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1. Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i

Author

Harada, Makoto, Adam, Jonathan, Covic, Marcela, Ge, Jianhong, Brandmaier, Stefan, Muschet, Caroline, Huang, Jialing, Han, Siyu, Rommel, Martina, Rotter, Markus, Heier, Margit, Mohney, Robert P., Krumsiek, Jan, Kastenmüller, Gabi, Rathmann, Wolfgang, Zou, Zhongmei, Zukunft, Sven, Scheerer, Markus F., Neschen, Susanne, Adamski, Jerzy, Gieger, Christian, Peters, Annette, Ankerst, Donna P., Meitinger, Thomas, Alderete, Tanya L., de Angelis, Martin Hrabe, Suhre, Karsten, and Wang-Sattler, Rui

Published
2024
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3. Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Author

Neumair, Matthias, Kattan, Michael W, Freedland, Stephen J, Haese, Alexander, Guerrios-Rivera, Lourdes, De Hoedt, Amanda M, Liss, Michael A, Leach, Robin J, Boorjian, Stephen A, Cooperberg, Matthew R, Poyet, Cedric, Saba, Karim, Herkommer, Kathleen, Meissner, Valentin H, Vickers, Andrew J, and Ankerst, Donna P

Subjects
Epidemiology, Public Health, Health Sciences, Urologic Diseases, Prevention, Prostate Cancer, Aging, Cancer, Clinical Research, Humans, Male, Digital Rectal Examination, Prostate-Specific Antigen, Prostatic Neoplasms, Risk Assessment, Clinical risk prediction, Missing data, Prostate cancer, Validation, Public Health and Health Services, General & Internal Medicine, Public health
Abstract

BackgroundWe compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user.MethodsTen North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.ResultsAmong 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.ConclusionDevelopers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

Published
2022

4. Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Author

Neumair, Matthias, Kattan, Michael W., Freedland, Stephen J., Haese, Alexander, Guerrios-Rivera, Lourdes, De Hoedt, Amanda M., Liss, Michael A., Leach, Robin J., Boorjian, Stephen A., Cooperberg, Matthew R., Poyet, Cedric, Saba, Karim, Herkommer, Kathleen, Meissner, Valentin H., Vickers, Andrew J., and Ankerst, Donna P.

Subjects
Statistics - Applications
Abstract

Objective: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. Study Design and Setting: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group greater or equal 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. Results: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.

Published
2021
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10. A Contemporary Prostate Biopsy Risk Calculator Based on Multiple Heterogeneous Cohorts

Author

Ankerst, Donna P, Straubinger, Johanna, Selig, Katharina, Guerrios, Lourdes, De Hoedt, Amanda, Hernandez, Javier, Liss, Michael A, Leach, Robin J, Freedland, Stephen J, Kattan, Michael W, Nam, Robert, Haese, Alexander, Montorsi, Francesco, Boorjian, Stephen A, Cooperberg, Matthew R, Poyet, Cedric, Vertosick, Emily, and Vickers, Andrew J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Prevention, Clinical Research, Prostate Cancer, Urologic Diseases, Cancer, Aged, Biopsy, Clinical Decision-Making, Decision Support Techniques, Digital Rectal Examination, Europe, Humans, Kallikreins, Male, Middle Aged, Neoplasm Grading, North America, Patient Selection, Predictive Value of Tests, Prospective Studies, Prostate-Specific Antigen, Prostatic Neoplasms, Reproducibility of Results, Risk Assessment, Risk Factors, Digital rectal exam, Family history, High-grade disease, Prostate cancer, Prostate-specific antigen, Risk prediction, Urology & Nephrology, Clinical sciences
Abstract

BackgroundProstate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems.ObjectiveWe prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool.Design, setting, and participantsWe obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006-2017 at eight North American institutions for model-building and three European institutions for validation.Outcome measurements and statistical analysisWe used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts.Results and limitationsCross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2-76.8), a small improvement over the AUC of 72.3% (70.9-73.7) for the PCPTRC (p

