Individualized model discovery: the case of anemia patients

Patient-Specific Modeling and Model Predictive Control Approach to Personalized Optimal Anemia Management

In the condition of anemia, kidneys produce less erythropoietin hormone to stimulate the bone marrow to make red blood cells (RBC) leading to a reduced hemoglobin (Hgb) level, also known as chronic kidney disease (CKD). External recombinant human erythropoietin (EPO) is administrated to maintain a healthy level of Hgb, i.e., 10 - 12 g/dl. The semi-blind robust model identification method is used to obtain a personalized patient model using minimum dose-response data points. The identified patient models are used as predictive models in the model predictive control (MPC) framework. The simulation results of MPC for different CKD patients are compared with those obtained from the existing clinical method, known as anemia management protocol (AMP), used in hospitals. The in-silico results show that MPC outperforms AMP to maintain healthy levels of Hgb without over-or-under- shoots. This offers a considerable performance improvement compared to AMP which is unable to stabilize EPO dosage and shows oscillations in Hgb levels throughout the treatment.Clinical Relevance-This research work provides a framework to help clinicians in decision-making for personalized EPO dose guidance using MPC with semi-blind robust model identification using minimum clinical patient dose-response data.

Control-Relevant Adaptive Personalized Modeling From Limited Clinical Data for Precise Warfarin Management

Warfarin is a challenging drug to administer due to the narrow therapeutic index of the International Normalized Ratio (INR), the inter- and intra-variability of patients, limited clinical data, genetics, and the effects of other medications. Goal: To predict the optimal warfarin dosage in the presence of the aforementioned challenges, we present an adaptive individualized modeling framework based on model (In)validation and semi-blind robust system identification. The model (In)validation technique adapts the identified individualized patient model according to the change in the patient's status to ensure the model's suitability for prediction and controller design. Results: To implement the proposed adaptive modeling framework, the clinical data of warfarin-INR of forty-four patients has been collected at the Robley Rex Veterans Administration Medical Center, Louisville. The proposed algorithm is compared with recursive ARX and ARMAX model identification methods. The results of identified models using one-step-ahead prediction and minimum mean squared analysis (MMSE) show that the proposed framework effectively predicts the warfarin dosage to keep the INR values within the desired range and adapt the individualized patient model to exhibit the true status of the patient throughout treatment. Conclusion: This paper proposes an adaptive personalized patient modeling framework from limited patientspecific clinical data. It is shown by rigorous simulations that the proposed framework can accurately predict a patient's doseresponse characteristics and it can alert the clinician whenever identified models are no longer suitable for prediction and adapt the model to the current status of the patient to reduce the prediction error.

Adaptive Individualized Drug-Dose Response Modeling from a Limited Clinical Data: Case of Warfarin Management

Administration of drugs requires sophisticated methods to determine the drug quantity for optimal results, and it has been a challenging task for the number of diseases. To solve these challenges, in this paper, we present the semi-blind robust model identification technique to find individualized patient models using the minimum number of clinically acquired patient-specific data to determine optimal drug dosage. To ensure the usability of these models for dosage predictability and controller design, the model (In)validation technique is also investigated. As a case study, the patients treated with warfarin are studied to demonstrate the semi-blind robust identification and model (In)validation techniques. The performance of models is assessed by calculating minimum means squared error (MMSE).Clinical Relevance- This work establishes a general framework for adaptive individualized drug-dose response models from a limited number of clinical patient-specific data. This work will help clinicians in decision-making for improved drug dosing, patient care, and limiting patient exposure to agents with a narrow therapeutic range.

A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease

The most severe form of kidney disease, End-Stage Renal Disease (ESRD) is treated with various forms of dialysis - artificial blood cleansing. Dialysis patients suffer many health burdens including high mortality and hospitalization rates, and symptomatic anemia: a low red blood cell count as indicated by a low hemoglobin (Hgb) level. ESRD-induced anemia is treated, with variable patient response, by erythropoiesis stimulating agents (ESAs): expensive injectable medications typically administered during dialysis sessions. The dosing protocol is typically a population level protocol based on original clinical trials, the use of which often results in Hgb cycling. This cycling phenomenon occurs primarily due to the mismatch in the time between dosing decisions and the time it takes for the effects of a dosing change to be fully realized. In this paper we develop a recurrent neural network approach that uses historic data together with future ESA and iron dosing data to predict the 1, 2, and 3 month Hgb levels of patients with ESRD-induced anemia. The results of extensive experimentation indicate that this approach generates predictions that are clinically relevant: the mean absolute error of the predictions is comparable to estimates of the intra-individual variability of the laboratory test for Hgb.

Individualized drug dosing using RBF-Galerkin method: Case of anemia management in chronic kidney disease

BACKGROUND AND OBJECTIVE

Anemia is a common comorbidity in patients with chronic kidney disease (CKD) and is frequently associated with decreased physical component of quality of life, as well as adverse cardiovascular events. Current treatment methods for renal anemia are mostly population-based approaches treating individual patients with a one-size-fits-all model. However, FDA recommendations stipulate individualized anemia treatment with precise control of the hemoglobin concentration and minimal drug utilization. In accordance with these recommendations, this work presents an individualized drug dosing approach to anemia management by leveraging the theory of optimal control.

METHODS

A Multiple Receding Horizon Control (MRHC) approach based on the RBF-Galerkin optimization method is proposed for individualized anemia management in CKD patients. Recently developed by the authors, the RBF-Galerkin method uses the radial basis function approximation along with the Galerkin error projection to solve constrained optimal control problems numerically. The proposed approach is applied to generate optimal dosing recommendations for individual patients.

RESULTS

Performance of the proposed approach (MRHC) is compared in silico to that of a population-based anemia management protocol and an individualized multiple model predictive control method for two case scenarios: hemoglobin measurement with and without observational errors. In silico comparison indicates that hemoglobin concentration with MRHC method has less variation among the methods, especially in presence of measurement errors. In addition, the average achieved hemoglobin level from the MRHC is significantly closer to the target hemoglobin than that of the other two methods, according to the analysis of variance (ANOVA) statistical test. Furthermore, drug dosages recommended by the MRHC are more stable and accurate and reach the steady-state value notably faster than those generated by the other two methods.

CONCLUSIONS

The proposed method is highly efficient for the control of hemoglobin level, yet provides accurate dosage adjustments in the treatment of CKD anemia.