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

Definition of an index to forecast intradialytic hypotension by a multi-variate statistical analysis

Aim: IntraDialysis Hypotension (IDH) is still one of the main hemodialysis related complications. The patient’s peculiar reaction to the treatment implies difficulties in preventing IDH. This work is aimed at defining an index to quantify the risk of IDH at the beginning of each session through a multivariate analysis of clinical data. Methods: Data referring to 516 sessions performed on 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 and hematochemical data were recorded and stored in a unique flexible structured database. Patients suffering from IDH in 2 or more sessions were classified as Hypotension Prone (HP), the others as Hypotension Resistant (HR). Statistical analysis was performed to identify the potential risk factor related to IDH onset. A new index, J, was defined as a weighted patient-specific combination of the statistically relevant 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 the parameters. J>1 points out the risk of IDH. J prediction accuracy was quantified as the percentage number of predicted IDH events versus the total number of IDHs. Results: Initial values of potassium concentration, systolic and diastolic blood pressure, and weight gain from the end of the previous treatment result to be statistically different between HP and HR patients. J allows recognising the 96% of the IDH episodes. Conclusions: The evaluation of J at the beginning of the dialysis session can provide the clinician useful information about the risk to develop IDH during the treatment and can advise physicians about the need to modify the prescription

A predictive index of intra-dialysis IDH

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

Patient-specific modeling of multicompartmental fluid and mass exchange during dialysis

Dialysis is associated with a non-negligible rate of morbidity, requiring treatment customization. Many mathematical models have been developed describing solute kinetics during hemodialysis (HD) for an average uremic patient. The clinical need can be more adequately addressed by developing a patient-specific, multicompartmental model

Preliminary results of dialysis study: single pool variable-volume calcium kinetic model

Background: The primary aim of the international study DialysIS (Dialysis therapy between Italy and Switzerland) is the increased personalization of hemodialytic treatments through a modellistic approach. Within the DialysIS study, we investigated the use of a single-pool variable-volume Ca kinetic model to assess the intradialytic calcium mass balance (Ca2 + MB) in chronic and stable dialysis patients. Methods: 34 patients on thrice-weekly bicarbonate high-flux hemodialysis were studied during 240 dialysis sessions (mean 6.5 ± 1.9 for each patient; range 3–9). All patients were dialyzed with a nominal d[Ca] of 1.50 mmol/l. Ionized calcium concentrations of plasma water (Ca2 + pw) and dialysate (Ca2 + di) were determined at the beginning and end of each session; calcium dialysance (DCa) was estimated from conductivity dialysance. The most useful variable for validating this methodology was considered being the difference between end-dialysis ionized plasma water calcium concentration measured value, normalized to pH 7.40 (adjCa2 + pwtM), and predicted by the model (Ca2 + pwtP) applying: Ca2 + pwtP = 1/∙(Ca2 + di-(Ca2 + di–∙Ca2 + pw0)∙(VtCa/V0Ca) (DCa∙∙(1/Qfecv-1/Qpwi))) With  (Donnan’s factor) equal to 0.938. Results shown as mean ± standard deviation when normal, median (range) when non-normal. Results: A mean negative Ca2 + MB (–0.83 ± 1.33 mmol) and a statistically significant temporary parathyroid hormone (PTH) reduction was found (PTHt-PTH0: –128 (–488 ÷ 432) pg/ml p <0.01). Figure 1 shows the difference between the distribution of the predicted values, the adjusted values (Ca2+pwtP – adjCa2+pwtM: 0.016 (–0.08 ÷ 0.16) mmol/l)and the non-corrected values (Ca2+pwtP – Ca2+pwtM: 0.073 (–0.03 ÷ 0.20) mmol/l). Conclusions: The very low differences between predicted and adjusted Capwt suggest that it is possible to model and predict Ca2+MB during dialysis with a nominal dialysate calcium concentration of 1.5 mmol/l and a final calcium level in physiological range

Preliminary results of dialysis study: accuracy of a single pool variable-volume calcium kinetic model with different calcium dialysate concentrations