Published
2018

11. Comparative Analysis of Biopsy Upgrading in Four Prostate Cancer Active Surveillance Cohorts.

Author

Inoue, Lurdes YT, Lin, Daniel W, Newcomb, Lisa F, Leonardson, Amy S, Ankerst, Donna, Gulati, Roman, Carter, H Ballentine, Trock, Bruce J, Carroll, Peter R, Cooperberg, Matthew R, Cowan, Janet E, Klotz, Laurence H, Mamedov, Alexandre, Penson, David F, and Etzioni, Ruth

Subjects
Clinical Research, Urologic Diseases, Prostate Cancer, Aging, Cancer, Adult, Aged, Aged, 80 and over, Biomarkers, Tumor, Biopsy, Disease Progression, Humans, Male, Middle Aged, Neoplasm Grading, Prostate-Specific Antigen, Prostatic Neoplasms, Risk Assessment, Risk Factors, United States, Watchful Waiting, Clinical Sciences, Public Health and Health Services
Abstract

BackgroundActive surveillance (AS) is increasingly accepted for managing low-risk prostate cancer, yet there is no consensus about implementation. This lack of consensus is due in part to uncertainty about risks for disease progression, which have not been systematically compared or integrated across AS studies with variable surveillance protocols and dropout to active treatment.ObjectiveTo compare risks for upgrading from a Gleason score (GS) of 6 or less to 7 or more across AS studies after accounting for differences in surveillance intervals and competing treatments and to evaluate tradeoffs of more versus less frequent biopsies.DesignJoint statistical model of longitudinal prostate-specific antigen (PSA) levels and risks for biopsy upgrading.SettingJohns Hopkins University (JHU); Canary Prostate Active Surveillance Study (PASS); University of California, San Francisco (UCSF); and University of Toronto (UT) AS studies.Patients2576 men aged 40 to 80 years with a GS between 2 and 6 and clinical stage T1 or T2 prostate cancer enrolled between 1995 and 2014.MeasurementsPSA levels and biopsy GSs.ResultsAfter variable surveillance intervals and competing treatments were accounted for, estimated risks for biopsy upgrading were similar in the PASS and UT studies but higher in UCSF and lower in JHU studies. All cohorts had a delay of 3 to 5 months in detecting upgrading with biennial biopsies starting after a first confirmatory biopsy versus annual biopsies.LimitationThe model does not account for possible misclassification of biopsy GS.ConclusionMen in different AS studies have different risks for biopsy upgrading after variable surveillance protocols and competing treatments are accounted for. Despite these differences, the consequences of more versus less frequent biopsies seem to be similar across cohorts. Biennial biopsies seem to be an acceptable alternative to annual biopsies.Primary funding sourceNational Cancer Institute.

Published
2018

17. Angpt2/Tie2 autostimulatory loop controls tumorigenesis

Author

Karabid, Ninelia Minaskan, Wiedemann, Tobias, Gulde, Sebastian, Mohr, Hermine, Segaran, Renu Chandra, Geppert, Julia, Rohm, Maria, Vitale, Giovanni, Gaudenzi, Germano, Dicitore, Alessandra, Ankerst, Donna Pauler, Chen, Yiyao, Braren, Rickmer, Kaissis, Georg, Schilling, Franz, Schillmaier, Mathias, Eisenhofer, Graeme, Herzig, Stephan, Roncaroli, Federico, Honegger, Jürgen B, and Pellegata, Natalia S

Published
2022
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18. Kidney Transplantation after Rescue Allocation - the Eurotransplant Experience: A Retrospective Multicenter Outcome Analysis

Author

Assfalg, Volker, Miller, Gregor, Stocker, Felix, van Meel, Marieke, Groenevelt, Tiny, Tieken, Ineke, Ankerst, Donna, Renders, Lutz, Novotny, Alexander, Hartmann, Daniel, Jell, Alissa, Rahmel, Axel, Wahba, Roger, Muehlfeld, Anja, Antonia, Bouts, Ysebaert, Dirk, Globke, Brigitta, Jacobs-Tulleneers-Thevissen, Daniel, Piros, László, Stippel, Dirk, Heller, Katharina, Eisenberger, Ute, van Laecke, Steven, Weimer, Rolf, Rosenkranz, Alexander R., Berger, Stefan, Fischer, Lutz, Kliem, Volker, Vondran, Florian, Sester, Urban, Schneeberger, Stefan, Harth, Ana, Kuypers, Dirk, Függer, Reinhold, Arnol, Miha, Christiaans, Maarten, Weinmann-Menke, Julia, Krüger, Bernd, Hilbrands, Luuk, Banas, Bernhard, Hakenberg, Oliver, Minnee, Robert, Schwenger, Vedat, Heyne, Nils, van Zuilen, Arjan, Reindl-Schwaighofer, Roman, Lopau, Kai, Huüser, Norbert, and Heemann, Uwe

