Clinical laboratory services for primary healthcare centers in urban cities: a pilot ACO model of ten primary healthcare centers.

BACKGROUND: Primary healthcare centers (PHC) ensure that patients receive comprehensive care from promotion and prevention to treatment, rehabilitation, and palliative care in a familiar environment. It is designed to provide first-contact, continuous, comprehensive, and coordinated patient care that will help achieve equity in the specialty healthcare system. The healthcare in Saudi Arabia is undergoing transformation to Accountable Care Organizations (ACO) model. In order for the Kingdom of Saudi Arabia (KSA) to achieve its transformational goals in healthcare, the improvement of PHCs' quality and utilization is crucial. An integral part of this service is the laboratory services. METHODS: This paper presents a pilot model for the laboratory services of PHC's in urban cities. The method was based on the FOCUS-PDCA quality improvement method focusing on the pre-analytical phase of the laboratory testing as well as the Saudi Central Board for Accreditation of Healthcare Institutes (CBAHI) gap analysis and readiness within the ten piloted primary healthcare centers. RESULTS: The Gap analysis, revealed in-consistency in the practice, lead to lower the quality of the service, which was seen in the low performance of the chosen key performance indicators (KPI's) (high rejection rates, lower turn-around times (TAT) for test results) and also in the competency of the staff. Following executing the interventions, and by using some of the ACO Laboratory strategies; the KPI rates were improved, and our results exceeded the targets that we have set to reach during the first year. Also introducing the electronic connectivity improved the TAT KPI and made many of the processes leaner. CONCLUSIONS: Our results revealed that the centralization of PHC's laboratory service to an accredited reference laboratory and implementing the national accreditation standards improved the testing process and lowered the cost, for the mass majority of the routine laboratory testing. Moreover, the model shed the light on how crucial the pre-analytical phase for laboratory quality improvement process, its effect on cost reduction, and the importance of staff competency and utilization.

Weight Loss and Exercise Alter the High-Density Lipoprotein Lipidome and Improve High-Density Lipoprotein Functionality in Metabolic Syndrome.

OBJECTIVE: High-density lipoprotein (HDL) lipid composition and function may better reflect cardiovascular risk than HDL cholesterol concentration. This study characterized the relationships between HDL composition, metabolism, and function in metabolic syndrome (MetS) patients and how changes in composition after weight loss (WL) and exercise treatments are related to function. APPROACH AND RESULTS: Plasma samples from MetS patients (n=95) and healthy individuals (n=40) were used in this study. Subsets of the MetS group underwent 12 weeks of no treatment (n=17), WL (n=19), or WL plus exercise (WLEX; n=17). HDL was isolated using density-gradient ultracentrifugation. The HDL lipidome was analyzed by mass spectrometry, and particle size determined by nuclear magnetic resonance. Cholesteryl ester transfer protein activity and ex vivo HDL cholesterol efflux capacity (CEC) were assessed. The HDL lipidome in the MetS patients was substantially different from that in healthy individuals, mean particle size was smaller, and CEC was lower. Several HDL phospholipid and sphingolipid species were associated with HDL diameter and CEC. The HDL lipidome and particle size were modified toward the healthy individuals after WL and WLEX treatments, with greater effects observed in the latter group. Cholesteryl ester transfer protein activity was reduced after WL and WLEX, and CEC was improved after WLEX. CONCLUSIONS: WLEX treatment in MetS patients normalizes the HDL lipidome and particle size profile and enhances CEC. HDL lipids associated with diminished CEC may represent novel biomarkers for early prediction of HDL dysfunction and disease risk and may represent potential therapeutic targets for future HDL therapies. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00163943.

Plasma Lipidomic Profiles Improve on Traditional Risk Factors for the Prediction of Cardiovascular Events in Type 2 Diabetes Mellitus.

