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Okita J, Nakata T, Uchida H, Kudo A, Fukuda A, Ueno T, Tanigawa M, Sato N, Shibata H. Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease. BMC Nephrol 2024; 25:101. [PMID: 38493099 PMCID: PMC10943785 DOI: 10.1186/s12882-024-03527-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Predicting time to renal replacement therapy (RRT) is important in patients at high risk for end-stage kidney disease. We developed and validated machine learning models for predicting the time to RRT and compared its accuracy with conventional prediction methods that uses the rate of estimated glomerular filtration rate (eGFR) decline. METHODS Data of adult chronic kidney disease (CKD) patients who underwent hemodialysis at Oita University Hospital from April 2016 to March 2021 were extracted from electronic medical records (N = 135). A new machine learning predictor was compared with the established prediction method that uses the eGFR decline rate and the accuracy of the prediction models was determined using the coefficient of determination (R2). The data were preprocessed and split into training and validation datasets. We created multiple machine learning models using the training data and evaluated their accuracy using validation data. Furthermore, we predicted the time to RRT using a conventional prediction method that uses the eGFR decline rate for patients who had measured eGFR three or more times in two years and evaluated its accuracy. RESULTS The least absolute shrinkage and selection operator regression model exhibited moderate accuracy with an R2 of 0.60. By contrast, the conventional prediction method was found to be extremely low with an R2 of -17.1. CONCLUSIONS The significance of this study is that it shows that machine learning can predict time to RRT moderately well with continuous values from data at a single time point. This approach outperforms the conventional prediction method that uses eGFR time series data and presents new avenues for CKD treatment.
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Affiliation(s)
- Jun Okita
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Takeshi Nakata
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan.
| | - Hiroki Uchida
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Akiko Kudo
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Akihiro Fukuda
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Tamio Ueno
- Department of Medical Technology and Sciences, School of Health Sciences at Fukuoka, International University of Health and Welfare, Okawa, Japan
| | - Masato Tanigawa
- Department of Biophysics, Faculty of Medicine, Oita University, Oita, Japan
| | - Noboru Sato
- Department of Healthcare AI Data Science, Faculty of Medicine, Oita University, Oita, Japan
| | - Hirotaka Shibata
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
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Titapiccolo JI, Lonati C, Goethel-Paal B, Bello AR, Bellocchio F, Pizzo A, Theodose M, Salvador MEB, Schofield M, Cioffi M, Basnayake K, Chisholm C, Mitrovic S, Trkulja M, Arens HJ, Stuard S, Neri L. Chronic kidney disease-associated pruritus (CKD-aP) is associated with worse quality of life and increased healthcare utilization among dialysis patients. Qual Life Res 2023; 32:2939-2950. [PMID: 37269433 DOI: 10.1007/s11136-023-03438-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/13/2023] [Indexed: 06/05/2023]
Abstract
PURPOSE Chronic pruritus significantly impairs hemodialysis patients' health status and quality of life (QOL) and it is associated with higher mortality rate, more frequent hospitalizations, poorer dialysis and medication adherence, and deteriorated mental status. However, pruritus is still underestimated, underdiagnosed, and undertreated in the real-life clinical scenario. We investigated prevalence, clinical characteristics, clinical correlates, severity as well as physical and psychological burden of chronic pruritus among adult hemodialysis patients in a large international real-world cohort. METHODS We conducted a retrospective cross-sectional study of patients registered in 152 Fresenius Medical Care (FMC) NephroCare clinics located in Italy, France, Ireland, United Kingdom, and Spain. Demographic and medical data were retrieved from the EuCliD® (European Clinical) database, while information on pruritus and QoL were abstracted from KDQOL™-36 and 5-D Itch questionnaire scores. RESULTS A total of 6221 patients were included, of which 1238 were from France, 163 Ireland, 1469 Italy, 2633 Spain, and 718 UK. The prevalence of mild-to-severe pruritus was 47.9% (n = 2977 patients). Increased pruritus severity was associated with increased use of antidepressants, antihistamines, and gabapentin. Patients with severe pruritus more likely suffered from diabetes, more frequently missed dialysis sessions, and underwent more hospitalizations due to infections. Both mental and physical QOL scores were progressively lower as the severity of pruritus increased; this association was robust to adjustment for potential confounders. CONCLUSION This international real-world analysis confirms that chronic pruritus is a highly prevalent condition among dialysis patients and highlights its considerable burden on several dimensions of patients' life.
