Development and validation of a model for the early prediction of the RRT requirement in patients with rhabdomyolysis

Predictive model for assessing the prognosis of rhabdomyolysis patients in the intensive care unit

Background

Rhabdomyolysis (RM) frequently gives rise to diverse complications, ultimately leading to an unfavorable prognosis for patients. Consequently, there is a pressing need for early prediction of survival rates among RM patients, yet reliable and effective predictive models are currently scarce.

Methods

All data utilized in this study were sourced from the MIMIC-IV database. A multivariable Cox regression analysis was conducted on the data, and the performance of the new model was evaluated based on the Harrell's concordance index (C-index) and the area under the receiver operating characteristic curve (AUC). Furthermore, the clinical utility of the predictive model was assessed through decision curve analysis (DCA).

Results

A total of 725 RM patients admitted to the intensive care unit (ICU) were included in the analysis, comprising 507 patients in the training cohort and 218 patients in the testing cohort. For the development of the predictive model, 37 variables were carefully selected. Multivariable Cox regression revealed that age, phosphate max, RR mean, and SOFA score were independent predictors of survival outcomes in RM patients. In the training cohort, the AUCs of the new model for predicting 28-day, 60-day, and 90-day survival rates were 0.818 (95% CI: 0.766-0.871), 0.810 (95% CI: 0.761-0.855), and 0.819 (95% CI: 0.773-0.864), respectively. In the validation cohort, the AUCs of the new model for predicting 28-day, 60-day, and 90-day survival rates were 0.840 (95% CI: 0.772-0.900), 0.842 (95% CI: 0.780-0.899), and 0.842 (95% CI: 0.779-0.897), respectively.

Conclusion

This study identified crucial demographic factors, vital signs, and laboratory parameters associated with RM patient prognosis and utilized them to develop a more accurate and convenient prognostic prediction model for assessing 28-day, 60-day, and 90-day survival rates.

Implications for clinical practice

This study specifically targets patients with RM admitted to ICU and presents a novel clinical prediction model that surpasses the conventional SOFA score. By integrating specific prognostic indicators tailored to RM, the model significantly enhances prediction accuracy, thereby enabling a more targeted and effective approach to managing RM patients.

Introducing the BlendedICU dataset, the first harmonized, international intensive care dataset

OBJECTIVE

This study introduces the BlendedICU dataset, a massive dataset of international intensive care data. This dataset aims to facilitate generalizability studies of machine learning models, as well as statistical studies of clinical practices in the intensive care units.

METHODS

Four publicly available and patient-level intensive care databases were used as source databases. A unique and customizable preprocessing pipeline extracted clinically relevant patient-related variables from each source database. The variables were then harmonized and standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Format. Finally, a brief comparison was carried out to explore differences in the source databases.

RESULTS

The BlendedICU dataset features 41 timeseries variables as well as the exposure times to 113 active ingredients extracted from the AmsterdamUMCdb, eICU, HiRID, and MIMIC-IV databases. This resulted in a database of more than 309000 intensive care admissions, spanning over 13 years and three countries. We found that data collection, drug exposure, and patient outcomes varied strongly between source databases.

CONCLUSION

The variability in data collection, drug exposure, and patient outcomes between the source databases indicated some dissimilarity in patient phenotypes and clinical practices between different intensive care units. This demonstrated the need for generalizability studies of machine learning models. This study provides the clinical data research community with essential data to build efficient and generalizable machine learning models, as well as to explore clinical practices in intensive care units around the world.

Earlier continuous renal replacement therapy is associated with reduced mortality in rhabdomyolysis patients

Background

Continuous renal replacement therapy (CRRT) is commonly employed for rhabdomyolysis (RM) patients. However, the optimal initiation timing of CRRT and prognostic factors were not well evaluated for patients with RM. We aimed to investigate the efficacy of CRRT timing on mortality and the risk factors for death in RM patients who received CRRT.

Methods

RM patients who received CRRT between 1 May 2010 and 31 May 2021 in our center were retrospectively included. Univariate and multivariate logistic analyses were performed to identify the risk factors for primary outcome (90-day mortality).

Results

A total of 134 patients were included in our study. The 90-day mortality rate was 38.06%. The median time from CRRT initiation to peak CK occurrence was 4.8 h (IQR −16, 14), 67 patients received CRRT before 4.8 h after peak CK occurrence (early CRRT), and 67 patients received CRRT beyond 4.8 h after peak CK occurrence (late CRRT). Multivariate logistic regression analysis showed that the time from CRRT initiation to the peak CK (per 1 h, OR 1.026, 95% CI 1.004–1.049, p = 0.023), late CRRT (OR 3.082, 95% CI 1.072–8.859, p = 0.037), elevated serum cardiac troponin I (cTnI) (per 1 ng/mL, OR 1.218, 95% CI 1.011–1.468, p = 0.038), older age (per 1 year, OR 1.042, 95% CI 1.003–1.081, p = 0.032), and need of mechanical ventilation support (OR 4.632, 95% CI 1.292–16.61, p = 0.019) were independent risk factors for 90-day mortality.

Conclusions

Earlier CRRT initiation before 4.8 h after peak CK occurrence was associated with lower 90-day patient mortality.