Clinical outcomes of hemodialysis patients in a public-private partnership care framework in Italy: a retrospective cohort study

Background

Innovative care models such as public-private partnerships (PPPs) may help meet the challenge of providing cost-effective high-quality care for the steadily growing and complex chronic kidney disease population since they combine the expertise and efficiency of a specialized dialysis provider with the population care approach of a public entity. We report the five-years main clinical outcomes of a population of patients treated on hemodialysis within a PPP-care model in Italy.

Methods

This descriptive retrospective cohort study consisted of all consecutive hemodialysis patients treated in the NephroCare-operated Nephrology and Dialysis unit of the Seriate Hospital in 2012–2016, which exercises a PPP-care model. Clinical and treatment information was obtained from the European Clinical Database. Hospitalization outcomes and cumulative all-cause mortality incidences that accounted for competing risks were calculated.

Results

We included 401 hemodialysis patients (197 prevalent and 204 incident patients) in our study. The mean cohort age and age-adjusted Charlson Comorbidity Index were 67.0 years and 6.7, respectively. Patients were treated with online high-volume hemodiafiltration or high-flux hemodialysis. Parameters of treatment efficiency were above the recommended targets throughout the study period. Patients in the PPP experienced benefits in terms of hospitalization (average number of hospital admissions/patient-year: 0.79 and 1.13 for prevalent and incident patients, respectively; average length of hospitalization: 8.9 days for both groups) and had low cumulative all-cause mortality rates (12 months: 10.6 and 7.8%, 5 years: 42.0 and 35.9%, for prevalent and incident patients, respectively).

Conclusions

Results of our descriptive study suggest that hemodialysis patients treated within a PPP-care model framework received care complying with recommended treatment targets and may benefit in terms of hospitalization and mortality outcomes.

Electronic supplementary material

The online version of this article (10.1186/s12882-019-1224-2) contains supplementary material, which is available to authorized users.

Effect of on-line conductivity plasma ultrafiltrate kinetic modeling on cardiovascular stability of hemodialysis patients

The aim of this multicenter, prospective, randomized cross-over study was to clarify whether on-line conductivity ultrafiltrate kinetic modeling (treatment B), as a substitute for sodium kinetic modeling, is capable of reducing intradialytic cardiovascular instability in comparison with standard treatment (treatment A), by reducing the sodium balance variability. Both treatments were performed by means of a modified hemodiafiltration technique. Treatment A was performed using fixed dialysate conductivity; treatment B made use of the dialysate conductivity derived from a conductivity kinetic model, in order to obtain an end-dialysis ultrafiltrate conductivity at each dialysis session that was equal to the mean value determined in the same patient during the four-week run-in period. Thus, during treatment B, the expected end-dialysis ultrafiltrate conductivity value of each patient should have been constant. The study was carried out according to a multicenter cross-over design of 16 weeks with two treatments (A or B), two sequences (1 = ABB and 2 = BAA), a run-in period of four weeks (period 1, treatment A), and three consecutive experimental periods of four weeks each. Analysis of variance for a cross-over design was used for the statistical analysis. Forty-nine hemodialysis patients prone to intradialytic hypotension (> 25% of sessions) were enrolled from 16 participating centers, and randomly assigned to either sequence 1 (26 patients) or sequence 2 (23 patients). Six patients dropped out and four were protocol violators, which left 39 patients selected for statistical analysis. There was no difference in the average dialysate conductivity, predialysis and end-dialysis plasma water ultrafiltrate conductivity or body weight between treatment A and treatment B. Thus, the observed mean sodium balance was not different and, as expected, only the intra-patient variability of end-dialysis ultrafiltrate conductivity (index of sodium balance variability) was reduced (21%). During treatment A, systolic blood pressure decreased by 23 mm Hg (95% confidence intervals 21 to 24 mm Hg) at the end of dialysis with respect to the pre-dialysis values. Treatment B reduced this intradialytic decrease (P = 0.001) with a maximum effect at the third hour of dialysis (4.4 mm Hg, 95% confidence intervals 1.9 to 6.9 mm Hg, 23% less than during treatment A, P 0.0005) without any period or carry-over effect (P = 0.53 and 0.08, respectively). There was no treatment effect on intradialytic diastolic blood pressure (P = 0.291). In conclusion, intradialytic cardiovascular stability was significantly improved by matching the interdialytic sodium load with intradialytic sodium removal using on-line conductivity ultrafiltrate kinetic modeling as an alternative to sodium kinetic modeling. Although highly significant, this effect was clinically not very large. By applying this conductivity kinetic model to patients with a more variable sodium intake from one session to another, a greater benefit can be expected.