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Affiliation(s)
- Jasmine Ion Titapiccolo
- International Data Science-Clinical Advanced Analytics, Global Medical Office, Fresenius Medical Care, Palazzo Pignano, Italy
| | - Caterina Lonati
- Center for Preclinical Research, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Berit Goethel-Paal
- Global Medical Office, EMEA CoE Clinical & Therapeutical Governance, Fresenius Medical Care, Bad Homburg, Germany
| | | | - Francesco Bellocchio
- International Data Science-Clinical Advanced Analytics, Global Medical Office, Fresenius Medical Care, Palazzo Pignano, Italy
| | | | | | | | | | | | | | - Chis Chisholm
- Fresenius Medical Care (UK) Ltd., 2HU, Sutton-in-Ashfield, UK
| | - Suzanne Mitrovic
- Nursing Care Care Operations EMEA, Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany
| | - Marjelka Trkulja
- Nursing Care Care Operations EMEA, Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany
| | - Hans-Juergen Arens
- Frenova International Clinical Research Services, Global Medical Office, Fresenius Medical Care, Bad Homburg, Germany
| | - Stefano Stuard
- Global Medical Office, EMEA CoE Clinical & Therapeutical Governance, Fresenius Medical Care, Bad Homburg, Germany
| | - Luca Neri
- International Data Science-Clinical Advanced Analytics, Global Medical Office, Fresenius Medical Care, Palazzo Pignano, Italy.
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Ji L, Zhang W, Huang J, Tian J, Zhong X, Luo J, Zhu S, He Z, Tong Y, Meng X, Kang Y, Bi Q. Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning. Front Public Health 2022; 10:1015952. [PMID: 36466509 PMCID: PMC9714267 DOI: 10.3389/fpubh.2022.1015952] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
Background Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well. Methods The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Results Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance. Conclusion Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.
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Affiliation(s)
- Lichen Ji
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,Department of Orthopedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wei Zhang
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
| | - Jiaqing Huang
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,The Second Clinic Medical College, Zhejiang Chinese Medicine University, Hangzhou, China
| | - Jinlong Tian
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China
| | - Xugang Zhong
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
| | - Junchao Luo
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,Department of Orthopedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Senbo Zhu
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,Department of Orthopedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zeju He
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,Department of Orthopedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yu Tong
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
| | - Xiang Meng
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China
| | - Yao Kang
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,Yao Kang
| | - Qing Bi
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China,*Correspondence: Qing Bi
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Russo GT, Giandalia A, Ceriello A, Di Bartolo P, Di Cianni G, Fioretto P, Giorda CB, Manicardi V, Pontremoli R, Viazzi F, Lucisano G, Nicolucci A, De Cosmo S. A prediction model to assess the risk of egfr loss in patients with type 2 diabetes and preserved kidney function: The amd annals initiative. Diabetes Res Clin Pract 2022; 192:110092. [PMID: 36167264 DOI: 10.1016/j.diabres.2022.110092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/05/2022] [Accepted: 09/19/2022] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To develop and validate a model for predicting 5-year eGFR-loss in type 2 diabetes mellitus (T2DM) patients with preserved renal function at baseline. RESEARCH DESIGN AND METHODS A cohort of 504.532 T2DM outpatients participating to the Medical Associations of Diabetologists (AMD) Annals Initiative was splitted into the Learning and Validation cohorts, in which the predictive model was respectively developed and validated. A multivariate Cox proportional hazard regression model including all baseline characteristics was performed to identify predictors of eGFR-loss. A weight derived from regression coefficients was assigned to each variable and the overall sum of weights determined the 0 to 8-risk score. RESULTS A set of demographic, clinical and laboratory parameters entered the final model. The eGFR-loss score showed a good performance in the Validation cohort. Increasing score values progressively identified a higher risk of GFR loss: a score ≥ 8 was associated with a HR of 13.48 (12.96-14.01) in the Learning and a HR of 13.45 (12.93-13.99) in the Validation cohort. The 5 years-probability of developing the study outcome was 55.9% higher in subjects with a score ≥ 8. CONCLUSIONS In the large AMD Annals Initiative cohort, we developed and validated an eGFR-loss prediction model to identify T2DM patients at risk of developing clinically meaningful renal complications within a 5-years time frame.
