TY - JOUR A1 - Böttinger, Erwin T1 - Wendepunkt für Gesundheit JF - Die Zukunft der Medizin : Disruptive Innovationen revolutionieren Medizin und Gesundheit Y1 - 2019 SN - 978-3-95466-398-9 SN - 978-3-95466-448-1 SP - 201 EP - 210 PB - Medizinisch Wissenschaftliche Verlagsgesellschaft CY - Berlin ER - TY - JOUR A1 - Chan, Lili A1 - Chaudhary, Kumardeep A1 - Saha, Aparna A1 - Chauhan, Kinsuk A1 - Vaid, Akhil A1 - Zhao, Shan A1 - Paranjpe, Ishan A1 - Somani, Sulaiman A1 - Richter, Felix A1 - Miotto, Riccardo A1 - Lala, Anuradha A1 - Kia, Arash A1 - Timsina, Prem A1 - Li, Li A1 - Freeman, Robert A1 - Chen, Rong A1 - Narula, Jagat A1 - Just, Allan C. A1 - Horowitz, Carol A1 - Fayad, Zahi A1 - Cordon-Cardo, Carlos A1 - Schadt, Eric A1 - Levin, Matthew A. A1 - Reich, David L. A1 - Fuster, Valentin A1 - Murphy, Barbara A1 - He, John C. A1 - Charney, Alexander W. A1 - Böttinger, Erwin A1 - Glicksberg, Benjamin A1 - Coca, Steven G. A1 - Nadkarni, Girish N. T1 - AKI in hospitalized patients with COVID-19 JF - Journal of the American Society of Nephrology : JASN N2 - Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associatedwith worse outcomes. However, AKI among hospitalized patients with COVID19 in the United States is not well described. Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged >= 18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patientswith AKI required dialysis. The proportionswith stages 1, 2, or 3 AKIwere 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge. KW - acute renal failure KW - clinical nephrology KW - dialysis KW - COVID-19 Y1 - 2021 U6 - https://doi.org/10.1681/ASN.2020050615 SN - 1046-6673 SN - 1533-3450 VL - 32 IS - 1 SP - 151 EP - 160 PB - American Society of Nephrology CY - Washington ER - TY - JOUR A1 - Chan, Lili A1 - Jaladanki, Suraj K. A1 - Somani, Sulaiman A1 - Paranjpe, Ishan A1 - Kumar, Arvind A1 - Zhao, Shan A1 - Kaufman, Lewis A1 - Leisman, Staci A1 - Sharma, Shuchita A1 - He, John Cijiang A1 - Murphy, Barbara A1 - Fayad, Zahi A. A1 - Levin, Matthew A. A1 - Böttinger, Erwin A1 - Charney, Alexander W. A1 - Glicksberg, Benjamin A1 - Coca, Steven G. A1 - Nadkarni, Girish N. T1 - Outcomes of patients on maintenance dialysis hospitalized with COVID-19 JF - Clinical journal of the American Society of Nephrology : CJASN KW - chronic dialysis KW - COVID-19 KW - end-stage kidney disease Y1 - 2021 U6 - https://doi.org/10.2215/CJN.12360720 SN - 1555-9041 SN - 1555-905X VL - 16 IS - 3 SP - 452 EP - 455 PB - American Society of Nephrology CY - Washington 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 - 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 - De Freitas, Jessica K. A1 - Johnson, Kipp W. A1 - Golden, Eddye A1 - Nadkarni, Girish N. A1 - Dudley, Joel T. A1 - Böttinger, Erwin A1 - Glicksberg, Benjamin S. A1 - Miotto, Riccardo T1 - Phe2vec BT - Automated disease phenotyping based on unsupervised embeddings from electronic health records JF - Patterns N2 - Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts. Y1 - 2021 U6 - https://doi.org/10.1016/j.patter.2021.100337 SN - 2666-3899 VL - 2 IS - 9 PB - Elsevier CY - Amsterdam ER - 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 - GEN 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 T2 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 21 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-585415 SN - 2590-0595 IS - 5 ER - TY - JOUR A1 - Freitas da Cruz, Harry A1 - Pfahringer, Boris A1 - Martensen, Tom A1 - Schneider, Frederic A1 - Meyer, Alexander A1 - Böttinger, Erwin A1 - Schapranow, Matthieu-Patrick T1 - Using interpretability approaches to update "black-box" clinical prediction models BT - an external validation study in