BLOod Test Trend for cancEr Detection (BLOTTED): protocol for an observational and prediction model development study using English primary care electronic health record data.

BACKGROUND: Simple blood tests can play an important role in identifying patients for cancer investigation. The current evidence base is limited almost entirely to tests used in isolation. However, recent evidence suggests combining multiple types of blood tests and investigating trends in blood test results over time could be more useful to select patients for further cancer investigation. Such trends could increase cancer yield and reduce unnecessary referrals. We aim to explore whether trends in blood test results are more useful than symptoms or single blood test results in selecting primary care patients for cancer investigation. We aim to develop clinical prediction models that incorporate trends in blood tests to identify the risk of cancer. METHODS: Primary care electronic health record data from the English Clinical Practice Research Datalink Aurum primary care database will be accessed and linked to cancer registrations and secondary care datasets. Using a cohort study design, we will describe patterns in blood testing (aim 1) and explore associations between covariates and trends in blood tests with cancer using mixed-effects, Cox, and dynamic models (aim 2). To build the predictive models for the risk of cancer, we will use dynamic risk modelling (such as multivariate joint modelling) and machine learning, incorporating simultaneous trends in multiple blood tests, together with other covariates (aim 3). Model performance will be assessed using various performance measures, including c-statistic and calibration plots. DISCUSSION: These models will form decision rules to help general practitioners find patients who need a referral for further investigation of cancer. This could increase cancer yield, reduce unnecessary referrals, and give more patients the opportunity for treatment and improved outcomes.

Consultations for clinical features of possible cancer and associated urgent referrals before and during the COVID-19 pandemic: an observational cohort study from English primary care.

BACKGROUND: It remains unclear to what extent reductions in urgent referrals for suspected cancer during the COVID-19 pandemic were the result of fewer patients attending primary care compared to GPs referring fewer patients. METHODS: Cohort study including electronic health records data from 8,192,069 patients from 663 English practices. Weekly consultation rates, cumulative consultations and referrals were calculated for 28 clinical features from the NICE suspected cancer guidelines. Clinical feature consultation rate ratios (CRR) and urgent referral rate ratios (RRR) compared time periods in 2020 with 2019. FINDINGS: Consultations for cancer clinical features decreased by 24.19% (95% CI: 24.04-24.34%) between 2019 and 2020, particularly in the 6-12 weeks following the first national lockdown. Urgent referrals for clinical features decreased by 10.47% (95% CI: 9.82-11.12%) between 2019 and 2020. Overall, once patients consulted with primary care, GPs urgently referred a similar or greater proportion of patients compared to previous years. CONCLUSION: Due to the significant fall in patients consulting with clinical features of cancer there was a lower than expected number of urgent referrals in 2020. Sustained efforts should be made throughout the pandemic to encourage the public to consult their GP with cancer clinical features.

Tsc1 (hamartin) confers neuroprotection against ischemia by inducing autophagy.

Previous attempts to identify neuroprotective targets by studying the ischemic cascade and devising ways to suppress it have failed to translate to efficacious therapies for acute ischemic stroke. We hypothesized that studying the molecular determinants of endogenous neuroprotection in two well-established paradigms, the resistance of CA3 hippocampal neurons to global ischemia and the tolerance conferred by ischemic preconditioning (IPC), would reveal new neuroprotective targets. We found that the product of the tuberous sclerosis complex 1 gene (TSC1), hamartin, is selectively induced by ischemia in hippocampal CA3 neurons. In CA1 neurons, hamartin was unaffected by ischemia but was upregulated by IPC preceding ischemia, which protects the otherwise vulnerable CA1 cells. Suppression of hamartin expression with TSC1 shRNA viral vectors both in vitro and in vivo increased the vulnerability of neurons to cell death following oxygen glucose deprivation (OGD) and ischemia. In vivo, suppression of TSC1 expression increased locomotor activity and decreased habituation in a hippocampal-dependent task. Overexpression of hamartin increased resistance to OGD by inducing productive autophagy through an mTORC1-dependent mechanism.