A systematic review of the natural history and biomarkers of primary lecithin:cholesterol acyltransferase deficiency.

Syndromes associated with LCAT deficiency, a rare autosomal recessive condition, include fish-eye disease (FED) and familial LCAT deficiency (FLD). FLD is more severe and characterized by early and progressive chronic kidney disease (CKD). No treatment is currently available for FLD, but novel therapeutics are under development. Furthermore, although biomarkers of LCAT deficiency have been identified, their suitability to monitor disease progression and therapeutic efficacy is unclear, as little data exist on the rate of progression of renal disease. Here, we systematically review observational studies of FLD, FED, and heterozygous subjects, which summarize available evidence on the natural history and biomarkers of LCAT deficiency, in order to guide the development of novel therapeutics. We identified 146 FLD and 53 FED patients from 219 publications, showing that both syndromes are characterized by early corneal opacity and markedly reduced HDL-C levels. Proteinuria/hematuria were the first signs of renal impairment in FLD, followed by rapid decline of renal function. Furthermore, LCAT activity toward endogenous substrates and the percentage of circulating esterified cholesterol (EC%) were the best discriminators between these two syndromes. In FLD, higher levels of total, non-HDL, and unesterified cholesterol were associated with severe CKD. We reveal a nonlinear association between LCAT activity and EC% levels, in which subnormal levels of LCAT activity were associated with normal EC%. This review provides the first step toward the identification of disease biomarkers to be used in clinical trials and suggests that restoring LCAT activity to subnormal levels may be sufficient to prevent renal disease progression.

Case contamination in electronic health records based case-control studies.

Clinically relevant information from electronic health records (EHRs) permits derivation of a rich collection of phenotypes. Unlike traditionally designed studies where scientific hypotheses are specified a priori before data collection, the true phenotype status of any given individual in EHR-based studies is not directly available. Structured and unstructured data elements need to be queried through preconstructed rules to identify case and control groups. A sufficient number of controls can usually be identified with high accuracy by making the selection criteria stringent. But more relaxed criteria are often necessary for more thorough identification of cases to ensure achievable statistical power. The resulting pool of candidate cases consists of genuine cases contaminated with noncase patients who do not satisfy the control definition. The presence of patients who are neither true cases nor controls among the identified cases is a unique challenge in EHR-based case-control studies. Ignoring case contamination would lead to biased estimation of odds ratio association parameters. We propose an estimating equation approach to bias correction, study its large sample property, and evaluate its performance through extensive simulation studies and an application to a pilot study of aortic stenosis in the Penn medicine EHR. Our method holds the promise of facilitating more efficient EHR studies by accommodating enlarged albeit contaminated case pools.

Phenotyping issues for exploring electronic health records to design clinical trials.

For utilizing electronic health records to help design and conduct clinical trials, an essential first step is to select eligible patients from electronic health records, that is, electronic health record phenotyping. We present two novel statistical methods that can be used in the context of electronic health record phenotyping. One mitigates the requirement for gold-standard control patients in developing phenotyping algorithms, and the other effectively corrects for bias in downstream analysis introduced by study samples contaminated by ineligible subjects.