topics, tweet characteristics such as tone and opinion expression, links, and specific sentiments toward refugees were analyzed. We found strong differences between the tweets. Public actor tweets were the main source of positive sentiment toward refugees and the main information source on refugee support. News media tweets mostly reflected traditional journalistic norms of impartiality and objectivity, whereas private actor tweets were more diverse in sentiments toward refugees. KW - refugee crisis 2015 KW - Germany KW - social media KW - Twitter KW - user types Y1 - 2023 UR - https://ijoc.org/index.php/ijoc/article/view/18172/4022 SN - 1932-8036 VL - 17 SP - 735 EP - 754 PB - The Annenberg Center for Communication CY - Los Angeles, Calif. ER -