Although direct measurement of LDL cholesterol (LDL-C) in blood is possible, there are several formulas for its estimation. The performance and concordance of these formulas have not been evaluated in Colombia. To determine the concordance between LDL-C directly measured using the enzymatic technique and existing methods to calculate it. Study of diagnostic tests, and concordance. We analyzed complete lipid profile samples, which included direct measurement of LDL-C, from 2014 to 2022 at Hospital Universitario San Ignacio (Bogotá, Colombia). The direct LDL-C measurements were compared with estimations using the DeLong, Sampson, Friedewald, extended Martin/Hopkins, Anandaraja, and Cordova methods. Lin's concordance correlation coefficient (CCC) and Bland-Altman plots were employed, conducting subgroup analyses based on triglycerides (TG), and LDL-C levels. Kappa coefficients assessed agreement in LDL-C risk categories according to dyslipidemia guidelines. A total of 2144 samples were evaluated. The formulas with the best CCC were DeLong (0.971) and Sampson (0.969), with no relevant differences. The extended Martin/Hopkins formula (0.964) and the Friedewald formula (0.964) also performed well. The Anandaraja (0.921) and Cordova (0.881) equations exhibited inferior performance. For all formulas, a decrease in concordance was observed when triglycerides were ≥400 mg/dL or when LDL-C was <100 mg/dL. Most formulas demonstrated optimal agreement when assessed using risk categories according to dyslipidemia guidelines, except for Anandaraja and Cordova. The DeLong, Sampson, extended Martin/Hopkins, and Friedewald formulas show the best concordance with directly measured LDL-C, so in most cases the results can be considered interchangeable. However, the Anandaraja and Cordova formulas are not recommended.

Background: Patients with non-ST-segment elevation myocardial infarction (NSTEMI) are frequently admitted to intensive care or monitored settings despite heterogeneous risk of ICU-requiring complications. We developed NSTEMI-CARE, a calibrated multilayer decision-support algorithm for early hospital level-of-care assignment. Methods: We conducted a retrospective cohort study of consecutive patients with NSTEMI and angiographically confirmed obstructive coronary artery disease admitted between 1 July 2017 and 31 December 2020 to a cardiovascular referral center in Bogotá, Colombia. The primary outcome was a composite of ICU-requiring complications. Candidate machine-learning models were evaluated for discrimination and calibration. The selected probabilistic model was integrated with predefined clinical triggers and a Gray-Zone decision rule to generate a binary recommendation for general ward versus ICU/monitored care. Results: Among 981 patients, 155 (15.8%) experienced the primary outcome. The isotonic-calibrated HistGradientBoosting model achieved an AUC of 0.821, average precision of 0.618, and Brier score of 0.092 during five-fold out-of-fold internal validation. The final algorithm assigned 464 patients (47.3%) to general ward care and 517 (52.7%) to ICU/monitored care. The observed event rate among ward-assigned patients was 3.9%. Sensitivity was 88.4%, specificity 54.0%, positive predictive value 26.5%, and negative predictive value 96.1%. In secondary comparative analysis, NSTEMI-CARE showed greater discrimination than ACTION-ICU (AUC 0.821 vs. 0.645; p < 0.001). Conclusions: NSTEMI-CARE identified a substantial subgroup potentially suitable for general ward management while maintaining a high negative predictive value. By integrating calibrated risk estimation with explicit clinical decision rules, the algorithm provides a clinically interpretable framework for early level-of-care assignment in NSTEMI. External and prospective validation is required before clinical implementation.

Context: In patients with COVID-19, diabetes mellitus and hyperglycemia are prevalent conditions. The literature is scarce regarding the association of these two variables with unfavorable outcomes in patients hospitalized for COVID-19. Objective: To evaluate the association between hyperglycemia and the occurrence of adverse outcomes in patients with COVID-19. Methodology: Retrospective cohort study evaluating the association between hyperglycemia at presentation with the occurrence of acute respiratory distress syndrome (ARDS), acute kidney injury (AKI) and in-hospital death in patients hospitalized with COVID-19. Results: 408 patients were included (mean age 60.48, 58.8% men, 34.1% with diabetes, 40.4% with in-hospital hyperglycemia). Hyperglycemia was independently associated with increased risk of ARDS (OR: 1.84; 95% CI: 1.15-2.94; p=0.001), AKI (OR: 2.73; CI: 1.24-5.96; p=0.012) and in-hospital death (OR: 2.61; CI: 1.20-5.68; p=0.015) after controlling for confounding variables. History of diabetes was not independently associated with increased risk for any of the three adverse outcomes. Conclusions: Hyperglycemia is independently associated with worse outcomes in patients hospitalized for COVID-19, unlike diabetes mellitus. Contexto: en pacientes con COVID-19, la diabetes mellitus y la hiperglucemia son condiciones prevalentes. La literatura es escasa en cuanto a la asociación de estas dos variables con desenlaces desfavorables en pacientes hospitalizados por COVID-19. Objetivo: evaluar la asociación entre hiperglucemia y la aparición de desenlaces adversos en pacientes con COVID-19. Metodología: estudio de cohorte retrospectiva en el que se evaluó la asociación entre presentar hiperglucemia con la aparición de síndrome de dificultad respiratoria aguda (SDRA), lesión renal aguda (LRA) y muerte intrahospitalaria en pacientes hospitalizados por COVID-19. Resultados: se incluyeron 408 pacientes (edad media 60.48, 58.8 % hombres, 34.1 % con diabetes, 40.4 % con hiperglucemia intrahospitalaria). La hiperglucemia se asoció de forma independiente a un mayor riesgo de SDRA (OR: 1.84, IC 95 %: 1.15 – 2.94; p=0.001), LRA (OR: 2.73; IC: 1.24–5.96; p=0.012) y muerte intrahospitalaria (OR: 2.61; IC: 1.20–5.68; p=0.015) después de controlar por variables de confusión. El antecedente de diabetes no se asoció de forma independiente con un mayor riesgo en ninguno de los tres desenlaces adversos. Conclusiones: la hiperglucemia se asocia de forma independiente con peores desenlaces en pacientes hospitalizados por COVID-19 a diferencia de la diabetes mellitus.