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Affiliation(s)
- G T Russo
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy.
| | - A Giandalia
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy.
| | - A Ceriello
- Department of Cardiovascular and Metabolic Diseases, IRCCS Gruppo Multimedica, MI, Italy.
| | | | - G Di Cianni
- Diabetes and Metabolic Diseases Unit, Health Local Unit North-West Tuscany, Livorno, Italy.
| | - P Fioretto
- Department of Medicine, University of Padua, Unit of Medical Clinic 3, Hospital of Padua, Padua, Italy.
| | - C B Giorda
- Diabetes and Metabolism Unit ASL Turin 5 Chieri (TO), Italy.
| | - V Manicardi
- Diabetes Consultant, Salus Hospital, Reggio Emilia, Italy.
| | - R Pontremoli
- Università degli Studi and IRCCS Ospedale Policlinico San Martino, Genova, Italy.
| | - F Viazzi
- Università degli Studi and IRCCS Ospedale Policlinico San Martino, Genova, Italy.
| | - G Lucisano
- Center for Outcomes Research and Clinical Epidemiology, CORESEARCH, Pescara, Italy.
| | - A Nicolucci
- Center for Outcomes Research and Clinical Epidemiology, CORESEARCH, Pescara, Italy.
| | - S De Cosmo
- Department of Medical Sciences, Scientific Institute "Casa Sollievo della Sofferenza", San Giovanni Rotondo (FG), Italy.
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Neri L, Lonati C, Titapiccolo JI, Nadal J, Meiselbach H, Schmid M, Baerthlein B, Tschulena U, Schneider MP, Schultheiss UT, Barbieri C, Moore C, Steppan S, Eckardt KU, Stuard S, Bellocchio F. The Cardiovascular Literature-Based Risk Algorithm (CALIBRA): Predicting Cardiovascular Events in Patients With Non-Dialysis Dependent Chronic Kidney Disease. FRONTIERS IN NEPHROLOGY 2022; 2:922251. [PMID: 37675027 PMCID: PMC10479593 DOI: 10.3389/fneph.2022.922251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/20/2022] [Indexed: 09/08/2023]
Abstract
Background and Objectives Cardiovascular (CV) disease is the main cause of morbidity and mortality in patients suffering from chronic kidney disease (CKD). Although it is widely recognized that CV risk assessment represents an essential prerequisite for clinical management, existing prognostic models appear not to be entirely adequate for CKD patients. We derived a literature-based, naïve-bayes model predicting the yearly risk of CV hospitalizations among patients suffering from CKD, referred as the CArdiovascular, LIterature-Based, Risk Algorithm (CALIBRA). Methods CALIBRA incorporates 31 variables including traditional and CKD-specific risk factors. It was validated in two independent CKD populations: the FMC NephroCare cohort (European Clinical Database, EuCliD®) and the German Chronic Kidney Disease (GCKD) study prospective cohort. CALIBRA performance was evaluated by c-statistics and calibration charts. In addition, CALIBRA discrimination was compared with that of three validated tools currently used for CV prediction in CKD, namely the Framingham Heart Study (FHS) risk score, the atherosclerotic cardiovascular disease risk score (ASCVD), and the Individual Data Analysis of Antihypertensive Intervention Trials (INDANA) calculator. Superiority was defined as a ΔAUC>0.05. Results CALIBRA showed good discrimination in both the EuCliD® medical registry (AUC 0.79, 95%CI 0.76-0.81) and the GCKD cohort (AUC 0.73, 95%CI 0.70-0.76). CALIBRA demonstrated improved accuracy compared to the benchmark models in EuCliD® (FHS: ΔAUC=-0.22, p<0.001; ASCVD: ΔAUC=-0.17, p<0.001; INDANA: ΔAUC=-0.14, p<0.001) and GCKD (FHS: ΔAUC=-0.16, p<0.001; ASCVD: ΔAUC=-0.12, p<0.001; INDANA: ΔAUC=-0.04, p<0.001) populations. Accuracy of the CALIBRA score was stable also for patients showing missing variables. Conclusion CALIBRA provides accurate and robust stratification of CKD patients according to CV risk and allows score calculations with improved accuracy compared to established CV risk scores also in real-world clinical cohorts with considerable missingness rates. Our results support the generalizability of CALIBRA across different CKD populations and clinical settings.
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Affiliation(s)
- Luca Neri
- Clinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Vaiano Cremasco, Italy
| | - Caterina Lonati
- Center for Preclinical Research, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Jasmine Ion Titapiccolo
- Clinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Vaiano Cremasco, Italy
| | - Jennifer Nadal
- Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Heike Meiselbach
- Department of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnber, Erlangen, Germany
| | - Matthias Schmid
- Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Barbara Baerthlein
- Medical Centre for Information and Communication Technology (MIK), University Hospital Erlangen, Erlangen, Germany
| | | | - Markus P. Schneider
- Department of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnber, Erlangen, Germany
| | - Ulla T. Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Department of Medicine IV – Nephrology and Primary Care, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Carlo Barbieri
- Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany
| | - Christoph Moore
- Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany
| | - Sonia Steppan
- Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnber, Erlangen, Germany
- Department of Nephrology and Medical Intensive Care, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Stefano Stuard
- Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany
| | - Francesco Bellocchio
- Clinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Vaiano Cremasco, Italy
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