Abstract. 1. Hosts experiencing frequent variation in density are thought to benefit from allocating more resources to parasite defence when density is high (‘density‐dependent prophylaxis’). However, high density conditions can increase intra‐specific competition and induce physiological stress, hence increasing host susceptibility to infection (‘crowding‐stress hypothesis’).2. We studied monarch butterflies (Danaus plexippus) and quantified the effects of larval rearing density on susceptibility to the protozoan parasite Ophryocystis elektroscirrha. Larvae were inoculated with parasite spores and reared at three density treatments: low, moderate, and high. We examined the effects of larval density on parasite loads, host survival, development rates, body size, and wing melanism.3. Results showed an increase in infection probability with greater larval density. Monarchs in the moderate and high density treatments also suffered the greatest negative effects of parasite infection on body size, development rate, and adult longevity.4. We observed greater body sizes and shorter development times for monarchs reared at moderate densities, and this was true for both unparasitised and parasite‐treated monarchs. We hypothesise that this effect could result from greater larval feeding rates at moderate densities, combined with greater physiological stress at the highest densities.5. Although monarch larvae are assumed to occur at very low densities in the wild, an analysis of continent‐wide monarch larval abundance data showed that larval densities can reach high levels in year‐round resident populations and during the late phase of the breeding season. Treatment levels used in our experiment captured ecologically‐relevant variation in larval density observed in the wild.

Comparisons of commonly used frontline regimens on survival outcomes in patients age 70 years and older with acute myeloid leukemia

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Introduction Patients older than 70 years with acute myeloid leukemia (AML) are generally considered to have poor prognosis. As a result, many patients are routinely not offered active treatment and/or are referred to palliative hospice care based on the assumption that their expected survival will be well below 6 months. However, a substantial number of patients live beyond 6 months indicating that management decisions ought to be individualized taking into considerations patients' preferences about benefits and harms of treatments and estimated survival prognostication. Methods Using large Moffitt AML database we identified all consecutive patients (n=305) with AML older than 70 who received high or low intensity chemotherapy to develop a multiple logistic regression model to assess the probability of survival at 12 month since diagnosis of AML. Patients who were censored prior to 12 months were considered not eligible (n=300). The final model was determined by the backward elimination method. We assessed discrimination of the model by performing ROC (receiver operating characteristic) analysis and calibration by using Hosmer-Lemeshow (H-L) goodness-of-fit test. We also performed regret-based decision curve analysis (DCA) to compare three decision strategies over all possible patient's preferences: "Do Not Treat/Refer to Hospice" vs. "Treat All" with chemotherapy vs. "Use Model" to guide decision about treatment (to treat or not to treat depending on the survival estimates in a relationship to the patient's preferences). In DCA, the preferences are captured by determining the threshold probability (T) of disease (AML) outcome at which a patient is indifferent between benefits (B) and harms (H) of treatment according to: T=1/[1+B/H]. The T can be elicited by asking a simple question concerning regret of omission (failure to benefit) vs. regret of commission (causing unnecessary harm): "how many more times would you regret not receiving a health intervention that could improve disease outcome (survival) compared with unnecessary and potentially harmful administration of treatments?" (http://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-10-51) Based on our previous study, we assumed that if treatment is administered, it is associated with hazard ratio (HR) of death reduction by 0.35 in the baseline analysis. The best strategy is the one associated with the least amount of regret. Results The prognostic model consisted of the following variables; cytogenetic status, ECOG PS, type of AML (De Novo vs. Secondary), and WBC level. A total of 112 patients (37%) survived at least 12 months. The model has good discrimination (area under curve=0.80) and excellent calibration [HL (chi2)=4.3; p=0.83] (Fig 1). DCA analysis showed that strategy "Do Not Treat/Refer to Hospice" was always inferior to the strategies "Treat" vs. "Use Model" (Fig 2). The decision strategy "Treat All" patients with AML older than 70 was best strategy for the threshold probability ranging from 1 to 46%. That is, as long as the patient would regret of not receiving benefit of treatment between 99 to 1.17 more than unnecessary receiving potentially harmful chemotherapy treatment, "Treat All" represents the best decision strategy for the management of elderly patients with AML. If the harms of treatments are more important to the patient (B/H<1.17), then "Use Model" becomes the best management choice. If we assumed that HR of treatment effect is equal to 0.1 and 0.65, then the threshold varied from 1% to 39% and 1% to 75%, respectively. That is, under these circumstances "Treat All" becomes best strategy if the patient values benefit of treatment 99 to 1.56 (or, 99 to 0.33 when HR of treatment=0.65) times more than avoiding harms of treatment. Limitation : Our model requires external validation before it can be exported for the use in a routine practice. Conclusion Our analysis indicates that not offering treatment to patients older than 70 with AML is never acceptable. The optimum decision is driven by the patients' preferences and estimated survival. If the patient regrets not receiving the potential benefits of treatments more than avoiding harms, treatment should be offered. The treatment should be avoided if the patient places more weight on harms than on benefits of treatment. Predictive model can help guide this decision. This research is supported by NIH grant 1-R01-CA168677-01A1 Disclosures Extermann: GTx: Research Funding.