Mechanism,Prediction And Prognosis Of Rhabdomyolysisinduced Renal Injury

Background Rhabdomyolysis(RM)is a complex set of clinical syndromes that involve the rapid dissolution of skeletal muscles and leak of toxic cellular contents and myoglobin into the systemic circulation,which in turn cause a series of physiological and biochemical disorders in the human body.RM is an acute and potentially fatal syndrome.The prevalence of acute kidney injury(AKI)following RM ranges from 13% to 50%.Mortality from RM is approximately 10%and is significantly increased in the setting of AKI.Myoglobin(MYO)plays a key role in the development of AKI.It can cause renal tubular obstruction,renal vasoconstriction,and the direct activation of oxidative stress,lipid p eroxidation and macrophages,which injure proximal tubular cells.Renal replacement therapy(RRT)is often used in the treatment of RM-induced AKI,in which a high permeability membrane eliminates circulating myoglobin.However,RRT is a non-specific treatment method that removes blood solutes,and it cannot regulate inflammation or promote the repair of damaged renal tubules.Moreover,current research has shown that RRT does not significantly improve the mortality.Parabiosis model has been used in several physiological studies,such as the migration of hematopoietic stem cells and neurodegenerative disease.Our previous study used a parabiosis model to show that exogenous biological renal support may attenuate inflammation and apoptosis and increase proli feration in an ischaemia-reperfusion injury(IRI)mouse model.In this study,we established a parabiosis model in mice and then used glycerol injections to induce AKI.To study the therapeutic effects of exogenous biological renal support on RM-induced AKI.Proteomic analysis was used to screen and study changes in protein expression and key pathways and preliminarily explore the mechanisms involved.In addition,the timing of RRT initiation is an important factor affecting the mortality of RM patients.It is necessary to consider all co-occurring factors in RM patients and individualize the treatment plan.Therefore,the use of more variables to support the early detection of RM patients who need RRT is very important.Meanwhile,early detection of patients who are at high risk of death is of great importance and may aid in delivering proper care and optimizing the use of limited resources.Objective1.To determine whether exogenous biological renal support promoted renal recovery from RM-induced AKI and preliminarily explore the mechanisms involved;2.Constructed a model based on the e ICU Collaborative Research Database(e ICU-CRD),the Medical Information Mart for Intensive Care III(MIMIC III)dataset and data collected from the First Medical Centre of the Chinese People’s Liberation Army General Hospital(PLAGH)to identify patients who need RRT at the time of hospital admission;3.Construct a risk prediction model based on the e ICU-CRD and MIMIC III to help identify patients at the time of hospital admission who are at high risk of death.Methods1.Exogenous biological renal support improves kidney function in RM-induced AKI in miceThree methods were used to verify the shared circulation created between the two mice.The mice were divided into three groups: the sham group with sterile saline administration;the RM group with glycerol administration;and the parabiosis + RM group.Three weeks after the parabiosis model was established,the recipient mouse was administered glycerol,and this mouse was defined as the P＿RM＿R.The other mouse in the parabiosis model supplied exogenous biological renal support and was defined as the P＿RM＿S.Blood samples and kidney tissue were collected for further processing 48 hours after the induction of RM.Bioinformatics analysis was conducted with Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis,functional enrichment analysis and clustering analysis;2.Development and Validation of a Model for the Early Prediction of the RRT Requirement in Patients with RMWe performed a longitudinal,multicenter,retrospective study based on the e ICUCRD(v2.0),MIMIC-III(v1.4)and PLAGH databases.All adult ICU patients(age≥ 18 years)diagnosed with RM according to the International Classification of Diseases(ICD-9)codes were considered.The exclusion criteria were(1)a peak CK level less than 1000 UL/L;(2)a hospital stay less than 2 days;(3)patients with unknown outcomes;and(4)outliers were present.The baseline characteristics were extracted within the first 24 h after patient admission.The data from the e ICU-CRD and MIMIC-III datasets were merged for further analysis.Variables with > 40% missing