Published
2021
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19. Tailored statistical methodology for large-scale registry-based clinical data to optimize online risk prediction

Author

Ankerst, Donna (Prof., Ph.D.), Ankerst, Donna (Prof., Ph.D.); Hüser, Norbert H. (Prof. Dr.); Mansmann, Ulrich (Prof. Dr.), Miller, Gregor M., Ankerst, Donna (Prof., Ph.D.), Ankerst, Donna (Prof., Ph.D.); Hüser, Norbert H. (Prof. Dr.); Mansmann, Ulrich (Prof. Dr.), and Miller, Gregor M.

Abstract

Longitudinal data presents various challenges depending on the desired outcome. In this work, for two areas, survival analysis and count models, we have shown how analyses must be adjusted to account for multiple outcomes, select appropriate covariates, and derive predictions. This is demonstrated for the survival after kidney transplantation and for count measures associated with the COVID-19 pandemic., Zeitaufgelöste Daten stellen je nach Zielvariable unterschiedliche Herausforderungen dar. In dieser Arbeit haben wir für zwei Bereiche, Überlebensanalysen und Zählmodelle, gezeigt, wie Analysen angepasst werden müssen, um mehrere Zielvariablen zu berücksichtigen, geeignete Kovariaten auszuwählen und Vorhersagen abzuleiten. Dies wird anhand von Überlebenszahlen nach einer Nierentransplantation und von Häufigkeitsmessungen im Zusammenhang mit der COVID-19-Pandemie demonstriert.

Published
2023

20. Maximizing risk prediction by synthesizing multiple data sets accommodating systematic missing data and heterogeneous scales

Author

Ankerst, Donna (Prof., Ph.D.), Ankerst, Donna (Prof., Ph.D.);Gelfond, Jonathan (Assoc. Prof., Ph.D.), Neumair, Matthias Josef Philipp, Ankerst, Donna (Prof., Ph.D.), Ankerst, Donna (Prof., Ph.D.);Gelfond, Jonathan (Assoc. Prof., Ph.D.), and Neumair, Matthias Josef Philipp

Abstract

Statistical analyses highly depend on the data quality and sample size. For lots of hypotheses, it is not sufficient to use only one data source. But data from different sources are often on a different scale and miss-aligned with the main data. Methods for treating this defiance additionally to high missingness and rare events were investigated in this thesis and shown in three studies from different study areas: prostate cancer, forestry, and fatal mountain accidents., Statistische Analysen hängen von der Datenqualität und Stichprobengröße ab. Für viele Hypothesen reicht es nicht, nur eine Datenquelle zu nutzen. Daten unterschiedlicher Quellen haben oft verschiedene Skalierungen. Der Umgang mit diesen Unstimmigkeiten, zusätzlich zu vielen fehlenden Werten und seltenen Ereignissen, wurde in dieser Arbeit untersucht und an drei Studien aus verschiedenen Untersuchungsgebieten veranschaulicht: Prostatakrebs, Forstwirtschaft und tödliche Bergunfälle.

Published
2023

22. Precision Medicine in Active Surveillance for Prostate Cancer: Development of the Canary–Early Detection Research Network Active Surveillance Biopsy Risk Calculator

Author

Ankerst, Donna P, Xia, Jing, Thompson, Ian M, Hoefler, Josef, Newcomb, Lisa F, Brooks, James D, Carroll, Peter R, Ellis, William J, Gleave, Martin E, Lance, Raymond S, Nelson, Peter S, Wagner, Andrew A, Wei, John T, Etzioni, Ruth, and Lin, Daniel W

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Prevention, Clinical Research, Aging, Urologic Diseases, Cancer, Prostate Cancer, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Good Health and Well Being, Aged, Aged, 80 and over, Biomedical Research, Biopsy, Early Detection of Cancer, Humans, Male, Middle Aged, Precision Medicine, Prospective Studies, Prostatic Neoplasms, Risk Assessment, Watchful Waiting, Active surveillance, Progression, Prostate-specific antigen, Urology & Nephrology, Clinical sciences
Abstract