Background: The primary aim of the international study DialysIS (Dialysis therapy between Italy and Switzerland) is the increased personalization of hemodialytic treatments through a modellistic approach. Within the DialysIS study, we compare the accuracy of a single-pool variable volume calcium kinetic model (SPVV-CaKM) using two different dialysate calcium concentrations (CaD). Methods: Pre- and post-treatment relevant variables of 34 patients treated with nominal CaD of 1.5 mmol/l (Group 1) and 22 patients with nominal CaD of 1.75 mmol/l (Group 2) were analyzed. The accuracy was evaluated determining the difference between predicted (Ca2+pwtP) and measured (Ca2+pwt) plasma water ionized calcium concentrations at the end of the dialysis sessions. To account for the changes in blood pH during dialysis session, which is known to affect plasma water ionized calcium concentrations, Ca2+pwt values were normalized at pH of 7.40. Results: Fig. 1 indicate that the predicted values almost overlap t he normalized values for Group 1, while it’s significantly higher for Group 2. Conclusion: The SPVV-CaKM is accurate in Group 1 while it overestimates the Ca2+pwt Group 2. The Ca2+pwt of the two groups doesn’t seem to account for the increased CaD. This suggests the presence of an additional compartment. Our hypothesis is that the administered calcium, predicted by our model, that doesn’t appear plasma could be deposited in bones and/or soft tissues. It is then theoretically possible to estimate the total calcium deposition or accumulation from the difference between predicted and measured post-treatment values

PRELIMINARY RESULTS OF DIALYSIS STUDY: ACCURACY OF A SINGLE POOL VARIABLE-VOLUME CALCIUM KINETIC MODEL WITH DIFFERENT CALCIUM DIALYSATE CONCENTRATIONS

INTRODUCTION AND AIMS: The primary aim of the international DialysIS study (Dialysis therapy between Italy and Switzerland) is to imptove the personalization of hemodialysis treatments through a modeling approach. Within the DialysIS study, we compared the accuracy of a single-pool variable volume calcium kinetic model (SPVV-CaKM) using two different dialysate calcium concentrations (CaD). METHODS: Pre- and post-treatment relevant variables of 31 patients treated with nominal CaD of 1.5 mmol/l (Group 1) and 22 patients with nominal CaD of 1.75 mmol/l (Group 2) were analyzed. The accuracy of the model was evaluated by determining the difference between predicted (Ca2+pwtP) and measured (Ca2+pwt) plasma water ionized calcium concentrations at the end of the dialysis sessions. To account for the changes in blood pH during dialysis session, which is known to affect plasma water ionized calcium concentrations, Ca2+pwt values were normalized at pH of 7.40. RESULTS: In Group 1 we found: Ca2+D = 1.26 + 0.04 mmol/l; a rise in Ca2+pw from 1.18 + 0.07 at the start to 1.32 + 0.04 mmol/l at the end of the dialysis session with a mean difference between Ca2+pwtP (1.33 + 0.04 mmol/l) and Ca2+pwt of 0.01 + 0.04 mmol/l. In Group 2 we found : Ca2+D = 1.41 + 0.04 mmol/l; a similar rise in Ca2+pw (from 1.18 + 0.07 mmol/l to 1.36 + 0.06 mmol/l) but with a mean difference between Ca2+pwtP (1.48 + 0.04 mmol/l) and Ca2+pwt of 0.12 + 0.05 mmol/l.CONCLUSIONS: The SPVV-CaKM was highly accurate when using a dialysate calcium concentration of 1.5 mmol/l while it wildly overestimated the post-treatment plasma water concentration when using a higher dialysate calcium concentration; the measured post-treatment values in the two groups did not seem to account for the increased dialysate calcium concentration, suggesting the presence of an additional compartment. We hypothesize that the administered calcium that according to our model did not appear in the plasma could be deposited in bones and/or soft tissues. It is then theoretically possible to estimate the total calcium deposition or accumulation from the difference between predicted and measured post-treatment in Ca2+pw values