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  - 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  - Rüther, Ferenc Darius
A1  - Sebode, Marcial
A1  - Lohse, Ansgar W.
A1  - Wernicke, Sarah
A1  - Böttinger, Erwin
A1  - Casar, Christian
A1  - Braun, Felix
A1  - Schramm, Christoph
T1  - Mobile app requirements for patients with rare liver diseases
BT  - a single center survey for the ERN RARE-LIVER
JF  - Clinics and research in hepatology and gastroenterology
N2  - Background: 
More patient data are needed to improve research on rare liver diseases. Mobile health apps enable an exhaustive data collection. Therefore, the European Reference Network on Hepatological diseases (ERN RARE-LIVER) intends to implement an app for patients with rare liver diseases communicating with a patient registry, but little is known about which features patients and their healthcare providers regard as being useful. 

Aims: 
This study aimed to investigate how an app for rare liver diseases would be accepted, and to find out which features are considered useful. 

Methods: 
An anonymous survey was conducted on adult patients with rare liver diseases at a single academic, tertiary care outpatient-service. Additionally, medical experts of the ERN working group on autoimmune hepatitis were invited to participate in an online survey. 

Results: 
In total, the responses from 100 patients with autoimmune (n = 90) or other rare (n = 10) liver diseases and 32 experts were analyzed. Patients were convinced to use a disease specific app (80%) and expected some benefit to their health (78%) but responses differed signifi-cantly between younger and older patients (93% vs. 62%, p < 0.001; 88% vs. 64%, p < 0.01). Comparing patients' and experts' feedback, patients more often expected a simplified healthcare pathway (e.g. 89% vs. 59% (p < 0.001) wanted access to one's own medical records), while healthcare providers saw the benefit mainly in improving compliance and treatment outcome (e.g. 93% vs. 31% (p < 0.001) and 70% vs. 21% (p < 0.001) expected the app to reduce mistakes in taking medication and improve quality of life, respectively).
KW  - Primary sclerosing cholangitis
KW  - Primary biliary cholangitis
KW  - Autoimmune
KW  - hepatitis
KW  - European reference networks
KW  - Mobile applications
KW  - Patient
KW  - reported out-come measures
Y1  - 2021
U6  - https://doi.org/10.1016/j.clinre.2021.101760
SN  - 2210-7401
SN  - 2210-741X
VL  - 45
IS  - 6
PB  - Elsevier Masson
CY  - Amsterdam
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  - 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  - 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  - 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  - Vaid, Akhil
A1  - Chan, Lili
A1  - Chaudhary, Kumardeep
A1  - Jaladanki, Suraj K.
A1  - Paranjpe, Ishan
A1  - Russak, Adam J.
A1  - Kia, Arash
A1  - Timsina, Prem
A1  - Levin, Matthew A.
A1  - He, John Cijiang
A1  - Böttinger, Erwin
A1  - Charney, Alexander W.
A1  - Fayad, Zahi A.
A1  - Coca, Steven G.
A1  - Glicksberg, Benjamin S.
A1  - Nadkarni, Girish N.
T1  - Predictive approaches for acute dialysis requirement and death in COVID-19
JF  - Clinical journal of the American Society of Nephrology : CJASN
N2  - Background and objectives
AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited. 

Design, setting, participants, & measurements
Using data from adult patients hospitalized with COVID-19 from five hospitals from theMount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to theMount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission.

Results
A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precisionrecall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction.

