Dataset Information

How well can familial hypercholesterolemia be identified in an electronic health record database?

ABSTRACT:

Background

Familial hypercholesterolemia (FH) is a condition characterized by high cholesterol levels and increased risk for coronary heart disease (CHD) that often goes undiagnosed. The Dutch Lipid Network Criteria (DLNC) are used to identify FH in clinical settings via physical examination, personal and family history of CHD, in addition to the presence of deleterious mutations of the LDLR, ApoB, and PCSK9 genes. Agreement between clinical and genetic diagnosis of FH varies. While an ICD diagnosis code was not available for coding FH until 2016, Systematized Nomenclature of Medicine (SNOMED) clinical concept codes, including genetic diagnoses, for FH have been utilized in electronic health records (EHRs).

Objective

To evaluate the concordance of identifying FH via SNOMED and ICD-10 CM codes vs the DLNC in an EHR database.

Methods

Using the Practice Fusion EHR database, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated comparing an FH cohort identified via SNOMED and ICD-10 CM codes to one identified via the DLNC.

Results

Among 907,616 patients with hypercholesterolemia, 2,180 were identified as FH via SNOMED code (zero were identified via ICD-10 CM), 259 had a DLNC score 6-8 (probable FH), and 45 had a DLNC score >8 (definite FH). Compared to DLNC score >8, the sensitivity, specificity, and PPV of the FH SNOMED code were 84.4%, 99.4%, and 6.4%, respectively. Compared to DLNC score ?6, the sensitivity was 36.8% and the specificity was 99.5% with a PPV of 18.7%.

Conclusion

Compared to the clinical criteria for FH, identification of FH patients via SNOMED diagnosis codes had high sensitivity and specificity, but low PPV. The discordance of these two techniques in identifying FH patients speaks to the challenges in identifying FH patients in large electronic databases such as administrative claims and EHR.

SUBMITTER: Mues KE

PROVIDER: S-EPMC6241698 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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How well can familial hypercholesterolemia be identified in an electronic health record database?

Mues Katherine E KE Bogdanov Alina N AN Monda Keri L KL Yedigarova Larisa L Liede Alexander A Kallenbach Lee L

Clinical epidemiology 20181115

<h4>Background</h4>Familial hypercholesterolemia (FH) is a condition characterized by high cholesterol levels and increased risk for coronary heart disease (CHD) that often goes undiagnosed. The Dutch Lipid Network Criteria (DLNC) are used to identify FH in clinical settings via physical examination, personal and family history of CHD, in addition to the presence of deleterious mutations of the <i>LDLR</i>, <i>ApoB</i>, and <i>PCSK9</i> genes. Agreement between clinical and genetic diagnosis of ...[more]

PMID: 30532597

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Project description:Background Familial hypercholesterolemia ( FH ), is a historically underdiagnosed, undertreated, high-risk condition that is associated with a high burden of cardiovascular morbidity and mortality. In this study, we use a population-based approach using electronic health record ( EHR )-based algorithms to identify FH . We report the major adverse cardiovascular events, mortality, and cost of medical care associated with this diagnosis. Methods and Results In our 1.18 million EHR- eligible cohort, International Classification of Diseases, Ninth Revision ( ICD -9) code-defined hyperlipidemia was categorized into FH and non- FH groups using an EHR algorithm designed using the modified Dutch Lipid Clinic Network criteria. Major adverse cardiovascular events, mortality, and cost of medical care were analyzed. A priori associated variables/confounders were used for multivariate analyses using binary logistic regression and linear regression with propensity score-based weighted methods as appropriate. EHR FH was identified in 32 613 individuals, which was 2.7% of the 1.18 million EHR cohort and 13.7% of 237 903 patients with hyperlipidemia. FH had higher rates of myocardial infarction (14.77% versus 8.33%; P<0.0001), heart failure (11.82% versus 10.50%; P<0.0001), and, after adjusting for traditional risk factors, significantly correlated to a composite major adverse cardiovascular events variable (odds ratio, 4.02; 95% CI, 3.88-4.16; P<0.0001), mortality (odds ratio, 1.20; CI, 1.15-1.26; P<0.0001), and higher total revenue per-year (incidence rate ratio, 1.30; 95% CI, 1.28-1.33; P<0.0001). Conclusions EHR -based algorithms discovered a disproportionately high prevalence of FH in our medical cohort, which was associated with worse outcomes and higher costs of medical care. This data-driven approach allows for a more precise method to identify traditionally high-risk groups within large populations allowing for targeted prevention and therapeutic strategies.