BackgroundMen on active surveillance (AS) face repeated biopsies. Most biopsy specimens will not show disease progression or change management. Such biopsies do not contribute to patient management and are potentially morbid and costly.ObjectiveTo use a contemporary AS prospective trial to develop a tool to predict AS biopsy outcomes.Design, setting, and participantsBiopsy samples (median: 2; range: 2-9 per patient) from 859 men participating in the Canary Prostate Active Surveillance Study and with Gleason 6 prostate cancer (median follow-up: 35.8 mo; range: 3.0-148.7 mo) were analyzed.Outcome measurements and statistical analysisLogistic regression was used to predict progression, defined as an increase in Gleason score from ≤6 to ≥7 or increase in percentage of cores positive for cancer from

Published
2015

24. Prevalence, lifestyle, and risk factors of erectile dysfunction, premature ejaculation, and low libido in middle‐aged men: first results of the Bavarian Men's Health‐Study.

Author

Herkommer, Kathleen, Meissner, Valentin H., Dinkel, Andreas, Jahnen, Matthias, Schiele, Stefan, Kron, Martina, Ankerst, Donna P., and Gschwend, Jürgen E.

Subjects
PREMATURE ejaculation, MIDDLE-aged men, IMPOTENCE, LIBIDO, SEXUAL dysfunction, WAIST circumference, PATIENT education
Abstract

Background: Erectile dysfunction (ED), premature ejaculation (PE), and low libido (LL) are reported as the most common male sexual dysfunctions. Objective: To evaluate the prevalence of ED, PE, and LL and associations with lifestyle risk factors and comorbidities in middle‐aged men. Materials and methods: This study included a population‐based random sample of 2500 50‐year‐old men who completed validated questionnaires, including the International Index of Erectile Function, the Erection Hardness Score, the Sexual Complaints Screener, and further questionnaires. Multiple logistic regression of outcomes ED, PE, and LL was used to model the association with explanatory factors. Results: The prevalence of at least one sexual dysfunction was 30%. 21%, 5.2%, and 7.2% of men had ED, PE, and LL, respectively. The risk of ED increased with PE (odds ratio [OR]: 1.94, 95% confidence interval [95%CI]: 1.22–3.08), LL (OR: 2.04, 95%CI: 1.26–3.29), higher waist circumference (OR: 2.23, 95%CI: 1.67–2.96), and lower urinary tract symptoms (LUTS) (OR: 1.88, 95%CI: 1.39–2.55), partnership was associated with a lower risk (OR: 0.57, 95%CI: 0.39–0.85). The risk of PE increased with ED (OR: 1.94, 95%CI: 1.23–3.07), partnership (OR:5.42, 95%CI: 1.30–22.60), depression (OR: 2.37, 95%CI: 1.09–5.14), and LUTS (OR: 2.42, 95%CI: 1.52–3.87), and decreased with physical activity (OR: 0.44, 95%CI: 0.21–0.93). The risk of LL increased with ED (OR: 2.09, 95%CI: 1.31–3.34) and poorer self‐rated health (OR: 2.97, 95%CI: 1.54–5.71). Discussion and conclusions: Roughly one in three 50‐year‐old men experience some form of sexual dysfunction and risk factors identified in this study underline the multifactorial nature of ED, PE, and LL. Many risk factors are modifiable which underlines the role of patient education. Modifiable risk factors should be addressed in patient education and men should take active measures to remove the risk posed by these factors. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Reply by Authors

Author

Pruthi, Deepak K., primary, Miller, Gregor, additional, Ankerst, Donna P., additional, Neumair, Matthias, additional, Capitanio, Umberto, additional, Correa, Andres F., additional, Lane, Brian R., additional, Roussel, Eduard, additional, McGregor, Thomas B., additional, Derweesh, Ithaar H., additional, Cordeiro, Mauricio, additional, Pierorazio, Phillip M., additional, Calvo, Carlos, additional, Bi, Hai, additional, Noyes, Sabrina L., additional, Meagher, Margaret, additional, Kutikov, Alexander, additional, Uzzo, Robert G., additional, Van Poppel, Hendrik, additional, Larcher, Alessandro, additional, Montorsi, Francesco, additional, Kattan, Michael W., additional, Kaushik, Dharam, additional, and Liss, Michael A., additional