Conclusions
 An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models.
KW  - COVID-19
KW  - dialysis
KW  - machine learning
KW  - prediction
KW  - AKI
Y1  - 2021
U6  - https://doi.org/10.2215/CJN.17311120
SN  - 1555-9041
SN  - 1555-905X
VL  - 16
IS  - 8
SP  - 1158
EP  - 1168
PB  - American Society of Nephrology
CY  - Washington
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  - BOOK
A1  - Meinel, Christoph
A1  - Döllner, Jürgen Roland Friedrich
A1  - Weske, Mathias
A1  - Polze, Andreas
A1  - Hirschfeld, Robert
A1  - Naumann, Felix
A1  - Giese, Holger
A1  - Baudisch, Patrick
A1  - Friedrich, Tobias
A1  - Böttinger, Erwin
A1  - Lippert, Christoph
A1  - Dörr, Christian
A1  - Lehmann, Anja
A1  - Renard, Bernhard
A1  - Rabl, Tilmann
A1  - Uebernickel, Falk
A1  - Arnrich, Bert
A1  - Hölzle, Katharina
T1  - Proceedings of the HPI Research School on Service-oriented Systems Engineering 2020 Fall Retreat
N2  - Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application.

Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns.

The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the research school, this technical report covers a wide range of topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment.
N2  - Der Entwurf und die Realisierung dienstbasierender Architekturen wirft eine Vielzahl von Forschungsfragestellungen aus den Gebieten der Softwaretechnik, der Systemmodellierung und -analyse, sowie der Adaptierbarkeit und Integration von Applikationen auf. Komponentenorientierung und WebServices sind zwei Ansätze für den effizienten Entwurf und die Realisierung komplexer Web-basierender Systeme. Sie ermöglichen die Reaktion auf wechselnde Anforderungen ebenso, wie die Integration großer komplexer Softwaresysteme.

"Service-Oriented Systems Engineering" repräsentiert die Symbiose bewährter Praktiken aus den Gebieten der Objektorientierung, der Komponentenprogrammierung, des verteilten Rechnen sowie der Geschäftsprozesse und berücksichtigt auch die Integration von Geschäftsanliegen und Informationstechnologien.

Die Klausurtagung des Forschungskollegs "Service-oriented Systems Engineering" findet einmal jährlich statt und bietet allen Kollegiaten die Möglichkeit den Stand ihrer aktuellen Forschung darzulegen. Bedingt durch die Querschnittstruktur des Kollegs deckt dieser Bericht ein weites Spektrum aktueller Forschungsthemen ab. Dazu zählen unter anderem Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; sowie Services Specification, Composition, and Enactment.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 138 
KW  - Hasso Plattner Institute
KW  - research school
KW  - Ph.D. retreat
KW  - service-oriented systems engineering
KW  - Hasso-Plattner-Institut
KW  - Forschungskolleg
KW  - Klausurtagung
KW  - Service-oriented Systems Engineering
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-504132
SN  - 978-3-86956-513-2
SN  - 1613-5652
SN  - 2191-1665
IS  - 138
PB  - Universitätsverlag Potsdam
CY  - Potsdam
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  - 
TY  - JOUR
A1  - Sigel, Keith Magnus
A1  - Swartz, Talia H.
A1  - Golden, Eddye
A1  - Paranjpe, Ishan
A1  - Somani, Sulaiman
A1  - Richter, Felix
A1  - De Freitas, Jessica K.
A1  - Miotto, Riccardo
A1  - Zhao, Shan
A1  - Polak, Paz
A1  - Mutetwa, Tinaye
A1  - Factor, Stephanie
A1  - Mehandru, Saurabh
A1  - Mullen, Michael
A1  - Cossarini, Francesca
A1  - Böttinger, Erwin
A1  - Fayad, Zahi
A1  - Merad, Miriam
A1  - Gnjatic, Sacha
A1  - Aberg, Judith
A1  - Charney, Alexander
A1  - Nadkarni, Girish
A1  - Glicksberg, Benjamin S.
T1  - Coronavirus 2019 and people living with human immunodeficiency virus
BT  - outcomes for hospitalized patients in New York City
JF  - Clinical infectious diseases : electronic edition
N2  - Background:
There are limited data regarding the clinical impact of coronavirus disease 2019 (COVID-19) on people living with human immunodeficiency virus (PLWH). In this study, we compared outcomes for PLWH with COVID-19 to a matched comparison group. 

