Traumatic Cerebral Microbleeds in the Subacute Phase Are Practical and Early Predictors of Abnormality of the Normal-Appearing White Matter in the Chronic Phase

BACKGROUND AND PURPOSE:In the chronic phase after traumatic brain injury, DTI findings reflect WM integrity. DTI interpretation in the subacute phase is less straightforward. Microbleed evaluation with SWI is straightforward in both phases. We evaluated whether the microbleed concentration in the subacute phase is associated with the integrity of normal-appearing WM in the chronic phase.MATERIALS AND METHODS:Sixty of 211 consecutive patients 18 years of age or older admitted to our emergency department ≤24 hours after moderate to severe traumatic brain injury matched the selection criteria. Standardized 3T SWI, DTI, and T1WI were obtained 3 and 26 weeks after traumatic brain injury in 31 patients and 24 healthy volunteers. At baseline, microbleed concentrations were calculated. At follow-up, mean diffusivity (MD) was calculated in the normal-appearing WM in reference to the healthy volunteers (MD_z). Through linear regression, we evaluated the relation between microbleed concentration and MD_z in predefined structures.RESULTS:In the cerebral hemispheres, MD_z at follow-up was independently associated with the microbleed concentration at baseline (left: B = 38.4 [95% CI 7.5–69.3], P = .017; right: B = 26.3 [95% CI 5.7–47.0], P = .014). No such relation was demonstrated in the central brain. MD_z in the corpus callosum was independently associated with the microbleed concentration in the structures connected by WM tracts running through the corpus callosum (B = 20.0 [95% CI 24.8–75.2], P < .000). MD_z in the central brain was independently associated with the microbleed concentration in the cerebral hemispheres (B = 25.7 [95% CI 3.9–47.5], P = .023).CONCLUSIONS:SWI-assessed microbleeds in the subacute phase are associated with DTI-based WM integrity in the chronic phase. These associations are found both within regions and between functionally connected regions.

The yearly incidence of traumatic brain injury (TBI) is around 300 per 100,000 persons.^1,2 Almost three-quarters of patients with moderate to severe TBI have traumatic axonal injury (TAI).³ TAI is a major predictor of functional outcome,^4,5 but it is mostly invisible on CT and conventional MR imaging.^6,7DTI provides direct information on WM integrity and axonal injury.^5,8 However, DTI abnormalities are neither specific for TAI nor stable over time. Possibly because of the release of mass effect and edema and resorption of blood products, the effects of concomitant (non-TAI) injury on DTI are larger in the subacute than in the chronic phase (>3 months).^4,9,10 Therefore, DTI findings are expected to reflect TAI more specifically in the chronic than in the subacute phase (1 week–3 months).⁴ Even in regions without concomitant injury, the effects of TAI on DTI are dynamic, possibly caused by degeneration and neuroplastic changes.^6,11,12 These ongoing pathophysiological processes possibly contribute to the emerging evidence that DTI findings in the chronic phase are most closely associated with the eventual functional outcome.^12,13Although DTI provides valuable information, its acquisition, postprocessing, and interpretation in individual patients are demanding. SWI, with which microbleeds can be assessed with high sensitivity, is easier to interpret and implement in clinical practice. In contrast to DTI, SWI-detected traumatic microbleeds are more stable¹ except in the hyperacute^14,15 and the late chronic phases.¹⁶ Traumatic cerebral microbleeds are commonly interpreted as signs of TAI. However, the relation is not straightforward. On the one hand, nontraumatic microbleeds may be pre-existing. On the other hand, even if traumatic in origin, microbleeds represent traumatic vascular rather than axonal injury.¹⁷ Indeed, TAI is not invariably hemorrhagic.¹⁸ Additionally, microbleeds may secondarily develop after trauma through mechanisms unrelated to axonal injury, such as secondary ischemia.¹⁸DTI is not only affected by pathophysiological changes but also by susceptibility.¹⁹ The important susceptibility-effect generated by microbleeds renders the interpretation of DTI findings at the location of microbleeds complex. In the chronic phase, mean diffusivity (MD) is the most robust marker of WM integrity.^4,6 For these reasons, we evaluated MD in the normal-appearing WM.Much TAI research focuses on the corpus callosum because it is commonly involved in TAI^5,18,20 and it can reliably be evaluated with DTI,^5,21 and TAI in the corpus callosum is related to clinical prognosis.^6,20 The corpus callosum consists of densely packed WM tracts that structurally and functionally connect left- and right-sided brain structures.²² The integrity of the corpus callosum is associated with the integrity of the brain structures it connects.²³ Therefore, microbleeds in brain structures that are connected through the corpus callosum may affect callosal DTI findings. Analogous to this, microbleeds in the cerebral hemispheres, which exert their function through WM tracts traveling through the deep brain structures and brain stem,^24,25 may affect DTI findings in the WM of the latter.Our purpose was to evaluate whether the microbleed concentration in the subacute phase is associated with the integrity of normal-appearing WM in the chronic phase. We investigated this relation within the cerebral hemispheres and the central brain and between regions that are functionally connected by WM tracts.     

