Prevalence of systemic anticancer therapy for patients within the last 30 days of life: experience in a private hospital oncology group.

BACKGROUND: In recent years, there has been a significant increase in the number of cancer treatments that have become available. However, it has remained difficult to choose the most appropriate time to cease active therapy in individual patients. AIMS: To determine the proportion of patients being treated with palliative intent who received systemic anticancer treatment in the last 30 days of life. METHODS: This is a retrospective cohort study conducted within the Melbourne Oncology Group at Cabrini Hospital. Patients managed with palliative intent who died between 1 January 2014 and 30 June 2014 were included. Outcomes measured were the percentage of patients who received systemic anticancer treatment in the last 30 days of life, palliative care referral status, Emergency Department presentations, hospital admissions and place of death. RESULTS: A total of 80 patients was included in the study. Of these patients, 21 (26%) received systemic anticancer treatment in the last 30 days of life. There was no statistically significant difference between patients who received treatment in the last month of life and those who did not in terms of the number of patients who were referred to palliative care, presented to an Emergency Department, were admitted to hospital or died in an acute ward. CONCLUSION: Although over a quarter of patients dying from advanced cancer received anticancer treatment in the last month of life, these patients did not present acutely to hospital more often and had the same extent of palliative care team involvement.

Stereotactic radiosurgery for treatment of Cushing disease: an Australian experience.

Stereotactic radiation therapy has emerged as an alternative to conventional radiotherapy for treatment of Cushing disease. The aim of this study was to investigate the efficacy and safety of this treatment. Records of patients with Cushing disease treated with stereotactic radiation were reviewed. Seventeen patients underwent stereotactic radiosurgery. Ten achieved remission after a mean of 23 (95% confidence interval, 15-31) months, and two developed hormone deficiencies.

Mathematically assessing the consequences of food terrorism scenarios.

We derive mathematical expressions for the mean number of casualties resulting from a deliberate release of a biological or chemical agent into a food supply chain. Our analysis first computes the amount of contaminated food as a function of the network topology and vessel sizes in the food processing plant. A probabilistic analysis, in which each potential consumer of contaminated food has his own random purchase time, infectious dose, and incubation period, determines the number of people who consume enough tainted food to get infected or poisoned before the attack is detected and food consumption is halted. These simple formulas can be used by the U.S. government and the food industry to develop a rough-cut prioritization of the threats from food terrorism, which would be a 1st step toward the allocation of appropriate prevention and mitigation resources.

Modeling and analysis of a virus that replicates selectively in tumor cells.

Replication-competent viruses have shown considerable promise in overcoming the inefficient gene transduction experienced by traditional gene therapy approaches to cancer treatment. The viruses infect tumor cells and replicate inside them, eventually causing lysis. Virus particles released during lysis are then able to infect other tumor cells, and, in this way, continuous rounds of infection and lysis allow the virus to spread throughout the tumor. Motivated by this novel cancer treatment, we formulate and analyse a system of partial differential equations that is essentially a radially-symmetric epidemic model embedded in a Stefan problem. We compare three, alternative virus-injection strategies: a fixed fraction of cells pre-infected with the virus are introduced throughout the entire tumor volume, within the tumor core, or within the tumor rim. For all three injection methods, simple and accurate conditions that predict whether the virus will control the tumor are derived.

Estimation of replicative senescence via a population dynamics model of cells in culture.

A simple mathematical model is developed for determining the time-varying fraction of senescent cells in culture in terms of the underlying probability distribution of the number of population doublings until senescence. This functional relationship is inverted, which allows for the estimation of the probability distribution of the number of population doublings until senescence given experimental data on the time-varying fraction of senescent cells. The relationship - in particular, the lag - between these two quantities is analyzed under the assumption that the number of population doublings until senescence follows the Weibull distribution. If the number of population doublings until senescence is geometrically distributed (i.e. the Weibull with shape parameter equal to one) then the cell culture appears immortal.

Analysis and comparison of multimodal cancer treatments.