Methods:
We identified 88 PLWH hospitalized with laboratory-confirmed COVID-19 in our hospital system in New York City between 12 March and 23 April 2020. We collected data on baseline clinical characteristics, laboratory values, HIV status, treatment, and outcomes from this group and matched comparators (1 PLWH to up to 5 patients by age, sex, race/ethnicity, and calendar week of infection). We compared clinical characteristics and outcomes (death, mechanical ventilation, hospital discharge) for these groups, as well as cumulative incidence of death by HIV status. 

Results:
Patients did not differ significantly by HIV status by age, sex, or race/ethnicity due to the matching algorithm. PLWH hospitalized with COVID-19 had high proportions of HIV virologic control on antiretroviral therapy. PLWH had greater proportions of smoking (P < .001) and comorbid illness than uninfected comparators. There was no difference in COVID-19 severity on admission by HIV status (P = .15). Poor outcomes for hospitalized PLWH were frequent but similar to proportions in comparators; 18% required mechanical ventilation and 21% died during follow-up (compared with 23% and 20%, respectively). There was similar cumulative incidence of death over time by HIV status (P = .94).

Conclusions:
We found no differences in adverse outcomes associated with HIV infection for hospitalized COVID-19 patients compared with a demographically similar patient group.
KW  - human immunodeficiency virus
KW  - coronavirus 2019
KW  - severe acute respiratory
KW  - syndrome coronavirus 2
Y1  - 2020
U6  - https://doi.org/10.1093/cid/ciaa880
SN  - 1058-4838
SN  - 1537-6591
VL  - 71
IS  - 11
SP  - 2933
EP  - 2938
PB  - Oxford Univ. Press
CY  - Cary, NC
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  - GEN
A1  - Zenner, Alexander M.
A1  - Böttinger, Erwin
A1  - Konigorski, Stefan
T1  - StudyMe
BT  - a new mobile app for user-centric N-of-1 trials
T2  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät
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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 18 
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-589763
IS  - 18
ER  - 
TY  - JOUR
A1  - Lewkowicz, Daniel
A1  - Wohlbrandt, Attila M.
A1  - Böttinger, Erwin
T1  - Digital therapeutic care apps with decision-support interventions for people with low back pain in Germany
BT  - Cost-effectiveness analysis
JF  - JMIR mhealth and uhealth
N2  - Background:
Digital therapeutic care apps provide a new effective and scalable approach for people with nonspecific low back pain (LBP). Digital therapeutic care apps are also driven by personalized decision-support interventions that support the user in self-managing LBP, and may induce prolonged behavior change to reduce the frequency and intensity of pain episodes. However, these therapeutic apps are associated with high attrition rates, and the initial prescription cost is higher than that of face-to-face physiotherapy. In Germany, digital therapeutic care apps are now being reimbursed by statutory health insurance; however, price targets and cost-driving factors for the formation of the reimbursement rate remain unexplored.

Objective: 
The aim of this study was to evaluate the cost-effectiveness of a digital therapeutic care app compared to treatment as usual (TAU) in Germany. We further aimed to explore under which circumstances the reimbursement rate could be modified to consider value-based pricing.

Methods:
We developed a state-transition Markov model based on a best-practice analysis of prior LBP-related decision-analytic models, and evaluated the cost utility of a digital therapeutic care app compared to TAU in Germany. Based on a 3-year time horizon, we simulated the incremental cost and quality-adjusted life years (QALYs) for people with nonacute LBP from the societal perspective. In the deterministic sensitivity and scenario analyses, we focused on diverging attrition rates and app cost to assess our model's robustness and conditions for changing the reimbursement rate. All costs are reported in Euro (euro1=US $1.12).