values were excluded from further analysis,and the median of the overall population was used to interpolate the remaining missing data.The study cohort was randomly divided into the following two parts: 70% of the data were used for the model training,and 30 %of the data were used for the model testing.The data collected from the Chinese PLAGH were used as an external validation dataset.A LASSO regression analysis was used to select the variables that were predictive of the need for RRT.The model was built in Python using the ML library scikit-learn.A logistic regression was selected as the algorithm.A propensity score matching(PSM)analysis was performed using Python.R software was used for the LASSO regression analysis,nomogram,concordance index,calibration,and decision and clinical impact curves;3.Interpretable Machine Learning Model for Early Prediction of Mortality in ICU Patients with RMData from the two datasets(e ICU-CRD and MIMIC-III)were merged for further analysis,and the data were evaluated on a 30% holdout sample of the merged datasets.Five machine learning methods(extreme gradient boosting-XGBoost,logistic regression-LR,support vector machine-SVM,random forest-RF and naive Bayesian – NB)were used to develop the predictive models.Five typical evaluation indexes(AUC,sensitivity,specificity,F1 and accuracy)were adopted to develop a generalizable model.Shapley additive explanation(SHAP)values were used to provide the interpretation of our early prediction model wit h contributing risk factors leading to death in patients with RM.Results1.The Scr and BUN levels were significantly increased in the RM group compared with the parabiosis + RM and sham groups.Sham mice did not show any significant tubular damage.RM mice showed the loss of tubular brush borders,cast formation,tubular dilatation and tubular necrosis,accompanied by an increase in the acute tubular injury score.Parabiosis + RM mice showed significantly improved renal histological injury.Proteomic analysis results showed that exogenous biological renal support led to an increase in proteins associated with extracellular,nuclear,cytoplasmic,mitochondrial and plasma membrane localisation in kidney tissue and serum compared with those in the RM group.The analysis of the functional enrichment of pathways identified the suppression of complement system activation;attenuation of oxidative stress,inflammation,and cell death;and increased proliferation.Furthermore,we further confirmed these effects with in vivo experiments;2.In total,1259 patients with RM(614 patients from e ICU-CRD,324 patients from the MIMIC-III database and 321 patients from the Chinese PLAGH)were eligible for this analysis.The rate of RRT was 15.0%(92/614)in the e ICU-CRD database,17.6%(57/324)in the MIMIC-III database and 5.6% in the Chinese PLAGH(18/321).After the LASSO regression selection,eight variables(age,creatinine,creatine kinase,aspartate aminotransferase,albumin,calcium,phosphate and atrial fibrillation(yes vs.no))were included in the RRT prediction model.The AUC of the model in the training dataset was 0.818(95% CI 0.78-0.87),the AUC in the test dataset was 0.794(95% CI 0.72-0.86),and the AUC in the Chinese PLAGH dataset(external validation dataset)was 0.820(95% CI0.70-0.86);This model(AUC = 0.818)performed better than Cr(AUC = 0.747)and CK(AUC = 0.667)alone in predicting the need for RRT;3.In total,938 patients with RM(614 patients from the e ICU-CRD and 324 patients from the MIMIC-III database)were eligible for this analysis.The hospital mortality was 10.1%(62/614)in the e ICU-CRD and 10.5%(34/324)in the MIMIC-III database.The AUC of the XGBoost model in predicting hospital mortality was 0.871,the sensitivity was 0.885,the specificity was 0.816,the accuracy was 0.915 and the F1 score was 0.624.The XGBoost model performance was superior to that of other models(LR(AUC = 0.862),SVM(AUC = 0.843),RF(AUC = 0.825)and NB(AUC = 0.805)and clinical scores(SOFA(AUC =0.747)and APS III(AUC = 0.721)).ConclusionIn summary,we demonstrated that exogenous biological renal support supplied by parabiosis can improve renal function in RM-induced AKI by suppressing complement system activation;decreasing oxidative stress,inflammation,and cell death;and promoting tubular cell proliferation.Our study provides new ideas for effectively preventing and treating RM-induced AKI and provides basic research evidence for the use of bioartificial kidneys to treat RM-induced AKI.In addition,predicting the need for RRT and estimating the risk of death among patients with RM could help ensure appropriate treatment and the optimization of the use of medical resources.