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Background Failure rates with cranioplasty procedures have driven efforts to improve graft material and reduce reoperation. One promising allograft source is a 3D-printed titanium mesh with calcium phosphate filler. This study evaluated failure rates and pertinent characteristics of these novel 3D-grafts compared to traditional materials. Methods Sixty patients were retrospectively identified who underwent a cranioplasty between January 2015–December 2017. Specific data points related to graft failure were collected for all surgical admissions, from the primary injury to their most recent. These included, but were not limited to, initial physical exam findings, vitals, comorbid conditions, surgery length, estimated blood loss, incision type, and need for revision. Failure rates of 3D-printed allografts were compared to traditional grafts. Results A total of 60 subjects were identified who underwent 71 unique cranioplasty procedures (3D = 13, Synthetic = 12, Autologous = 46). There were 14 total failures, demonstrating a 19.7% overall failure rate. Specifically, 15.4% (n = 2) of 3D, 19.6% (n = 9) of autologous, and 25.0% (n = 3) of synthetic grafts required revision. Patients receiving 3D-grafts had the shortest overall mean surgery times (200.8 ± 54.3 min) and lowest infection rates (7.7%) compared to autologous (210.5 ± 47.9 min | 25.0%) and synthetic models (217.6 ± 77.3 min | 8.7%), though significance was unable to be determined. Tobacco use and trap-door incisions were associated with increased failure rates relative to straight or curved incisions in autologous grafts. Cranioplasties performed less than 3 months after craniectomy appeared to fail more often than those performed at least three months after craniectomy, for the synthetic group. Conclusion We concluded that 3D-printed cranioplasty grafts may lead to lower failure rates and shorter surgery times compared to traditional cranioplasty materials in our limited population. 3D-implants hold promise for cranial reconstruction after TBI.

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Background The manual extraction of valuable data from electronic medical records is cumbersome, error-prone, and inconsistent. By automating extraction in conjunction with standardized terminology, the quality and consistency of data utilized for research and clinical purposes would be substantially improved. Here, we set out to develop and validate a framework to extract pertinent clinical conditions for traumatic brain injury (TBI) from computed tomography (CT) reports. Methods We developed tbiExtractor, which extends pyConTextNLP, a regular expression algorithm using negation detection and contextual features, to create a framework for extracting TBI common data elements from radiology reports. The algorithm inputs radiology reports and outputs a structured summary containing 27 clinical findings with their respective annotations. Development and validation of the algorithm was completed using two physician annotators as the gold standard. Results tbiExtractor displayed high sensitivity (0.92-0.94) and specificity (0.99) when compared to the gold standard. The algorithm also demonstrated a high equivalence (94.6%) with the annotators. A majority of clinical findings (85%) had minimal errors (F1 Score � 0.80). When compared to annotators, tbiExtractor extracted information in significantly less time (0.3 sec vs 1.7 min per report).