Results:
Our base case results indicated that the digital therapeutic care strategy led to an incremental cost of euro121.59, but also generated 0.0221 additional QALYs compared to the TAU strategy, with an estimated incremental cost-effectiveness ratio (ICER) of euro5486 per QALY. The sensitivity analysis revealed that the reimbursement rate and the capability of digital therapeutic care to prevent reoccurring LBP episodes have a significant impact on the ICER. At the same time, the other parameters remained unaffected and thus supported the robustness of our model. In the scenario analysis, the different model time horizons and attrition rates strongly influenced the economic outcome. Reducing the cost of the app to euro99 per 3 months or decreasing the app's attrition rate resulted in digital therapeutic care being significantly less costly with more generated QALYs, and is thus considered to be the dominant strategy over TAU.

Conclusions:
The current reimbursement rate for a digital therapeutic care app in the statutory health insurance can be considered a cost-effective measure compared to TAU. The app's attrition rate and effect on the patient's prolonged behavior change essentially influence the settlement of an appropriate reimbursement rate. Future value-based pricing targets should focus on additional outcome parameters besides pain intensity and functional disability by including attrition rates and the app's long-term effect on quality of life.
KW  - cost-utility analysis
KW  - low back pain
KW  - back pain
KW  - cost-effectiveness
KW  - Markov model
KW  - digital therapy
KW  - digital health app
KW  - mHealth
KW  - orthopedic;
KW  - eHealth
KW  - mobile health
KW  - digital health
KW  - pain management
KW  - health apps
Y1  - 2022
U6  - https://doi.org/10.2196/35042
SN  - 2291-5222
VL  - 10
IS  - 2
PB  - JMIR Publications
CY  - Toronto
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  - 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  - 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
JF  - Kidney international : official journal of the International Society of Nephrology
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.
KW  - acute kidney injury
KW  - end-stage kidney disease
KW  - genome-wide association
KW  - study
KW  - rapid eGFRcrea decline
Y1  - 2020
U6  - https://doi.org/10.1016/j.kint.2020.09.030
SN  - 0085-2538
SN  - 1523-1755
VL  - 99
IS  - 4
SP  - 926
EP  - 939
PB  - Elsevier
CY  - New York
ER  - 
TY  - GEN
A1  - Horowitz, Carol R.
A1  - Fei, Kezhen
A1  - Ramos, Michelle A.
A1  - Hauser, Diane
A1  - Ellis, Stephen B.
A1  - Calman, Neil
A1  - Böttinger, Erwin
T1  - Receipt of genetic risk information significantly improves blood pressure control among African anecestry adults with hypertension
