Sharing reference intervals and monitoring patients across laboratories - findings from a likely commutable external quality assurance program.

OBJECTIVES: Laboratory results are increasingly interpreted against common reference intervals (CRIs), published clinical decision limits, or previous results for the same patient performed at different laboratories. However, there are no established systems to determine whether current analytical performance justifies these interpretations. We analysed data from a likely commutable external quality assurance program (EQA) to assess these interpretations. METHODS: The use of CRIs was assessed by evaluating instrument group medians against minimum specifications for bias. The use of clinical decision limits was assessed using specifications from professional bodies, and the monitoring of patients by testing at different laboratories was assessed by comparing all-laboratory imprecision to within-subject biological variation. RESULTS: Five of the 18 analytes with Australasian CRIs did not meet specification for all instrument groups. Among these, calcium and magnesium failed for one instrument group out of seven, while bicarbonate, chloride, and lipase failed for two instrument groups. Of the 18 analytes reviewed currently without CRIs in Australasia, 10 candidates were identified. Among analytes with clinical decision limits, i.e. lipids, glucose, and vitamin D, only triglycerides met both bias and imprecision specifications, while vitamin D met the imprecision specification. Monitoring patients by testing at different laboratories was supported for 15 of the 46 (33 %) analyte-method principles groups that met minimum imprecision specifications. CONCLUSIONS: Analysis of data from commutable EQA programs can provide a mechanism for monitoring whether analytical performance justifies the interpretations made in contemporary laboratory practice. EQA providers should establish systems for routinely providing this information to the laboratory community.

What's to Be Done About Laboratory Quality? Process Indicators, Laboratory Stewardship, the Outcomes Problem, Risk Assessment, and Economic Value: Responding to Contemporary Global Challenges.

OBJECTIVES: For 50 years, structure, process, and outcomes measures have assessed health care quality. For clinical laboratories, structural quality has generally been assessed by inspection. For assessing process, quality indicators (QIs), statistical monitors of steps in the clinical laboratory total testing, have proliferated across the globe. Connections between structural and process laboratory measures and patient outcomes, however, have rarely been demonstrated. METHODS: To inform further development of clinical laboratory quality systems, we conducted a selective but worldwide review of publications on clinical laboratory quality assessment. RESULTS: Some QIs, like seven generic College of American Pathologists Q-Tracks monitors, have demonstrated significant process improvement; other measures have uncovered critical opportunities to improve test selection and result management. The College of Pathologists of Australasia Key Indicator Monitoring and Management System has deployed risk calculations, introduced from failure mode effects analysis, as surrogate measures for outcomes. Showing economic value from clinical laboratory testing quality is a challenge. CONCLUSIONS: Clinical laboratories should converge on fewer (7-14) rather than more (21-35) process monitors; monitors should cover all steps of the testing process under laboratory control and include especially high-risk specimen-quality QIs. Clinical laboratory stewardship, the combination of education interventions among clinician test orderers and report consumers with revision of test order formats and result reporting schemes, improves test ordering, but improving result reception is more difficult. Risk calculation reorders the importance of quality monitors by balancing three probabilities: defect frequency, weight of potential harm, and detection difficulty. The triple approach of (1) a more focused suite of generic consensus quality indicators, (2) more active clinical laboratory testing stewardship, and (3) integration of formal risk assessment, rather than competing with economic value, enhances it.

Enhancing the Clinical Value of Medical Laboratory Testing.

The value of medical laboratory testing is often directed to the cost of testing however the clinical benefits of these tests are at least as important. Laboratory testing has an acknowledged widespread role in clinical decision making, and therefore a role in determining clinical outcome. Consequently, the value of laboratory testing should be considered in its role in affecting beneficial actions and outcomes. This includes both the requesting phase of choosing tests which will influence clinical decision making as well as the reporting phase in a way that guides clinical decisions and actions. Clinical decision support systems and software can enhance the value of medical laboratory testing if they are directed toward facilitating those clinical decisions where there is either evidence, or agreed consensus, addressing patient outcomes.

Assuring the quality of interpretative comments in clinical chemistry.

