Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 4 de 4
Filter
Add more filters










Database
Language
Publication year range
1.
J Am Heart Assoc ; 13(12): e034434, 2024 Jun 18.
Article in English | MEDLINE | ID: mdl-38879446

ABSTRACT

BACKGROUND: Familial hypercholesterolemia (FH), while highly prevalent, is a significantly underdiagnosed monogenic disorder. Improved detection could reduce the large number of cardiovascular events attributable to poor case finding. We aimed to assess whether machine learning algorithms outperform clinical diagnostic criteria (signs, history, and biomarkers) and the recommended screening criteria in the United Kingdom in identifying individuals with FH-causing variants, presenting a scalable screening criteria for general populations. METHODS AND RESULTS: Analysis included UK Biobank participants with whole exome sequencing, classifying them as having FH when (likely) pathogenic variants were detected in their LDLR, APOB, or PCSK9 genes. Data were stratified into 3 data sets for (1) feature importance analysis; (2) deriving state-of-the-art statistical and machine learning models; (3) evaluating models' predictive performance against clinical diagnostic and screening criteria: Dutch Lipid Clinic Network, Simon Broome, Make Early Diagnosis to Prevent Early Death, and Familial Case Ascertainment Tool. One thousand and three of 454 710 participants were classified as having FH. A Stacking Ensemble model yielded the best predictive performance (sensitivity, 74.93%; precision, 0.61%; accuracy, 72.80%, area under the receiver operating characteristic curve, 79.12%) and outperformed clinical diagnostic criteria and the recommended screening criteria in identifying FH variant carriers within the validation data set (figures for Familial Case Ascertainment Tool, the best baseline model, were 69.55%, 0.44%, 65.43%, and 71.12%, respectively). Our model decreased the number needed to screen compared with the Familial Case Ascertainment Tool (164 versus 227). CONCLUSIONS: Our machine learning-derived model provides a higher pretest probability of identifying individuals with a molecular diagnosis of FH compared with current approaches. This provides a promising, cost-effective scalable tool for implementation into electronic health records to prioritize potential FH cases for genetic confirmation.


Subject(s)
Apolipoprotein B-100 , Hyperlipoproteinemia Type II , Machine Learning , Proprotein Convertase 9 , Humans , Hyperlipoproteinemia Type II/genetics , Hyperlipoproteinemia Type II/diagnosis , Hyperlipoproteinemia Type II/epidemiology , Female , Male , Proprotein Convertase 9/genetics , Apolipoprotein B-100/genetics , Middle Aged , Receptors, LDL/genetics , United Kingdom/epidemiology , Exome Sequencing , Genetic Testing/methods , Adult , Predictive Value of Tests , Genetic Predisposition to Disease , Mutation
2.
Am J Nephrol ; 38(1): 50-7, 2013.
Article in English | MEDLINE | ID: mdl-23817179

ABSTRACT

Spurious electrolyte disorders refer to an artifactually elevated or decreased serum electrolyte values that do not correspond to their actual systemic levels. When a clinician is confronted with a case of electrolyte disturbance, the first question should be whether it is an artifact. Spurious electrolyte disorders (pseudohyponatremia, pseudohypernatremia, pseudohypokalemia, pseudohyperkalemia, pseudohypomagnesemia, pseudohypophosphatemia, pseudohyperphosphatemia, pseudohypocalcemia and pseudohypercalcemia) are not infrequently observed in clinical practice. The recognition that an electrolyte disturbance may be an artifact may prevent inappropriate therapeutic interventions that could potentially have unfavorable outcomes. Clinicians must be alert to the possibility of spurious laboratory abnormalities when faced with conflicting laboratory values or measurements that are discordant with the clinical presentation. Moreover, in the presence of conditions that predispose to spurious electrolyte disorders, the normal measured electrolyte levels should raise the suspicion that true electrolyte disorders may be present.


