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Additional file 2: Table A2. Disparities in dates of death between the datasets where death is reported from both sources. Description: Information on the differences between the dates of death in the two sources.

Additional file 1: Table A1. Dates of data freeze dd-mmm-yyyy. Description: Table of dates of the data freeze for each data source.

Additional file 3: Table A3. Check of death reporting prior data freeze. Description: Information on how close to the data freeze date deaths are reported.

Additional file 4: Table A4. Guideline table for use of RCHD in the UK. Description: A possible table for the recording of comparisons of trial and RCHD.

Raman spectroscopy (RS) is a powerful technique that permits the non‐destructive chemical analysis of cells and tissues without the need for expensive and complex sample preparation. To date, samples have been routinely mounted onto calcium fluoride (CaF2) as this material possesses the desired mechanical and optical properties for analysis, but CaF2 is both expensive and brittle and this prevents the technique from being routinely adopted. Furthermore, Raman scattering is a weak phenomenon and CaF2 provides no means of increasing signal. For RS to be widely adopted, particularly in the clinical field, it is crucial that spectroscopists identify an alternative, low‐cost substrate capable of providing high spectral signal to noise ratios with good spatial resolution. Results show that these desired properties are attainable when using mirrored stainless steel as a Raman substrate. When compared with CaF2, data show that stainless steel has a low background signal and provides an average signal increase of 1.43 times during tissue analysis and 1.64 times when analyzing cells. This result is attributed to a double‐pass of the laser beam through the sample where the photons from the source laser and the forward scattered Raman signal are backreflected and retroreflected from the mirrored steel surface and focused towards collection optics. The spatial resolution on stainless steel is at least comparable to that on CaF2 and it is not compromised by the reflection of the laser. Steel is a fraction of the cost of CaF2 and the reflection and focusing of photons improve signal to noise ratios permitting more rapid mapping. The low cost of steel coupled with its Raman signal increasing properties and robust durability indicates that steel is an ideal substrate for biological and clinical RS as it possesses key advantages over routinely used CaF2. © 2016 The Authors. Journal of Raman Spectroscopy Published by John Wiley & Sons Ltd.

The techniques and procedures associated with histology are, in most cases, suitable for the diagnosis of colonic carcinomas. However, in cases such as epithelial misplacement the morphology of a stained tissue sample is homologous to that of cancer. This can lead to patients being misdiagnosed and undergoing unnecessary surgery. To prevent this surgery we suggest that the epithelium of tissue samples be examined using infrared (IR) spectroscopy. In this study, IR maps of tissue sections were registered to standard histology images so that epithelial specific spectra could be collected. The differences between these spectra were explored by using Principal Component Analysis (PCA). This paper provides a novel protocol detailing how histology specific spectra can be collected. The potential usefulness of these spectra is demonstrated through the separation of epithelial misplacement cases and colonic carcinomas within PCA space.

Raman Spectroscopy for the identification and classification of malignancy in the oesophagus has been demonstrated in this thesis. The potential of Raman spectroscopy in this field is twofold; as a adjunct for the pathologist and as a biopsy targeting tool at endoscopy. This study has demonstrated the feasibility of these potential applications in vitro. Spectral diagnostic models have been developed by correlating spectral information with histopathology. This is the current 'gold standard' diagnostic method for the identification of dysplasia, the established risk factor for the development of oesophageal cancer. Histopathology is a subjective assessment and widely acknowledged to have limitations. A more rigorous gold standard was therefore developed, as part of this study, using the consensus opinion of three independent expert pathologists to train the diagnostic models. Raman spectra have been measured from oesophageal tissue covering the full spectrum of malignant disease in the oesophagus, using a near infrared Raman spectrometer customised for tissue spectral measurements. Two spectral datasets were measured with different volumes of tissue probed using twenty and eighty times magnification ultra long working distance objectives. Multivariate statistical analysis has been used to extract the required spectral information with the greatest discriminative power. Principal component fed linear discriminant spectral models have been tested with leave one out cross validation procedures. Three pathology group models have correctly classified up to 91% of spectra, and eight group models have correctly classified up to 82% of spectra. Optimisation of the spectral models by selection of significant principal components, filtering the data and using staggered models was investigated. Effort has been made to understand the findings in their clinical context, with review of patient history and clinical progress, long term follow up is required. Preliminary work projecting independent data on to the models has been encouraging with 76% of the spectra in the three group model correctly classified, approaching classification levels of the training dataset. Formalin fixed tissue models were demonstrated to perform well, with 80% of the spectra were correctly classified in the seven group model. This further demonstrates the potential of Raman spectroscopy as a pathology tool. If Raman spectroscopy is to be implemented in a clinical setting it must be transferable between different measurement systems. This has been evaluated with oesophageal tissue spectra measured on two systems using three objectives. Simple calibration has demonstrated the use of multiple systems and measurement parameters in the development and application of spectral classification models. Testing of a new design of fibre probe has provided encouraging preliminary results. There is potential for the application of Raman spectroscopy in vivo, however the technology remains immature. Spectral maps of samples taken from across the spectrum of disease have shown clear delineation of the morphological features seen on the H&E images. Furthermore the biochemical information elicited has been analysed. Initial measurements of oesophageal tissue using multiphoton imaging have demonstrated the potential of collagen autofuorescence in the diagnosis of malignant change.

