Biomedical subjects

L M Wein

Publications and source records attributed to L M Wein.

4 recordsLinked to original sources

PubMed1999

Evidence-based organ allocation.

BACKGROUND: There are not enough cadaveric kidneys to meet the demands of transplant candidates. The equity and efficiency of alternative organ allocation strategies have not been rigorously compared. METHODS: We developed a five-compartment Monte Carlo simulation model to compare alternative organ allocation strategies, accommodating dynamic changes in recipient and donor characteristics, patient and graft survival rates, and quality of life. The model simulated the operations of a single organ procurement organization and attempted to predict the evolution of the transplant waiting list for 10 years. Four allocation strategies were compared: a first-come first-transplanted system; a point system currently utilized by the United Network of Organ Sharing; an efficiency-based algorithm that incorporated correlates of patient and graft survival; and a distributive efficiency algorithm, which had an additional goal of promoting equitable allocation among African-American and other candidates. RESULTS: A 10-year computer simulation was performed. The distributive efficiency policy was associated with a 3.5%+/-0.8% (mean +/- SD) increase in quality-adjusted life expectancy (33.9 months vs 32.7 months), a decrease in the median waiting time to transplantation among those who were transplanted (6.6 months vs 16.3 months), and an increase in the overall likelihood of transplantation (61% vs 45%), compared with the United Network of Organ Sharing algorithm. Improved equity and efficiency were also seen by race (African-American vs other), sex, and age (<50 or > or =50 years). Sensitivity analyses did not appreciably change the qualitative results. CONCLUSION: Evidence-based organ allocation strategies in cadaveric kidney transplantation would yield improved equity and efficiency measures compared with existing algorithms.

Algorithms↗

PubMed1998

Pooled testing for HIV prevalence estimation: exploiting the dilution effect.

We study pooled (or group) testing as a method for estimating the prevalence of HIV; rather than testing each sample individually, this method combines various samples into a pool and then tests the pool. Existing pooled testing procedures estimate the prevalence using dichotomous test outcomes. However, HIV test outcomes are inherently continuous, and their dichotomization may eliminate useful information. To overcome this problem, we develop a parametric procedure that utilizes the continuous outcomes. This procedure employs a hierarchical pooling model and estimates the prevalence using the likelihood equation. The likelihood equation is solved using an iterative algorithm, and a simulation study shows that our procedure yields very accurate estimates at a fraction of the cost of existing procedures.

Algorithms↗

PubMed1998

Mathematical analysis of antiretroviral therapy aimed at HIV-1 eradication or maintenance of low viral loads.

Motivated by the ability of combinations of antiretroviral agents to sustain viral suppression in HIV-1-infected individuals, we analyse the transient and steady-state behavior of a mathematical model of HIV-1 dynamics in vivo in order to predict whether these drug regimens can eradicate HIV-1 or maintain viral loads at low levels. The model incorporates two cell types (CD4+ T cells and a long-lived pool of cells), two strains of virus (drug-sensitive wild type and drug-resistant mutant) and two types of antiretroviral agents (reverse transcriptase and protease inhibitors). The transient behavior of the cells and virus and the eventual eradication of the virus are determined primarily by the strength of the combination therapy against the mutant strain and the maximum achievable increase in the uninfected CD4+ T cell concentration. We also predict, if the parameters of the model remain constant during therapy, that less intensive maintenance regimens will be unable to maintain low viral loads for extensive periods of time. However, if the reduction in viral load produced by therapy reduces the state of activation of the immune system, the number of cells susceptible for HIV-1 infection may decrease even though total CD4+ T cells increase. Our model predicts that if this occurs strong inductive therapy that reduces viral load followed by weaker maintenance regimes may succeed.

Antiviral Agents↗

PubMed1997

Dynamic multidrug therapies for HIV: a control theoretic approach.

Motivated by the inability of current drug treatment to provide long-term benefit to HIV-infected individuals, we derive HIV therapeutic strategies by formulating and analyzing a mathematical control problem. The model tracks the dynamics of uninfected and infected CD4+ cells and free plasma virus, and allows the virus to mutate into various strains. At each point in time, several different therapeutic options are available, where each option corresponds to a combination of reverse transcriptase inhibitors. The controller observes the individual's current status and chooses among the therapeutic options in a dynamic fashion in order to minimize the total viral load. Our initial numerical results suggest that dynamic therapies have the potential to significantly outperform the static protocols that are currently in use; by anticipating and responding to the disease progression, the dynamic strategy reduces the total free virus, increases the uninfected CD4+ count, and delays the emergence of drug-resistant strains.

CD4-Positive T-Lymphocytes↗