SALAD‐BAAR: A numerical risk score for hospital admission or emergency department presentation in ambulatory patients with cardiovascular disease

BackgroundWhile many interventions to reduce hospital admissions and emergency department (ED) visits for patients with cardiovascular disease have been developed, identifying ambulatory cardiac patients at high risk for admission can be challenging.HypothesisA computational model based on readily accessible clinical data can identify patients at risk for admission.MethodsElectronic health record (EHR) data from a tertiary referral center were used to generate decision tree and logistic regression models. International Classification of Disease (ICD) codes, labs, admissions, medications, vital signs, and socioenvironmental variables were used to model risk for ED presentation or hospital admission within 90 days following a cardiology clinic visit. Model training and testing were performed with a 70:30 data split. The final model was then prospectively validated.ResultsA total of 9326 patients and 46 465 clinic visits were analyzed. A decision tree model using 75 patient characteristics achieved an area under the curve (AUC) of 0.75 and a logistic regression model achieved an AUC of 0.73. A simplified 9‐feature model based on logistic regression odds ratios achieved an AUC of 0.72. A further simplified numerical score assigning 1 or 2 points to each variable achieved an AUC of 0.66, specificity of 0.75, and sensitivity of 0.58. Prospectively, this final model maintained its predictive performance (AUC 0.63–0.60).ConclusionNine patient characteristics from routine EHR data can be used to inform a highly specific model for hospital admission or ED presentation in cardiac patients. This model can be simplified to a risk score that is easily calculated and retains predictive performance.     

Accuracy of MRI-guided Versus Systematic Prostate Biopsy in Patients Under Active Surveillance: A Systematic Review and Meta-analysis

This meta-analysis focuses on the accuracy of upgrading to clinically significant prostate cancer (PCa) by multiparametric magnetic resonance imaging-targeted biopsy (MRI-TB) versus systematic biopsy (SB). We searched the Medline, Embase, Cochrane Central Register of Controlled Trials, Web of Science, Scopus, and Literatura Latino Americana em Ciências da Saúde databases through January 2020 for comparative, retrospective/prospective, paired-cohort, and randomized clinical trials with paired comparisons. The population consisted of patients with low-risk PCa in active surveillance with at least 1 index lesion on imaging. We evaluated the quality of evidence by using the Quality Assessment of Diagnostic Accuracy Studies-2 score. Group comparisons considered the differences between the area under the curve summary receiver operating characteristic curve in a 2-tailed method. We also compared the positive predictive value of the best single method (MRI-TB or SB) and the referral study test (combined biopsy, a combination of MRI-TB and SB). The meta-analysis included 6 studies enrolling 741 patients. The pooled sensitivity for the 2 groups was 0.79 (95% confidence interval, 0.74-0.83; I² = 75%) and 0.67 (95% confidence interval, 0.63-0.74; I² = 55.4%), respectively. The area under the curve for the MRI-TB and SB groups were 0.99 and 0.92 (P < .001), respectively. The positive predictive value for the MRI-TB and combined biopsy groups were similar. The accumulated evidence suggests better results for MRI-TB compared with SB. Therefore, use of MRI-TB alone may be preferable in patients in active surveillance harboring low-risk PCa.