BACKGROUND: Neuroendocrine dysfunction after traumatic brain injury (TBI) has received increased attention due to its impact on the recovery of neural function. The purpose of this study is to investigate the incidence and risk factors of adrenocortical insuffi ciency (AI) after TBI to reveal independent predictors and build a prediction model of AI after TBI.METHODS: Enrolled patients were grouped into the AI and non-AI groups. Fourteen preset impact factors were recorded. Patients were regrouped according to each impact factor as a categorical variable. Univariate and multiple logistic regression analyses were performed to screen the related independent risk factors of AI after TBI and develop the predictive model. RESULTS: A total of 108 patients were recruited, of whom 34 (31.5%) patients had AI. Nine factors (age, Glasgow Coma Scale [GCS] score on admission, mean arterial pressure [MAP], urinary volume, serum sodium level, cerebral hernia, frontal lobe contusion, diff use axonal injury [DAI], and skull base fracture) were probably related to AI after TBI. Three factors (urinary volume [X 4 ], serum sodium level [X 5 ], and DAI [X 8 ]) were independent variables, based on which a prediction model was developed (logit P= -3.552+2.583X 4 +2.235X 5 +2.269X 8 ). CONCLUSIONS:The incidence of AI after TBI is high. Factors such as age, GCS score, MAP, urinary volume, serum sodium level, cerebral hernia, frontal lobe contusion, DAI, and skull base fracture are probably related to AI after TBI. Urinary volume, serum sodium level, and DAI are the independent predictors of AI after TBI.

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The global epidemic of coronavirus disease 2019 (COVID‐19) endangers more and more people. Many studies on cutaneous manifestations related to COVID‐19 have emerged, but their prevalence has varied widely. The objective of this study was to conduct a meta‐analysis estimating the prevalence of skin manifestations in COVID‐19. Four databases PubMed, Web of Science, CBM, and CNKI were searched, and the results were screened by two reviewers. A random‐effects model was used to evaluate the overall prevalence. Heterogeneity was assessed by I 2 . Further subgroup analyses were conducted by region, sample size, sex, age, and severity of COVID‐19. A funnel plot and Egger's test were performed to assess publication bias. The pooled prevalence of cutaneous manifestation of 61 089 patients in 33 studies was 5.6% (95% confidence intervals [CI] = 0.040–0.076, I 2 = 98.3%). Severity of COVID‐19 was probably the source of heterogeneity. Studies with sample size <200 report higher prevalence estimates (10.2%). The prevalence of detailed types was as follows: maculopapular rash 2%, livedoid lesions 1.4%, petechial lesions 1.1%, urticaria 0.8%, pernio‐like lesions 0.5%, vesicular lesions 0.3%. Petechial lesions and livedoid lesions contain a higher proportion of severe patients than other skin manifestations. The prevalence rates of pernio‐like lesions, urticaria and petechial lesions vary greatly in different regions.