Letter to the editor in reference to : "A web-based prediction score for head and neck cancer referrals"

Sir, We read with interest the article by Lau et al (2018) on the design of a web‐based prediction score for head and neck cancer (HNC) referrals. The authors state that there are no similar scoring systems available in the literature that are web‐based and applicable to the two‐week‐wait referrals. Over the last years, risk calculators for common cancer have been extensively explored and are available online, aiming to improve cancer referral pathways and detection.1, 2 A HNC risk calculator (HNC‐RC) based on symptoms and demographics has also been developed and is available online (Sensitivity: 74.8%; specificity: 65.9%; and overall predictive power (AUC): 0.77).3 It has also been externally validated maintaining its discriminatory ability (sensitivity: 79.3%; specificity: 68.6%; and AUC: 0.81). In their abstract, Lau et al have stated that logistic regression and artificial network machine approached have been used. Despite this, only the former was employed, as mentioned in their methodology section, due to time‐related restrictions and effort required to ensure an error‐free algorithm

Head and Neck Cancer Risk Calculator (HaNC-RC) - v.2. Adjustments and addition of symptoms and social history factors.

Objectives: Head and neck cancer (HNC) diagnosis through the 2-week wait, urgent suspicion of cancer (USOC) pathway has failed to increase early cancer detection rates in the UK. A head and neck cancer risk calculator (HaNC-RC) has previously been designed to aid referral of high-risk patients to USOC clinics (predictive power: 77%). Our aim was to refine the HaNC-RC to increase its prediction potential. Design: Following sample size calculation, prospective data collection and statistical analysis of referral criteria and outcomes. Setting: Large tertiary care cancer centre in Scotland. Participants: 3531 new patients seen in routine, urgent and USOC head and neck (HaN) clinics. Main outcome measures: Data collected were as follows: demographics, social history, presenting symptoms and signs and HNC diagnosis. Univariate and multivariate regression analysis were performed to identify significant predictors of HNC. Internal validation was performed using 1000 sample bootstrapping to estimate model diagnostics included the area under the receiver operator curve (AUC), sensitivity and specificity. Results: The updated version of the risk calculator (HaNC-RC v.2) includes age, gender, unintentional weight loss, smoking, alcohol, positive and negative symptoms and signs of HNC. It has achieved an AUC of 88.6% with two recommended triage referral cut-offs to USOC (cut-off: 7.1%; sensitivity: 85%, specificity: 78.3%) or urgent clinics (cut-off: 2.2%; sensitivity: 97.1%; specificity of 52.9%). This could redistribute cancer detection through USOC clinics from the current 60.9%–85.2%, without affecting total numbers seen in each clinical setting. Conclusions: The use of the HaNC-RC v.2 has a significant potential in both identifying patients at high risk of HNC early thought USOC clinics but also improving health service delivery practices by reducing the number of inappropriately urgent referrals

Prolonged but not short-duration blast waves elicit acute inflammation in a rodent model of primary blast limb trauma

BackgroundBlast injuries from conventional and improvised explosive devices account for 75% of injuries from current conflicts; over 70% of injuries involve the limbs. Variable duration and magnitude of blast wave loading occurs in real-life explosions and is hypothesised to cause different injuries. While a number of in vivo models report the inflammatory response to blast injuries, the extent of this response has not been investigated with respect to the duration of the primary blast wave. The relevance is that explosions in open air are of short duration compared to those in confined spaces.MethodsHindlimbs of adult Sprauge-Dawley rats were subjected to focal isolated primary blast waves of varying overpressure (1.8–3.65 kPa) and duration (3.0–11.5 ms), utilising a shock tube and purpose-built experimental rig. Rats were monitored during and after the blast. At 6 and 24 h after exposure, blood, lungs, liver and muscle tissues were collected and prepared for histology and flow cytometry.ResultsAt 6 h, increases in circulating neutrophils and CD43Lo/His48Hi monocytes were observed in rats subjected to longer-duration blast waves. This was accompanied by increases in circulating pro-inflammatory chemo/cytokines KC and IL-6. No changes were observed with shorter-duration blast waves irrespective of overpressure. In all cases, no histological damage was observed in muscle, lung or liver. By 24 h post-blast, all inflammatory parameters had normalised.ConclusionsWe report the development of a rodent model of primary blast limb trauma that is the first to highlight an important role played by blast wave duration and magnitude in initiating acute inflammatory response following limb injury in the absence of limb fracture or penetrating trauma. The combined biological and mechanical method developed can be used to further understand the complex effects of blast waves in a range of different tissues and organs in vivo