The provision of interpretative advice on laboratory results is a post-analytic activity and an integral part of clinical laboratory services. It is valued by healthcare workers and has the potential to prevent or reduce errors and improve patient outcomes. It is important to ensure that interpretative comments provided by laboratory personnel are of high quality: comments should be patient-focused and answer the implicit or explicit question raised by the requesting clinician. Comment providers need to be adequately trained and qualified and be able to demonstrate their proficiency to provide advice on laboratory reports. External quality assessment (EQA) schemes can play a part in assessing and demonstrating the competence of such laboratory staff and have an important role in their education and continuing professional development. A standard structure is proposed for EQA schemes for interpretative comments in clinical chemistry, which addresses the scope and method of assessment including nomenclature and marking scales. There is a need for evidence that participation in an EQA program for interpretative commenting facilitates improved quality of comments. It is proposed that standardizing goals and methods of assessment as well as nomenclature and marking scales may help accumulate evidence to demonstrate the impact of participation in EQA for interpretative commenting on patient outcome.

Uncertainty in measurement and total error - are they so incompatible?

There appears to be a growing debate with regard to the use of "Westgard style" total error and "GUM style" uncertainty in measurement. Some may argue that the two approaches are irreconcilable. The recent appearance of an article "Quality goals at the crossroads: growing, going, or gone" on the well-regarded Westgard Internet site requires some comment. In particular, a number of assertions which relate to ISO 15189 and uncertainty in measurement appear misleading. An alternate view of the key issues raised by Westergard may serve to guide and enlighten others who may accept such statements at face value.

Patterns of type 2 diabetes monitoring in rural towns: How does frequency of HbA1c and lipid testing compare with existing guidelines?

OBJECTIVE: To indicate levels of monitoring of type 2 diabetes in rural and regional Australia by examining patterns of glycated haemoglobin (HbA1c) and blood lipid testing. DESIGN AND SETTING: Retrospective analysis of pathology services data from twenty regional and rural towns in eastern Australia over 24 months. PARTICIPANTS: Of 13 105 individuals who had either a single HbA1c result ≥7.0% (53 mmol mol-1 ); or two or more HbA1c tests within the study period. MAIN OUTCOME MEASURES: Frequency of testing of HbA1c and blood lipids (cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides) were compared with guideline recommendations. RESULTS: About 58.3% of patients did not have the recommended 6-monthly HbA1c tests and 30.6% did not have annual lipid testing. For those who did not receive tests at the recommended interval, the mean between-test interval was 10.5 months (95% CI = 7.5-13.5) rather than 6 months for HbA1c testing; and 15.7 (95% CI = 13.3-18.1) months rather than annually for blood lipids. For those with at least one out-of-range test result, 77% of patients failed to receive a follow-up HbA1c test and 86.5% failed to receive a follow-up blood lipid test within the recommended 3 months. Patients less than 50 years of age, living in a more remote area and with poor diabetes control were less likely to have testing at the recommended intervals (P < 0.0001). CONCLUSIONS: Although poor diabetes testing is not limited to rural areas, more intensive diabetes monitoring is likely to be needed for patients living in non-metropolitan areas, particularly for some subgroups.

Performance criteria of the post-analytical phase.

Quality in healthcare is ideally at an optimal benchmark, but must be at least above the minimal standards for care. While laboratory quality is ideally judged in clinical terms, laboratory medicine has also used biological variations and state-of-the-art criteria when, as is often the case, clinical outcome studies or clinical consensus are not available. The post-analytical phase involves taking quality technical results and providing the means for clinical interpretation in the report. Reference intervals are commonly used as a basis for data interpretation; however, laboratories vary in the reference intervals they use, even when analysis is similar. Reference intervals may have greater clinical value if they are both optimised to account for physiological individuality, as well as if they are harmonised through professional consensus. Clinical decision limits are generally superior to reference intervals as a basis for interpretation because they address the specific clinical concern in any patient. As well as providing quality data and interpretation, the knowledge of laboratory experts can be used to provide targeted procedural knowledge in a patient report. Most profoundly critically abnormal results should to be acted upon to minimise the risk of mortality. The three steps in quality report interpretation, (i) describing the abnormal data, (ii) interpreting the clinical information within that data and (iii) providing knowledge for clinical follow-up, highlight that the quality of all laboratory testing is reflected in its impact on clinical management and improving patient outcomes.

Comparison of IFCC-calibrated HbA(1c) from laboratory and point of care testing systems.