Published
2023
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26. Diabetes, Obesity, and Pathological Upstaging in Renal Cell Carcinoma: Results From a Large Multi-institutional Consortium

Author

Pruthi, Deepak K., primary, Miller, Gregor, additional, Ankerst, Donna P., additional, Neumair, Matthias, additional, Capitanio, Umberto, additional, Correa, Andres F., additional, Lane, Brian R., additional, Roussel, Eduard, additional, McGregor, Thomas B., additional, Derweesh, Ithaar H., additional, Cordeiro, Mauricio, additional, Pierorazio, Phillip M., additional, Calvo, Carlos, additional, Bi, Hai, additional, Noyes, Sabrina L., additional, Meagher, Margaret, additional, Kutikov, Alexander, additional, Uzzo, Robert G., additional, Van Poppel, Hendrik, additional, Larcher, Alessandro, additional, Montorsi, Francesco, additional, Kattan, Michael W., additional, Kaushik, Dharam, additional, and Liss, Michael A., additional

Published
2023
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29. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

Author

Abdallah, Kald, Aittokallio, Tero, Airola, Antti, Anghe, Catalina, Azima, Helia, Baertsch, Robert, Ballester, Pedro J, Bare, Chris, Bhandari, Vinayak, Bot, Brian M, Dang, Cuong C, Dunbar, Maria Bekker-Nielsen, Buchardt, Ann-Sophie, Buturovic, Ljubomir, Cao, Da, Chalise, Prabhakar, Cho, Junwoo, Chu, Tzu-Ming, Coley, R Yates, Conjeti, Sailesh, Correia, Sara, Costello, James C, Dai, Ziwei, Dai, Junqiang, Dargatz, Philip, Delavarkhan, Sam, Deng, Detian, Dhanik, Ankur, Du, Yu, Elangovan, Aparna, Ellis, Shellie, Elo, Laura L, Espiritu, Shadrielle M, Fan, Fan, Farshi, Ashkan B, Freitas, Ana, Fridley, Brooke, Friend, Stephen, Fuchs, Christiane, Gofer, Eyal, Peddinti, Gopalacharyulu, Graw, Stefan, Greiner, Russ, Guan, Yuanfang, Guinney, Justin, Guo, Jing, Gupta, Pankaj, Guyer, Anna I, Han, Jiawei, Hansen, Niels R, Chang, Billy HW, Hirvonen, Outi, Huang, Barbara, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph G, Jayaswa, Vivek, Jeon, Jouhyun, Ji, Zhicheng, Juvvadi, Deekshith, Jyrkkiö, Sirkku, Kanigel-Winner, Kimberly, Katouzian, Amin, Kazanov, Marat D, Khan, Suleiman A, Khayyer, Shahin, Kim, Dalho, Golinska, Agnieszka K, Koestler, Devin, Kokowicz, Fernanda, Kondofersky, Ivan, Krautenbacher, Norbert, Krstajic, Damjan, Kumar, Luke, Kurz, Christoph, Kyan, Matthew, Laajala, Teemu D, Laimighofer, Michael, Lee, Eunjee, Lesinski, Wojciech, Li, Miaozhu, Li, Ye, Lian, Qiuyu, Liang, Xiaotao, Lim, Minseong, Lin, Henry, Lin, Xihui, Lu, Jing, Mahmoudian, Mehrad, Manshaei, Roozbeh, Meier, Richard, Miljkovic, Dejan, Mirtti, Tuomas, Mnich, Krzysztof, Navab, Nassir, Neto, Elias C, Newton, Yulia, Norman, Thea, Pahikkala, Tapio, Pal, Subhabrata, Park, Byeongju, Patel, Jaykumar, Pathak, Swetabh, Pattin, Alejandrina, Ankerst, Donna P, Peng, Jian, Petersen, Anne H, Philip, Robin, Piccolo, Stephen R, Pölsterl, Sebastian, Polewko-Klim, Aneta, Rao, Karthik, Ren, Xiang, Rocha, Miguel, Rudnicki, Witold R., Ryan, Charles J, Ryu, Hyunnam, Sartor, Oliver, Scherb, Hagen, Sehgal, Raghav, Seyednasrollah, Fatemeh, Shang, Jingbo, Shao, Bin, Shen, Liji, Sher, Howard, Shiga, Motoki, Sokolov, Artem, Söllner, Julia F, Song, Lei, Soule, Howard, Stolovitzky, Gustavo, Stuart, Josh, Sun, Ren, Sweeney, Christopher J, Tahmasebi, Nazanin, Tan, Kar-Tong, Tomaziu, Lisbeth, Usset, Joseph, Vang, Yeeleng S, Vega, Roberto, Vieira, Vitor, Wang, David, Wang, Difei, Wang, Junmei, Wang, Lichao, Wang, Sheng, Wang, Tao, Wang, Yue, Wolfinger, Russ, Wong, Chris, Wu, Zhenke, Xiao, Jinfeng, Xie, Xiaohui, Xie, Yang, Xin, Doris, Yang, Hojin, Yu, Nancy, Yu, Thomas, Yu, Xiang, Zahedi, Sulmaz, Zanin, Massimiliano, Zhang, Chihao, Zhang, Jingwen, Zhang, Shihua, Zhang, Yanchun, Zhou, Fang Liz, Zhu, Hongtu, Zhu, Shanfeng, Zhu, Yuxin, Winner, Kimberly Kanigel, Bare, J Christopher, Neto, Elias Chaibub, Peddinti, Gopal, and Scher, Howard I