Subject(s)
Artifacts , Water-Electrolyte Imbalance/diagnosis , Humans
3.
J Cardiovasc Pharmacol Ther ; 18(2): 113-8, 2013 Mar.
Article in English | MEDLINE | ID: mdl-23113965

ABSTRACT

AIM: To compare the effect of manidipine 20 mg plus rosuvastatin 10 mg versus olmesartan 20 mg plus rosuvastatin 10 mg on markers of insulin resistance in patients with mixed dyslipidemia, hypertension, and impaired fasting glucose (IFG). METHODS: This study had a prospective, randomized, open-label, blinded endpoint (PROBE) design. A total of 40 patients with IFG, mixed dyslipidemia, and stage 1 hypertension were included. Following dietary intervention, patients were randomly allocated to rosuvastatin (10 mg/d) plus olmesartan (20 mg/d) or manidipine (20 mg/d). The primary end point was the between-group difference in changes in the Homeostasis Model Assessment Insulin Resistance (HOMA-IR) index following 3 months of treatment. Secondary end points included changes in fasting plasma glucose (FPG), fasting insulin levels, and glucosylated hemoglobin. RESULTS: At the end of the 3-month treatment period, a significant increase in HOMA-IR index by 14% (from 2.4 [0.5-7.9] to 2.7 [0.5-5.2], P = .02 versus baseline) was seen in the olmesartan plus rosuvastatin group. On the contrary, no significant change in HOMA-IR index was observed in the manidipine plus rosuvastatin group (1.7 [0.5-5.2] to 1.7 [0.8-6.0], P = NS versus baseline, P = .04 versus olmesartan plus rosuvastatin group). An increase in fasting insulin levels was observed in the olmesartan plus rosuvastatin group (+8%, from 10.1 [2.0-29.6] to 10.9 [2.0-19.1] µU/mL, P < .05 versus baseline), while no significant change was seen in the manidipine plus rosuvastatin group (+3%, from 7.3 [2.0-17.6] to 7.5 [1.9-15.6] µU/mL, P = NS versus baseline, P = .02 versus olmesartan plus rosuvastatin group). Fasting plasma glucose and glycosylated hemoglobin did not change significantly in any group. CONCLUSION: Manidipine seems to ameliorate the possible statin-associated increase in insulin resistance as compared with olmesartan in patients with IFG, hypertension, and mixed dyslipidemia.


Subject(s)
Blood Glucose/drug effects , Dihydropyridines/administration & dosage , Dyslipidemias/drug therapy , Fluorobenzenes/administration & dosage , Hypertension/drug therapy , Imidazoles/administration & dosage , Pyrimidines/administration & dosage , Sulfonamides/administration & dosage , Tetrazoles/administration & dosage , Aged , Blood Glucose/metabolism , Drug Therapy, Combination , Dyslipidemias/blood , Dyslipidemias/epidemiology , Endpoint Determination/methods , Fasting/blood , Female , Humans , Hypertension/blood , Hypertension/epidemiology , Insulin Resistance/physiology , Male , Middle Aged , Nitrobenzenes , Piperazines , Prospective Studies , Rosuvastatin Calcium , Single-Blind Method , Treatment Outcome
4.
Ann Gastroenterol ; 26(1): 23-28, 2013.
Article in English | MEDLINE | ID: mdl-24714322

ABSTRACT

INFLAMMATORY BOWEL DISEASE (IBD) IS A CHRONIC INFLAMMATORY INTESTINAL DISORDER ENCOMPASSING TWO MAJOR ENTITIES: Crohn's disease and ulcerative colitis. Intestinal inflammatory processes reduce the absorption of sodium, chloride and calcium, while they increase potassium secretion. In addition, mild to severe metabolic alkalosis may occur in IBD patients, mainly depending on the severity of the disease and the part of the gastrointestinal tract being affected. The aim of this review is the presentation of the electrolyte and acid-base disturbances in IBD and how the activity state of the disease and/or treatment may affect them.

SELECTION OF CITATIONS
SEARCH DETAIL
...