Brain Metastases: Insights from Statistical Modeling of Size Distribution.

BACKGROUND AND PURPOSE: Brain metastases are a common finding on brain MRI. However, the factors that dictate their size and distribution are incompletely understood. Our aim was to discover a statistical model that can account for the size distribution of parenchymal metastases in the brain as measured on contrast-enhanced MR imaging. MATERIALS AND METHODS: Tumor volumes were calculated on the basis of measured tumor diameters from contrast-enhanced T1-weighted spoiled gradient-echo images in 68 patients with untreated parenchymal metastatic disease. Tumor volumes were then placed in rank-order distributions and compared with 11 different statistical curve types. The resultant R 2 values to assess goodness of fit were calculated. The top 2 distributions were then compared using the likelihood ratio test, with resultant R values demonstrating the relative likelihood of these distributions accounting for the observed data. RESULTS: Thirty-nine of 68 cases best fit a power distribution (mean R 2 = 0.938 ± 0.050), 20 cases best fit an exponential distribution (mean R 2 = 0.957 ± 0.050), and the remaining cases were scattered among the remaining distributions. Likelihood ratio analysis revealed that 66 of 68 cases had a positive mean R value (1.596 ± 1.316), skewing toward a power law distribution. CONCLUSIONS: The size distributions of untreated brain metastases favor a power law distribution. This finding suggests that metastases do not exist in isolation, but rather as part of a complex system. Furthermore, these results suggest that there may be a relatively small number of underlying variables that substantially influence the behavior of these systems. The identification of these variables could have a profound effect on our understanding of these lesions and our ability to treat them.

Pulseless electrical activity following traumatic cardiac arrest: Sign of life or death?

BACKGROUND: Generally considered a sign of life, PEA is the most common arrhythmia encountered following pre-hospital traumatic cardiac arrest. Some recommend cardiac ultrasound (CUS) to determine cardiac wall motion (CWM) prior to terminating resuscitation efforts. This purpose of this study was to evaluate the outcomes of patients with traumatic cardiac arrest presenting with PEA, with and without CWM. METHODS: Trauma patients who underwent pre-hospital CPR were identified from the registries of two level-1 trauma centers. Pre-hospital management by emergency medical transport services was guided by advanced life support protocols. The on-duty trauma surgeon directed the resuscitations and performed or supervised CUS and determined CWM. RESULTS: Among 277 patients who underwent pre-hospital CPR, 110 patients had PEA on arrival to ED. 69 (62.7%) were injured by blunt mechanisms. Median CPR duration was 20.0 and 8.0 min for pre-hospital and ED, respectively. Sixty-three patients (22.7%) underwent resuscitative thoracotomy. One hundred seventy-two patients (62.1%) received CUS and of these 32 (18.6%) had CWM. CWM was significantly associated with survival to hospital admission (21.9% vs. 1.4%; P < 0.001); however, no patient with CUS survived to hospital discharge. Overall, only one patient with PEA on arrival survived to discharge. CONCLUSION: Following pre-hospital traumatic cardiac arrest, PEA on arrival portends death. Although CWM is associated with survival to admission, it is not associated with meaningful survival. Heroic resuscitative measures may be unwarranted for PEA following pre-hospital traumatic arrest, regardless of CWM.