Published
2017
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30. Individual participant data modeling approaches for clinical risk model validation and bias adjustment

Author

Ankerst, Donna (Prof., Ph.D.), Ankerst, Donna (Prof., Ph.D.);Byeongyeob, Choi (Prof., Ph.D.), Chen, Yiyao, Ankerst, Donna (Prof., Ph.D.), Ankerst, Donna (Prof., Ph.D.);Byeongyeob, Choi (Prof., Ph.D.), and Chen, Yiyao

Abstract

Heterogeneity between validation and training data used to build clinical risk prediction models biases the performance of the models on validation samples. A framework is developed to accommodate selection bias coming from heterogeneous distributions of risk factors and verification bias coming from different verification mechanisms between training and validation cohorts. Adjustments result in weighted versions of the usual performance metrics with different weights addressing different types of bias., Die Heterogenität zwischen den Validierungsdaten und den Trainingsdaten, die zur Erstellung der klinischen Risikovorhersagemodelle verwendet wurden, verzerrt die Leistung der Modelle in den Validierungsproben. Es wird ein Rahmen entwickelt, um die Selektionsverzerrung aufgrund heterogener Verteilungen von Risikofaktoren und die Verifizierungsverzerrung aufgrund unterschiedlicher Verifizierungsmechanismen zwischen Trainings- und Validierungskohorten zu berücksichtigen.

Published
2022

31. Data from Epidemiologic Risk Factors in a Comparison of a Barrett Esophagus Registry (BarrettNET) and a Case–Control Population in Germany

Author

Schmidt, Melissa, primary, Ankerst, Donna P., primary, Chen, Yiyao, primary, Wiethaler, Maria, primary, Slotta-Huspenina, Julia, primary, Becker, Karl-Friedrich, primary, Horstmann, Julia, primary, Kohlmayer, Florian, primary, Lehmann, Andreas, primary, Linkohr, Birgit, primary, Strauch, Konstantin, primary, Schmid, Roland M., primary, Quante, Anne S., primary, and Quante, Michael, primary

Published
2023
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32. Supplemental Table 1 from Epidemiologic Risk Factors in a Comparison of a Barrett Esophagus Registry (BarrettNET) and a Case–Control Population in Germany

Author

Schmidt, Melissa, primary, Ankerst, Donna P., primary, Chen, Yiyao, primary, Wiethaler, Maria, primary, Slotta-Huspenina, Julia, primary, Becker, Karl-Friedrich, primary, Horstmann, Julia, primary, Kohlmayer, Florian, primary, Lehmann, Andreas, primary, Linkohr, Birgit, primary, Strauch, Konstantin, primary, Schmid, Roland M., primary, Quante, Anne S., primary, and Quante, Michael, primary

Published
2023
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39. Translational Risk Models

