Effect of clinical decision support for severe hypercholesterolemia on low-density lipoprotein cholesterol levels.

Severe hypercholesterolemia/possible familial hypercholesterolemia (FH) is relatively common but underdiagnosed and undertreated. We investigated whether implementing clinical decision support (CDS) was associated with lower low-density lipoprotein cholesterol (LDL-C) in patients with severe hypercholesterolemia/possible FH (LDL-C ≥ 190 mg/dL). As part of a pre-post implementation study, a CDS alert was deployed in the electronic health record (EHR) in a large health system comprising 3 main sites, 16 hospitals and 53 clinics. Data were collected for 3 months before ('silent mode') and after ('active mode') its implementation. Clinicians were only able to view the alert in the EHR during active mode. We matched individuals 1:1 in both modes, based on age, sex, and baseline lipid lowering therapy (LLT). The primary outcome was difference in LDL-C between the two groups and the secondary outcome was initiation/intensification of LLT after alert trigger. We identified 800 matched patients in each mode (mean ± SD age 56.1 ± 11.8 y vs. 55.9 ± 11.8 y; 36.0% male in both groups; mean ± SD initial LDL-C 211.3 ± 27.4 mg/dL vs. 209.8 ± 23.9 mg/dL; 11.2% on LLT at baseline in each group). LDL-C levels were 6.6 mg/dL lower (95% CI, -10.7 to -2.5; P = 0.002) in active vs. silent mode. The odds of high-intensity statin use (OR, 1.78; 95% CI, 1.41-2.23; P < 0.001) and LLT initiation/intensification (OR, 1.30, 95% CI, 1.06-1.58, P = 0.01) were higher in active vs. silent mode. Implementation of a CDS was associated with lowering of LDL-C levels in patients with severe hypercholesterolemia/possible FH, likely due to higher rates of clinician led LLT initiation/intensification.

Clinician Perspectives on Clinical Decision Support for Familial Hypercholesterolemia.

Familial Hypercholesterolemia (FH) is underdiagnosed in the United States. Clinical decision support (CDS) could increase FH detection once implemented in clinical workflows. We deployed CDS for FH at an academic medical center and sought clinician insights using an implementation survey. In November 2020, the FH CDS was deployed in the electronic health record at all Mayo Clinic sites in two formats: a best practice advisory (BPA) and an in-basket alert. Over three months, 104 clinicians participated in the survey (response rate 11.1%). Most clinicians (81%) agreed that CDS implementation was a good option for identifying FH patients; 78% recognized the importance of implementing the tool in practice, and 72% agreed it would improve early diagnosis of FH. In comparing the two alert formats, clinicians found the in-basket alert more acceptable (p = 0.036) and more feasible (p = 0.042) than the BPA. Overall, clinicians favored implementing the FH CDS in clinical practice and provided feedback that led to iterative refinement of the tool. Such a tool can potentially increase FH detection and optimize patient management.

Tropomyosin Isoform Diversity in the Cynomolgus Monkey Heart and Skeletal Muscles Compared to Human Tissues.

Old world monkeys separated from the great apes, including the ancestor of humans, about 25 million years ago, but most of the genes in humans and various nonhuman primates are quite similar even though their anatomical appearances are quite different. Like other mammals, primates have four tropomyosin genes (TPM1, TPM2, TPM3, and TPM4) each of which generates a multitude of TPM isoforms via alternative splicing. Only TPM1 produces two sarcomeric isoforms (TPM1α and TPM1κ), and TPM2, TPM3, and TPM4 each generate one sarcomeric isoform. We have cloned and sequenced TPM1α, TPM1κ, TPM2α, TPM3α, and TPM4α with RNA from cynomolgus (Cyn) monkey hearts and skeletal muscle. We believe this is the first report of directly cloning and sequencing of these monkey transcripts. In the Cyn monkey heart, the rank order of TPM isoform expression is TPM1α > TPM2α > TPM1κ > TPM3α > TPM4α. In the Cyn monkey skeletal muscle, the rank order of expression is TPM1α > TPM2α > TPM3α > TPM1κ > TPM4α. The major differences in the human heart are the increased expression of TPM1κ, although TPM1α is still the dominant transcript. In the Cyn monkey heart, the only sarcomeric TPM isoform at the protein level is TPM1α. This is in contrast to human hearts where TPM1α is the major sarcomeric isoform but a lower quantity of TPM1κ, TPM2α, and TPM3α is also detected at the protein level. These differences of tropomyosin and/or other cardiac protein expression in human and Cyn monkey hearts may reflect the differences in physiological activities in daily life.

Effect of TAVR Approach and Other Baseline Factors on the Incidence of Acute Kidney Injury: A Systematic Review and Meta-Analysis.

