Peter Willshaw

Investigation of uremic analytes in hemodialysate and their structural elucidation from accurate mass maps generated by a multi-dimensional liquid chromatography/mass spectrometry approach

Historically, structural elucidation of unknown analytes by mass spectrometry alone has involved tandem mass spectrometry experiments using electron ionization. Most target molecules for bioanalysis in the metabolome are unsuitable for detection by this previous methodology. Recent publications have used high-resolution accurate mass analysis using an LTQ-Orbitrap with the more modern approach of electrospray ionization to identify new metabolites of known metabolic pathways. We have investigated the use of this methodology to build accurate mass fragmentation maps for the structural elucidation of unknown compounds. This has included the development and validation of a novel multi-dimensional LC/MS/MS methodology to identify known uremic analytes in a clinical hemodialysate sample. Good inter- and intra-day reproducibility of both chromatographic stages with a high degree of mass accuracy and precision was achieved with the multi-dimensional liquid chromatography/tandem mass spectrometry (LC/MS/MS) system. Fragmentation maps were generated most successfully using collision-induced dissociation (CID) as, unlike high-energy CID (HCD), ions formed by this technique could be fragmented further. Structural elucidation is more challenging for large analytes >270 Da and distinguishing between isomers where their initial fragmentation pattern is insufficiently different. For small molecules (<200 Da), where fragmentation data may be obtained without loss of signal intensity, complete structures can be proposed from just the accurate mass fragmentation data. This methodology has led to the discovery of a selection of known uremic analytes and two completely novel moieties with chemical structural assignments made.

Using artificial intelligence to predict the equilibrated postdialysis blood urea concentration.

Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) postdialysis blood urea or equilibrated Kt/V results in an inadequate hemodialysis prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated postdialysis blood urea (eqU) is therefore crucial in order to estimate the equilibrated (true) Kt/V. In this work we propose a supervised neural network to predict the eqU at 60 min after the end of hemodialysis. The use of this model is new in this field and is shown to be better than the currently accepted methods (Smye for eqU and Daugirdas for eqKt/V). With this approach we achieve a mean difference error of 0.22 ± 7.71 mg/ml (mean % error: 1.88 ± 13.46) on the eqU prediction and a mean difference error for eqKt/V of –0.01 ± 0.15 (mean % error: –0.95 ± 14.73). The equilibrated Kt/V estimated with the eqU calculated using the Smye formula is not appropriate because it showed a great dispersion. The Daugirdas double-pool Kt/V estimation formula appeared to be accurate and in agreement with the results of the HEMO study.

Dialysate-side urea kinetics. Neural network predicts dialysis dose during dialysis

Determination of the adequacy of dialysis is a routine but crucial procedure in patient evaluation. The total dialysis dose, expressed as Kt/V, has been widely recognised to be a major determinant of morbidity and mortality in haemodialysed patients. Many different factors influence the correct determination of Kt/V, such as urea sequestration in different body compartments, access and cardiopulmonary recirculation. These factors are responsible for urea rebound after the end of the haemodialysis session, causing poor Kt/V estimation. There are many techniques that try to overcome this problem. Some of them use analysis of blood-side urea samples, and in recent years, on-line urea monitors have become available to calculate haemodialysis dose from dialysate-side urea kinetics. All these methods require waiting until the end of the session to calculate the Kt/V dose. In this work, a neural network (NN) method is presented for early prediction of the Kt/V dose. Two different portions of the dialysate urea concentration-time profile (provided by an on-line urea minitor) were analysed: the entire curve A and the first half B, using an NN to predict the Kt/V and compare this with that provided by the monitor. The NN was able to predict Kt/V is the middle of the 4h session (B data) without a significant increase in the percentage error (B data: 6.69%±2.46%; A data: 5.58%±8.77%, mean±SD) compared with the monitor Kt/V.