nephrology JF - Artificial intelligence in medicine : AIM N2 - Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies. KW - Clinical predictive modeling KW - Nephrology KW - Validation KW - Interpretability KW - methods Y1 - 2021 U6 - https://doi.org/10.1016/j.artmed.2020.101982 SN - 0933-3657 SN - 1873-2860 VL - 111 PB - Elsevier CY - Amsterdam ER - TY - GEN A1 - Gorski, Mathias A1 - Jung, Bettina A1 - Li, Yong A1 - Matias-Garcia, Pamela R. A1 - Wuttke, Matthias A1 - Coassin, Stefan A1 - Thio, Chris H. L. A1 - Kleber, Marcus E. A1 - Winkler, Thomas W. A1 - Wanner, Veronika A1 - Chai, Jin-Fang A1 - Chu, Audrey Y. A1 - Cocca, Massimiliano A1 - Feitosa, Mary F. A1 - Ghasemi, Sahar A1 - Hoppmann, Anselm A1 - Horn, Katrin A1 - Li, Man A1 - Nutile, Teresa A1 - Scholz, Markus A1 - Sieber, Karsten B. A1 - Teumer, Alexander A1 - Tin, Adrienne A1 - Wang, Judy A1 - Tayo, Bamidele O. A1 - Ahluwalia, Tarunveer S. A1 - Almgren, Peter A1 - Bakker, Stephan J. L. A1 - Banas, Bernhard A1 - Bansal, Nisha A1 - Biggs, Mary L. A1 - Boerwinkle, Eric A1 - Böttinger, Erwin A1 - Brenner, Hermann A1 - Carroll, Robert J. A1 - Chalmers, John A1 - Chee, Miao-Li A1 - Chee, Miao-Ling A1 - Cheng, Ching-Yu A1 - Coresh, Josef A1 - de Borst, Martin H. A1 - Degenhardt, Frauke A1 - Eckardt, Kai-Uwe A1 - Endlich, Karlhans A1 - Franke, Andre A1 - Freitag-Wolf, Sandra A1 - Gampawar, Piyush A1 - Gansevoort, Ron T. A1 - Ghanbari, Mohsen A1 - Gieger, Christian A1 - Hamet, Pavel A1 - Ho, Kevin A1 - Hofer, Edith A1 - Holleczek, Bernd A1 - Foo, Valencia Hui Xian A1 - Hutri-Kahonen, Nina A1 - Hwang, Shih-Jen A1 - Ikram, M. Arfan A1 - Josyula, Navya Shilpa A1 - Kahonen, Mika A1 - Khor, Chiea-Chuen A1 - Koenig, Wolfgang A1 - Kramer, Holly A1 - Kraemer, Bernhard K. A1 - Kuehnel, Brigitte A1 - Lange, Leslie A. A1 - Lehtimaki, Terho A1 - Lieb, Wolfgang A1 - Loos, Ruth J. F. A1 - Lukas, Mary Ann A1 - Lyytikainen, Leo-Pekka A1 - Meisinger, Christa A1 - Meitinger, Thomas A1 - Melander, Olle A1 - Milaneschi, Yuri A1 - Mishra, Pashupati P. A1 - Mononen, Nina A1 - Mychaleckyj, Josyf C. A1 - Nadkarni, Girish N. A1 - Nauck, Matthias A1 - Nikus, Kjell A1 - Ning, Boting A1 - Nolte, Ilja M. A1 - O'Donoghue, Michelle L. A1 - Orho-Melander, Marju A1 - Pendergrass, Sarah A. A1 - Penninx, Brenda W. J. H. A1 - Preuss, Michael H. A1 - Psaty, Bruce M. A1 - Raffield, Laura M. A1 - Raitakari, Olli T. A1 - Rettig, Rainer A1 - Rheinberger, Myriam A1 - Rice, Kenneth M. A1 - Rosenkranz, Alexander R. A1 - Rossing, Peter A1 - Rotter, Jerome A1 - Sabanayagam, Charumathi A1 - Schmidt, Helena A1 - Schmidt, Reinhold A1 - Schoettker, Ben A1 - Schulz, Christina-Alexandra A1 - Sedaghat, Sanaz A1 - Shaffer, Christian M. A1 - Strauch, Konstantin A1 - Szymczak, Silke A1 - Taylor, Kent D. A1 - Tremblay, Johanne A1 - Chaker, Layal A1 - van der Harst, Pim A1 - van der Most, Peter J. A1 - Verweij, Niek A1 - Voelker, Uwe A1 - Waldenberger, Melanie A1 - Wallentin, Lars A1 - Waterworth, Dawn M. A1 - White, Harvey D. A1 - Wilson, James G. A1 - Wong, Tien-Yin A1 - Woodward, Mark A1 - Yang, Qiong A1 - Yasuda, Masayuki A1 - Yerges-Armstrong, Laura M. A1 - Zhang, Yan A1 - Snieder, Harold A1 - Wanner, Christoph A1 - Boger, Carsten A. A1 - Kottgen, Anna A1 - Kronenberg, Florian A1 - Pattaro, Cristian A1 - Heid, Iris M. T1 - Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline T2 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät N2 - Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function. T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 19 KW - acute kidney injury KW - end-stage kidney disease KW - genome-wide association KW - study KW - rapid eGFRcrea decline Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-565379 IS - 19 ER -