BT  - results of a randomized trail
T2  - Journal of General Internal Medicine
Y1  - 2018
U6  - https://doi.org/10.1007/s11606-018-4413-y
SN  - 0884-8734
SN  - 1525-1497
VL  - 33
SP  - S322
EP  - S323
PB  - Springer
CY  - New York
ER  - 
TY  - JOUR
A1  - Wuttke, Matthias
A1  - Li, Yong
A1  - Li, Man
A1  - Sieber, Karsten B.
A1  - Feitosa, Mary F.
A1  - Gorski, Mathias
A1  - Tin, Adrienne
A1  - Wang, Lihua
A1  - Chu, Audrey Y.
A1  - Hoppmann, Anselm
A1  - Kirsten, Holger
A1  - Giri, Ayush
A1  - Chai, Jin-Fang
A1  - Sveinbjornsson, Gardar
A1  - Tayo, Bamidele O.
A1  - Nutile, Teresa
A1  - Fuchsberger, Christian
A1  - Marten, Jonathan
A1  - Cocca, Massimiliano
A1  - Ghasemi, Sahar
A1  - Xu, Yizhe
A1  - Horn, Katrin
A1  - Noce, Damia
A1  - Van der Most, Peter J.
A1  - Sedaghat, Sanaz
A1  - Yu, Zhi
A1  - Akiyama, Masato
A1  - Afaq, Saima
A1  - Ahluwalia, Tarunveer Singh
A1  - Almgren, Peter
A1  - Amin, Najaf
A1  - Arnlov, Johan
A1  - Bakker, Stephan J. L.
A1  - Bansal, Nisha
A1  - Baptista, Daniela
A1  - Bergmann, Sven
A1  - Biggs, Mary L.
A1  - Biino, Ginevra
A1  - Boehnke, Michael
A1  - Boerwinkle, Eric
A1  - Boissel, Mathilde
A1  - Böttinger, Erwin
A1  - Boutin, Thibaud S.
A1  - Brenner, Hermann
A1  - Brumat, Marco
A1  - Burkhardt, Ralph
A1  - Butterworth, Adam S.
A1  - Campana, Eric
A1  - Campbell, Archie
A1  - Campbell, Harry
A1  - Canouil, Mickael
A1  - Carroll, Robert J.
A1  - Catamo, Eulalia
A1  - Chambers, John C.
A1  - Chee, Miao-Ling
A1  - Chee, Miao-Li
A1  - Chen, Xu
A1  - Cheng, Ching-Yu
A1  - Cheng, Yurong
A1  - Christensen, Kaare
A1  - Cifkova, Renata
A1  - Ciullo, Marina
A1  - Concas, Maria Pina
A1  - Cook, James P.
A1  - Coresh, Josef
A1  - Corre, Tanguy
A1  - Sala, Cinzia Felicita
A1  - Cusi, Daniele
A1  - Danesh, John
A1  - Daw, E. Warwick
A1  - De Borst, Martin H.
A1  - De Grandi, Alessandro
A1  - De Mutsert, Renee
A1  - De Vries, Aiko P. J.
A1  - Degenhardt, Frauke
A1  - Delgado, Graciela
A1  - Demirkan, Ayse
A1  - Di Angelantonio, Emanuele
A1  - Dittrich, Katalin
A1  - Divers, Jasmin
A1  - Dorajoo, Rajkumar
A1  - Eckardt, Kai-Uwe
A1  - Ehret, Georg
A1  - Elliott, Paul
A1  - Endlich, Karlhans
A1  - Evans, Michele K.
A1  - Felix, Janine F.
A1  - Foo, Valencia Hui Xian
A1  - Franco, Oscar H.
A1  - Franke, Andre
A1  - Freedman, Barry I.
A1  - Freitag-Wolf, Sandra
A1  - Friedlander, Yechiel
A1  - Froguel, Philippe
A1  - Gansevoort, Ron T.
A1  - Gao, He
A1  - Gasparini, Paolo
A1  - Gaziano, J. Michael
A1  - Giedraitis, Vilmantas
A1  - Gieger, Christian
A1  - Girotto, Giorgia
A1  - Giulianini, Franco
A1  - Gogele, Martin
A1  - Gordon, Scott D.
A1  - Gudbjartsson, Daniel F.
A1  - Gudnason, Vilmundur
A1  - Haller, Toomas