OBJECTIVE: WHO, IDF and ADA recommend HbA(1c) ≥6.5% (48 mmol/mol) for diagnosis of diabetes with pre-diabetes 6.0% (42 mmol/mol) [WHO] or 5.7% (39 mmol/mol) [ADA] to 6.4% (47 mmol/mol). We have compared HbA(1c) from several methods for research relating glycaemic markers. RESEARCH DESIGN AND METHODS: HbA1c was measured in EDTA blood from 128 patients with diabetes on IE HPLC analysers (Bio-Rad Variant II NU, Menarini HA8160 and Tosoh G8), point of care systems, POCT, (A1cNow+ disposable cartridges and DCA 2000(®)+ analyser), affinity chromatography (Primus Ultra2) and the IFCC secondary reference method (Menarini HA8160 calibrated using IFCC SRM protocol). RESULTS: Median (IQ range) on IFCC SRM was 7.5% (6.8-8.4) (58(51-68) mmol/mol) HbA(1c) with minimum 5.3%(34 mmol/mol)/maximum 11.9%(107 mmol/mol). There were positive offsets between IFCC SRM and Bio-Rad Variant II NU, mean difference (1SD), +0.33%(0.17) (+3.6(1.9) mmol/mol), r(2)=0.984, p<0.001 and Tosoh G8, +0.22%(0.20) (2.4(2.2) mmol/mol), r(2)=0.976, p<0.001 with a very small negative difference -0.04%(0.11) (-0.4(1.2) mmol/mol), r(2)=0.992, p<0.001 for Menarini HA8160. POCT methods were less precise with negative offsets for DCA 2000(®)+ analyser -0.13%(0.28) (-1.4(3.1) mmol/mol), r(2)=0.955, p<0.001 and A1cNow+ cartridges -0.70%(0.67) (-7.7(7.3) mmol/mol), r(2)=0.699, p<0.001 (n=113). Positive biases for Tosoh and Bio-Rad (compared with IFCC SRM) have been eliminated by subsequent revision of calibration. CONCLUSIONS: Small differences observed between IFCC-calibrated and NGSP certified methods across a wide HbA(1c) range were confirmed by quality control and external quality assurance. As these offsets affect estimates of diabetes prevalence, the analyser (and calibrator) employed should be considered when evaluating diagnostic data.

Physiology and its importance for reference intervals.

Reference intervals are ideally defined on apparently healthy individuals and should be distinguished from clinical decision limits that are derived from known diseased patients. Knowledge of physiological changes is a prerequisite for understanding and developing reference intervals. Reference intervals may differ for various subpopulations because of differences in their physiology, most obviously between men and women, but also in childhood, pregnancy and the elderly. Changes in laboratory measurements may be due to various physiological factors starting at birth including weaning, the active toddler, immunological learning, puberty, pregnancy, menopause and ageing. The need to partition reference intervals is required when there are significant physiological changes that need to be recognised. It is important that laboratorians are aware of these changes otherwise reference intervals that attempt to cover a widened inter-individual variability may lose their usefulness. It is virtually impossible for any laboratory to directly develop reference intervals for each of the physiological changes that are currently known, however indirect techniques can be used to develop or validate reference intervals in some difficult situations such as those for children. Physiology describes our life's journey, and it is only when we are familiar with that journey that we can appreciate a pathological departure.

The de ritis ratio: the test of time.

De Ritis described the ratio between the serum levels of aspartate transaminase (AST) and alanine transaminase (ALT) almost 50 years ago. While initially described as a characteristic of acute viral hepatitis where ALT was usually higher than AST, other authors have subsequently found it useful in alcoholic hepatitis, where AST is usually higher than ALT. These interpretations are far too simplistic however as acute viral hepatitis can have AST greater than ALT, and this can be a sign of fulminant disease, while alcoholic hepatitis can have ALT greater than AST when several days have elapsed since alcohol exposure. The ratio therefore represents the time course and aggressiveness of disease that would be predicted from the relatively short half-life of AST (18 h) compared to ALT (36 h). In chronic viral illnesses such as chronic viral hepatitis and chronic alcoholism as well as non-alcoholic fatty liver disease, an elevated AST/ALT ratio is predictive of long terms complications including fibrosis and cirrhosis. There are methodological issues, particularly whether or not pyridoxal phosphate is used in the transaminase assays, and although this can have specific effects when patient samples are deficient in this vitamin, these method differences generally have mild effects on the usefulness of the assays or the ratio. Ideally laboratories should be using pyridoxal phosphate supplemented assays in alcoholic, elderly and cancer patients who may be pyridoxine deplete. Ideally all laboratories reporting abnormal ALT should also report AST and calculate the De Ritis ratio because it provides useful diagnostic and prognostic information.