Implications of True and Perceived Treatment Burden on Cardiovascular Medication Use.

Background: Clinical decisions require weighing possible risks and benefits, which are often based on the provider's sense of treatment burden. Patients often have a different view of how heavily treatment burden should be weighted. Objective: To examine how much small variations in patient treatment burden would influence optimal use of antihypertensive medications and how much over- and undertreatment can result from clinicians misunderstanding their patients' values. Methods: Analysis-Markov chain model. Data sources-Existing literature, including an individual-level meta-analysis of blood pressure trials. Target population-US representative sample, ages 40 to 85, no history of cardiovascular disease. Time horizon-Effect of 10 years of treatment on estimated lifetime quality-adjusted life-year (QALY) burden. Perspective-Patient. OUTCOME MEASURES: QALYs gained by treatment. Results: Fairly small differences in true patient burden from blood pressure treatment alter the number of blood pressure medications that should be recommended and alters treatment's potential benefit dramatically. We also found that a clinician misunderstanding the patient's burden could lead to almost 30% of patients being treated inappropriately. Limitations: Our results are based on simulation modeling. Conclusions: Clinical decisions that fail to account for patient treatment burden can mistreat a very large proportion of the public. Successful treatment choices closely depend on a clinician's ability to accurately gauge a patient's treatment burden.

Strategic modeling of the neonatal nurse practitioner workforce.

BACKGROUND: Neonatal nurse practitioners (NNPs) play a vital role in the medical care of newborns and infants. There is expected to be a shortage of NNPs in the near future. PURPOSE: To assess the present NNP workforce and study the impact of potential policy changes to alleviate forecasted shortages. METHODS: We modeled the education and workforce system for NNPs. Forecasting models were combined with optimal decision-making to derive best-case scenario admission levels for graduate and undergraduate programs. DISCUSSION: Under the best-case scenario for the current system, the shortage of NNPs is expected to last 10 years. We analyzed the impact of improving the certification examination passing rate, increasing the annual growth rate of master's programs, and reducing the workforce annual attrition rate. We found that policy changes may reduce the forecasted shortage to 4 years. CONCLUSION: Present forecasts of demand for NNPs indicate that the existing workforce and education system will be unable to satisfy the growing demand. Policy changes may reduce the expected shortage and potentially improve access to care for newborns and infants.

Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning.

Background: Markov decision process (MDP) models are powerful tools. They enable the derivation of optimal treatment policies but may incur long computational times and generate decision rules that are challenging to interpret by physicians. Methods: In an effort to improve usability and interpretability, we examined whether Poisson regression can approximate optimal hypertension treatment policies derived by an MDP for maximizing a patient's expected discounted quality-adjusted life years. Results: We found that our Poisson approximation to the optimal treatment policy matched the optimal policy in 99% of cases. This high accuracy translates to nearly identical health outcomes for patients. Furthermore, the Poisson approximation results in 104 additional quality-adjusted life years per 1000 patients compared to the Seventh Joint National Committee's treatment guidelines for hypertension. The comparative health performance of the Poisson approximation was robust to the cardiovascular disease risk calculator used and calculator calibration error. Limitations: Our results are based on Markov chain modeling. Conclusions: Poisson model approximation for blood pressure treatment planning has high fidelity to optimal MDP treatment policies, which can improve usability and enhance transparency of more personalized treatment policies.

Strategic modeling of the pediatric nurse practitioner workforce.

OBJECTIVE: To assess the current pediatric nurse practitioner (PNP) workforce and to investigate the impact of potential policy changes to address forecasted shortages. METHODS: We modeled the admission of students into nursing bachelor's programs and followed them through advanced clinical programs. Prediction models were combined with optimal decision-making to determine best-case scenario admission levels. We computed 2 measures: (1) the absolute shortage and (2) the expected number of years until the PNP workforce will be able to fully satisfy PNP demand (ie, self-sufficiency). RESULTS: There is a forecasted shortage of PNPs in the workforce over the next 13 years. Under the best-case scenario, it would take at least 13 years for the workforce to fully satisfy demand. Our analysis of potential policy changes revealed that increasing the specialization rate for PNPs by 4% would decrease the number of years required until there are enough PNPs from 13 years to 5 years. Increasing the certification examination passing rate to 96% from the current average of 86.9% would lead to self-sufficiency in 11 years. In addition, increasing the annual growth rate of master's programs to 36% from the current maximum of 10.7% would result in self-sufficiency in 5 years. CONCLUSIONS: Current forecasts of demand for PNPs indicate that the current workforce will be incapable of satisfying the growing demand. Policy changes can result in a reduction in the expected shortage and potentially improve access to care for pediatric patients.

Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma.

PURPOSE: To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG). DESIGN: Secondary analyses using longitudinal data from 2 randomized controlled trials. PARTICIPANTS: A total of 571 participants from the Advanced Glaucoma Intervention Study (AGIS) and the Collaborative Initial Glaucoma Treatment Study (CIGTS). METHODS: Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant's disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant's disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared with 1-, 1.5-, and 2-year fixed interval schedules of obtaining VF and IOP measurements. MAIN OUTCOME MEASURES: Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, and number of VF and IOP measurements needed to assess for progression. RESULTS: Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (P<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (P = 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well for patients with mild and advanced disease. The model performed significantly more testing of patients who exhibited OAG progression than nonprogressing patients (1.3 vs. 1.0 tests per year; P<0.0001). CONCLUSIONS: Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management.

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

BACKGROUND: Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. METHODS: Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. RESULTS: The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. CONCLUSION: A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.