Author

Ankerst, Donna Pauler, Seifert-Klauss, Vanadin, Kiechle, Marion, Klüppelberg, Claudia, editor, Straub, Daniel, editor, and Welpe, Isabell M., editor

Published
2014
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40. Kidney Transplantation after Rescue Allocation - The Eurotransplant Experience

Author

Assfalg, Volker, Miller, Gregor, Stocker, Felix, van Meel, Marieke, Groenevelt, Tiny, Tieken, Ineke, Ankerst, Donna, Renders, Lutz, Novotny, Alexander, Hartmann, Daniel, Jell, Alissa, Rahmel, Axel, Wahba, Roger, Mühlfeld, Anja, Bouts, Antonia, Ysebaert, Dirk, Globke, Brigitta, Jacobs-Tulleneers-Thevissen, Daniel, Piros, László, Stippel, Dirk, Heller, Katharina, Eisenberger, Ute, van Laecke, Steven, Weimer, Rolf, Rosenkranz, Alexander R., Berger, Stefan, Fischer, Lutz, Kliem, Volker, Vondran, Florian, Sester, Urban, Schneeberger, Stefan, Harth, Ana, Kuypers, Dirk, Függer, Reinhold, Arnol, Miha, Christiaans, Maarten, Weinmann-Menke, Julia, Krüger, Bernd, Hilbrands, Luuk, Banas, Bernhard, Hakenberg, Oliver, Minnee, Robert, Schwenger, Vedat, Heyne, Nils, van Zuilen, Arjan, Reindl-Schwaighofer, Roman, Lopau, Kai, Hüser, Norbert, Heemann, Uwe, Huüser, Norbert, Surgery, Groningen Kidney Center (GKC), Groningen Institute for Organ Transplantation (GIOT), Paediatric Nephrology, ARD - Amsterdam Reproduction and Development, Interne Geneeskunde, MUMC+: MA Nefrologie (9), RS: NUTRIM - R3 - Respiratory & Age-related Health, Pediatrics, Basic (bio-) Medical Sciences, and Diabetes Pathology & Therapy

Subjects
Waiting time, kidney, PREDICTOR, medicine.medical_specialty, Tissue and Organ Procurement, Eurotransplant, medicine.medical_treatment, Medizin, Outcome analysis, deceased donor renal transplantation, COLD ISCHEMIA TIME, surgery, All institutes and research themes of the Radboud University Medical Center, Internal medicine, DIALYSIS, Medicine and Health Sciences, Humans, Medicine, standard allocation, FRAILTY, Kidney transplantation, Dialysis, Retrospective Studies, RISK, GRAFT FAILURE, Transplantation, Deceased donor, rescue allocation, business.industry, MORTALITY, Incidence (epidemiology), Graft Survival, algorithms Eurotransplant kidney allocation system, ASSOCIATION, medicine.disease, Kidney Transplantation, Tissue Donors, Confidence interval, Eurotransplant senior program, RECIPIENTS, Treatment Outcome, SURVIVAL, Human medicine, Renal disorders Radboud Institute for Health Sciences [Radboudumc 11], business
Abstract

BACKGROUND: At Eurotransplant (ET), kidneys are transferred to "rescue allocation" (RA), whenever the standard allocation (SA) algorithms Eurotransplant Kidney Allocation System (ETKAS) and Eurotransplant Senior Program (ESP) fail. We analyzed the outcome of RA. METHODS: Retrospective patient clinical and demographic characteristics association analyses were performed with graft outcomes for 2422 recipients of a deceased donor renal transplantation (DDRT) after RA versus 25 481 after SA from 71 centers across all ET countries from 2006 to 2018. RESULTS: Numbers of DDRTs after RA increased over the time, especially in Germany. RA played a minor role in ESP versus ETKAS (2.7% versus 10.4%). RA recipients and donors were older compared with SA recipients and donors, cold ischemia times were longer, waiting times were shorter, and the incidence of primary nonfunction was comparable. Among ETKAS recipients, HLA matching was more favorable in SA (mean 3.7 versus 2.5). In multivariate modeling, the incidence of graft loss in ETKAS recipients was reduced in RA compared with SA (subdistribution hazard ratio, 0.80; 95% confidence interval [0.70-0.91], P

Published
2022
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