Determinants affecting the prognosis of decompressive craniectomy for traumatic brain injury

Clinical observation of individualized nutritional formula on inflammation index, immune status and gastrointestinal tolerance in patients with severe head injury

Objectives:

To evaluate the clinical significance of individualized nutritional formulas on inflammatory factors, immune status and gastrointestinal tolerance in patients with severe head injury.

Methods:

A total of 80 patients with severe head injury who were hospitalized in Baoding No.1 Central Hospital from March 2017 to March 2020 were randomly divided into two groups with 40 cases in each group. Patients in both groups were given enteral nutrition (EN), the control group was given conventional enteral nutrition formula through nasointestinal tube, and the experimental group was given individualized nutrition formula. All patients were tested for tumor necrosis factor(TNF-α), C-reactive protein(CRP), interleukin 6(IL-6), IgA, IgM, IgG, serum intestinal fatty acid binding protein(I-FABP) and D-lactic acid concentration before and after enteral nutrition treatment. The incidence of adverse reactions such as abdominal distension, diarrhea, constipation, and gastric retention within seven days after treatment of two groups were compared and analyzed.

Results:

There was no significant difference in inflammatory factors such as TNF-a, CRP, IL-6, immunoglobulin levels, I-FABP and D-lactic acid concentration between the two groups before treatment (p>0.05). After treatment, the above indicators of the two groups of patients were better than before treatment, the difference was statistically significant (p<0.05), and the experimental group was significantly better than the control group (p<0.05). The experimental group had a gastrointestinal adverse reaction rate of 10%, and the control group had 27.5%, the difference was statistically significant (p=0.04).

Conclusions:

Individualized nutritional formula has more significant advantages than conventional nutritional formula for patients with severe head injury, which can reduce inflammatory response, increase the patient’s immune level, improve the intestinal mucosal barrier function, have good gastrointestinal tolerance, and have a low incidence of adverse reactions.

Random Forest-Based Prediction of Outcome and Mortality in Patients with Traumatic Brain Injury Undergoing Primary Decompressive Craniectomy

BACKGROUND

Various prognostic models are used to predict mortality and functional outcome in patients after traumatic brain injury with a trend to incorporate machine learning protocols. None of these models is focused exactly on the subgroup of patients indicated for decompressive craniectomy. Evidence regarding efficiency of this surgery is still incomplete, especially in patients undergoing primary decompressive craniectomy with evacuation of traumatic mass lesions.

METHODS

In a prospective study with a 6-month follow-up period, we assessed postoperative outcome and mortality of 40 patients who underwent primary decompressive craniectomy for traumatic brain injuries during 2018-2019. The results were analyzed in relation to a wide spectrum of preoperatively available demographic, clinical, radiographic, and laboratory data. Random forest algorithms were trained for prediction of both mortality and unfavorable outcome, with their accuracy quantified by area under the receiver operating curves (AUCs) for out-of-bag samples.

RESULTS

At the end of the follow-up period, we observed mortality of 57.5%. Favorable outcome (Glasgow Outcome Scale [GOS] score 4-5) was achieved by 30% of our patients. Random forest-based prediction models constructed for 6-month mortality and outcome reached a moderate predictive ability, with AUC = 0.811 and AUC = 0.873, respectively. Random forest models trained on handpicked variables showed slightly decreased AUC = 0.787 for 6-month mortality and AUC = 0.846 for 6-month outcome and increased out-of-bag error rates.

CONCLUSIONS

Random forest algorithms show promising results in prediction of postoperative outcome and mortality in patients undergoing primary decompressive craniectomy. The best performance was achieved by Classification Random forest for 6-month outcome.