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Objective:To explore the selection of surgical methods for different sites of symptomatic Rathke's cleft cyst (RCC) and the clinical efficacies of these patients.Methods:Forty-seven patients with symptomatic RCC, admitted to our hospital from January 2016 to December 2019, were chosen in our study; 21 patients with intrasellar symptomatic RCC accepted surgery via unilateral nasal approach at the right side, 19 patients with intra-suprasellar symptomatic RCC accepted surgery via bilateral nasal approach, 3 patients with suprasellar symptomatic RCC accepted endonasal transsphenoidal surgery under endoscope, and 4 patients with suprasellar symptomatic RCC accepted craniotomy via pterion approach. The clinical efficacies and complications of patients accepted different surgical methods were compared. All patients were followed up for 3-36 months to observe the recurrence.Results:The postoperative symptoms of the patients were effectively improved, including headache relief ratio of 27/31, vision loss improvement ratio of 5/5, high prolactin relief ratio of 11/13, pituitary function improvement ratio of 9/18. Complications occurred in 6 patients, presenting as diabetes insipidus. Four patients recurred during follow-up.Conclusion:Intrasellar and intra-suprasellar symptomatic RCC accepted surgery via endoscopic transnasal transsphenoidal approach are safe and effective; selection of surgical methods for suprasellar symptomatic RCC should be determined according to the sizes and growth directions of cysts.
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Objective To explore the awakening probabilistic prediction models of coma patients with traumatic brain injury on admission and six months after treatment,and develop and apply the software of the models.Methods Clinical data of 190 coma patients with traumatic brain injury,admitted to our hospital from September 2010 to October 2012,were analyzed retrospectively.Potential predictive factors at admission and after awakening were analyzed by binary Logistic regression analysis; based on these factors,the awakening probabilistic prediction models of coma patients with traumatic brain injury were established; C++ language was used to write the computer software that could predict the awakening probability of 103 patients with traumatic brain injury.Results Multinomial Logistic regression analysis showed that 6 factors,including age,pupillary light reflex,movement Glasgow coma scale (mGCS) scores,morphology changes of mesencephalon surrounding cisterna,eye opening time after treatment,and percentages of ischemic brain volume in CT images,were independent factors to predict the awakening probability of coma patients with traumatic brain injury.Model A and B owned high performance (C statistics of models:0.955 and 0.975; accept rate of models:90.5％ and 94.0％).The established software based on models was easy to use with reliable results (the accept rate of 103 patients were 87.3％ and 93.2％).Conclusion The established models can timely and accurately predict the awakening probability of coma patients with traumatic brain injury; the software named sober probabilistic prediction for coma patients with traumatic brain injury can help in decision-making in clinics.