MétaCan
Menu
Retour à la cohorte
Enregistrement W3185437709 · doi:10.1093/jalm/jfab064

Pushing the New NIH LDL-Cholesterol Equation to Its Limits

2021· letter· en· W3185437709 sur OpenAlexaff
Victoria Higgins, Sarah Delaney, Daniel R. Beriault

Notice bibliographique

RevueThe Journal of Applied Laboratory Medicine · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueLipoproteins and Cardiovascular Health
Établissements canadiensSt. Michael's HospitalUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésLdl cholesterolCholesterolComputer scienceMedicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

LDL cholesterol (LDL-C) concentration is a key component for the clinical management of patients at risk for cardiovascular disease (1, 2). LDL-C is most commonly calculated by the Friedewald equation as LDL-C = total cholesterol (TC) − HDL cholesterol (HDL-C) − (triglycerides [TG]/5), in mg/dL (3). The factor 5 represents the average mass ratio of TG to VLDL-C (3). The Friedewald equation has several limitations mainly stemming from its fixed TG: VLDL-C mass ratio, including LDL-C underestimation in samples containing chylomicrons, low LDL-C concentration, or TG >400 mg/dL (4.52 mmol/L), and LDL-C overestimation in type III hyperlipoproteinemic patients due to their lower TG: VLDL-C mass ratio. The Martin-Hopkins LDL-C equation replaces the fixed factor of 5 with an empirical factor that varies with TG and non-HDL-C concentration (4). This equation more accurately estimates LDL-C when LDL-C < 70 mg/dL (1.81 mmol/L) and TG ≤400 mg/dL but is still not suited for type III hyperlipoproteinemic patients or patients with TG >400 mg/dL, and laboratories require licensing agreements to use its patented 180-cell factor table. The National Institutes of Health (NIH) equation (5), LDL-C=TC0.948-HDL-C0.971-(TG8.56+TGxNon-HDL-C2140-TG216100⁠) – 9.44, provides a better estimate of LDL-C in patients with hypertriglyceridemia (up to 800 mg/dL [9.02 mmol/L]) and/or low LDL-C and rarely produces negative LDL-C values (in contrast to the preceding equations). With the expected increase in clinical laboratories implementing the new NIH LDL-C equation and a paucity of real-world data on restrictions to its use, we offer practical solutions to limit negative LDL-C values and gross LDL-C overestimation. We validated the NIH LDL-C equation at our laboratory (St. Michael’s Hospital) using 3161 ultracentrifugation results and encountered 10 negative LDL-C results and 2 gross LDL-C overestimations. VLDL-C, HDL-C, and LDL-C were obtained by ultracentrifugation (Optima L-90K ultracentrifuge, Beckman) at 37 000 rpm for 16.5 h at 10 °C. LDL-C was calculated using the Friedewald equation (3), Martin-Hopkins equation (4), and NIH equation (5). Here, we report 5 scenarios of patient results encountered in our study cohort (Table 1) that push the NIH equation to its limit and can be prevented by implementing simple restrictions. Five patient scenarios resulting in LDL-C underestimation or overestimation by the NIH equation (conventional units). To convert cholesterol from mg/dL to mmol/L, divide 38.67. To convert triglycerides from mg/dL to mmol/L, divide by 88.57. UC, ultracentrifugation. Five patient scenarios resulting in LDL-C underestimation or overestimation by the NIH equation (conventional units). To convert cholesterol from mg/dL to mmol/L, divide 38.67. To convert triglycerides from mg/dL to mmol/L, divide by 88.57. UC, ultracentrifugation. LDL-C underestimation from hypertriglyceridemia: LDL-C was underestimated in scenarios 1 to 3 primarily due to TG >800 mg/dL, resulting in nonsensical negative LDL-C concentration. Scenario 1 exhibited the greatest LDL-C underestimation (−342 mg/dL [−8.84 mmol/L] compared to the reference method). This is likely due to grossly elevated TC, TG, and non-HDL-C (even exceeding the maximum concentrations of the NIH equation derivation cohort), leading to a grossly elevated VLDL-C estimation and LDL-C underestimation. While the NIH equation is more robust to variable VLDL particle composition than preceding equations, it underestimates LDL-C when TG >800 mg/dL (5). In line with our observations and those by Sampson et al. (5), we propose this equation is not used when TG >800 mg/dL, which is a great improvement from the Friedewald equation restriction of TG >400 mg/dL. LDL-C underestimation from low LDL-C: In scenario 4, TC, non-HDL-C, and LDL-C concentrations were low. The inaccuracy in VLDL-C estimation can generally be tolerated because VLDL-C concentration is small compared to LDL-C. However, with very low LDL-C concentration, the inaccuracy in VLDL-C becomes more pronounced and leads to inaccurate LDL-C estimation. We propose that the lower reporting limit for LDL-C calculated by the NIH equation should be 20 mg/dL (0.52 mmol/L), as the bias from ultracentrifugation exceeded −7.72 mg/dL (−0.20 mmol/L) below this concentration even after excluding samples with TG >800 mg/dL. LDL-C overestimation from extreme hypertriglyceridemia: In scenario 5, LDL-C was overestimated due to grossly high TG concentration. This was likely due to the TG2 factor of the NIH equation, which adjusts for extreme TG elevations that have proportionally more TG-rich chylomicrons and VLDL. Due to its high denominator, this term only becomes quantitatively important at very high TG values. However, since the TG concentration in this scenario is over double the maximum concentration observed in the NIH derivation cohort, this led to an overadjustment and subsequent LDL-C overestimation. The NIH equation exhibits superior accuracy for LDL-C estimation than preceding equations in most situations. However, here we review scenarios where even this improved equation is unable to accurately estimate LDL-C, requiring ultracentrifugation for LDL-C measurement. Additionally, LDL-C in patients with type III hyperlipoproteinemia, who were excluded from the cohort used to derive the NIH equation (5), was overestimated by 86.4 mg/dL (2.23 mmol/L; percentage difference: 75.0%), on average, compared to ultracentrifugation. We also examined the effects of applying limitations to TC and non-HDL-C concentrations, but this did not add any value beyond the 3 criteria already applied. In summary, the NIH equation should not be used in patients with type III hyperlipoproteinemia or TG >800 mg/dL and the lower reporting limit for LDL-C should be 20 mg/dL. In our cohort, these restrictions would have affected 7.05% of patients (78.9% of which had an LDL-C estimation >12% from the reference method) and prevented all 12 grossly inaccurate LDL-C estimations from being reported. Nonstandard Abbreviations: LDL-C, LDL cholesterol; TC, total cholesterol; HDL-C, HDL cholesterol; TG, triglycerides; VLDL-C, VLDL cholesterol. Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 4 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Authors' Disclosures or Potential Conflicts of Interest: No authors declared any potential conflicts of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,065
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,066
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,065
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,006
Communication savante0,0060,006
Science ouverte0,0020,003
Intégrité de la recherche0,0660,076
Charge utile insuffisante (le modèle a refusé de juger)0,0050,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,038
Tête enseignante GPT0,278
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2021
Routes d'admission1
Résumé présentnon

Explorer davantage

Même revueThe Journal of Applied Laboratory MedicineMême sujetLipoproteins and Cardiovascular HealthTravaux en français237 207