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Deshpande S, Doyle FJ, Dassau E. Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY : A PUBLICATION OF THE IEEE CONTROL SYSTEMS SOCIETY 2023; 31:2261-2274. [PMID: 38525198 PMCID: PMC10958373 DOI: 10.1109/tcst.2023.3291573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
We present design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a joint function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB were jointly low, independent of glucose magnitude, to limit hyperglycemia while aggressively reduces for negative glucose ROC to avoid hypoglycemia. The proposed controller was evaluated using two simulation scenarios: an induced resistance scenario and a nominal scenario to highlight the performance over a reference zone MPC with glucose ROC weighting only. The continuous adaption scheme resulted in consistent improvement for the entire glucose range without incurring additional risk of hypoglycemia. For the induced resistance and no feedforward bolus scenario, the percent time in 70-180 mg/dL was higher (53.5% versus 48.9%, p<0.001) with larger improvement in the overnight percent time in tighter glucose range 70-140 mg/dL (70.9% versus 52.9%, p<0.001). The results from extensive simulations, as well as clinical validation in three different outpatient studies demonstrate the utility and safety of the proposed zone MPC.
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Affiliation(s)
- Sunil Deshpande
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
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Pinsker JE, Dassau E, Deshpande S, Raghinaru D, Buckingham BA, Kudva YC, Laffel LM, Levy CJ, Church MM, Desrochers H, Ekhlaspour L, Kaur RJ, Levister C, Shi D, Lum JW, Kollman C, Doyle FJ. Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4. Diabetes Technol Ther 2022; 24:635-642. [PMID: 35549708 PMCID: PMC9422791 DOI: 10.1089/dia.2022.0084] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background: Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings. Methods: Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome. Results: Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%], P = 0.22). Median time <70 mg/dL improved from 3.0% with SAP to 1.6% with AID (-1.5% [-2.4% to -0.5%], P = 0.002). The adaptation system decreased initial basal rates by a median of 4% (-8%, 16%) and increased initial carbohydrate ratios by a median of 45% (32%, 59%) after 13 weeks. Conclusions: Automated adaptation of insulin delivery settings with AID use did not significantly improve time-in-range in this very well-controlled population. Additional study and further refinement of the adaptation system are needed, especially in populations with differing degrees of baseline glycemic control, who may show larger benefits from adaptation.
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Affiliation(s)
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Sunil Deshpande
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Dan Raghinaru
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Bruce A. Buckingham
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Yogish C. Kudva
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Lori M. Laffel
- Research Division, Joslin Diabetes Center and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Carol J. Levy
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Hannah Desrochers
- Research Division, Joslin Diabetes Center and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Laya Ekhlaspour
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Ravinder Jeet Kaur
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Camilla Levister
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dawei Shi
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - John W. Lum
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Craig Kollman
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
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Ozaslan B, Levy CJ, Kudva YC, Pinsker JE, O'Malley G, Kaur RJ, Castorino K, Levister C, Trinidad MC, Desjardins D, Church MM, Plesser M, McCrady-Spitzer S, Ogyaadu S, Nelson K, Reid C, Deshpande S, Kremers WK, Doyle FJ, Rosenn B, Dassau E. Feasibility of Closed-Loop Insulin Delivery with a Pregnancy-Specific Zone Model Predictive Control Algorithm. Diabetes Technol Ther 2022; 24:471-480. [PMID: 35230138 PMCID: PMC9464083 DOI: 10.1089/dia.2021.0521] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Objective: Evaluating the feasibility of closed-loop insulin delivery with a zone model predictive control (zone-MPC) algorithm designed for pregnancy complicated by type 1 diabetes (T1D). Research Design and Methods: Pregnant women with T1D from 14 to 32 weeks gestation already using continuous glucose monitor (CGM) augmented pump therapy were enrolled in a 2-day multicenter supervised outpatient study evaluating pregnancy-specific zone-MPC based closed-loop control (CLC) with the interoperable artificial pancreas system (iAPS) running on an unlocked smartphone. Meals and activities were unrestricted. The primary outcome was the CGM percentage of time between 63 and 140 mg/dL compared with participants' 1-week run-in period. Early (2-h) postprandial glucose control was also evaluated. Results: Eleven participants completed the study (age: 30.6 ± 4.1 years; gestational age: 20.7 ± 3.5 weeks; weight: 76.5 ± 15.3 kg; hemoglobin A1c: 5.6% ± 0.5% at enrollment). No serious adverse events occurred. Compared with the 1-week run-in, there was an increased percentage of time in 63-140 mg/dL during supervised CLC (CLC: 81.5%, run-in: 64%, P = 0.007) with less time >140 mg/dL (CLC: 16.5%, run-in: 30.8%, P = 0.029) and time <63 mg/dL (CLC: 2.0%, run-in:5.2%, P = 0.039). There was also less time <54 mg/dL (CLC: 0.7%, run-in:1.6%, P = 0.030) and >180 mg/dL (CLC: 4.9%, run-in: 13.1%, P = 0.032). Overnight glucose control was comparable, except for less time >250 mg/dL (CLC: 0%, run-in:3.9%, P = 0.030) and lower glucose standard deviation (CLC: 23.8 mg/dL, run-in:42.8 mg/dL, P = 0.007) during CLC. Conclusion: In this pilot study, use of the pregnancy-specific zone-MPC was feasible in pregnant women with T1D. Although the duration of our study was short and the number of participants was small, our findings add to the limited data available on the use of CLC systems during pregnancy (NCT04492566).