Detection of abnormality in the electrocardiogram without prior knowledge by using the quantisation error of a self-organising map, tested on the European ischaemia database

Most systems for the automatic detection of abnormalities in the ECG require prior knowledge of normal and abnormal ECG morphology from pre-existing databases. An automated system for abnormality detection has been developed based on learning normal ECG morphology directly from the patient. The quantisation error from a self-organising map ‘learns’ the form of the patients ECG and detects any change in its morphology. The system does not require prior knowledge of normal and abnormal morphologies. It was tested on 76 records from the European Society of Cardiology database and detected 90.5% of those first abnormalities declared by the database to be ischaemic. The system also responded to abnormalities arising from ECG axis changes and slow baseline drifts and revealed that ischaemic episodes are often followed by long-term changes in ECG morphology.

Molecular Kinetics Modeling in Hemodialysis: On-Line Molecular Monitoring and Spectral Analysis

The knowledge of the underlying molecular kinetics is a key point for the development of a dialysis treatment as well as for patient monitoring. In this work, we propose a kinetic inference method that is general enough to be used on different molecular types measured in the spent dialysate. It estimates the number and significance of the compartments involved in the overall process of dialysis by means of a spectral deconvolution technique, characterizing therefore the kinetic behavior of the patient. The method was applied to 52 patients to reveal the underlying kinetics from dialysate time-concentration profiles of urea, which has a well-known molecular kinetic. Three types of behaviors were found: one-compartmental (exponential decay Tau = 180 ± 61.64 minutes), bicompartmental (Tau1 = 24.96 ± 19.33 minutes, Tau2 = 222.32 ± 76.59 minutes), and tricompartmental (Tau1 = 23.03 ± 14.21 minutes; Tau2 = 85.75 ± 27.48 minutes; and Tau3 = 337 ± 85.52 minutes). In patients with bicompartmental kinetics, the Tau2 was related to the level of dialysis dose. The study concluded that spectral deconvolution technique can be considered a powerful tool for molecular kinetics inference that could be integrated in on-line molecular analysis devices. Furthermore, the method could be used in the analysis of poorly understood molecules as well as in new hemodialysis target biomarkers.

Bone Density Changes during Adjuvant Aromatase Inhibitor Treatment in Breast Cancer Patients: Single Centre Experience

e11565 Background: Adjuvant aromatase inhibitors (AI) therapy in breast cancer accelerates bone loss resulting in increased incidence of fracture. We evaluated the effect of AI on bone density changes among breast cancer patients. METHOD A total of 85 patients who had adjuvant AI for more than 24 months and 3 Dual energy X-ray Absorptiometry (DXA) scans were included in this study. Annual bone density change was calculated and correlated with various risk and prognostic factors. RESULTS Adjuvant AI was given in up-front (50), sequential (15) and extended adjuvant (17) therapy settings, with a median duration of 31 months. Intervals between scans varied from 6 - 33 months (median = 13 months), 63 patients had baseline DXA scans undertaken within 6 months from start of AI. Bisphosphonate was prescribed to 35 patients during AI therapy according to local practice. Changes in bone density according to WHO classification is shown in the table. Annual bone density change in the spine at 2nd scan was (-9.2% to +11%, mean = -0.89±4.11), after 3rd scan, (-11.7% to +10.7%, mean = 0.021±3.84). In the group of patients who had adjuvant chemotherapy (59 patients), significant bone loss was seen in the patients who did not have bisphosphonate (n=39), (-0.011 in BMD, Mean=0.88±0.02) compared to those who had bisphosphonate (n=20), (0.036 in BMD, Mean=0.93±0.02). In the patients who did not have chemotherapy (n=26), no bone loss was seen whether bisphosphonate was used or not. No clinical bone fracture was reported among patients who had osteoporosis at baseline. CONCLUSIONS The patients who had chemotherapy are more likely to lose bone density and close monitoring is required for bisphosphonate therapy. Bone loss is more significant in the lumbar spine compared to the hip. Following intervention with bisphosphonate, the rate of bone loss was reduced, and as a result only 2.3% of patients developed osteoporosis during AI treatment. AI can be safely given from the bone health point of view with the appropriate use of bisphosphonate. [Table: see text] No significant financial relationships to disclose.