Search

Aim: To determine whether continuous venovenous hemodiafiltration (CVVHDF) plus standard medical therapy (SMT) vs. SMT alone prevents rhabdomyolysis (RM)-induced acute kidney injury (AKI) and to analyze the related health economics.Methods: This retrospective cohort study involved patients with RM without AKI, coronary heart disease, or chronic kidney disease treated with CVVHDF plus SMT (treatment group, n = 9). Matched patients with RM without AKI treated with SMT only served as controls (1:1 ratio). Baseline characteristics, biochemical indexes, renal survival data, and health economic data were compared between groups. In the treatment group, some biochemical data were compared at different time points.Results: At 2 and 7 days after admission, serum biochemical indices (e.g., myoglobin, creatine kinase, creatinine, and blood urea nitrogen) did not differ between the two groups. Total (P = 0.011) and daily hospitalization costs (P = 0.002) were higher in the treatment group than in the control group. After 53 months of follow-up, no patient developed increased serum creatinine, except for 1 treatment-group patient who died of acute myocardial infarction. In the treatment group, myoglobin levels significantly differed before and after the first CVVHDF treatment (P = 0.008), and serum myoglobin, serum creatinine, and blood urea nitrogen decreased significantly at different time points after CVVHDF.Conclusions: Although CVVHDF facilitated myoglobin elimination, its addition to SMT did not improve serum myoglobin or other biochemical indices or the long-term renal prognosis. Despite similar hospitalization durations, both total and daily hospitalization costs were higher in the treatment group.

BACKGROUND: In the event of a sudden shortage of medical resources, a rapid, simple, and accurate prediction model is essential for the 30-day mortality rate of patients with COVID-19. METHODS: This retrospective study compared the characteristics of the survivals and non-survivals of 278 patients with COVID-19. Logistic regression analysis was performed to obtain the “COVID-19 death risk score” (CDRS) model. Using the area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow goodness-of-fit test, discrimination and calibration were assessed. Internal validation was conducted using a regular bootstrap method. RESULTS: A total of 63 (22.66%) of 278 included patients died. The logistic regression analysis revealed that high-sensitivity C-reactive protein (hsCRP; odds ratio [OR]=1.018), D-dimer (OR=1.101), and respiratory rate (RR; OR=1.185) were independently associated with 30-day mortality. CDRS was calculated as follows: CDRS=−10.245+(0.022×hsCRP)+(0.172×D-dimer)+(0.203×RR). CDRS had the same predictive effect as the sequential organ failure assessment (SOFA) and “confusion, uremia, respiratory rate, blood pressure, and age over 65 years” (CURB-65) scores, with AUROCs of 0.984 for CDRS, 0.975 for SOFA, and 0.971 for CURB-65, respectively. And CDRS showed good calibration. The AUROC through internal validations was 0.980 (95% confidence interval [CI]: 0.965–0.995). Regarding the clinical value, the decision curve analysis of CDRS showed a net value similar to that of CURB-65 in this cohort. CONCLUSION: CDRS is a novel, efficient and accurate prediction model for the early identification of COVID-19 patients with poor outcomes. Although it is not as advanced as the other models, CDRS had a similar performance to that of SOFA and CURB-65.