Trends in the full blood count blood test and colorectal cancer detection: a longitudinal, case-control study of UK primary care patient data

Study design

We performed a retrospective case-control longitudinal study to explore changes in FBC results before a diagnosis compared to patients without a diagnosis. FBC data were obtained from a UK primary care database, the Clinical Practice Research Datalink (CPRD) GOLD, and diagnosis data from the UK National Cancer Registration and Analysis Service (NCRAS). CPRD has ethical approval from the Health Research Authority to hold anonymised patient data and support research using that data. The CPRD Independent Scientific Advisory Committee’s approval of data access for individual research projects includes ethics approval and consent for those projects. Ethical approval was therefore covered for this study by the CPRD (protocol 14_195RMn2A2R). Clinical codes to extract data from CPRD and NCRAS are in Table 1 and Table 2.

Study entry was defined as the latest date of registration with the practice, patient’s 40^th birthday, or 1^st January 2000. Study exit was defined as the earliest date of leaving the practice, date of death, or 14^th January 2014 (the NCRAS data-cut date).

Participants

Patients with at least one FBC blood test within 10 years before index date (defined below) were included. Patients were excluded if registered with their primary care practice for less than one year, had a history of colorectal cancer before study entry, or diagnosed after study exit. Patients diagnosed with another cancer type before or simultaneously with colorectal cancer diagnosis were excluded. Patients with an available date of diagnosis but no indication of the cancer type were excluded.

Clinical outcome

The outcome was the first diagnosis of colorectal cancer in the NCRAS database. For cases (patients diagnosed), the index date was the date of colorectal cancer diagnosis in the patient’s study period. For controls (patients without a diagnosis), the index date was a randomly selected date in the patient’s study period. A random date was chosen to mimic the sporadic nature of diagnoses in the overall study period for cases.

Demographic and FBC variables

Year of birth and sex were available for all patients in the CPRD dataset. We extracted the date of each FBC test and included 14 of the 20 parameters (exposure variables of interest) that make up the FBC in this study. We excluded five: percentage basophils, eosinophils, lymphocytes, monocytes, and neutrophils because we used the corresponding counts (also FBC parameters). Additionally, red blood cell distribution width was excluded because this parameter is not recorded in general practice so was missing for almost all FBCs. We excluded FBC results outside biologically plausible ranges, such as negative values (see Table 3 for further details), FBCs performed earlier than 10 years before index date, and FBCs performed after index date.

Iron-deficiency anaemia warrants further investigation for colorectal cancer. Anaemia was defined as haemoglobin level <13 g/dL for men and <12 g/dL for women, as recommended by the National Institute for Health and Care Excellence (NICE) (NICE: Suspected cancer recognition and referral, NICE: anaemia - iron deficiency) and World Health Organisation (WHO) (WHO: anaemia). Microcytic anaemia, commonly present in iron-deficiency anaemia (Mean Corpuscular Volume), was defined as the presence of microcytosis (mean corpuscular volume <80 fL, as recommended by NICE (NICE: Investigations to confirm iron deficiency anaemia)) with anaemia.

Each FBC parameter has a normal or reference range of values considered to reflect a healthy individual (Mayo Clinic: Complete blood count). Reference ranges differ based on age and sex and may differ slightly across primary care practices (Royal Wolverhampton NHS UK, York Hospitals NHS UK, Maidstone and Tunbridge Wells NHS UK, Gloucestershire Hospitals NHS UK). We used reference ranges provided by the Department of Laboratory Haematology at Oxford University Hospitals Trust in this study (Table 4) (Oxford University Hospitals NHS UK) for comparison with trends.

A detailed account of our data preparation and validation processes has previously been reported⁷. We also previously provided summary statistics for each FBC parameter.

Statistical analysis

Trends in raw data: We used LOWESS smoothing to describe trends in FBC parameters graphically for 10-yearly age groups. Controls with many FBCs are likely to have some other condition/disease, which could affect blood levels. Therefore, for both cases and controls, we randomly selected three FBCs per patient (if there were more than three) to reduce the influence of these many effected FBCs on LOWESS trends.

Modelling trends: Mixed effects models were developed for each FBC parameter separately (using all available FBCs per patient), using restricted maximum likelihood estimation. To model differences in FBC levels between cases and controls over time, colorectal cancer status (yes/no) and time to index date (years) were included as fixed effects together with an interaction between them. Each model was adjusted for age at index date (years) as a fixed effect and interactions between age and time and age and colorectal cancer status were included if trends over time or by colorectal cancer status differed by age group upon graphical inspection of LOWESS plots. Each patient was modelled using a random intercept and time using a random slope with an unstructured covariance matrix to account for correlation in repeated measures.

Non-linearity of continuous variables was based on visual inspection, Akaike information criteria, and Bayesian information criteria, which compared linear splines, restricted cubic splines, and fractional polynomials, and number of knots and knot locations^8–10. Where non-linear, time to index date was modelled using piecewise linear splines with three knots: at one, two, and four years before index date. Age at index date was modelled using piecewise linear splines with knots at ages 60, 70, and 80 years for red blood cell-related parameters and platelet count and were variable for white blood cell count-related parameters.