Development and validation of a head and neck cancer risk calculator

Background: Most new head and neck cancer (HNC) cases in the UK are diagnosed in an advanced disease stage. This is despite the availability of the 2-week wait (2ww), urgent suspected cancer referral pathway from primary to secondary care. Most HNCs are diagnosed from routes other than the 2ww, despite an increasing number of 2ww referrals. A symptom-based risk calculator had been previously designed to identify patients at high risk of HNC (AUC: 77%) but has not been widely adopted to date, having a lower AUC compared to other common cancer risk calculators (AUC >80%). Aim and Objectives: The aim of this study was to develop and validate a refined version of the HNC symptom-based calculator with the objective of increasing its prediction potential to be more in line with other cancer risk calculators. Design, Setting and Participants: The study was performed in two stages. The calculator development phase was based on a prospective cohort of new head and neck referrals to a secondary care centre in Glasgow (n=3,531, following sample size calculation). The validation phase was performed in a new prospective cohort of patients referred via the 2ww pathway in 41 secondary care centres across the UK (n=4,569) during the first wave of the COVID-19 pandemic. Main outcome measures: The main outcome measure was the area under the curve (AUC) and sensitivity and specificity combination of the final selected model at internal and external validation. Data collected included demographics, social history, presenting symptoms and signs and HNC diagnosis. Binary logistic regression analysis and random forest modelling with internal validation were performed to identify the best-performing model, followed by logistic regression external validation of the updated (HaNC-RC v.2) model. Results: The HaNC-RC v.2 had an improved AUC of 88.6% at internal validation. The model included age, gender, unintentional weight loss, smoking and alcohol history and a refined list of positive and negative symptoms of HNC. Two recommended referral thresholds were introduced based on sensitivity and specificity combinations for a 2ww referral (cut-off: 7.1%; sensitivity: 85%, specificity: 78.3%) and urgent referral (cut-off: 2.2%; sensitivity: 97.1%; specificity: 52.9%). The AUC remained high at external validation (AUC: 83.96%; sensitivity:70%; specificity: 81%). The use of the HaNC-RC v.2 resulted in a reduction of the 2ww appointments by 70% during the first wave of the COVID-19 pandemic. Of the total of 256 cancers, 73.2% were seen in the high-risk group (2ww referral) and 16.5% in the moderate-risk group (urgent referral). These figures were much improved compared to those based on GP triaging using the national referral guidelines (59.9% and 25.4%, respectively) in the Glasgow region, without affecting the total numbers seen in each clinical setting. Conclusions: This study achieved its aim and objectives of developing and validating an updated version of a previously designed HNC risk calculator. The HaNC-RC v.2 has a much-improved AUC that remained high at external validation, and it could be used as a triaging aid for head and neck referrals in secondary or primary care pathways.Background: Most new head and neck cancer (HNC) cases in the UK are diagnosed in an advanced disease stage. This is despite the availability of the 2-week wait (2ww), urgent suspected cancer referral pathway from primary to secondary care. Most HNCs are diagnosed from routes other than the 2ww, despite an increasing number of 2ww referrals. A symptom-based risk calculator had been previously designed to identify patients at high risk of HNC (AUC: 77%) but has not been widely adopted to date, having a lower AUC compared to other common cancer risk calculators (AUC >80%). Aim and Objectives: The aim of this study was to develop and validate a refined version of the HNC symptom-based calculator with the objective of increasing its prediction potential to be more in line with other cancer risk calculators. Design, Setting and Participants: The study was performed in two stages. The calculator development phase was based on a prospective cohort of new head and neck referrals to a secondary care centre in Glasgow (n=3,531, following sample size calculation). The validation phase was performed in a new prospective cohort of patients referred via the 2ww pathway in 41 secondary care centres across the UK (n=4,569) during the first wave of the COVID-19 pandemic. Main outcome measures: The main outcome measure was the area under the curve (AUC) and sensitivity and specificity combination of the final selected model at internal and external validation. Data collected included demographics, social history, presenting symptoms and signs and HNC diagnosis. Binary logistic regression analysis and random forest modelling with internal validation were performed to identify the best-performing model, followed by logistic regression external validation of the updated (HaNC-RC v.2) model. Results: The HaNC-RC v.2 had an improved AUC of 88.6% at internal validation. The model included age, gender, unintentional weight loss, smoking and alcohol history and a refined list of positive and negative symptoms of HNC. Two recommended referral thresholds were introduced based on sensitivity and specificity combinations for a 2ww referral (cut-off: 7.1%; sensitivity: 85%, specificity: 78.3%) and urgent referral (cut-off: 2.2%; sensitivity: 97.1%; specificity: 52.9%). The AUC remained high at external validation (AUC: 83.96%; sensitivity:70%; specificity: 81%). The use of the HaNC-RC v.2 resulted in a reduction of the 2ww appointments by 70% during the first wave of the COVID-19 pandemic. Of the total of 256 cancers, 73.2% were seen in the high-risk group (2ww referral) and 16.5% in the moderate-risk group (urgent referral). These figures were much improved compared to those based on GP triaging using the national referral guidelines (59.9% and 25.4%, respectively) in the Glasgow region, without affecting the total numbers seen in each clinical setting. Conclusions: This study achieved its aim and objectives of developing and validating an updated version of a previously designed HNC risk calculator. The HaNC-RC v.2 has a much-improved AUC that remained high at external validation, and it could be used as a triaging aid for head and neck referrals in secondary or primary care pathways