Project description:BACKGROUND:Sarcopenia, cachexia and frailty have overlapping features and clinical consequences, but often go unrecognized. The objective was to detect patients described by clinicians as having sarcopenia, cachexia or frailty within electronic health records (EHR) and compare clinical variables between cases and matched controls. METHODS:We conducted a case-control study using retrospective data from the Indiana Network for Patient Care multi-health system database from 2016 to 2017. The computable phenotype combined ICD codes for sarcopenia, cachexia and frailty, with clinical note text terms for sarcopenia, cachexia and frailty detected using natural language processing. Cases with these codes or text terms were matched to controls without these codes or text terms matched on birth year, sex and race. Two physicians reviewed EHR for all cases and a subset of controls. Comorbidity codes, laboratory values, and other coded clinical variables were compared between groups using Wilcoxon matched-pair sign-rank test for continuous variables and conditional logistic regression for binary variables. RESULTS:Cohorts of 9594 cases and 9594 matched controls were generated. Cases were 59% female, 69% white, and a median (1st, 3rd quartiles) age 74.9 (62.2, 84.8) years. Most cases were detected by text terms without ICD codes n =?8285 (86.4%). All cases detected by ICD codes (total n =?1309) also had supportive text terms. Overall 1496 (15.6%) had concurrent terms or codes for two or more of the three conditions (sarcopenia, cachexia or frailty). Of text term occurrence, 97% were used positively for sarcopenia, 90% for cachexia, and 95% for frailty. The remaining occurrences were negative uses of the terms or applied to someone other than the patient. Cases had lower body mass index, albumin and prealbumin, and significantly higher odds ratios for diabetes, hypertension, cardiovascular and peripheral vascular diseases, chronic kidney disease, liver disease, malignancy, osteoporosis and fractures (all p <?0.05). Cases were more likely to be prescribed appetite stimulants and caloric supplements. CONCLUSIONS:Patients detected with a computable phenotype for sarcopenia, cachexia and frailty differed from controls in several important clinical variables. Potential uses include detection among clinical cohorts for targeting recruitment for research and interventions.

Project description:BackgroundHigh expectations have been set for the implementations of health information systems (HIS) in health care. However, nurses have been dissatisfied after implementations of HIS. In particular, poorly functioning electronic health records (EHRs) have been found to induce stress and cognitive workload. Moreover, the need to learn new systems may require considerable effort from nurses. Thus, EHR implementations may have an effect on the well-being of nurses.ObjectiveThis study aimed to examine the associations of EHR-to-EHR implementations and the sufficiency of related training with perceived stress related to information systems (SRIS), time pressure, and cognitive failures among registered nurses. Moreover, we examined the moderating effect of the employment sector (hospital, primary care, social services, and others) on these associations.MethodsThis study was a cross-sectional survey study of 3610 registered Finnish nurses in 2020. EHR implementation was measured by assessing whether the work unit of each respondent had implemented or will implement a new EHR (1) within the last 6 months, (2) within the last 12 months, (3) in the next 12 months, and (4) at no point within the last 12 months or in the forthcoming 12 months. The associations were examined using analyses of covariance adjusted for age, gender, and employment sector.ResultsThe highest levels of SRIS (adjusted mean 4.07, SE 0.05) and time pressure (adjusted mean 4.55, SE 0.06) were observed among those who had experienced an EHR implementation within the last 6 months. The lowest levels of SRIS (adjusted mean 3.26, SE 0.04), time pressure (adjusted mean 4.41, SE 0.05), and cognitive failures (adjusted mean 1.84, SE 0.02) were observed among those who did not experience any completed or forthcoming implementations within 12 months. Nurses who perceived that they had received sufficient implementation-related training experienced less SRIS (F1=153.40, P<.001), time pressure (F1=80.95, P<.001), and cognitive failures (F1=34.96, P<.001) than those who had received insufficient training. Recent implementations and insufficient training were especially strongly associated with high levels of SRIS in hospitals.ConclusionsEHR implementations and insufficient training related to these implementations may endanger the well-being of nurses and even lead to errors. Thus, it is extremely important for organizations to offer comprehensive training before, during, and after implementations. Moreover, easy-to-use systems that allow transition periods, a re-engineering approach, and user involvement may be beneficial to nurses in the implementation process. Training and other improvements would be especially important in hospitals.

Dataset Information

How well can familial hypercholesterolemia be identified in an electronic health record database?

Background

Objective

Methods

Results

Conclusion

Publications

How well can familial hypercholesterolemia be identified in an electronic health record database?

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