Background: Acute kidney injury (AKI) is a well-known complication following a transcatheter aortic valve replacement (TAVR) and is associated with higher morbidity and mortality. Objective: We aim to compare the risk of developing AKI after transfemoral (TF), transapical (TA), and transaortic (TAo) approaches following TAVR. Methods: We searched Medline and EMBASE databases from January 2009 to January 2021. We included studies that evaluated the risk of AKI based on different TAVR approaches. After extracting each study's data, we calculated the risk ratio and 95% confidence intervals using RevMan software 5.4. Publication bias was assessed by the forest plot. Results: Thirty-six (36) studies, consisting of 70,406 patients undergoing TAVR were included. Thirty-five studies compared TF to TA, and only seven investigations compared TF to TAo. AKI was documented in 4,857 out of 50,395 (9.6%) patients that underwent TF TAVR compared to 3,155 out of 19,721 (16%) patients who underwent TA-TAVR, with a risk ratio of 0.49 (95% CI, 0.36-0.66; p < 0.00001). Likewise, 273 patients developed AKI out of the 1,840 patients (14.8%) that underwent TF-TAVR in contrast to 67 patients out of the 421 patients (15.9%) that underwent TAo-TAVR, with a risk ratio of 0.51 (95% CI, 0.27-0.98; p = 0.04). There was no significant risk when we compared TA to TAo approaches, with a risk ratio of 0.89 (95% CI, 0.29-2.75; p = 0.84). Conclusion: The risk of post-TAVR AKI is significantly lower in patients who underwent TF-TAVR than those who underwent TA-TAVR or TAo-TAVR.

Pathogenic variants in arteriopathy genes detected in a targeted sequencing study: Penetrance and 1-year outcomes after return of results.

PURPOSE: We estimated the penetrance of pathogenic/likely pathogenic (P/LP) variants in arteriopathy-related genes and assessed near-term outcomes following return of results. METHODS: Participants (N = 24,520) in phase III of the Electronic Medical Records and Genomics network underwent targeted sequencing of 68 actionable genes, including 9 genes associated with arterial aneurysmal diseases. Penetrance was estimated on the basis of the presence of relevant clinical traits. Outcomes occurring within 1 year of return of results included new diagnoses, referral to a specialist, new tests ordered, surveillance initiated, and new medications started. RESULTS: P/LP variants were present in 34 participants. The average penetrance across genes was 59%, ranging from 86% for FBN1 variants to 25% for SMAD3. Of 16 participants in whom results were returned, 1-year outcomes occurred in 63%. A new diagnosis was made in 44% of the participants, 56% were referred to a specialist, a new test was ordered in 44%, surveillance was initiated in 31%, and a new medication was started in 31%. CONCLUSION: Penetrance of P/LP variants in arteriopathy-related genes, identified in a large, targeted sequencing study, was variable and overall lower than that reported in clinical cohorts. Meaningful outcomes within the first year were noted in 63% of participants who received results.

Penetrance and outcomes at 1-year following return of actionable variants identified by genome sequencing.

PURPOSE: We estimated penetrance of actionable genetic variants and assessed near-term outcomes following return of results (RoR). METHODS: Participants (n = 2,535) with hypercholesterolemia and/or colon polyps underwent targeted sequencing of 68 genes and 14 single-nucleotide variants. Penetrance was estimated based on presence of relevant traits in the electronic health record (EHR). Outcomes occurring within 1-year of RoR were ascertained by EHR review. Analyses were stratified by tier 1 and non-tier 1 disorders. RESULTS: Actionable findings were present in 122 individuals and results were disclosed to 98. The average penetrance for tier 1 disorder variants (67%; n = 58 individuals) was higher than in non-tier 1 variants (46.5%; n = 58 individuals). After excluding 45 individuals (decedents, nonresponders, known genetic diagnoses, mosaicism), ≥1 outcomes were noted in 83% of 77 participants following RoR; 78% had a process outcome (referral to a specialist, new testing, surveillance initiated); 68% had an intermediate outcome (new test finding or diagnosis); 19% had a clinical outcome (therapy modified, risk reduction surgery). Risk reduction surgery occurred more often in participants with tier 1 than those with non-tier 1 variants. CONCLUSION: Relevant phenotypic traits were observed in 57% whereas a clinical outcome occurred in 19% of participants with actionable genomic variants in the year following RoR.

Sarcomeric TPM3 expression in human heart and skeletal muscle.

In mammals, four tropomyosin genes TPM1, TPM2, TPM3, and TPM4 are known. One isoform of the TPM3 gene, encoding 285 amino acid residues designated as TPM3α, has been reported. TPM3α protein expression in human hearts is not definitively established. We have cloned from human heart and skeletal muscle transcripts of TPM3α and three novel TPM3 isoforms, TPM3ν, TPM3ξ, and TPM3ο. TPM3ν and TPM3ο are alternatively spliced RNAs with different 3'-UTRs encoding an identical novel protein with 285 amino acid differing from TPM3α and TPM3ξ in exon 6 only. TPM3α and TPM3ξ, which have different 3'UTRs, also encode an identical protein. qRT-PCR data show that the transcripts of TPM3α, TPM3ν, TPM3ξ, and TPM3ο are expressed in both heart and skeletal muscle. We have evaluated the expression of various TPM proteins in fetal and adult human hearts, and also in skeletal muscle samples. Western blots using CG3 antibody show a stronger signal of TPM3 protein in fetal heart and adult skeletal muscle compared to adult heart. LC-MS/MS studies with the protein spots separated and identified by CH1 antibody after 2D Western blot analyses, confirm the expression of TPM3α/TPM3ξ in heart, but some peptides detected could be either TPM3α or TPM3ν. In heart samples, TPM1 protein was the dominant with varying amount of TPM2 and TPM3, while TPM4 expression was not observed. In skeletal muscles, TPM2 was the majority TPM protein expressed. The biological consequences of these varying expression of individual tropomyosin proteins are yet to be established.

Deploying Clinical Decision Support for Familial Hypercholesterolemia.

Objective: Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center. Methods: CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify "possible FH"; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS. Results: The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine. Conclusion: We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.