Background: Lesser toe plantar plate attenuation or disruption is being increasingly implicated in a variety of common clinical conditions. A multitude of surgical techniques and devices have been recently developed to facilitate surgical repair of the plantar plate. However, the microvascular anatomy, and therefore the healing potential in large part, has not been defined. We investigated the microvasculature of the plantar plate by employing a novel technique involving microvascular perfusion and nano–computed tomography (nano-CT) imaging. Methods: Twelve human adult cadaveric lower extremities were amputated distal to the knee. The anterior and posterior tibial arteries were perfused with a barium solution. The soft tissues of each foot were then counterstained with phosphomolybdic acid (PMA). The second through fourth toe metatarsophalangeal (MTP) joints of 12 feet were imaged with nano-CT at 14-micron resolution. Images were then reconstructed for analysis of the plantar plate microvasculature and calculation of the vascular density along the length of the plantar plate. Results: A microvascular network extends from the surrounding soft tissues at the attachments of the plantar plate on both the metatarsal and proximal phalanx. The midsubstance of the plantar plate appears to be relatively hypovascular. Analysis of the vascular density along the length of the plantar plate demonstrated a consistent trend with increased vascular density at approximately the proximal 29% and distal 22% of the plantar plate. Conclusion: There is a vascular network extending from the surrounding soft tissues into the proximal and distal attachments of the plantar plate. Clinical relevance: The hypovascular midportion of the plantar plate may play an important role in the underlying pathoanatomy and pathophysiology of this area. These findings may have significant clinical implications for the reparative potential of this region and the surgical procedures currently described to accomplish anatomic plantar plate repair.
INTRODUCTION Severe traumatic brain injury (TBI) associated with acute subdural hematomas (aSDH) is common and represents around 10% to 20% of all TBI. Predictive models have been used in an attempt to modulate the morbidity and mortality of patient outcomes. We used machine learning (ML) to identify risk factors predictive of in-hospital mortality in the severe TBI patient population with aSDH. METHODS We included 74 patients with severe TBI and aSDH in the analysis. Random forest, ML architecture, was used to create a predictive model of in-hospital mortality with a pre-set precision of 97.4% (RStudio-3.5). A total of 133 input variables including demographics, in-hospital laboratory values, and outcome measures were included and mean accuracy ranks were assessed RESULTS The highest scoring input variables were length of stay, last sodium value collected, last platelet value collected, and Glasgow Coma Scale (GCS) motor exam score on day two. Mean length of stay was significantly shorter in the group of patients that died (4.114 ± 4.241 d vs 22.72 ± 11.72; P < .0001) The mean sodium that was last collected was significantly more elevated in the group of patients who died (139.9 ± 3.299 vs 148.9 ± 8.825 mEq/L; P < .0001). The mean platelet values last collected during the hospitalization were significantly lower in the group of patients who died (440.8 ± 240.4 vs 165.6 ± 113.7 × 109/L; P < .0001). GCS motor exam score at day 2 following the injury was also significantly greater in the survival group (4.872 ± 1.005 vs 2.143 ± 1.574; P < .0001). CONCLUSION ML is an efficient tool that can provide a reasonable level of accuracy in predicting mortality in our patient population. Adequate monitoring of sodium and platelet levels, as well as the GCS motor examination, can promote goal-directed therapy. Integration of ML into the severe TBI algorithm may help early identification of high-risk patients.
INTRODUCTION Acute subdural hematoma (aSDH) in the context of severe traumatic brain injury (TBI) is a neurosurgical emergency. Predictive models have been used in an attempt to modulate the morbidity and mortality of patient outcomes. We used machine learning (ML) to identify admission risk factors predictive of long-term morbidity in the severe TBI patient population with aSDH. METHODS Between 2013 and 2016, 85 patients with severe TBI and aSDH were included in the analysis. Random forest, ML architecture, was used to create a predictive model of long-term morbidity stratification. About 46 patients were included in the high morbidity group [Glasgow Outcome Scale (GOS) 1-2] and 39 patients were in the low morbidity group (GOS 3-5). We included 30 admission input variables including medical and surgical co-morbidities, neurological examination, laboratory values, and radiographic findings. RESULTS The predictive model showed a 78% precision. The highest scoring input variable was the pupillary examination in predicting high vs low morbidity (bilaterally unreactive vs symmetrically reactive; P < .0001). GCS on admission was higher in the low morbidity group (4 [3-7] vs 7 [3-7]; P < .0101). Rotterdam scores were higher in the high-morbidity group (3 [3-5] vs 4 [4-5]; P < .0032). GCS motor examination on admission was higher in the low-morbidity group (5 [1-5] vs. 2 [1-5]; P < .0106). The basal cisterns were found to be more patent in patients with the low-morbidity group (P = .0012). CONCLUSION ML is an efficient tool that can provide a reasonable level of accuracy in predicting long-term morbidity in patients with severe TBI and aSDH. Monitoring these admission criteria can help with risk-stratification of patients into higher and low risk tracks. Integration of ML into the treatment algorithm may allow the development of more refined guidelines to guide goal-directed therapy.
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