We analyse the sequence in which the three most commonly prescribed cancer treatments--surgery (S), chemotherapy (C) and radiotherapy (R)--should be administered. A system of ordinary differential equations is formulated that captures the various local and systemic effects of the three modes of treatment, as well as the first-order effects of the inter-relationship between the primary tumour and the distant metastatic tumours, including primary tumour shedding and the primary tumour's effect on the rate of angiogenesis in the metastatic tumours. Under a set of stated assumptions on the parameter values, we find the exact cancer cure probability (subject to toxicity constraints) for the six permutation schedules (i.e. SCR, CSR, CRS, SRC, RSC, RCS) and for two novel schedules, SRCR and RSCR, that apply radiotherapy in disjoint, optimally timed portions. We show analytically that SRCR and RSCR are the two best-performing (i.e. highest cure probability) schedules among the eight considered. Further, SRCR is shown to be optimal among all possible schedules, provided a modest condition is satisfied on the delay of initial angiogenesis experienced by the patient's dormant tumours.

Dynamic optimization of a linear-quadratic model with incomplete repair and volume-dependent sensitivity and repopulation.

PURPOSE: The linear-quadratic model typically assumes that tumor sensitivity and repopulation are constant over the time course of radiotherapy. However, evidence suggests that the growth fraction increases and the cell-loss factor decreases as the tumor shrinks. We investigate whether this evolution in tumor geometry, as well as the irregular time intervals between fractions in conventional hyperfractionation schemes, can be exploited by fractionation schedules that employ time-varying fraction sizes. METHODS: We construct a mathematical model of a spherical tumor with a hypoxic core and a viable rim, which is most appropriate for a prevascular tumor, and is only a caricature of a vascularized tumor. This model is embedded into the traditional linear-quadratic model by assuming instantaneous reoxygenation. Dynamic programming is used to numerically compute the fractionation regimen that maximizes the tumor-control probability (TCP) subject to constraints on the biologically effective dose of the early and late tissues. RESULTS: In several numerical examples that employ five or 10 fractions per week on a 1-cm or 5-cm diameter tumor, optimally varying the fraction sizes increases the TCP significantly. The optimal regimen incorporates large Friday (afternoon, if 10 fractions per week) fractions that are escalated throughout the course of treatment, and larger afternoon fractions than morning fractions. CONCLUSION: Numerical results suggest that a significant increase in tumor cure can be achieved by allowing the fraction sizes to vary throughout the course of treatment. Several strategies deserve further investigation: using larger fractions before overnight and weekend breaks, and escalating the dose (particularly on Friday afternoons) throughout the course of treatment.

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.

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.

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.

Management of antiretroviral therapy for HIV infection: modelling when to change therapy.

OBJECTIVE: To evaluate four strategies for monitoring plasma HIV RNA levels and/or resistance genotypes to decide when to change antiretroviral therapy. The strategies include: (i) 1997 guidelines recommending a therapy switch when plasma RNA exceeds a threshold level; (ii) a viral load policy, using a fixed increase in viral load as the trigger; (iii) a genotype policy, requiring a smaller viral rebound than (ii) and detection of genotypic resistance before switching; and (iv) a proactive policy, switching drug regimens at a predetermined time if viral load has not rebounded. DESIGN AND SETTING: A Monte Carlo simulation tracks patients' viral loads and presence of opportunistic infection during therapy. The model uses clinical and virological data and statistical variation in patient parameters for the evaluation of therapeutic strategies. MAIN OUTCOME MEASURES: To determine which strategies minimize viral rebound detection delay while maintaining a low (prespecified) probability of switching therapy before rebound. RESULTS: 1997 Guidelines and the viral load policy create lengthy delays in detection of rebound, particularly when patients are drug-naive and the detection limit of the viral load assay is 500 copies/ml. A detection limit of 20 copies/ml decreases this delay substantially. Genotyping achieves only minor additional delay reductions. Of the strategies tested, the proactive policy leads to the shortest delays. CONCLUSIONS: This model indicates that prolonged periods may be required for viral load to rebound to detectable levels following prolonged suppression. Proactive switching produces the best outcome in our model because it may reduce the duration of viral replication under pressure of a failing regimen before detection of viral rebound. This strategy should be evaluated in clinical trials.

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.