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Introduction: As a disorder of the brain in adults and children, traumatic brain injury (TBI) is considered the major cause of mortality and morbidity. As a serious complication of TBI, post-traumatic hydrocephalus (PTH) is commonly identified and significantly associated with neurocognitive impairment, motor dysfunction, and growth impairment. The long-term functional outcomes after shunt dependence are totally not clear. Methods: This study included 6279 patients between 2012 and 2022. To identify the unfavorable functional outcomes and the PTH-related factors, we carried out univariable logistic regression analyses. To identify the occurrence time of PTH, we conducted the log-rank test and Kaplan–Meier analysis. Results: Mean patient age was 51.03 ± 22.09 years. Of the 6279 patients with TBI, 327 developed PTH (5.2%). Several PTH development-associated factors, such as intracerebral hematoma, diabetes, longer initial hospital stay, craniotomy, low GCS (Glasgow Coma Scale), EVD (external ventricular drain), and DC (decompressive craniectomy) (p < 0.01), were identified. We also analyzed the factors of unfavorable outcomes after TBI including > 80 years, repeated operations, hypertension, EVD, tracheotomy, and epilepsy (p < 0.01). Ventriculoperitoneal shunt (VPS) itself is not an independent factor of the unfavorable outcome but shunt complication is a strong independent factor of unfavorable outcome (p < 0.05). Conclusion: We should emphasize the practices that can minimize the risks of shunt complications. Additionally, the rigorous radiographic and clinical surveillance will benefit those patients at high risk of developing PTH. Trial registration: ClinicalTrials.gov identifier, ChiCTR2300070016. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of hydrocephalus is mainly based on imaging findings. However, the significance of many imaging indicators may change, especially in some degenerative diseases, and even lead to misdiagnosis. Methods: This study explored the effectiveness of commonly used morphological parameters and typical radiographic findings in hydrocephalus diagnosis. The patients' imaging data were divided into three groups, including the hydrocephalus group, the symptomatic group, and the normal control group. The diagnostic validity and weight of various parameters were compared between groups by multiple machine learning methods. Results: Our results demonstrated that Evans' ratio is the most valuable diagnostic indicator compared to the hydrocephalus group and the normal control group. But frontal horns' ratio is more useful in diagnosing patients with symptoms. Meanwhile, the sign of disproportionately enlarged subarachnoid space and third ventricle enlargement could be effective diagnostic indicators in all situations. Conclusion: Both morphometric parameters and radiological features were essential in diagnosing hydrocephalus, but the weights are different in different situations. The machine learning approaches can be applied to optimize the diagnosis of other diseases and consistently update the clinical diagnostic criteria. [ABSTRACT FROM AUTHOR]