For many years now there has been a growing frustration with the statistics of head and neck cancer. Despite the many advances in diagnosis and therapy, there has been little change in the prognosis for most cancers of the head and neck in the last 50 years, so what is the point of yet another journal? Well, it is not all bad news.

This translational research evaluates a novel, custom-built Raman probe for potential application as an in vivo diagnostic tool in the oesophagus. Raman spectroscopy (RS) is a well established analytical technique which is capable of probing the biochemical changes associated with neoplastic progression in oesophageal tissue. RS relies on measurement of subtle inelastic scattering signals following monochromatic laser excitation. Clinical utilisation of RS within hollow organs requires accurate collection and transmission of signal through fibre-optic cables. This research aims to evaluate the ability of a custom built fibre-optic Raman probe, in conjunction with multivariate classification models, to differentiate between benign and neoplastic oesophageal pathologies ex vivo. The need for spectral stability and reproducibility are addressed, as are difficulties associated with multisystem reliability. In addition, the biochemical basis of spectral classification is evaluated and discussed. Cont/d.

This study has involved the analysis of volatile fingerprints using a hybrid electronic nose (e-nose) to discriminate between and diagnose the microorganisms which cause ventilator–associated pneumonia (VAP), one of the most important infections in the hospital environment. This infection occurs in hospitalised patients with 48-72 hrs of mechanical ventilation. VAP diagnostics still remains a problem due to the lack of a precise diagnostic tool. The current tests are mostly based on quantitative cultures of samples from the lower lung airways with clinical findings, which do not often result in accurate diagnoses of the disease. Cont/d.

Ventilator associated pneumonia (VAP) is a significant challenge for the Intensive Care doctors worldwide. It is both difficult to diagnose accurately and quickly and to treat effectively once the diagnosis has been established. Current diagnostic microbiological methods of diagnosis can take up to 48 hours to yield results. Early diagnosis and treatment remain the best way of improving outcome for patients with VAP. In this study we look at novel diagnostic techniques for VAP. Electronic nose (Enose) technology was used to identify to identify the presence of microorganisms in bronchoalveolar lavage (BAL) fluid samples taken from the respiratory tracts of ventilated patients. The results were compared with standard microbiological culture and sensitivities. The Enose was able to discriminate 83% of samples into growth or no growth groups on samples grown in the lab. When the technique was employed to samples taken directly from patients the accuracy fell to 68.2%. This suggests that patient related factors may be reducing the accuracy of the Enose classification. The use of antimicrobial drugs prior to patient sampling is likely to have played a major role. The second part of this study used Gas Chromatography-Mass Spectrometry (GC-MS) analysis of patient’s breath in an attempt to identify patients with VAP. Breath samples were taken at the same time as the bronchoalveolar lavage samples described above. The use of this technique did show differences between the breath samples of patients who did not have any microbiological growth from their BAL samples and those that did. Leave one out cross validation of a PC fed LDA model showed 84% correct classification between healthy volunteers, no growth and growth groups. Finally, we evaluated the Breathotron, which is a breath analysis device designed and built at Cranfield Health. It allows for analysis of breath samples using a single sensor system as opposed to a sensor array employed in traditional Enose devices. This allows it to be more portable and cheaper to build. The Breathotron also allows collection of breath onto sorbent cartridges for GC-MS analysis. Its single sensor did not allow for accurate discrimination between samples.

Raman spectroscopy is a technique that utilises inelastic scattering processes to provide a biochemical fingerprint that has been shown to successfully discriminate oesophageal pathologies. The aim of this study was to develop Raman spectroscopy as a clinical tool; both in vivo for ‘targeted biopsy’, and in ex vivo for ‘automated histopathology’. Two different Raman probes were evaluated and compared and tissue classification models generated ex vivo. A preliminary classification model of a novel single collection fibre probe demonstrated potential for the probe design. Both probes were shown to discriminate three different oesophageal pathology groups. A cross-validated tissue classification model (88 samples) discriminated normal, Barrett’s and neoplasia with an overall accuracy of 86.5% with a sensitivity of 83.3-89.5% and specificity of 89.2-97.1%. A novel rapid Raman mapping technique was evaluated. It was shown that sufficient biochemical information for pathology diagnosis could be extracted from low signal to noise ratio data using multivariate analysis providing the dataset was sufficiently large, thus demonstrating the feasibility of automated histopathology in a clinically realistic time frame. Furthermore, it was demonstrated that high spatial resolution imaging was not necessarily required for automated histopathology using novel interpretation of multivariate techniques. A tissue classification model generated from two rapid Raman maps containing separated substrate, normal, HGD, luminescence and fibrous connective tissue with an overall training performance of 97.5% Problems limiting clinical implementation of Raman techniques were investigated and methods of overcoming devised.