A model‐based meta analysis study of sodium glucose co‐transporter‐2 inhibitors

Yao, Xinglei; Zhou, Jiawei; Lee, Song; Ren, Yupeng; Hu, Pei; Liu, Dongyang

doi:10.1002/psp4.12934

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…covariates [35][36][37][38]. Researchers have utilized methods from logistic regression to machine learning [39][40][41][42][43][44]. Within machine learning researchers are starting to utilize transparent methods for visualization [24,45].…”

Section: Plos Onementioning

confidence: 99%

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

Huang

2023

PLoS ONE

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Background There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality. Objective The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency. Methods Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map. Results Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics. Conclusion Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

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Section: Plos Onementioning

confidence: 99%

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

Huang

2023

PLoS ONE

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A model‐based meta analysis study of sodium glucose co‐transporter‐2 inhibitors

Yao

Zhou

Lee

et al. 2023

CPT Pharmacom & Syst Pharma

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Type 2 diabetes mellitus (T2DM) agent sodium-glucose co-transporter 2 (SGLT2) inhibitors show special benefits in reducing body weight and heart failure risks.To accelerate clinical development for novel SGLT2 inhibitors, a quantitative relationship among pharmacokinetics, pharmacodynamics, and disease end points (PK/PD/end points) in healthy subjects and patients with T2DM was developed. PK/PD/end point data in published clinical studies for three globally marketed SGLT2 inhibitors (dapagliflozin, canagliflozin, and empagliflozin) were collected according to pre-set criteria. Overall, 80 papers with 880 PK, 27 PD, 848 fasting plasma glucose (FPG), and 1219 hemoglobin A1c (HbA1c) data were collected.A two-compartmental model with Hill's equation was utilized to capture PK/ PD profiles. A novel translational biomarker, the change of urine glucose excretion (UGE) from baseline normalized by FPG (ΔUGE c ) was identified to bridge healthy subjects and patients with T2DM with different disease statuses. ΔUGE c was found to have a similar maximum increase with different half-maximal effective concentration values of 56.6, 2310, and 841 mg/mL•h for dapagliflozin, canagliflozin, and empagliflozin respectively. ΔUGE c will change FPG based on linear function. HbA1c profiles were captured by indirect response model. Additional placebo effect was also considered for both end points. The PK/ΔUGE c /FPG/ HbA1c relationship was validated internally using diagnostic plots and visual assessment and further validated externally using the fourth globally approved same-in-class drug (ertugliflozin). This validated quantitative PK/PD/end point relationship offers novel insight into long-term efficacy prediction for SGLT2 inhibitors. The novelty identified ΔUGE c could make the comparison of different SGLT2 inhibitors' efficacy characteristics easier, and achieve early prediction from healthy subjects to patients.

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