A Chinese localization protocol on scar prevention and management is needed, due to the ethnic differences and complicated clinical environment. Therefore, the "Chinese Expert Consensus Report on Clinical Prevention and Treatment of Scar" was released in 2017, after two-year extensive discussions, among 12 independent experts in burn/trauma, plastic surgery and dermatology. A number of recommendations were put forward to keep pace with the times, and adapt to the current clinical situation in China. This review is focused on some sections of the Expert Consensus, including the classification, assessment, prevention and treatment of scar, and related novel techniques, along with a briefly introduction of the rationales and theoretical evidences underlying the consensus.

OBJECTIVETo appraise the significance of extravascular lung water index (EVLWI), pulmonary vascular permeability index (PVPI), and intrathoracic blood volume index (ITBVI) in the differential diagnosis of the type of burn-induced pulmonary edema.

METHODSThe clinical data of 38 patients, with severe burn hospitalized in our burn ICU from December 2011 to September 2014 suffering from the complication of pulmonary edema within one week post burn and treated with mechanical ventilation accompanied by pulse contour cardiac output monitoring, were retrospectively analyzed. The patients were divided into lung injury group ( L, n = 17) and hydrostatic group (H, n = 21) according to the diagnosis of pulmonary edema. EVLWI, PVPI, ITBVI, oxygenation index, and lung injury score ( LIS) were compared between two groups, and the correlations among the former four indexes and the correlations between each of the former three indexes and types of pulmonary edema were analyzed. Data were processed with t test, chi-square test, Mann-Whitney U test, Pearson correlation test, and accuracy test [receiver operating characteristic (ROC) curve].

RESULTSThere was no statistically significant difference in EVLWI between group L and group H, respectively (12.9 ± 3.1) and (12.1 ± 2.1) mL/kg, U = 159.5, P > 0.05. The PVPI and LIS of patients in group L were respectively 2.6 ± 0.5 and (2.1 ± 0.6) points, and they were significantly higher than those in group H [1.4 ± 0.3 and (1.0 ± 0.6) points, with U values respectively 4.5 and 36.5, P values below 0.01]. The ITBVI and oxygenation index of patients in group L were respectively (911 197) mL/m2 and (136 ± 69) mmHg (1 mmHg = 0.133 kPa), which were significantly lower than those in group H [(1,305 ± 168) mL/m2 and (212 ± 60) mmHg, with U values respectively 21.5 and 70.5, P values below 0.01]. In group L, there was obviously positive correlation between EVLWI and PVPI, or EVLWI and ITBVI (with r values respectively 0.553 and 0.807, P < 0.05 or P < 0.01), and there was obviously negative correlation between oxygenation index and EVLWI, or oxygenation index and PVPI (with r values respectively -0.674 and -0.817, P values below 0.01). In group H, there was obviously positive correlation between EVLWI and ITBVI (r = 0.751, P < 0.01) but no obvious correlation between EVLWI and PVPI, oxygenation index and EVLWI, or oxygenation index and PVPI (with r values respectively -0.275, 0.197, and 0:062, P values above 0.05). The total area under ROC curve of PVPI value for differentiating the type of pulmonary edema was 0.987 [with 95% confidence interval (CI) 0.962-1.013, P < 0.01], and 1.9 was the cutoff value with sensitivity of 94.1% and specificity of 95.2% . The total area under ROC curve of ITBVI value for differentiating the type of pulmonary edema was 0.940 (with 95% CI 0.860-1.020, P < 0.01), and 1,077. 5 mL/m2 was the cutoff value with sensitivity of 95.2% and specificity of 88.2%.

CONCLUSIONSEVLWI, PVPI, and ITBVI have an important significance in the differential diagnosis of the type of burn-induced pulmonary edema, and they may be helpful in the early diagnosis and management of burn-induced pulmonary edema.

Blood Gas Analysis ; Blood Volume ; Burns ; complications ; Capillary Permeability ; Diagnosis, Differential ; Extravascular Lung Water ; Humans ; Lung ; blood supply ; Lung Injury ; physiopathology ; therapy ; Monitoring, Physiologic ; Pulmonary Edema ; diagnosis ; etiology ; ROC Curve ; Respiration, Artificial ; Retrospective Studies

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.

The healing process of burn wounds is strongly associated with the depth of wounds, and the depth of wounds is dependent to initial temperature and duration of contact with source of heat, infection, and secondary damage in the debridement process. On this basis, some experts present a concept of accurate debridement, which denotes removal of all necrotic tissue and at the same time protecting viable tissue for repair of raw wounds in order to maximally maintain patients' appearance and functions. A new technology of burn wound debridement--hydrosurgery system has been applied clinically. This paper summarizes the characteristics of hydrosurgery in the aspects of its technology, suitable wounds, bacterial load, amount of blood loss, and degree of pain produced during operation, and also the author's opinions regarding its efficacy to realize an accurate debridement for burn injury.
Burns ; pathology ; surgery ; Debridement ; methods ; Humans ; Necrosis ; Surgical Wound Infection ; Treatment Outcome ; Wound Healing