Continuous venovenous hemodiafiltration versus standard medical therapy for the prevention of rhabdomyolysis-induced acute kidney injury: a retrospective cohort study.

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 analyze the related health economics. METHODS: This retrospective cohort study involved 9 RM patients without AKI, coronary heart disease, or chronic kidney disease treated with CVVHDF plus SMT (CVVHDF + SMT group). Nine matched RM patients without AKI treated with SMT only served as controls (SMT group). Baseline characteristics, biochemical indexes, renal survival data, and health economic data were compared between groups. In the CVVHDF + SMT group, 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 groups. Total (P = 0.011) and daily hospitalization costs (P = 0.002) were higher in the CVVHDF + SMT group than in the SMT group. After 53 months of follow-up, no patient developed increased serum creatinine, except for 1 CVVHDF + SMT-group patient who died of acute myocardial infarction. In the CVVHDF + SMT 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 biochemical indices like serum myoglobin, serum creatine kinase, creatinine, blood urea nitrogen, and lactate dehydrogenase or the long-term renal prognosis. Despite similar hospitalization durations, both total and daily hospitalization costs were higher in the CVVHDF + SMT group.

Exploratory COVID-19 death risk score based on basic laboratory tests and physiological clinical measurements.

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.