ArticleProduction and operations management2019
Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma.
Article in Production and operations management, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Personalized Prediction of Glaucoma Progression Under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods.Ophthalmology · 2018Trial
- Ocular and Systemic Pharmacokinetics of Baicalein and Baicalin After Intravitreal Injection and Oral Administration in Mice.Pharmaceutics · 2026Article
- Win-Win Pricing of Follow-Up Policies Under Healthcare Warranties for Chronic Diseases: A Mathematical Modeling Approach.Healthcare (Basel, Switzerland) · 2025Article
- Estimating the Relationship Between the Symptom-Free Waiting Period and Injury Rates After Return-to-Play from Concussion: A Simulation Analysis Using CARE Consortium Data.Sports medicine (Auckland, N.Z.) · 2023Article
- Diagnostic Policies Optimization for Chronic Diseases Based on POMDP Model.Healthcare (Basel, Switzerland) · 2022Article
- Predicting rapid progression phases in glaucoma using a soft voting ensemble classifier exploiting Kalman filtering.Health care management science · 2021Article
- Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases.Health care management science · 2021Article
- A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management.Health care management science · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
To manage chronic disease patients effectively, clinicians must know (1) how to monitor each patient (i.e., when to schedule the next visit and which tests to take), and (2) how to control the disease (i.e., what levels of controllable risk factors will sufficiently slow progression). Our research addresses these questions simultaneously and provides the optimal solution to a novel linear quadratic Gaussian state space model. For the objective of minimizing the relative change in state over time (i.e., disease progression), which is necessary for managing irreversible chronic diseases while also considering the cost of tests and treatment, we show that the classical two-way separation of estimation and control holds. This makes a previously intractable problem solvable by decomposition into two separate, tractable problems while maintaining optimality. The resulting optimization is applied to the management of glaucoma. Based on data from two large randomized clinical trials, we validate our model and demonstrate how our decision support tool can provide actionable insights to the clinician caring for a patient with glaucoma. This methodology can be applied to a broad range of irreversible chronic diseases to devise patient-specific monitoring and treatment plans optimally.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.