A1  - Hamet, Pavel
A1  - Harris, Tamara B.
A1  - Hartman, Catharina A.
A1  - Hayward, Caroline
A1  - Hellwege, Jacklyn N.
A1  - Heng, Chew-Kiat
A1  - Hicks, Andrew A.
A1  - Hofer, Edith
A1  - Huang, Wei
A1  - Hutri-Kahonen, Nina
A1  - Hwang, Shih-Jen
A1  - Ikram, M. Arfan
A1  - Indridason, Olafur S.
A1  - Ingelsson, Erik
A1  - Ising, Marcus
A1  - Jaddoe, Vincent W. V.
A1  - Jakobsdottir, Johanna
A1  - Jonas, Jost B.
A1  - Joshi, Peter K.
A1  - Josyula, Navya Shilpa
A1  - Jung, Bettina
A1  - Kahonen, Mika
A1  - Kamatani, Yoichiro
A1  - Kammerer, Candace M.
A1  - Kanai, Masahiro
A1  - Kastarinen, Mika
A1  - Kerr, Shona M.
A1  - Khor, Chiea-Chuen
A1  - Kiess, Wieland
A1  - Kleber, Marcus E.
A1  - Koenig, Wolfgang
A1  - Kooner, Jaspal S.
A1  - Korner, Antje
A1  - Kovacs, Peter
A1  - Kraja, Aldi T.
A1  - Krajcoviechova, Alena
A1  - Kramer, Holly
A1  - Kramer, Bernhard K.
A1  - Kronenberg, Florian
A1  - Kubo, Michiaki
A1  - Kuhnel, Brigitte
A1  - Kuokkanen, Mikko
A1  - Kuusisto, Johanna
A1  - La Bianca, Martina
A1  - Laakso, Markku
A1  - Lange, Leslie A.
A1  - Langefeld, Carl D.
A1  - Lee, Jeannette Jen-Mai
A1  - Lehne, Benjamin
A1  - Lehtimaki, Terho
A1  - Lieb, Wolfgang
A1  - Lim, Su-Chi
A1  - Lind, Lars
A1  - Lindgren, Cecilia M.
A1  - Liu, Jun
A1  - Liu, Jianjun
A1  - Loeffler, Markus
A1  - Loos, Ruth J. F.
A1  - Lucae, Susanne
A1  - Lukas, Mary Ann
A1  - Lyytikainen, Leo-Pekka
A1  - Magi, Reedik
A1  - Magnusson, Patrik K. E.
A1  - Mahajan, Anubha
A1  - Martin, Nicholas G.
A1  - Martins, Jade
A1  - Marz, Winfried
A1  - Mascalzoni, Deborah
A1  - Matsuda, Koichi
A1  - Meisinger, Christa
A1  - Meitinger, Thomas
A1  - Melander, Olle
A1  - Metspalu, Andres
A1  - Mikaelsdottir, Evgenia K.
A1  - Milaneschi, Yuri
A1  - Miliku, Kozeta
A1  - Mishra, Pashupati P.
A1  - Program, V. A. Million Veteran
A1  - Mohlke, Karen L.
A1  - Mononen, Nina
A1  - Montgomery, Grant W.
A1  - Mook-Kanamori, Dennis O.
A1  - Mychaleckyj, Josyf C.
A1  - Nadkarni, Girish N.
A1  - Nalls, Mike A.
A1  - Nauck, Matthias
A1  - Nikus, Kjell
A1  - Ning, Boting
A1  - Nolte, Ilja M.
A1  - Noordam, Raymond
A1  - Olafsson, Isleifur
A1  - Oldehinkel, Albertine J.
A1  - Orho-Melander, Marju
A1  - Ouwehand, Willem H.
A1  - Padmanabhan, Sandosh
A1  - Palmer, Nicholette D.
A1  - Palsson, Runolfur
A1  - Penninx, Brenda W. J. H.
A1  - Perls, Thomas
A1  - Perola, Markus
A1  - Pirastu, Mario
A1  - Pirastu, Nicola
A1  - Pistis, Giorgio
A1  - Podgornaia, Anna I.
A1  - Polasek, Ozren
A1  - Ponte, Belen
A1  - Porteous, David J.
A1  - Poulain, Tanja
A1  - Pramstaller, Peter P.
A1  - Preuss, Michael H.
A1  - Prins, Bram P.
A1  - Province, Michael A.
A1  - Rabelink, Ton J.
A1  - Raffield, Laura M.
A1  - Raitakari, Olli T.
A1  - Reilly, Dermot F.
