Acute kidney injury in traumatic brain injury intensive care unit patients.

BACKGROUND: The exact definition of Acute kidney injury (AKI) for patients with traumatic brain injury (TBI) is unknown. AIM: To compare the power of the "Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease" (RIFLE), Acute Kidney Injury Network (AKIN), Creatinine kinetics (CK), and Kidney Disease Improving Global Outcomes (KDIGO) to determine AKI incidence/stage and their association with the in-hospital mortality rate of patients with TBI. METHODS: This retrospective study collected the data of patients admitted to the intensive care unit for neurotrauma from 2001 to 2012, and 1648 patients were included. The subjects in this study were assessed for the presence and stage of AKI using RIFLE, AKIN, CK, and KDIGO. In addition, the propensity score matching method was used. RESULTS: Among the 1648 patients, 291 (17.7%) had AKI, according to KDIGO. The highest incidence of AKI was found by KDIGO (17.7%), followed by AKIN (17.1%), RIFLE (12.7%), and CK (11.5%) (P = 0.97). Concordance between KDIGO and RIFLE/AKIN/CK was 99.3%/99.1%/99.3% for stage 0, 36.0%/91.5%/44.5% for stage 1, 35.9%/90.6%/11.3% for stage 2, and 47.4%/89.5%/36.8% for stage 3. The in-hospital mortality rates increased with the AKI stage in all four definitions. The severity of AKI by all definitions and stages was not associated with in-hospital mortality in the multivariable analyses (all P > 0.05). CONCLUSION: Differences are seen in AKI diagnosis and in-hospital mortality among the four AKI definitions or stages. This study revealed that KDIGO is the best method to define AKI in patients with TBI.

Validated tool for early prediction of intensive care unit admission in COVID-19 patients.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2. AIM: To develop and validate a risk stratification tool for the early prediction of intensive care unit (ICU) admission among COVID-19 patients at hospital admission. METHODS: The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital. We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest (RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. The predicted models were carefully calibrated, and the predictive performance was evaluated and compared with two previously published models. RESULTS: There were 681 and 296 patients in the training and validation cohorts, respectively. The patients in the training cohort were older than those in the validation cohort (median age: 63.0 vs 49.0 years, P < 0.001), and the percentages of male gender were similar (49.6% vs 49.3%, P = 0.958). The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio, age, lactate dehydrogenase, C-reactive protein, creatinine, D-dimer, albumin, procalcitonin, glucose, platelet, total bilirubin, lactate and creatine kinase. The accuracy, sensitivity and specificity for the RF model were 91%, 88% and 93%, respectively, higher than those for the logistic regression model. The area under the receiver operating characteristic curve of our model was much better than those of two other published methods (0.90 vs 0.82 and 0.75). Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%, whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata. Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A. CONCLUSION: Our model can identify ICU admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.

Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule.

BACKGROUND: The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial. AIM: To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care. METHODS: This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People's Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model. RESULTS: Among the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO2)/(FiO2 × respiratory rate, RR) showed positive correlations with severe COVID-19. In multivariate logistic regression analysis, only six parameters including BMI [odds ratio (OR) 3.939; 95% confidence interval (CI): 1.409-11.015; P = 0.009], time from onset to admission (≥ 5 d) (OR 7.107; 95%CI: 1.449-34.849; P = 0.016), fever (OR 6.794; 95%CI: 1.401-32.951; P = 0.017), Charlson index (OR 2.917; 95%CI: 1.279-6.654; P = 0.011), PaO2/FiO2 ratio (OR 17.570; 95%CI: 1.117-276.383; P = 0.041), and neutrophil/lymphocyte ratio (OR 3.574; 95%CI: 1.048-12.191; P = 0.042) were found to be independent predictors of COVID-19. These factors were found to be significant risk factors for severe patients confirmed with COVID-19. The AUROC was 0.941 (95%CI: 0.901-0.981) and 0.936 (95%CI: 0.886-0.987) in both datasets. The calibration properties were good. CONCLUSION: The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.

Virus load and virus shedding of SARS-CoV-2 and their impact on patient outcomes.

BACKGROUND: Understanding a virus shedding patterns in body fluids/secretions is important to determine the samples to be used for diagnosis and to formulate infection control measures. AIM: To investigate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding patterns and its risk factors. METHODS: All laboratory-confirmed coronavirus disease 2019 patients with complete medical records admitted to the Shenzhen Third People's Hospital from January 28, 2020 to March 8, 2020 were included. Among 145 patients (54.5% males; median age, 46.1 years), three (2.1%) died. The bronco-alveolar lavage fluid (BALF) had the highest virus load compared with the other samples. The viral load peaked at admission (3.3 × 108 copies) and sharply decreased 10 d after admission. RESULTS: The viral load was associated with prolonged intensive care unit (ICU) duration. Patients in the ICU had significantly longer shedding time compared to those in the wards (P < 0.0001). Age > 60 years [hazard ratio (HR) = 0.6; 95% confidence interval (CI): 0.4-0.9] was an independent risk factor for SARS-CoV-2 shedding, while chloroquine (HR = 22.8; 95%CI: 2.3-224.6) was a protective factor. CONCLUSION: BALF had the highest SARS-CoV-2 load. Elderly patients had higher virus loads, which was associated with a prolonged ICU stay. Chloroquine was associated with shorter shedding duration and increased the chance of viral negativity.

Quantification of iodiconazole by LC-MS/MS, a new topical transdermal cream, in dermatophytosis patients' plasma.

BACKGROUND: A rapid and sensitive LC-MS/MS method was established to measure iodiconazole (ADKZ) in dermatophytosis patients treated topically with 2% ADKZ cream. METHODOLOGY: ADKZ was extracted by liquid-liquid extraction (LLE) and separated by an Agilent Zorbax SB-C18 column (2.1 × 150 mm, 3.5 µm) using methanol - 0.01% formic acid (70:30, v/v) as the mobile phase. All the validation assays met the acceptable criteria and the linearity ranged from 10 to 1000 pg/ml. CONCLUSION: The method has been validated to be simple, sensitive and successfully applied to the study. The average amount of ADKZ absorbed into blood was approximately 0.51 µg daily, and ADKZ blood concentrations were consistent during the four-week treatment course. The cumulation of ADKZ in vivo was almost negligible.