BACKGROUND: Clinical lipid measurements do not show the full complexity of the altered lipid metabolism associated with diabetes mellitus or cardiovascular disease. Lipidomics enables the assessment of hundreds of lipid species as potential markers for disease risk. METHODS: Plasma lipid species (310) were measured by a targeted lipidomic analysis with liquid chromatography electrospray ionization-tandem mass spectrometry on a case-cohort (n=3779) subset from the ADVANCE trial (Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation). The case-cohort was 61% male with a mean age of 67 years. All participants had type 2 diabetes mellitus with ≥1 additional cardiovascular risk factors, and 35% had a history of macrovascular disease. Weighted Cox regression was used to identify lipid species associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) and cardiovascular death during a 5-year follow-up period. Multivariable models combining traditional risk factors with lipid species were optimized with the Akaike information criteria. C statistics and NRIs were calculated within a 5-fold cross-validation framework. RESULTS: Sphingolipids, phospholipids (including lyso- and ether- species), cholesteryl esters, and glycerolipids were associated with future cardiovascular events and cardiovascular death. The addition of 7 lipid species to a base model (14 traditional risk factors and medications) to predict cardiovascular events increased the C statistic from 0.680 (95% confidence interval [CI], 0.678-0.682) to 0.700 (95% CI, 0.698-0.702; P<0.0001) with a corresponding continuous NRI of 0.227 (95% CI, 0.219-0.235). The prediction of cardiovascular death was improved with the incorporation of 4 lipid species into the base model, showing an increase in the C statistic from 0.740 (95% CI, 0.738-0.742) to 0.760 (95% CI, 0.757-0.762; P<0.0001) and a continuous net reclassification index of 0.328 (95% CI, 0.317-0.339). The results were validated in a subcohort with type 2 diabetes mellitus (n=511) from the LIPID trial (Long-Term Intervention With Pravastatin in Ischemic Disease). CONCLUSIONS: The improvement in the prediction of cardiovascular events, above traditional risk factors, demonstrates the potential of plasma lipid species as biomarkers for cardiovascular risk stratification in diabetes mellitus. CLINICAL TRIAL REGISTRATION: URL: https://clinicaltrials.gov. Unique identifier: NCT00145925.

Novel Lipid Species for Detecting and Predicting Atrial Fibrillation in Patients With Type 2 Diabetes.

The incidence of atrial fibrillation (AF) is higher in patients with diabetes. The goal of this study was to assess if the addition of plasma lipids to traditional risk factors could improve the ability to detect and predict future AF in patients with type 2 diabetes. Logistic regression models were used to identify lipids associated with AF or future AF from plasma lipids (n = 316) measured from participants in the ADVANCE trial (n = 3,772). To gain mechanistic insight, follow-up lipid analysis was undertaken in a mouse model that has an insulin-resistant heart and is susceptible to AF. Sphingolipids, cholesteryl esters, and phospholipids were associated with AF prevalence, whereas two monosialodihexosylganglioside (GM3) ganglioside species were associated with future AF. For AF detection and prediction, addition of six and three lipids, respectively, to a base model (n = 12 conventional risk factors) increased the C-statistics (detection: from 0.661 to 0.725; prediction: from 0.674 to 0.715) and categorical net reclassification indices. The GM3(d18:1/24:1) level was lower in patients in whom AF developed, improved the C-statistic for the prediction of future AF, and was lower in the plasma of the mouse model susceptible to AF. This study demonstrates that plasma lipids have the potential to improve the detection and prediction of AF in patients with diabetes.

Changes in plasma lipids predict pravastatin efficacy in secondary prevention.

BACKGROUNDStatins have pleiotropic effects on lipid metabolism. The relationship between these effects and future cardiovascular events is unknown. We characterized the changes in lipids upon pravastatin treatment and defined the relationship with risk reduction for future cardiovascular events.METHODSPlasma lipids (n = 342) were measured in baseline and 1-year follow-up samples from a Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) study subcohort (n = 4991). The associations of changes in lipids with treatment and cardiovascular outcomes were investigated using linear and Cox regression. The effect of treatment on future cardiovascular outcomes was examined by the relative risk reduction (RRR).RESULTSPravastatin treatment was associated with changes in 206 lipids. Species containing arachidonic acid were positively associated while phosphatidylinositol species were negatively associated with pravastatin treatment. The RRR from pravastatin treatment for cardiovascular events decreased from 23.5% to 16.6% after adjustment for clinical risk factors and change in LDL-cholesterol (LDL-C) and to 3.0% after further adjustment for the change in the lipid ratio PI(36:2)/PC(38:4). Change in PI(36:2)/PC(38:4) mediated 58% of the treatment effect. Stratification of patients into quartiles of change in PI(36:2)/PC(38:4) indicated no benefit of pravastatin in the fourth quartile.CONCLUSIONThe change in PI(36:2)/PC(38:4) predicted benefit from pravastatin, independent of change in LDL-C, demonstrating its potential as a biomarker for monitoring the clinical benefit of statin treatment in secondary prevention.TRIAL REGISTRATIONAustralian New Zealand Clinical Trials Registry identifier ACTRN12616000535471.FUNDINGBristol-Myers Squibb; NHMRC grants 211086, 358395, and 1029754; NHMRC program grant 1149987; NHMRC fellowship 108026; and the Operational Infrastructure Support Program of the Victorian government of Australia.

Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention.

BACKGROUND: Plasma lipidomic measures may enable improved prediction of cardiovascular outcomes in secondary prevention. The aim of this study is to determine the association of plasma lipidomic measurements with cardiovascular events and assess their potential to predict such events. METHODS: Plasma lipids (n = 342) were measured in a retrospective subcohort (n = 5,991) of the LIPID study. Proportional hazards regression was used to identify lipids associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) and cardiovascular death. Multivariable models adding lipid species to traditional risk factors were created using lipid ranking established from the Akaike information criterion within a 5-fold cross-validation framework. The results were tested on a diabetic case cohort from the ADVANCE study (n = 3,779). RESULTS: Specific ceramide species, sphingolipids, phospholipids, and neutral lipids containing omega-6 fatty acids or odd-chain fatty acids were associated with future cardiovascular events (106 species) and cardiovascular death (139 species). The addition of 7 lipid species to a base model (11 conventional risk factors) resulted in an increase in the C-statistics from 0.629 (95% CI, 0.628-0.630) to 0.654 (95% CI, 0.653-0.656) for prediction of cardiovascular events and from 0.673 (95% CI, 0.671-0.675) to 0.727 (95% CI, 0.725-0.728) for prediction of cardiovascular death. Categorical net reclassification improvements for cardiovascular events and cardiovascular death were 0.083 (95% CI, 0.081-0.086) and 0.166 (95% CI, 0.162-0.170), respectively. Evaluation on the ADVANCE case cohort demonstrated significant improvement on the base models. CONCLUSIONS: The improvement in the prediction of cardiovascular outcomes, above conventional risk factors, demonstrates the potential of plasma lipidomic profiles as biomarkers for cardiovascular risk stratification in secondary prevention. FUNDING: Bristol-Myers Squibb, the National Health and Medical Research Council of Australia (grants 211086, 358395, and 1029754), and the Operational Infrastructure Support Program of the Victorian government of Australia.

An Efficient Single Phase Method for the Extraction of Plasma Lipids.

Lipidomic approaches are now widely used to investigate the relationship between lipid metabolism, health and disease. Large-scale lipidomics studies typically aim to quantify hundreds to thousands of lipid molecular species in a large number of samples. Consequently, high throughput methodology that can efficiently extract a wide range of lipids from biological samples is required. Current methods often rely on extraction in chloroform:methanol with or without two phase partitioning or other solvents, which are often incompatible with liquid chromatography electrospray ionization-tandem mass spectrometry (LC ESI-MS/MS). Here, we present a fast, simple extraction method that is suitable for high throughput LC ESI-MS/MS. Plasma (10 µL) was mixed with 100 µL 1-butanol:methanol (1:1 v/v) containing internal standards resulting in efficient extraction of all major lipid classes (including sterols, glycerolipids, glycerophospholipids and sphingolipids). Lipids were quantified using positive-ion mode LC ESI-MS/MS. The method showed high recovery (>90%) and reproducibility (%CV < 20%). It showed a strong correlation of all lipid measures with an established chloroform:methanol extraction method (R2 = 0.976). This method uses non-halogenated solvents, requires no drying or reconstitution steps and is suitable for large-scale LC ESI-MS/MS-based lipidomic analyses in research and clinical laboratories.