Volume Management with Kidney Replacement Therapy in the Critically Ill Patient.

While the administration of intravenous fluids remains an important treatment, the negative consequences of subsequent fluid overload have raised questions about when and how clinicians should pursue avenues of fluid removal. Decisions regarding fluid removal during critical illness are complex even for patients with preserved kidney function. This article seeks to apply general concepts of fluid management to the care of patients who also require KRT. Because optimal fluid management for any specific patient is likely to change over the course of critical illness, conceptual models using phases of care have been developed. In this review, we will examine the implications of one such model on the use of ultrafiltration during KRT for volume removal in distributive shock. This will also provide a useful lens to re-examine published data of KRT during critical illness. We will highlight recent prospective trials of KRT as well as recent retrospective studies examining ultrafiltration rate and mortality, review the results, and discuss applications and shortcomings of these studies. We also emphasize that current data and techniques suggest that optimal guidelines will not consist of recommendations for or against absolute fluid removal rates but will instead require the development of dynamic protocols involving frequent cycles of reassessment and adjustment of net fluid removal goals. If optimal fluid management is dynamic, then frequent assessment of fluid responsiveness, fluid toxicity, and tolerance of fluid removal will be needed. Innovations in our ability to assess these parameters may improve our management of ultrafiltration in the future.

Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model.

Glioblastomas are the most aggressive primary brain tumors, characterized by their rapid proliferation and diffuse infiltration of the brain tissue. Survival patterns in patients with glioblastoma have been associated with a number of clinicopathologic factors including age and neurologic status, yet a significant quantitative link to in vivo growth kinetics of each glioma has remained elusive. Exploiting a recently developed tool for quantifying glioma net proliferation and invasion rates in individual patients using routinely available magnetic resonance images (MRI), we propose to link these patient-specific kinetic rates of biological aggressiveness to prognostic significance. Using our biologically based mathematical model for glioma growth and invasion, examination of serial pretreatment MRIs of 32 glioblastoma patients allowed quantification of these rates for each patient's tumor. Survival analyses revealed that even when controlling for standard clinical parameters (e.g., age and Karnofsky performance status), these model-defined parameters quantifying biological aggressiveness (net proliferation and invasion rates) were significantly associated with prognosis. One hypothesis generated was that the ratio of the actual survival time after whatever therapies were used to the duration of survival predicted (by the model) without any therapy would provide a therapeutic response index (TRI) of the overall effectiveness of the therapies. The TRI may provide important information, not otherwise available, about the effectiveness of the treatments in individual patients. To our knowledge, this is the first report indicating that dynamic insight from routinely obtained pretreatment imaging may be quantitatively useful in characterizing the survival of individual patients with glioblastoma. Such a hybrid tool bridging mathematical modeling and clinical imaging may allow for stratifying patients for clinical studies relative to their pretreatment biological aggressiveness.

Complementary but distinct roles for MRI and 18F-fluoromisonidazole PET in the assessment of human glioblastomas.

UNLABELLED: Glioblastoma multiforme is a primary brain tumor known for its rapid proliferation, diffuse invasion, and prominent neovasculature and necrosis. This study explores the in vivo link between these characteristics and hypoxia by comparing the relative spatial geometry of developing vasculature inferred from gadolinium-enhanced T1-weighted MRI (T1Gd), edematous tumor extent revealed on T2-weighted MRI (T2), and hypoxia assessed by 18F-fluoromisonidazole PET (18F-FMISO). Given the role of hypoxia in upregulating angiogenic factors, we hypothesized that the distribution of hypoxia seen on 18F-FMISO is correlated spatially and quantitatively with the amount of leaky neovasculature seen on T1Gd. METHODS: A total of 24 patients with glioblastoma underwent T1Gd, T2, and 18F-FMISO-11 studies preceded surgical resection or biopsy, 7 followed surgery and preceded radiation therapy, and 11 followed radiation therapy. Abnormal regions seen on the MRI scan were segmented, including the necrotic center (T0), the region of abnormal blood-brain barrier associated with disrupted vasculature (T1Gd), and infiltrating tumor cells and edema (T2). The 18F-FMISO images were scaled to the blood 18F-FMISO activity to create tumor-to-blood ratio (T/B) images. The hypoxic volume (HV) was defined as the region with T/Bs greater than 1.2, and the maximum T/B (T/Bmax) was determined by the voxel with the greatest T/B value. RESULTS: The HV generally occupied a region straddling the outer edge of the T1Gd abnormality and into the T2. A significant correlation between HV and the volume of the T1Gd abnormality that relied on the existence of a large outlier was observed. However, there was consistent correlation between surface areas of all MRI-defined regions and the surface area of the HV. The T/Bmax, typically located within the T1Gd region, was independent of the MRI-defined tumor size. Univariate survival analysis found the most significant predictors of survival to be HV, surface area of HV, surface area of T1Gd, and T/Bmax. CONCLUSION: Hypoxia may drive the peripheral growth of glioblastomas. This conclusion supports the spatial link between the volumes and surface areas of the hypoxic and MRI regions; the magnitude of hypoxia, T/Bmax, remains independent of size.