Differences of anticholinergic drug burden between older hospitalized patients with and without delirium: a systematic review and meta-analysis based on prospective cohort studies.

OBJECTIVES: This review aims to comprehensively summarize the differences in anticholinergic drug burden (ADB) scores between older hospitalized patients with and without delirium. METHODS: We searched PubMed, Embase, Web of Science, Cochrane Library and CINAHL EBSCOhost databases to identify prospective cohort studies exploring the relationship between ADB and the occurrence of delirium in older hospitalized patients. The primary outcome of the review was the mean ADB scores for the delirium and non-delirium groups, and the secondary outcome was the scores for the subsyndromal and non-delirium groups. The standardized mean difference (SMD) and corresponding 95% confidence intervals (95% CI) were incorporated using a fixed-effect method. Moreover, we performed subgroup analysis according to the admission type, age, the ADB scale type and the ADB classification. RESULTS: Nine prospective cohort studies involving 3791 older patients with a median age of 75.1 (71.6-83.9) were included. The ADB score was significantly higher in the delirium group than in the non-delirium group (SMD = 0.21, 95%CI 0.13-0.28). In subgroup analysis, the age subgroup was split into < 75 and ≥ 75 according to the median age of the older people. There were significant differences in ADB scores between older people with delirium and those without delirium in various subgroups: surgical (SMD = 0.20, 95%CI 0.12-0.28), internal medicine (SMD = 0.64, 95%CI 0.25-1.02), age < 75 (SMD = 0.17, 95%CI 0.08-0.26), age ≥ 75 (SMD = 0.27, 95%CI 0.15-0.39), ADS scale (SMD = 0.13, 95%CI 0.13-0.40), ARS scale (SMD = 0.15, 95%CI 0.03-0.26), ACB scale (SMD = 0.13, 95%CI 0.01-0.25), pre-admission ADB (SMD = 0.24, 95%CI 0.05-0.43) and ADB during hospitalization (SMD = 0.20, 95%CI 0.12-0.27). CONCLUSIONS: We found a quantitative relationship between ADB and delirium in older patients admitted for internal medicine and surgery. And this relationship remained significant in different age, ADB scale type and ADB classification subgroups. However, the actual difference in ADB scores between patients with delirium and without delirium was small. More high-quality observational studies should be conducted to explore the impact of ADB on delirium and subsyndromal delirium. CLINICAL TRIAL REGISTRATION: The protocol was published in the International Prospective Register of Systematic Reviews (PROSPERO) [Ref: CRD42022353649].

Tacrolimus in the treatment of childhood nephrotic syndrome: Machine learning detects novel biomarkers and predicts efficacy.

STUDY OBJECTIVE: The pharmacokinetics and pharmacodynamics of tacrolimus (TAC) vary greatly among individuals, hindering its precise utilization. Moreover, effective models for the early prediction of TAC efficacy in patients with nephrotic syndrome (NS) are lacking. We aimed to identify key factors affecting TAC efficacy and develop efficacy prediction models for childhood NS using machine learning algorithms. DESIGN: This was an observational cohort study of patients with pediatric refractory NS. SETTING: Guangzhou Women and Children's Medical Center between June 2013 and December 2018. PATIENTS: 203 patients with pediatric refractory NS were used for model generation and 35 patients were used for model validation. INTERVENTION: All patients regularly received double immunosuppressive therapy comprising TAC and low-dose prednisone or methylprednisolone. In this observational cohort study of 203 pediatric patients with refractory NS, clinical and genetic variables, including single-nucleotide polymorphism (SNPs), were identified. TAC efficacy was evaluated 3 months after administration according to two different evaluation criteria: response or non-response (Group 1) and complete remission, partial remission, or non-remission (Group 2). MEASUREMENTS: Logistic regression, extremely random trees, gradient boosting decision trees, random forest, and extreme gradient boosting algorithms were used to develop and validate the models. Prediction models were validated among a cohort of 35 patients with NS. MAIN RESULTS: The random forest models performed best in both groups, and the area under the receiver operating characteristics curve of these two models was 80.7% (Group 1) and 80.3% (Group 2). These prediction models included urine erythrocyte count before administration, steroid types, and eight SNPs (ITGB4 rs2290460, TRPC6 rs3824934, CTGF rs9399005, IL13 rs20541, NFKBIA rs8904, NFKBIA rs8016947, MAP3K11 rs7946115, and SMARCAL1 rs11886806). CONCLUSIONS: Two pre-administration models with good predictive performance for TAC response of patients with NS were developed and validated using machine learning algorithms. These accurate models could assist clinicians in predicting TAC efficacy in pediatric patients with NS before utilization to avoid treatment failure or adverse effects.

Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning.

Background and Aims: Tacrolimus(TAC)-induced nephrotoxicity, which has a large individual variation, may lead to treatment failure or even the end-stage renal disease. However, there is still a lack of effective models for the early prediction of TAC-induced nephrotoxicity, especially in nephrotic syndrome(NS). We aimed to develop and validate a predictive model of TAC-induced tubular toxicity in children with NS using machine learning based on comprehensive clinical and genetic variables. Materials and Methods: A retrospective cohort of 218 children with NS admitted between June 2013 and December 2018 was used to establish the models, and 11 children were prospectively enrolled for external validation. We screened 47 clinical features and 244 genetic variables. The changes in urine N- acetyl- ß-D- glucosaminidase(NAG) levels before and after administration was used as an indicator of renal tubular toxicity. Results: Five machine learning algorithms, including extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), extremely random trees (ET), random forest (RF), and logistic regression (LR) were used for model generation and validation. Four genetic variables, including TRPC6 rs3824934_GG, HSD11B1 rs846910_AG, MAP2K6 rs17823202_GG, and SCARB2 rs6823680_CC were incorporated into the final model. The XGBoost model has the best performance: sensitivity 75%, specificity 77.8%, accuracy 77.3%, and AUC 78.9%. Conclusion: A pre-administration model with good performance for predicting TAC-induced nephrotoxicity in NS was developed and validated using machine learning based on genetic factors. Physicians can estimate the possibility of nephrotoxicity in NS patients using this simple and accurate model to optimize treatment regimen before administration or to intervene in time after administration to avoid kidney damage.

Early Prediction of Clinical Response to Etanercept Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.

BACKGROUND AND AIMS: At present, there is a lack of simple and reliable model for early prediction of the efficacy of etanercept in the treatment of juvenile idiopathic arthritis (JIA). This study aimed to generate and validate prediction models of etanercept efficacy in patients with JIA before administration using machine learning algorithms based on electronic medical record (EMR). MATERIALS AND METHODS: EMR data of 87 JIA patients treated with etanercept between January 2011 and December 2018 were collected retrospectively. The response of etanercept was evaluated by using DAS44/ESR-3 simplified standard. The stepwise forward and backward method based on information gain was applied to select features. Five machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extremely Random Trees (ET) and Logistic Regression (LR) were used for model generation and validation with fifty-fold stratified cross-validation. EMR data of additional 14 patients were collected for external validation of the model. RESULTS: Tender joint count (TJC), Time interval, Lymphocyte percentage (LYM), and Weight were screened out and included in the final model. The model generated by the XGBoost algorithm based on the above 4 features had the best predictive performance: sensitivity 75%, specificity 66.67%, accuracy 72.22%, AUC 79.17%, respectively. CONCLUSION: A pre-administration model with good prediction performance for etanercept response in JIA was developed using advanced machine learning algorithms. Clinicians and pharmacists can use this simple and accurate model to predict etanercept response of JIA early and avoid treatment failure or adverse effects.