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Affiliation(s)
- Basak Ozaslan
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Carol J. Levy
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Grenye O'Malley
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Camilla Levister
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Mitchell Plesser
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Selassie Ogyaadu
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kristen Nelson
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | | | - Sunil Deshpande
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | | | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
| | - Barak Rosenn
- Robert Wood Johnson Barnabas Health, New Brunswick, New Jersey, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
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Askari MR, Rashid M, Sun X, Sevil M, Shahidehpour A, Kawaji K, Cinar A. Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes. BIOMEDINFORMATICS 2022; 2:297-317. [PMID: 36968645 PMCID: PMC10038808 DOI: 10.3390/biomedinformatics2020019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Objective: The interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID). Methods: Two challenges in time-series data collected in daily living are addressed: data quality improvement and the detection of unannounced disturbances of BGC. CGM data have missing values for varying periods of time and outliers. People may neglect reporting their meal and physical activity information. In this work, novel methods for preprocessing real-world data collected from people with T1D and the detection of meal and exercise events are presented. Four recurrent neural network (RNN) models are investigated to detect the occurrences of meals and physical activities disjointly or concurrently. Results: RNNs with long short-term memory (LSTM) with 1D convolution layers and bidirectional LSTM with 1D convolution layers have average accuracy scores of 92.32% and 92.29%, and outperform other RNN models. The F1 scores for each individual range from 96.06% to 91.41% for these two RNNs. Conclusions: RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation.
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Affiliation(s)
- Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Xiaoyu Sun
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Andrew Shahidehpour
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Keigo Kawaji
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
- Correspondence: ; Tel.:(312) 567-3042
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Kaur RJ, Deshpande S, Pinsker JE, Gilliam WP, McCrady-Spitzer S, Zaniletti I, Desjardins D, Church MM, Doyle III FJ, Kremers WK, Dassau E, Kudva YC. Outpatient Randomized Crossover Automated Insulin Delivery Versus Conventional Therapy with Induced Stress Challenges. Diabetes Technol Ther 2022; 24:338-349. [PMID: 35049354 PMCID: PMC9271334 DOI: 10.1089/dia.2021.0436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Background: Automated insulin delivery (AID) systems have not been evaluated in the context of psychological and pharmacological stress in type 1 diabetes. Our objective was to determine glycemic control and insulin use with Zone Model Predictive Control (zone-MPC) AID system enhanced for states of persistent hyperglycemia versus sensor-augmented pump (SAP) during outpatient use, including in-clinic induced stress. Materials and Methods: Randomized, crossover, 2-week trial of zone-MPC AID versus SAP in 14 adults with type 1 diabetes. In each arm, each participant was studied in-clinic with psychological stress induction (Trier Social Stress Test [TSST] and Socially Evaluated Cold Pressor Test [SECPT]), followed by pharmacological stress induction with oral hydrocortisone (total four sessions per participant). The main outcomes were 2-week continuous glucose monitor percent time in range (TIR) 70-180 mg/dL, and glucose and insulin outcomes during and overnight following stress induction. Results: During psychological stress, AID decreased glycemic variability percentage by 13.4% (P = 0.009). During pharmacological stress, including the following overnight, there were no differences in glucose outcomes and total insulin between AID and physician-assisted SAP. However, with AID total user-requested insulin was lower by 6.9 U (P = 0.01) for pharmacological stress. Stress induction was validated by changes in heart rate and salivary cortisol levels. During the 2-week AID use, TIR was 74.4% (vs. SAP 63.1%, P = 0.001) and overnight TIR was 78.3% (vs. SAP 63.1%, P = 0.004). There were no adverse events. Conclusions: Zone-MPC AID can reduce glycemic variability and the need for user-requested insulin during pharmacological stress and can improve overall glycemic outcomes. Clinical Trial Identifier NCT04142229.