A1  - Rettig, Rainer
A1  - Rheinberger, Myriam
A1  - Rice, Kenneth M.
A1  - Ridker, Paul M.
A1  - Rivadeneira, Fernando
A1  - Rizzi, Federica
A1  - Roberts, David J.
A1  - Robino, Antonietta
A1  - Rossing, Peter
A1  - Rudan, Igor
A1  - Rueedi, Rico
A1  - Ruggiero, Daniela
A1  - Ryan, Kathleen A.
A1  - Saba, Yasaman
A1  - Sabanayagam, Charumathi
A1  - Salomaa, Veikko
A1  - Salvi, Erika
A1  - Saum, Kai-Uwe
A1  - Schmidt, Helena
A1  - Schmidt, Reinhold
A1  - Ben Schottker, 
A1  - Schulz, Christina-Alexandra
A1  - Schupf, Nicole
A1  - Shaffer, Christian M.
A1  - Shi, Yuan
A1  - Smith, Albert V.
A1  - Smith, Blair H.
A1  - Soranzo, Nicole
A1  - Spracklen, Cassandra N.
A1  - Strauch, Konstantin
A1  - Stringham, Heather M.
A1  - Stumvoll, Michael
A1  - Svensson, Per O.
A1  - Szymczak, Silke
A1  - Tai, E-Shyong
A1  - Tajuddin, Salman M.
A1  - Tan, Nicholas Y. Q.
A1  - Taylor, Kent D.
A1  - Teren, Andrej
A1  - Tham, Yih-Chung
A1  - Thiery, Joachim
A1  - Thio, Chris H. L.
A1  - Thomsen, Hauke
A1  - Thorleifsson, Gudmar
A1  - Toniolo, Daniela
A1  - Tonjes, Anke
A1  - Tremblay, Johanne
A1  - Tzoulaki, Ioanna
A1  - Uitterlinden, Andre G.
A1  - Vaccargiu, Simona
A1  - Van Dam, Rob M.
A1  - Van der Harst, Pim
A1  - Van Duijn, Cornelia M.
A1  - Edward, Digna R. Velez
A1  - Verweij, Niek
A1  - Vogelezang, Suzanne
A1  - Volker, Uwe
A1  - Vollenweider, Peter
A1  - Waeber, Gerard
A1  - Waldenberger, Melanie
A1  - Wallentin, Lars
A1  - Wang, Ya Xing
A1  - Wang, Chaolong
A1  - Waterworth, Dawn M.
A1  - Bin Wei, Wen
A1  - White, Harvey
A1  - Whitfield, John B.
A1  - Wild, Sarah H.
A1  - Wilson, James F.
A1  - Wojczynski, Mary K.
A1  - Wong, Charlene
A1  - Wong, Tien-Yin
A1  - Xu, Liang
A1  - Yang, Qiong
A1  - Yasuda, Masayuki
A1  - Yerges-Armstrong, Laura M.
A1  - Zhang, Weihua
A1  - Zonderman, Alan B.
A1  - Rotter, Jerome I.
A1  - Bochud, Murielle
A1  - Psaty, Bruce M.
A1  - Vitart, Veronique
A1  - Wilson, James G.
A1  - Dehghan, Abbas
A1  - Parsa, Afshin
A1  - Chasman, Daniel I.
A1  - Ho, Kevin
A1  - Morris, Andrew P.
A1  - Devuyst, Olivier
A1  - Akilesh, Shreeram
A1  - Pendergrass, Sarah A.
A1  - Sim, Xueling
A1  - Boger, Carsten A.
A1  - Okada, Yukinori
A1  - Edwards, Todd L.
A1  - Snieder, Harold
A1  - Stefansson, Kari
A1  - Hung, Adriana M.
A1  - Heid, Iris M.
A1  - Scholz, Markus
A1  - Teumer, Alexander
A1  - Kottgen, Anna
A1  - Pattaro, Cristian
T1  - A catalog of genetic loci associated with kidney function from analyses of a million individuals
JF  - Nature genetics
N2  - Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
Y1  - 2019
U6  - https://doi.org/10.1038/s41588-019-0407-x
SN  - 1061-4036
SN  - 1546-1718
VL  - 51
IS  - 6
SP  - 957
EP  - +
PB  - Nature Publ. Group
CY  - New York
ER  -