The standard-of-care for postoperative care following elective craniotomy has historically been ICU admission. However, recent literature interrogating complications and interventions during this postoperative ICU stay suggests that all patients may not require this level of care. Thus, hospitals began implementing non-ICU postoperative care pathways for elective craniotomy. This systematic review aims to summarize and evaluate the existing literature regarding outcomes and costs for patients receiving non-ICU care after elective craniotomy. DATA SOURCES:A systematic review of the PubMed database was performed following PRISMA guidelines from database inception to August 2021. STUDY SELECTION:Included studies were published in peer-reviewed journals, in English, and described outcomes for patients undergoing elective craniotomies without postoperative ICU care.DATA EXTRACTION: Data regarding study design, patient characteristics, and postoperative care pathways were extracted independently by two authors. Quality and risk of bias were evaluated using the Oxford Centre for Evidence-Based Medicine Levels of Evidence tool and Risk Of Bias In Non-Randomized Studies-of Interventions tool, respectively. DATA SYNTHESIS:In total, 1,131 unique articles were identified through the database search, with 27 meeting inclusion criteria. Included articles were published from 2001 to 2021 and included non-ICU inpatient care and same-day discharge pathways. Overall, the studies demonstrated that postoperative non-ICU care for elective craniotomies led to length of stay reduction ranging from 6 hours to 4 days and notable cost reductions. Across 13 studies, 53 of the 2,469 patients (2.1%) intended for postoperative management in a non-ICU setting required subsequent care escalation. CONCLUSIONS: Overall, these studies suggest that non-ICU care pathways for appropriately selected postcraniotomy patients may represent a meaningful opportunity to improve care value. However, included studies varied greatly in patient selection, postoperative care protocol, and outcomes reporting. Standardization and multi-institutional collaboration are needed to draw definitive conclusions regarding non-ICU postoperative care for elective craniotomy.
OBJECTIVE In recent years, machine learning models for clinical prediction have become increasingly prevalent in the neurosurgical literature. However, little is known about the quality of these models, and their translation to clinical care has been limited. The aim of this systematic review was to empirically determine the adherence of machine learning models in neurosurgery with standard reporting guidelines specific to clinical prediction models. METHODS Studies describing the development or validation of machine learning predictive models published between January 1, 2020, and January 10, 2023, across five neurosurgery journals (Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, Neurosurgery, and World Neurosurgery) were included. Studies where the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were not applicable, radiomic studies, and natural language processing studies were excluded. RESULTS Forty-seven studies featuring a machine learning–based predictive model in neurosurgery were included. The majority (53%) of studies were single-center studies, and only 15% of studies externally validated the model in an independent cohort of patients. The median compliance across all 47 studies was 82.1% (IQR 75.9%–85.7%). Giving details of treatment (n = 17 [36%]), including the number of patients with missing data (n = 11 [23%]), and explaining the use of the prediction model (n = 23 [49%]) were identified as the TRIPOD criteria with the lowest rates of compliance. CONCLUSIONS Improved adherence to TRIPOD guidelines will increase transparency in neurosurgical machine learning predictive models and streamline their translation into clinical care.
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