Phenotyping of Korean patients with better-than-expected efficacy of moderate-intensity statins using tensor factorization

Abstract

Several studies have been conducted to evaluate the efficacy of statins in Korean and Asian patients. However, most previous studies only observed the percent reduction in low-density lipoprotein cholesterol (LDL-C) and did not consider the effects of various patient conditions simultaneously, such as abnormal test results, patient demographics, and prescribed drugs before taking a statin. Moreover, the characteristics of the patients whose percent reduction in LDL-C was higher than expected were not provided. Therefore, in this study, we aimed to derive meaningful phenotypes by using tensor factorization to observe the characteristics of the patients whose percent reduction in LDL-C was higher than expected among patients taking moderate-intensity statins. In addition, we used the derived phenotypes to predict how much the LDL-C levels of new patients decreased. We consequently identified eight phenotypes that represented the characteristics of the patients whose percent reduction in LDL-C was higher than expected. Moreover, the latent representations of the derived phenotypes achieved prediction performance similar to that obtained using the raw data. These results demonstrate that the derived phenotypes and latent representations are useful tools for observing the characteristics of patients and predicting LDL-C levels. Additionally, our findings provide direction on how to conduct clinical studies in the future.

Comparative Effectiveness of Combination Therapy with Statins and Angiotensin‐Converting Enzyme Inhibitors versus Angiotensin II Receptor Blockers in Patients with Coronary Heart Disease: A Nationwide Population‐Based Cohort Study in Korea

“…This result seems acceptable because Asians achieve similar benefits as Westerners at lower statin doses . Furthermore, moderate‐intensity statins have demonstrated a low‐density lipoprotein cholesterol (LDL‐C)‐lowering effect of more than a 50% reduction in Korean patients with baseline LDL‐C levels above 190 mg/dl . According to the 2013 ACC/AHA guideline definitions, these patients received high‐intensity statins because high‐intensity statin therapy was defined as statins lowering LDL‐C levels by ~50% or more.…”

“…35 Furthermore, moderate-intensity statins have demonstrated a low-density lipoprotein cholesterol (LDL-C)-lowering effect of more than a 50% reduction in Korean patients with baseline LDL-C levels above 190 mg/dl. [36][37][38] According to the 2013 ACC/ AHA guideline definitions, 26 these patients received high-intensity statins because highintensity statin therapy was defined as statins lowering LDL-C levels by~50% or more. Due to these intensity disparities, recommendations from the ACC/AHA guidelines should be carefully interpreted when applying them to Asian patients.…”

Multimodal Phenotyping of Alzheimer’s Disease with Longitudinal Magnetic Resonance Imaging and Cognitive Function Data

“…This means that phenotypes are defined as a probabilistic membership to medical components, and patients also have a probabilistic membership to the phenotypes. Nonnegative tensor factorization (NTF) is particularly popular due to its ability to model interactions between multiple data sources, its flexibility to adapt regularization methods, and the interpretability of latent medical concepts from its outputs [13][14][15] . The input for NTF is the interactions between different modalities (e.g., co-occurrence of medication and diagnosis within a time window).…”

“…One important characteristic that phenotypes should have is the ability to discriminate between the types of diseases under consideration, such as AD vs MCI, MCI vs CN, and AD vs CN. For this purpose, we added supervised regularization to the objective function 15,48 . Supervised regularization encourages the phenotypes to be separated according to the diagnostic label (i.e., AD, MCI, CN).…”

“…Recent advances in computational phenotyping methodologies have introduced data-driven phenotyping of AD and related dementias. This computational approach is automated and non-biased, high-throughput, and can handle vast amounts of noisy healthcare data [13][14][15] . One method has examined patterns of cortical atrophy on brain imaging to suggest AD subtypes 16 .…”