Association of trends with cancer: Joint modelling of longitudinal and time-to-event data was used to quantify the association between FBC trends and cancer diagnosis. Joint modelling uses mixed effects modelling to model longitudinal data and are linked to a Cox model to provide hazard ratios (HRs) for outcomes. Univariate joint models (one for each FBC parameter) were developed. Mixed methods were as described above, except colorectal cancer status terms were removed because these are treated as the outcome in the linked time-to-event outcome for cancer presence. Due to the computationally intensive nature of joint models, these models were limited to a random sample of 50,000 patients.

Early detection opportunities: To identify opportunities for early detection, we assessed differences in FBC levels over time between cases diagnosed at Duke’s tumour Stage A (earliest stage) and D (latest stage). Mixed effects models were developed using the same methods described above but were limited to cases alone and included Duke’s tumour stage (A versus D) accordingly as fixed effects instead of colorectal cancer status. Joint models were not developed for Stage A versus D tumours due to limited sample sizes.

To explore the association between microcytic anaemia and diagnosis, we calculated the proportion of patients with microcytic anaemia per six-monthly time band, up to five years before index date. Microcytic anaemia presence was based on any FBC in the time band, if a patient had multiple, and proportions were calculated out of the number of patients in the time band. Additionally, we derived age-adjusted odds ratios (95% confidence interval (CI)) for microcytic anaemia presence using logistic regression for each time band separately. We visually compared trends to microcytic anaemia thresholds and FBC reference ranges to identify whether trends can pre-date single-value, iron-deficiency referral thresholds and blood-abnormality.

All analyses were stratified by sex. A two-sided significance level of 5% was used for all statistical analyses. Analyses were conducted using Stata/SE 15.1 (RRID: SCR_012763). Alternative, open-access software, such as R (RRID: SCR_001905), can perform the equivalent analyses.

Sensitivity analysis

We recreated the trends and mixed effects models for each FBC parameter using a matched design. Cases and controls were matched 1:5 on age at index and follow-up time. Follow-up was time (years) from first FBC to index, converted into six-monthly bands for matching. A random index date within the control’s study period was used instead of the index date of their matched case because the latter heavily reduced the sample size. For example, many controls had an index date after study exit or had no FBCs before index.

The FBC test

On average, 14 of the 15 parameters were available within a FBC across FBCs for both males and females separately. Red blood cell distribution width was the FBC parameter missing for almost 100% of FBC tests for both males and females. This is likely because this parameter was historically not reported to the general practice by haematology laboratories, despite being automatically derived by haematology analysers (i.e. machines). Consequently, red blood cell distribution width was excluded from further analyses. Haemoglobin had the least amount of missing data, missing for 1.9% (n = 22,637) and 1.8% (n = 34,678) of FBC tests for males and females, respectively. Missing data for each parameter are provided in Table 3.

Colorectal cancer and the FBC

Of 399,405 males, 2.3% (n = 9,255) had a colorectal cancer diagnosis and of 540,544 females, 1.5% (n = 8,153) were diagnosed. Median (min, max) follow-up was 3.0 (0, 10.0) for male cases, 2.7 (0, 10.0) for male controls, 3.3 (0, 10.0) for female cases and 2.8 (0, 10.0) for female controls (Figure 2). Median (min, max) time between the last FBC and index date was 0.2 (0, 9.9) years for male cases, 1.0 (0, 10.0) years for male controls, 0.2 (0, 10.0) years for female cases and 0.9 (0, 10.0) years for female controls (Figure 3).

Figure 2. Histogram of follow-up time from first FBC to index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²The spike at time=0 in cases is likely due to patients undergoing cancer investigation. This was not expected to influence trends, as the trends rely on sufficient data at each time-point, not comparability of follow-up.

Figure 3. Histogram of time between last FBC and index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²The spike at time=0 in cases is likely due to patients undergoing cancer investigation. This was not expected to influence trends, as the trends rely on sufficient data at each time-point, not comparability of follow-up.

Mixed models for red blood cell-related parameters are in Table 6 (males) and Table 7 (females), platelet-related in Table 8, and white blood cell count-related in Table 9 (males) and Table 10 (females). The presence of colorectal cancer was statistically significantly associated with all parameter levels (p <0.05 for each model) except white blood cell count and eosinophil count for both males and females.

Trends in cases and controls for all FBC parameters are in Figure 4–Figure 17. In the raw data (LOWESS curves), there was no apparent difference in trends measured from 10 to four years before index date between cases and controls for both males and females. Within four years before index date, levels changed steadily over time in patients without a diagnosis, such as the reduction in haemoglobin over time that may be due to increasing age. However, cases had trends in FBC levels that diverged from controls for all parameters, except basophil count and eosinophil count. Additionally for cases, our Figures indicate that the rate of change in many FBC levels increased as the time to diagnosis approached. Individualised trends (raw and predicted) are given for four cases (Figure 18) and four controls (Figure 19).

