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Objective@#To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learning method and logistic regression model.@*Methods@#The clinical data of 157 severely burned patients in August 2nd Kunshan factory aluminum dust explosion accident conforming to the inclusion criteria were collected. Patients suffering AKI within 90 days after admission were enrolled in group AKI, while the others were enrolled in non-AKI group. Single factor analysis was used to choose independent factors associated with AKI, including sex, age, admission time, features of basic injuries, initial score on admission, treatment condition, and mortality on post injury days 30, 60, and 90. Data were processed with Mann-Whitney U test, chi-square test, and Fisher′s exact test. Variables with P<0.1 in single factor analysis and those with possible clinical significance were brought into the establishment of prediction model. Logistic regression and XGBoost machine learning algorithm were used to build the prediction model of AKI. The area under receiver operating characteristic curve (AUC) was calculated, and the sensitivity and specificity for optimal threshold value were also calculated for each model. Nonparametric resampling test was used to compare the significance of difference of AUC of the two models.@*Results@#(1) Eighty-nine (56.7%) patients developed AKI within 90 days from admission. Compared with 68 patients in non-AKI group, 89 patients in group AKI were older (Z=-2.203, P<0.05), with larger total burn area and full-thickness burn area (Z=-5.200, -6.297, P<0.01), worse acute physical and chronic health evaluation (APACHE) Ⅱ score, abbreviated burn severity index score, and sequential organ failure assessment (SOFA) score on admission (Z=-7.485, -4.739, -4.590, P<0.01), higher occurrence rate of sepsis (χ2=33.087, P<0.01), higher rates of accepting tracheotomy, mechanical ventilation, and continuous renal replacement therapy (χ2=12.373, 17.201, 43.763, P<0.01), larger first excision area (Z=-2.191, P<0.05), and higher mortality on post injury days 30, 60, and 90 (χ2=7.483, 37.259, 45.533, P<0.01). There were no statistically significant differences in sex, open decompression, admission time, 24-hour fluid volume after admission, 48-hour fluid volume after admission, the first 24-hour urine volume, the second 24 hour urine volume, the first excision time, and inhalation injury (χ2=0.529, 3.318, Z=-1.746, -0.016, -1.199, -1.824, -0.625, -1.747, P>0.05). The rates of deep vein catheterization of patients in the two groups were both 100%. (2) There were twenty possible prediction variables for preliminary establishment of model according to the difference results of single factor analysis and clinical significance of variables. (3) The logistic regression prediction model had three variables: APACHE Ⅱ score [odds ratio (OR)=1.36, 95% confidence interval (CI)=1.20-1.53, P<0.001], sepsis (OR=2.63, 95% CI=0.90-7.66, P>0.05), and the first 24-hour urine volume (OR=0.71, 95% CI=0.50-1.01, P>0.05). The AUC of the logistic regression prediction model was 0.875 (95% CI=0.821-0.930), with the specificity and sensitivity of optimal threshold value 84.4% and 77.7%, respectively. (4) XGBoost machine learning model had seven main predictive variables: APACHE Ⅱ score, full-thickness burn area, 24-hour fluid volume after admission, sepsis, the first 24-hour urine volume, SOFA score, and 48-hour fluid volume after admission. The AUC of machine learning model was 0.920 (95% CI=0.879-0.962), higher than that of logistic regression model (P<0.001), with the specificity and sensitivity of optimal threshold value 89.7% and 82.0%, respectively.@*Conclusions@#Sepsis and fluid resuscitation are two important predictive variables that can be intervened for AKI in severely burned patients. Machine learning method has a better performance and can provide more accurate prediction for individuals than logistic regression prediction model, and therefore has good clinical application prospect.
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Objective To explore the therapeutic effects of different surgery methods on early hypertensive intracerebral hemorrhage(HICH)in basal ganglia region in elderly patients and on prognostic factors analysis.Methods 89 elderly patients with early HICH were randomly divided into four groups according to surgery methods and whether their ICP was monitored.Group A(n=21)was given minimally invasive hematoma drainage,group B(n=23)was given small bone window for removal of hematoma,group C(n=21)and group D(n=24)was given ICP monitoring and corresponding management of ICP on the basis of group A and group B,respectively.The changes of intracranial pressure before and after operation,prognosis and post-operative complications were compared.Results The intracranial pressure was significantly decreased at 3rd day,7th day after operation in group C and D as compared with those in group A and B at the same time points(F=11.76,P<0.05),and the score of GCS was also higher in group C and D at 7th day after operation than in group A,B at the same time points(F=4.72,P<0.05).At 14th and 28th day after operation,the score of GCS was higher in group C than in group A and B(F=19.24,P<0.05),and higher in group C than in group D at 28th day after operation(F=22.26,P<0.05).The dosage of mannitol was significantly lower in group C and group D than in group A and group B(F=18.87,P<0.05).The incidence rate of post-operative complications was 14.3% in group C vs.28.6% in group A(P<0.05)and 20.8% in group D vs.47.8% in group B(χ2=7.04,P<0.05).The proportion of a good recovery and a light disability was significantly higher in group C and D(76.2% and 75.0%)than in group A and B(42.9% and 39.1%)respectively(χ2=14.99,all P<0.05).Conclusions Minimally invasive hematoma drainage shows the advantages of small trauma and a few complications for the treatment of elderly patients with early HICH,and its combination with ICP can early change intracranial pressure and further improves the prognosis.