A Nomogram To Predict In-hospital Mortality Of Patients With Diabetes And Congestive Heart Failure And An Analysis Of Differentially Expressed Genes In Diabetic Cardiomyopathy

Objective:(1)To analyze whether diabetes was an independent risk factor for the patient’s diagnosis of congestive heart failure.It was conducted on patients admitted to the intensive care unit(ICU)and the outcome of the study was a diagnosis of congestive heart failure.(2)To analyze the risk factors for in-hospital mortality in patients with diabetes and congestive heart failure and to develop a nomogram to predict the risk of in-hospital mortality.Patients were included for the study if they met the following inclusion criteria:(ⅰ)they had an ICU admission record,(ⅱ)they had a diagnosis of diabetes in the hospital record,and(ⅲ)they had a diagnosis of congestive heart failure in the hospital record.The study outcome was death of the patient during hospitalization as recorded in the hospital records.(3)To investigate key genes leading to diabetic cardiomyopathy through mRNA chip related to diabetic cardiomyopathy in the GEO database.Methods:(1)The data of this study from the Medical Information Mart for Intensive Care(MIMIC).PgAdmin PostgreSQL and Navicat Premium were used for database construction and collection of raw data.Patients were divided into two cohorts,with or without CHF,and patients’ baseline characteristics were compared between the two groups.Univariate logistic regression was used to analyses potential risk factors for the development of congestive heart failure in patients,and multivariate logistic regression was used to correct for potential confounders.(2)The least absolute shrinkage and selection operator was used for variables screening,the simplified model is visualized in the form of nomogram;the bootstrap resampling method was used for internal validation of the model,and the number of resamples is 500;the receiver operating characteristic curve was used to evaluate the discrimination of the model,the area under the ROC curve was used as the evaluation index.The calibration curve was used to evaluate the calibration of the model,and the Decision Curve Analysis curve was used to evaluate the clinical value of the model.(3)The online analysis tool GEO2R of the GEO database was used to analyze the differentially expressed genes in the data sets GSE4745 and GSE5606.R was used to draw the volcano map of differentially expressed genes,and the online drawing tool VENN map was used to analyze the differentially expressed genes of the two data sets.The clusterProfile package in R was used for Gene Ontology(GO)enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis for differentially expressed genes.GSEA software was used for gene set enrichment analysis.STRING online database was used to construct a protein-protein interaction(PPI)network.The maximal clique centrality algorithm in Cytoscape was used to calculate the top 10 key genes.The expression of key genes was observed and compared in vitro.Results:(1)The presence of diabetes,after adjusting for confounding factors,was associated with an increased risk of congestive heart failure diagnosis by 1.43 times(95%CI:1.38-1.48).(2)In the nomogram model,the following factors were used to predict the risk of in-hospital mortality in patients with diabetes and congestive heart failure:age,myocardial infarction(including previous infarction),atrial fibrillation,hyperlipidemia,β-blockers,ACEI/ARB,diuretics,insulin,metformin,erythrocyte distribution width≥15.5%,heart rate≥100 bpm,systolic blood pressure≥130 mmHg,anion gap≥20 mEq/L,bicarbonate≤22 mEq/L,blood urea nitrogen≥20 mg/dl,white blood cell count≥10 K/μL.In the ROC analysis,the AUC for the model was 0.792(95%CI:0.774-0.811).DCA curves demonstrated that the model achieved a greater net benefit at thresholds of 0.01-0.81.A Bootstrap resampling study of the nomogram model for the risk of in-hospital mortality in patients with diabetes and congestive heart failure showed a corrected C-statistic of 0.787,and a calibration curve showed that the nomogram model was performing well.(3)In an in vitro model of diabetic cardiomyopathy,Pdk4,Ucp3,Hmgcs2,Asl6,Slc2a4 are consistent with the chip analysis results.The expression of Pdk4,Ucp3and Hmgcs2 increased in cardiomyocytes stimulated by high glucose(25 mmol/l)for 72 hours,while the expression of Acsl6 and Slc2a4 decreased in cardiomyocytes stimulated by high glucose(25 mmol/l).Conclusion:(1)Diabetes increases the likelihood of a diagnosis of congestive heart failure independently.(2)The nomogram model developed in this study for predicting the risk of inhospital death in diabetes patients with congestive heart failure was internally validated and proved to have good discrimination.calibration,and clinical applicability.(3)Pdk4,Ucp3,Hmgcs2,Asl6,Slc2a4 may be related to the occurrence and development of diabetic cardiomyopathy,and may be potential biomarkers of diabetic cardiomyopathy.