High red blood cell distribution width is associated with a risk of short-term mortality in hospitalized surgical, but not clinical patients

Machine learning for hospital readmission prediction in pediatric population

BACKGROUND AND OBJECTIVE

Pediatric readmissions are a burden on patients, families, and the healthcare system. In order to identify patients at higher readmission risk, more accurate techniques, as machine learning (ML), could be a good strategy to expand the knowledge in this area. The aim of this study was to develop predictive models capable of identifying children and adolescents at high risk of potentially avoidable 30-day readmission using ML.

METHODS

Retrospective cohort study was carried out with 9,080 patients under 18 years old admitted to a tertiary university hospital. Demographic, clinical, and biochemical data were collected from electronic databases. We randomly divided the dataset into training (75 %) and testing (25 %), applied downsampling, repeated cross-validation with five folds and ten repetitions, and the hyperparameter was optimized of each technique using a grid search via racing with ANOVA models. We applied six ML classification algorithms to build the predictive models, including classification and regression tree (CART), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), decision tree and logistic regression (LR). The area under the receiver operating curve (AUC), sensitivity, specificity, Youden's J-index and accuracy were used to evaluate the performance of each model.

RESULTS

The avoidable 30-day hospital readmissions rate was 9.5 %. Some algorithms presented similar AUC, both in the dataset training and in the dataset testing, such as XGBoost, RF, GBM and CART. Considering the Youden's J-index, the algorithm that presented the best index was XGBoost with bagging imputation, with AUC of 0.814 (J-index of 0.484). Cancer diagnosis, age, red blood cells, leukocytes, red cell distribution width and sodium levels, elective admission, and multimorbidity were the most important characteristics to classify between readmission and non-readmission groups.

CONCLUSION

Machine learning approaches, especially XGBoost, can predict potentially avoidable 30-day pediatric hospital readmission into tertiary assistance. If implemented in the computer hospital system, our model can help in the early and more accurate identification of patients at readmission risk, targeting health strategic interventions.

Nutritional Status Associated to Red Cell Distribution Width, Length of Stay, and Clinical Outcome patient with Chronic Kidney Diseases

INTRODUCTION: Red cell distribution width (RDW) shows the heterogeneity of erythrocyte size associated with inflammation and various clinical conditions including in patients with chronic kidney disease (CKD). Systemic inflammation and oxidative stress were commonly found in CKD patients. AIM: This study aimed to examine the relationship of nutritional status, length of hospital stay (LOS), and clinical outcome to RDW in CKD patients. METHODS: We conducted a retrospective cohort study of 1736 patients CKD patients who admitted from January 2017 to August 2020, aged between 18 and 60 years and were hospitalized at Wahidin General Hospital. From those, 239 were consulted with clinical nutrition specialist, of which 59 patients eligible with the criteria inclusion. Data were collected through medical records and through electronic data (biochemical data). RDW was categorized into normal and high RDW group, nutritional status based on subjective global assessment (SGA), LOS <10 days and clinical outcome based on conditions at the time of hospital discharged. Data were analyzed using SPSS version 25.0. RESULTS: The data of 59 patients were analyzed, the mean age was 50.42 years (normal RDW) and 47.24 years (high RDW), most of them are women (57.7% vs. 60.6%). There were 42 patients with moderate malnutrition (23 normal RDW and 19 high RDW) and 17 patients with severe malnutrition (3 normal RDW and 14 with high RDW). The study found a significant differences between normal RDW and high RDW (p 0.021), but not significant differences in LOS (p 0.890) and clinical outcome (p 0.968). There were a significant differences in the levels of hemoglobin (p = 0.001), RBG (p = 0.030), and serum sodium level (p = 0.010). Patient with LOS < 10 days had lower sodium levels and more severe anemia when compared with LOS > 10 days and the poor clinical outcome had a heavier degree of anemia compared to the good clinical outcomes. CONCLUSION: Poor nutritional status was associated with an increase in RDW, degree of anemia, and sodium level.