Hydrodynamic focusing of an elastic capsule in stokes flow: An exploratory numerical study

We study numerically the motion of an elastic capsule in a simple microfluidic device, a sheath flow focuser, designed to align (or focus) particles at the center of a microchannel. The geometry of the device is given, while the flow conditions are varied, and the outflux is constant. The efficiency is expressed in terms of the distance required to achieve focusing and the functioning is characterized by the deformations and stresses undergone by the capsule membrane. Calculations are performed for a ‘stiff’ and ‘soft’ capsule, corresponding to a capillary number equal to Ca = 0.05 and Ca = 0.3 based on the outflow rate. We report that as the sheath flow is increased the focusing efficiency grows and plateaus beyond a certain threshold, displaying a drop in the efficiency gain. Differently, the deformations and stresses undergone by the capsule membrane grow for the entire range of the flow parameters considered. This should be kept in mind if the stresses exerted on the membrane have to be minimized or need to be below certain characteristic thresholds typical of the specific application considered. Especially large initial offsets and stiff capsules benefit from focusing

A Bayesian approach for the identification of patient-specific parameters in a dialysis kinetic model

Hemodialysis is the most common therapy to treat renal insufficiency. However, notwithstanding the recent improvements, hemodialysis is still associated with a non-negligible rate of comorbidities, which could be reduced by customizing the treatment. Many differential compartment models have been developed to describe the mass balance of blood electrolytes and catabolites during hemodialysis, with the goal of improving and controlling hemodialysis sessions. However, these models often refer to an average uremic patient, while on the contrary the clinical need for customization requires patient-specific models. In this work, we assume that the customization can be obtained by means of patient-specific model parameters. We propose and validate a Bayesian approach to estimate the patient-specific parameters of a multi-compartment model, and to predict the single patient’s response to the treatment, in order to prevent intra-dialysis complications. The likelihood function is obtained by means of a discretized version of the multi-compartment model, where the discretization is in terms of a Runge–Kutta method to guarantee convergence, and the posterior densities of model parameters are obtained through Markov Chain Monte Carlo simulation. Results show fair estimations and the applicability in the clinical practice

A predictive index of intra-dialysis IDH. A statistical clinical data mining approach.

Intra-Dialysis Hypotension (IDH) is one of the main hemodialysis related complications, occurring in 25-30% of the sessions. The factors involved in the onset of hypotension in patients undergoing dialysis are due both to clinical conditions (e.g. presence of vascular or cardiac diseases, neuropathology, anemia) and treatment settings such as temperature of the dialysate, sodium concentration, buffer composition, ultrafiltration rate, etc. The patient’s peculiar reaction to the treatment implies difficulties in preventing IDH episodes. This work explores the possibility to use a multivariate analysis of clinical data to quantify the risk to develop IDH at the beginning of each session. The study is framed in the DialysIS project (Dialysis therapy between Italy and Switzerland) funded by INTERREG – Italy – Switzerland and Co-funded by European Union. Data referring to a total of 516 sessions performed on 70 adult patients undergoing dialysis treatment (50 patients enrolled at A. Manzoni Hospital Lecco, Italy and 20 patients at Regional Hospital of Lugano, Switzerland) were collected. Clinical prescriptions, hydration status, dialysis machine data and hematochemical data were recorded and stored in a unique flexible structured MySQL® database. A statistical analysis was performed to find the potential risk factor related to IDH onset. IDH episodes were automatically detected during the monitored sessions, according to the literature criteria. Patients suffering from IDH in 2 or more sessions were classified as Hypotension Prone (HP), the others as Hypotension Resistant (HR). Initial values of potassium concentration [K+], systolic (SBP) and diastolic (DBP) blood pressure, and weight gain (ΔW) from the end of the previous treatment result to be statistically different between the HP and HR groups. A new index, J, was defined as a weighted patient-specific combination of these parameters and calculated for each session of each patient. The weight of the index coefficients can be dynamically adjourned based on the longitudinal analysis of [K+], SBP, DBP, and ΔW. The results reported in this paper were calculated based on a longitudinal analysis of a minimum of three sessions for each patient. The accuracy of the J index in predicting IDH events has been evaluated and quantified in terms of percentage number of predicted IDH events, with respect to the total number of IDHs. Values of J index higher than 1 point out the risk of IDH onset. J allows the prediction of 100% of IDH episodes using 5 sessions, the 90% using 3 sessions. More specifically, at Lecco Hospital 43 IDH events were detected by the automatic system of which 100% and 95% were respectively predicted by the new index calculated using 5 or 3 sessions. Similarly, at Lugano Hospital 58 IDH were detected by the automatic system of which 100% and 87,5% were predicted using 5 or 3 sessions respectively. A longer longitudinal dataset will allow a higher matching of J to actual IDH episodes. In conclusion, the evaluation of this new index at the beginning of the dialysis session prior to connecting the patient to the machine can provide the clinician with useful information about the risk for the patient to develop cardiovascular instabilities (IDH) during the treatment and can advise the physician about the need to modify the prescription

An interoperable common storage system for shared dialysis clinical data

Interoperability stands in the ability of different information systems to exchange data. Today interoperability is highly requested in several departments of healthcare organizations. Dialysis facilities register a high delay in the possibility of data exchange, because the strong lack of standards results in a huge gap in having interoperable systems. This work represents a first effort to overcome the problems of interoperability of data related to dialysis units. A Federated DataBase System (FDBS) approach has been adopted to construct a common data repository. The storage system has been built by the Dialysis Data Infrastructure (DDI), a unique multilevel standardized data structure supported by the Dialysis MATlib (DM), an embedded Matlab® library, that’s able to convert, harmonize and query the raw data coming from the dialysis treatment units into a common interoperable format. The DDI and DM have been developed in the context of the Italy- Switzerland cooperation project INTERREG DialysIS, and currently contains 1018 dialysis sessions recorded referred to 145 patients