[Prevalence and treatment of anemia in chronic kidney disease patients based on regional medical big data]

Objective: To assess the prevalence, risk factors and treatment of anemia in patients with chronic kidney disease (CKD). Methods: A descriptive method was used to analyze the prevalence and treatment of anemia in CKD patients based on regional health data in Yinzhou District of Ningbo during 2012-2018. The multivariate logistic regression analysis was used to identify independent influence factors of anemia in the CKD patients. Results: In 52 619 CKD patients, 15 639 suffered from by anemia (29.72%), in whom 5 461 were men (26.41%) and 10 178 were women (31.87%), and anemia prevalence was higher in women than in men, the difference was significant (P<0.001). The prevalence of anemia increased with stage of CKD (24.77% in stage 1 vs. 69.42% in stage 5, trend χ2 test P<0.001). Multivariate logistic regression analysis revealed that being women (aOR=1.57, 95%CI: 1.50-1.63), CKD stage (stage 2: aOR=1.10, 95%CI: 1.04-1.16;stage 3: aOR=2.28,95%CI: 2.12-2.44;stage 4: aOR=4.49,95%CI :3.79-5.32;stage 5: aOR=6.31,95%CI: 4.74-8.39), age (18-30 years old: aOR=2.40,95%CI: 2.24-2.57, 61-75 years old: aOR=1.35,95%CI:1.28-1.42, ≥76 years old: aOR=2.37,95%CI:2.20-2.55), BMI (<18.5 kg/m2:aOR=1.29,95%CI: 1.18-1.41;23.0-24.9 kg/m2:aOR=0.79,95%CI: 0.75-0.83;≥25.0 kg/m2:aOR=0.70,95%CI: 0.66-0.74), abdominal obesity (aOR=0.91, 95%CI: 0.86-0.96), chronic obstructive pulmonary disease (aOR=1.15, 95%CI: 1.09-1.22), cancer (aOR=3.03, 95%CI: 2.84-3.23), heart failure (aOR=1.44, 95%CI: 1.35-1.54) and myocardial infarction (aOR=1.54, 95%CI:1.16-2.04) were independent risk factors of anemia in CKD patients. Among stage 3-5 CKD patients with anemia, 12.03% received iron therapy, and 4.78% received treatment with erythropoiesis-stimulating agent (ESA) within 12 months after anemia was diagnosed. Conclusions: The prevalence of anemia in CKD patients was high in Yinzhou. However, the treatment rate of iron therapy and ESA were low. More attention should be paid to the anemia management and treatment in CKD patients.

[Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU]

OBJECTIVE

To construct an in-hospital mortality prediction model for patients with acute kidney injury (AKI) in intensive care unit (ICU) by using support vector machine (SVM), and compare it with the simplified acute physiology score II (SAPS-II) which is commonly used in the ICU.

METHODS

We used Medical Information Mart for Intensive Care III (MIMIC-III) database as data source. The AKI patients in the MIMIC-III database were selected according to the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) definition of AKI. We employed the same predictor variable set as used in SAPS-II to construct an SVM model. Meanwhile, we also developed a customized SAPS-II model using MIMIC-III database, and compared performances between the SVM model and the customized SAPS-II model. The performance of each model was evaluated via area under the receiver operation characteristic curve (AUROC), root mean squared error (RMSE), sensitivity, specificity, Youden's index and accuracy based on 5-fold cross-validation. The agreement of the results between the SVM model and the customized SAPS-II model was illustrated using Bland-Altman plots.

RESULTS

A total number of 19 044 patients with AKI were included. The observed in-hospital mortality of the AKI patients was 13.58% in MIMIC-III. The results based on the 5-fold cross validation showed that the average AUROC of the SVM model and the customized SAPS-II model was 0.86 and 0.81, respectively (The difference between the two models was statistically significant with t=13.0, P<0.001). The average RMSE of the SVM model and the customized SAPS-II model was 0.29 and 0.31, respectively (The difference was statistically significant with t=-9.6, P<0.001). The SVM model also outperformed the customized SAPS-II model in terms of sensitivity and Youden's index with significant statistical differences (P=0.002 and <0.001, respectively).The Bland-Altman plot showed that the SVM model and the customized SAPS-II model had similar mortality prediction results when the mortality of a patient was certain, but the consistency between the mortality prediction results of the two models was poor when the mortality of a patient was with high uncertainty.

CONCLUSION

Compared with the SAPS-II model, the SVM model has a better performance, especially when the mortality of a patient is with high uncertainty. The SVM model is more suitable for predicting the mortality of patients with AKI in ICU and early intervention in patients with AKI in ICU. The SVM model can effectively help ICU clinicians improve the quality of medical treatment, which has high clinical value.

Correlation analysis of the PNPLA7 gene polymorphism and susceptibility to menstrual disorder

We examined the correlation between PNPLA7 gene polymorphisms at the rs61754920 and rs11137410 loci and menstrual disorder in women of reproductive age in the Central Plain. Genomic DNA was extracted from peripheral blood; polymerase chain reaction-ligase detection reaction and SNaPshot genotyping were used to detect polymorphisms in the rs61754920 and rs11137410 gene loci, respectively. The results for the 2 loci in individuals of different blood types were statistically analyzed. The proportion of the AA homozygote at the rs61754920 locus in the PNPLA7 gene was the lowest, while the proportion of the CC homozygote at the rs11137410 locus in the PNPLA7 gene was the highest. There were no statistical differences in the frequency distribution of genotypes and alleles at the 2 loci between control and test groups. The frequency of the TT genotype at the rs11137410 locus in women with type O blood was significantly lower in the test group than in the control group. Frequencies of the C and T alleles were significantly different between the 2 groups. There may be an association between the PNPLA7 gene and type O blood or a combined effect of the 2 genes.