Figure 4. Red blood cell count trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 5. Haemoglobin trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 6. Haematocrit trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 7. Mean corpuscular volume trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 8. Mean corpuscular haemoglobin trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 9. Mean corpuscular haemoglobin concentration trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 10. Platelets trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 11. Mean platelet volume trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 12. White blood cell count trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 13. Basophil count trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 14. Eosinophil count trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 15. Lymphocyte trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 16. Monocyte count trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 17. Neutrophil count trends between cases and controls, by age at index date^1,2.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. ²LOWESS trends are age (+/- 3 years) and modelled (fixed effects) trends are taken at that specific age. Legend: males (left) and females (right). Colorectal cancer (blue line) and no cancer (red line). LOWESS trend (solid line) and modelled trend (dashed line).

Figure 18. Individualised haemoglobin (left) and platelet (right) trends for four cases by age at index¹.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. Legend: raw data (green triangle), modelled population-level trend (fixed effects) (blue line), modelled individualised patient-level trend (fixed and random effects) (red circle).

Figure 19. Individualised haemoglobin (left) and platelet (right) trends for four controls by age at index¹.

¹Index date was the date of diagnosis for cases and a randomly selected date in the patient’s study period for controls. Legend: raw data (green triangle), modelled population-level trend (fixed effects) (blue line), modelled individualised patient-level trend (fixed and random effects) (red circle).

Colorectal cancer and FBC trends

Using joint modelling, we quantified the relationship between FBC trends and cancer presence (Table 11). The HRs represent how a change in a patient-level trend from the background trend (i.e. trend in patients without cancer) is associated with cancer presence. For example, a decrease in patient-level haemoglobin from the background haemoglobin trend is associated with an increased diagnosis rate (males: 1.783, 95% CI: 1.730, 1.835 and females: 2.037, 95% CI: 1.953, 2.128). There was no statistically significant association between trends in basophil count and eosinophil (both males and females), neutrophil count (males), and white blood cell count (females) and colorectal cancer diagnosis.

Colorectal cancer and microcytic anaemia

Anaemia was present in 48.8% of male cases and 54.3% of female cases on any FBC within one year of diagnosis. At each six-monthly interval up to five years before index date, the proportion with microcytic anaemia was higher in cases than controls (Figure 20). In cases, the proportion increased as the time to diagnosis approached and was highest at 0–3 months prior to diagnosis: 23.3% (n = 1,188) of 5,107 males and 28.4% (n = 1,286) of 4,521 females with FBCs in that period. The odds of diagnosis (corresponding to microcytic anaemia presence) increased as time to index date approached (Table 12). Presence of microcytic anaemia statistically significantly increased odds of diagnosis at each time band within three years before index date for both males (three-year OR = 2.2 (95% CI = 1.5, 3.1)) and females (three-year OR = 1.7 (95% CI = 1.3, 2.3)). No odds ratio achieved statistical significance at earlier time points.

Figure 20. Microcytic anaemia prevalence over six-monthly (+/- 3 months) time band.

Legend: males (top) and females (bottom).

We compared our graphical trends to microcytic anaemia thresholds. For haemoglobin, trends suggest that the threshold is on average only reached in cases very close to the time of diagnosis, except in the oldest age groups, where the threshold is reached slightly earlier but even controls in this age group reach the threshold. For mean corpuscular volume, trends suggest the threshold is on average not reached, regardless of age group. This suggests only a minority of patients have iron-deficiency determined from the FBC test (maximum 23.3% males and 28.4% females, Figure 20).

Colorectal cancer and FBC reference ranges

For all FBC parameters, the graphical trends showed that levels remained in the reference range for both cases and controls, except red blood cell count, haemoglobin, haematocrit, mean corpuscular volume, and mean platelet volume. In these five parameters, the trends suggest blood levels often only reach abnormal thresholds within approximately six months of diagnosis in younger cases. However, in older cases, levels are abnormal for approximately three years before diagnosis, which was also observed for older controls.

Tumour staging and the FBC

The number of cases diagnosed per Duke’s tumour stage is in Table 13. Mixed models including Duke’s stage at diagnosis, developed using cases alone, are provided for red blood cell-related parameters in 14 (males) and 15 (males), platelet-related in Table 16, and white blood cell-related in 17 (males) and 18 (females). In the raw data (LOWESS curves), there appeared to be no difference in trends over time between Stage A and Stage D tumours among older patients. However, changes started up to one year earlier in patients with Stage D in younger patients (see Figure 21 for haemoglobin and Figure 22 for platelets – for the remaining parameters, please see ‘Data availability’). This was observed in all FBC parameters except mean platelet volume, basophil count, eosinophil count, and lymphocyte count for both males and females, which showed no apparent difference between tumour stages.