Six-canal video-Head-Impulse-Test in patients with labyrinthine and retro-labyrinthine pathology: detecting vestibulo-ocular reflex deficits

Background: Abnormal gains in six-canal video-Head-Impulse-Test (vHIT) are attributed to semicircular canal deficits. However, as vHIT responses are linked to vestibulo-ocular reflex (VOR), we hypothesized that abnormal gains can be due to VOR pathway deficits. Methods: We compared vHIT gains and correlations between them (Mann-Kendall trend test) in 20 patients with superior semi-circular canal dehiscence (SSCD; labyrinthine cause) and 20 side and gender-matched patients with vestibular schwannomas (VS, retrolabyrinthine cause). Results: VS but not SSCD was significantly associated with abnormal lateral (OR: 9.00 (95% CI:1.638;49.44), p:0.011) and posterior canal status (OR: 9.00 (95% CI: 2.151;37.659), p:0.003). In VS we found a statistically significant degree of dependence between all ipsilesional canal vHIT gains; such dependence was not observed in SSCD. Conclusions: VOR gains differ in patients with labyrinthine and retrolabyrinthine disease, suggesting that abnormal gains can indicate not only deficits in the semi-circular canals but also elsewhere along the VOR pathway

A Comparison of Cochlear Nerve Size in Normal-Hearing Adults Using Magnetic Resonance Imaging.

OBJECTIVE Cochlear implantation is a clinical and cost-effective treatment for severe hearing loss. Cochlear nerve size assessment by magnetic resonance imaging (MRI) has been investigated for use as a prognostic indicator following cochlear implantation. This study aimed to further that research by assessing nerve size in normal-hearing adults for symmetry. MATERIALS AND METHODS Patients with tinnitus presenting to our center retrospectively had their nerve size assessed by MRI. RESULTS The study found no significant differences between right and left cochlear nerves in normal-hearing adults, supporting our hypothesis of symmetry in these individuals. This was a previously unproven and uninvestigated hypothesis. CONCLUSION Nerve size assessment should remain an active area of research in otological disease

Rapid implementation of an evidence-based remote triaging system for assessment of suspected referrals and patients with head and neck cancer on follow-up after treatment during the COVID-19 pandemic: Model for international collaboration.

BACKGROUND Outpatient telemedicine consultations are being adopted to triage patients for head and neck cancer. However, there is currently no established structure to frame this consultation. METHODS For suspected referrals with cancer, we adapted the Head and Neck Cancer Risk Calculator (HaNC-RC)-V.2, generated from 10 244 referrals with the following diagnostic efficacy metrics: 85% sensitivity, 98.6% negative predictive value, and area under the curve of 0.89. For follow-up patients, a symptom inventory generated from 5123 follow-up consultations was used. A customized Excel Data Tool was created, trialed across professional groups and made freely available for download at www.entintegrate.co.uk/entuk2wwtt, alongside a user guide, protocol, and registration link for the project. Stakeholder support was obtained from national bodies. RESULTS No remote consultations were refused by patients. Preliminary data from 511 triaging episodes at 13 centers show that 77.1% of patients were discharged directly or have had their appointments deferred. DISCUSSION Significant reduction in footfall can be achieved using a structured triaging system. Further refinement of HaNC-RC-V.2 is feasible and the authors welcome international collaboration