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Affiliation(s)
- Ravinder Jeet Kaur
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunil Deshpande
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | | | | | - Shelly McCrady-Spitzer
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, Minnesota, USA
| | - Isabella Zaniletti
- Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Donna Desjardins
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, Minnesota, USA
| | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Francis J. Doyle III
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Walter K. Kremers
- Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Yogish C. Kudva
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, Minnesota, USA
- Address correspondence to: Yogish C. Kudva, MBBS, Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, 200 First Street SW, Rochester MN 55902, USA
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Ozaslan B, Deshpande S, Doyle FJ, Dassau E. Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes. Front Endocrinol (Lausanne) 2022; 12:768639. [PMID: 35392357 PMCID: PMC8982146 DOI: 10.3389/fendo.2021.768639] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/30/2021] [Indexed: 01/13/2023] Open
Abstract
Type 1 diabetes (T1D) increases the risk for pregnancy complications. Increased time in the pregnancy glucose target range (63-140 mg/dL as suggested by clinical guidelines) is associated with improved pregnancy outcomes that underscores the need for tight glycemic control. While closed-loop control is highly effective in regulating blood glucose levels in individuals with T1D, its use during pregnancy requires adjustments to meet the tight glycemic control and changing insulin requirements with advancing gestation. In this paper, we tailor a zone model predictive controller (zone-MPC), an optimization-based control strategy that uses model predictions, for use during pregnancy and verify its robustness in-silico through a broad range of scenarios. We customize the existing zone-MPC to satisfy pregnancy-specific glucose control objectives by having (i) lower target glycemic zones (i.e., 80-110 mg/dL daytime and 80-100 mg/dL overnight), (ii) more assertive correction bolus for hyperglycemia, and (iii) a control strategy that results in more aggressive postprandial insulin delivery to keep glucose within the target zone. The emphasis is on leveraging the flexible design of zone-MPC to obtain a controller that satisfies glycemic outcomes recommended for pregnancy based on clinical insight. To verify this pregnancy-specific zone-MPC design, we use the UVA/Padova simulator and conduct in-silico experiments on 10 subjects over 13 scenarios ranging from scenarios with ideal metabolic and treatment parameters for pregnancy to extreme scenarios with such parameters that are highly deviant from the ideal. All scenarios had three meals per day and each meal had 40 grams of carbohydrates. Across 13 scenarios, pregnancy-specific zone-MPC led to a 10.3 ± 5.3% increase in the time in pregnancy target range (baseline zone-MPC: 70.6 ± 15.0%, pregnancy-specific zone-MPC: 80.8 ± 11.3%, p < 0.001) and a 10.7 ± 4.8% reduction in the time above the target range (baseline zone-MPC: 29.0 ± 15.4%, pregnancy-specific zone-MPC: 18.3 ± 12.0, p < 0.001). There was no significant difference in the time below range between the controllers (baseline zone-MPC: 0.5 ± 1.2%, pregnancy-specific zone-MPC: 3.5 ± 1.9%, p = 0.1). The extensive simulation results show improved performance in the pregnancy target range with pregnancy-specific zone MPC, suggest robustness of the zone-MPC in tight glucose control scenarios, and emphasize the need for customized glucose control systems for pregnancy.
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Affiliation(s)
| | | | | | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, United States
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Herzog AL, Busch J, Wanner C, von Jouanne-Diedrich HK. Survey about do-it-yourself closed loop systems in the treatment of diabetes in Germany. PLoS One 2020; 15:e0243465. [PMID: 33332410 PMCID: PMC7746287 DOI: 10.1371/journal.pone.0243465] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 11/22/2020] [Indexed: 11/19/2022] Open
Abstract
Continuous glucose monitoring (CGM) improves treatment with lower blood glucose levels and less patient effort. In combination with continuous insulin application, glycemic control improves and hypoglycemic episodes should decrease. Direct feedback of CGM to continuous subcutaneous insulin application, using an algorithm is called a closed-loop (CL) artificial pancreas system. Commercial devices stop insulin application by predicting hypoglycemic blood glucose levels through direct interaction between the sensor and pump. The prediction is usually made for about 30 minutes and insulin delivery is restarted at the previous level if a rise in blood glucose is predicted within the next 30 minutes (hybrid closed loop system, HCL this is known as a predictive low glucose suspend system (PLGS)). In a fully CL system, sensor and pump communicate permanently with each other. Hybrid closed-loop (HCL) systems, which require the user to estimate the meal size and provide a meal insulin basis, are commercially available in Germany at the moment. These systems result in fewer hyperglycemic and hypoglycemic episodes with improved glucose control. Open source initiatives have provided support by building do-it-yourself CL (DIYCL) devices for automated insulin application since 2014, and are used by a tech-savvy subgroup of patients. The first commercial hybrid CL system has been available in Germany since September 2019. We surveyed 1054 patients to determine which devices are currently used, which features would be in demand by potential users, and the benefits of DIYCL systems. 9.7% of these used a DIYCL system, while 50% would most likely trust these systems but more than 85% of the patients would use a commercial closed loop system, if available. The DIYCL users had a better glucose control regarding their time in range (TIR) and glycated hemoglobin (HbA1c).
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Affiliation(s)
- Anna Laura Herzog
- Division of Nephrology, Transplantationszentrum, University of Würzburg, Universitätsklinikum, Würzburg, Germany
- * E-mail:
| | - Jonas Busch
- TH Aschaffenburg (University of Applied Sciences), Aschaffenburg, Germany
| | - Christoph Wanner
- Division of Nephrology, Medizinische Klinik I, University of Würzburg, Universitätsklinikum, Würzburg, Germany
| | - Holger K. von Jouanne-Diedrich
- Competence Center for Artificial Intelligence, Faculty of Engineering, TH Aschaffenburg (University of Applied Sciences), Aschaffenburg, Germany
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