Population Health Identification and Management of Low Density Lipoprotein Cholesterol via a Remote, Algorithmic, Navigator-Executed Program

Am Heart J. 2021 Sep 2;S0002-8703(21)00223-4. doi: 10.1016/j.ahj.2021.08.017. Online ahead of print.

Abstract

Background: Implementation of guideline-directed cholesterol management remains low despite definitive evidence establishing their reduction of cardiovascular (CV) events, especially in high atherosclerotic CV disease (ASCVD) risk patients. Modern electronic resources now exist that may help improve health care delivery. While electronic medical records (EMR) allow for population health screening, the potential for coupling EMR screening to remotely delivered algorithmic population-based management has been less studied as a way of overcoming barriers to optimal cholesterol management.

Methods: In an academically affiliated healthcare system, using EMR screening, we sought to identify 1,000 high ASCVD risk patients not meeting guideline-directed low-density lipoprotein-cholesterol (LDL-C) goals within specific system-affiliated primary care practices. Contacted patients received cholesterol education and were offered a remote, guideline-directed algorithmic cholesterol management program executed by trained but non-licensed "navigators" under professional supervision. Navigators used telephone and internet resources to facilitate algorithm-driven, guideline-based medication initiation/titration and laboratory testing until patients achieved LDL-C goals or exited the program. As a clinical effectiveness program for cholesterol guideline implementation, comparison was made to those contacted patients who declined program-based medication management and received education only, along with their usual care.

Results: 1021 patients falling into guideline-defined high ASCVD risk groups warranting statin therapy (ASCVD, type 2 diabetes, LDL ≥ 190 mg/dL, calculated 10-year ASCVD risk ≥7.5%) and not achieving guideline-defined target LDL-C levels and/or therapy were identified and contacted. Among the 698 such patients who elected to enter program medication management, significant LDL-C reductions were evident in the total cohort (mean -65.4 mg/dL, 45% decrease) and each high ASCVD risk subgroup: ASCVD (-57.2 mg/dL, -48.0%); diabetes mellitus (-53.1 mg/dL, -40.0%); severe hypercholesterolemia (-76.3 mg/dL, -45.7%); elevated ASCVD 10 year risk (-62.8 mg/dL, -41.1%) (P<0.001 for all), without any significant complications. Among 20% of participants with reported statin intolerance, average LDL-C decreased from baseline 143 mg/dL to 85 mg/dL using mainly statins and ezetimibe, without requiring use of PCSK9 inhibitors. In comparison, eligible high ASCVD risk patients who were contacted but opted for education only, a 17% LDL-C decrease occurred over a similar timeframe, with 80% remaining with an LDL-C over 100 mg/dL.

Conclusions: A remote, algorithm-driven, navigator-executed cholesterol management program successfully identified high ASCVD risk undertreated patients using EMR screening and was associated with significantly improved guideline-directed LDL-C control, supporting this approach as a novel strategy for improving health care access and delivery.

Keywords: Hypercholesterolemia; adherence; cardiovascular risk; coronary artery disease; low density lipoprotein; population health; statin intolerance; statins.