Faecal immunochemical testing (FIT): sources of result variation based on three years of routine testing of symptomatic patients in English primary care.

Introduction: We aimed to determine the analytical capabilities of a commonly used faecal immunochemical test (FIT) to detect faecal haemoglobin (Hb) in symptomatic people attending primary care in the context of the English NICE DG30 guidance.Materials and Methods: Data obtained from independent verification studies and clinical testing of the HM-JACKarc FIT method in routine primary care practice were analysed to derive performance characteristics.Results: Detection capabilities for the FIT method were 0.5 µg/g (limit of blank), 1.3 µg/g (limit of detection) and 3.0 µg/g (limit of quantitation). Of 33 non-homogenized specimens, 31 (93.9%) analysed in triplicate were consistently categorized relative to 10 µg/g, compared to all 33 (100%) homogenized specimens. Imprecision was higher (median 27.8%, (range 20.5% to 48.6%)) in non-homogenized specimens than in homogenized specimens (10.2%, (7.0 to 13.5%)). Considerable variation was observed in sequential clinical specimens from individual patients but no positive or negative trend in specimen degradation was observed over time (p = 0.26).Discussion: The FIT immunoassay evaluated is capable of detecting faecal Hb at concentrations well below the DG30 threshold of 10 µg/g and is suitable for application in this context. The greatest practical challenge to FIT performance is reproducible sampling, the pre-analytical step associated with most variability. Further research should focus on reducing sampling variability, particularly as post-COVID-19 guidance recommends greater FIT utilization.

Individual inflammatory marker abnormalities or inflammatory marker scores to identify primary care patients with unexpected weight loss for cancer investigation?

BACKGROUND: Combinations of inflammatory markers are used as prognostic scores in cancer patients with cachexia. We investigated whether they could also be used to prioritise patients attending primary care with unexpected weight loss for cancer investigation. METHODS: We used English primary care electronic health records data linked to cancer registry data from 12,024 patients with coded unexpected weight loss. For each individual inflammatory marker and score we estimated the sensitivity, specificity, likelihood ratios, positive predictive value (PPV) and the area under the curve along with 95% confidence intervals for a cancer diagnosis within six months. RESULTS: The risk of cancer associated with two abnormal inflammatory markers combined in a score was higher than the risk associated with individual inflammatory marker abnormalities. However, the risk of cancer in weight loss associated with individual abnormalities, notably a raised C-reactive protein, was sufficient to trigger further investigation for cancer under current NICE guidelines. CONCLUSIONS: If scores including pairs of inflammatory marker abnormalities were to be used, in preference to individual abnormalities, fewer people would be investigated to diagnose one cancer with fewer false positives, but fewer people with cancer would be diagnosed overall.

The use of statistical methodology to determine the accuracy of grading within a diabetic retinopathy screening programme.

AIMS: We aimed to use longitudinal data from an established screening programme with good quality assurance and quality control procedures and a stable well-trained workforce to determine the accuracy of grading in diabetic retinopathy screening. METHODS: We used a continuous time-hidden Markov model with five states to estimate the probability of true progression or regression of retinopathy and the conditional probability of an observed grade given the true grade (misclassification). The true stage of retinopathy was modelled as a function of the duration of diabetes and HbA1c . RESULTS: The modelling dataset consisted of 65 839 grades from 14 187 people. The median number [interquartile range (IQR)] of examinations was 5 (3, 6) and the median (IQR) interval between examinations was 1.04 (0.99, 1.17) years. In total, 14 227 grades (21.6%) were estimated as being misclassified, 10 592 (16.1%) represented over-grading and 3635 (5.5%) represented under-grading. There were 1935 (2.9%) misclassified referrals, 1305 were false-positive results (2.2%) and 630 were false-negative results (1.0%). Misclassification of background diabetic retinopathy as no detectable retinopathy was common (3.4% of all grades) but rarely preceded referable maculopathy or retinopathy. CONCLUSION: Misclassification between lower grades of retinopathy is not uncommon but is unlikely to lead to significant delays in referring people for sight-threatening retinopathy.

Establishing an evidence base for frequency of monitoring glycated haemoglobin levels in patients with Type 2 diabetes: projections of effectiveness from a regression model.

AIMS: Glycated haemoglobin (HbA1c) is monitored to guide treatment decisions in relation to glycaemic goals over time. Changes between two consecutive HbA1c tests result not only from deterioration or improvement in glycaemic control, but also from biovariability and measurement error. We model how this short-term variability impacts on HbA1c monitoring. METHODS: Using data from a randomized trial of non-insulin treated patients with Type 2 diabetes we fitted a random-effects model for progression and variability of HbA1c. We estimated how many tests where HbA1c ≥ 7.5% (58.5 mmol/mol) would be false-positive (underlying HbA1c < 7.5% but test ≥ 7.5% owing to variability) vs. true-positive, in people with initial HbA1c between 6.5% and 7.3% (48 mmol/mol and 56 mmol/mol). RESULTS: Participants (n = 320) had mean (SD) age 66 (10) years, BMI 31.3 (6.0) kg/m2 and median HbA1c was 7.1% (54 mmol/mol) with interquartile range 6.6% (49 mmol/mol) to 7.7% (61 mmol/mol). Mean (95% CI) change in HbA1c was 0.1% (1 mmol/mol) with 95% confidence interval 0.05% (0.5 mmol/mol) to 0.15% (2 mmol/mol) per 6 months. The minimum interval at which a true-positive test is more likely than a false positive test is 270 days for a starting HbA1c of 6.9% (52 mmol/mol) and 360 days at a starting value of 6.5% (48 mmol/mol). CONCLUSION: In patients with initial HbA1c close to treatment goal, retesting at 6 months would yield more true-positive than false-positive tests. For patients with lower initial HbA1c, retesting at 6 months would yield more false than true-positive tests. In all patients, retesting at 12 months yields more true than false-positive tests. In very few patients would retesting at 3 months be justified.