Power spectrum analysis of the fetal heart rate during noradrenaline infusion and acute hypoxemia in the chronic fetal lamb preparation

In a chronically instrumented fetal lamb the effect on heart rate variability of noradrenaline as well as hypoxemia is studied by the use of power spectral analysis. Subsequent to both noradrenaline infusion and hypoxemia the very low frequency components of the variability are markedly decreased as compared with control conditions. After hypoxemia also a high frequency peak appears in the spectrum.

Power spectral analysis of the heart rate of the human fetus at 26 and 36 weeks of gestation

The fetal electrocardiogram (ECG) was recorded from the maternal abdomen together with sonographic detection of fetal states in four pregnant patients at 26 and 36 weeks of gestation. Computerised algorithms obtained a good recognition of maternal ECG and fetal ECG. The corresponding power spectral density (PSD) was estimated during epochs of quiet or breathing movements for 256 consecutive heart beats. At 36 weeks of gestation the PSD analysis showed heart rate variability components similar to an adult heart. A low frequency component was observed together with a high frequency component in the range of fetal breathing movements. This high frequency component (0.6 - 0.9 Hz) disappeared from the PSD analysis during epochs of apnea. To our knowledge this is the first demonstration of such a mechanism in the human fetus in utero. At 26 weeks no respiratory component was observed in the PSD analysis. The maturation of control mechanisms could be responsible for this difference.

Spectral analysis of antepartum heart rate variability

The present paper illustrates the basic methodological aspects of the power spectral analysis applied to fetal ECG starting from a gestational age around the 25th week of gestation until term. An abdominal ECG signal is recorded and fetal and maternal QRSs are recognised through selective digital filtering and averaging techniques. Power Spectral Density (PSD) of the discrete R-R series is then calculated through an autoregressive (AR) technique. In analogy with what happens in adult human subjects, fetuses manifest variability in correspondence to particular bands in the spectrum which are sensitive to the sympatho-vagal balance which controls many cardiovascular functions and respiration in different physiological conditions. Such a method is easily implemented on a personal computer and may be used to asses the development of the autonomic nervous system in fetuses as well as to show the physiopathological reasons which perturbate the relevant control mechanisms.

Compressed spectral arrays for the analysis of 24-hr heart rate variability signal: enhancement of parameters and data reduction

Heart rate variability signal in the form of an R-R interval tachogram is detected in Holter type 24-hr ECG recordings. Spectral analysis is carried out over consecutive nonoverlapping records, and the information is displayed in the form of a compressed spectral array through parametric techniques. The trends of spectral parameters such as low-frequency (LF) and high-frequency (HF) powers and central frequencies are also plotted, together with the classical mean R-R value and variance relative to each single spectrum. These parameters quantify the effect of sympatho-vagal balance on heart rate control during the 24-hr period and provide important elements for the diagnostic evaluation of various pathologies, like hypertension. A spectral compression algorithm which checks the position of the poles relative to LF and HF bands inside the unitary circle in the complex zeta-plane is also developed. Applications of this procedure are foreseen in the clinical evaluation of ambulant patients as well as in the study of physical and psychological stress.

Parameter extraction from heart rate and arterial blood pressure variability signals in dogs for the validation of a physiological model

The paper describes an automatic procedure for improving the extraction of parameters in heart rate (HR) and arterial blood pressure (ABP) beat-to-beat variability signals. Auto- and cross-spectral analysis of such signals is carried out through parametric models and the distribution of the power of the spectra and the phase relationships are compared in various physiological situations induced in the dogs via drug infusion or surgical interventions which do influence the control mechanisms of HR and ABP. This "black-box" approach allows the obtention, directly from the processing of the above-mentioned signals, of the estimation of parameters relative to cardiovascular models, as the ones described by simple equations (Windkessel and Starling laws are introduced as examples). These parameters seem to validate significantly the capability of such models to describe the physiological interactions existing between the two considered signals. Applications may be foreseen both for research and clinical purposes.

Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies

The heart rate variability (HRV) signal carries important information about the systems controlling heat rate and blood pressure, mainly elicited by autonomic nervous system (sympathetic and parasympathetic) controls. The present paper illustrates methods of HRV signal processing by using autoregressive (AR) modeling and power spectral density estimate. The information enhanced in this way seems to be particularly sensitive in discriminating various cardiovascular pathologies (hypertension, myocardial infarction, diabetic neuropathy, etc.). This method provides a simple non-invasive analysis, based on the processing of spontaneous oscillations in heart rate. Particular emphasis is directed to the algorithms used and to their direct application by using proper computerized techniques: only a few paradigmatical examples will be illustrated as preliminary results.

Spectral and cross-spectral analysis of heart rate and arterial blood pressure variability signals

A parametric method for autoregressive (AR) auto- and cross-spectral analysis is presented for the contemporaneous processing of heart rate and arterial blood pressure variability signals. In particular, the introduced bivariate spectral analysis (phase and coherence spectra) provides quantitative and objective means which are useful to measure the role played by the neural controlling systems (sympathetic and parasympathetic systems) on the cardiovascular signals under different pathophysiological conditions. Algorithmic aspects, connected to the way of processing discrete numerical series synchronized to single cardiac beats, are particularly stressed. Important applications are foreseen both in physiological studies and in clinical practice as an aid to the detection of various relevant cardiovascular pathologies such as hypertension and diabetes.

The clinical relevance of the abdominal fetal electrocardiogram

We investigated the duration of fetal electrocardiographic events during normal pregnancies and during pregnancies with fetal abnormalities. The fetal abdominal signal was processed and enhanced by means of the averaging technique after removing the maternal complex. In normal pregnancies P wave and QRS complex duration increases progressively from the 17th week up to the term: this increase parallels the gain in weight of the fetal heart and particularly of the ventricular mass. These results indicated that the duration of fetal complexes could be used as an index of the size, development and maturity of the fetal heart. When fetal growth retardation (FGR) is present, the weight of the fetal heart is significantly reduced, and is reflected in a decrease in QRS duration. In a series of 107 cases the sensitivity of this parameter in detecting FGR was 81% and the specificity 93%. Moreover no perinatal death nor Apgar values below 7 occurred in growth retarded fetuses with normal QRS duration, while in the group with shortened QRS neonatal deaths were 11% and Apgar scores below 7 26%. Abdominal FECG do provide important auxiliary information for prenatal diagnosis of congenital heart defects (CHD). Anomalies with abnormal atrioventricular connection were reflected in longer PR interval. Ventricular hypertrophia and hypoplasia were associated with increased or decreased QRS duration, respectively. Furthermore, the three fetuses which developed congestive heart failure showed prolonged QRS duration. In severe RH disease, chronic fetal anemia can lead to myocardial hypertrophy and cardiac enlargement.(ABSTRACT TRUNCATED AT 250 WORDS)

Variability analysis of fetal heart rate signals as obtained from abdominal electrocardiographic recordings

The present paper introduces an original method of digital signal processing for an automatic analysis of non-invasive abdominal ECG recordings on pregnant women starting from the 25th week of gestation. The procedure has been implemented on a DEC-VAX 750 digital computer at the Department of Electrical Engineering, Polytechnic of Milano and the signals are recorded at the Department of Obstetrics and Gynecology "L. Mangiagalli", University of Milano, Italy. The experimental results presented in here are still preliminary as only few cases have been considered up to now (about 20) and the goal of the paper is mainly focused on the algorithmic aspects of the whole procedure implemented in the computer and on the approach of heart rate variability (HRV) signal analysis both in the mother and in the fetus. Abdominal ECG lead processing is illustrated starting from the step of maternal (M) and fetal (F) QRS recognitions through linear digital filtering (derivative and low-pass FIR filter, Weber-Cappellini window) and weighted averaging techniques synchronized with maternal QRS's. Figure 1 a shows the original abdominal lead; figure 1 b the filtered signal for MQRS recognitions; figure 2 a the template of maternal cardiac cycle as obtained after the averaging operation synchronized with the instants of MQRS occurrence. The subtraction of the template results in the abdominal lead shown in figure 1 c in which the contribution of MECG is practically entirely reduced even in the case of MQRS and FQRS overlapping.(ABSTRACT TRUNCATED AT 250 WORDS)