Innovations in randomized clinical trials in Nephrology: from study design to patient’s activism

Abstract

Current clinical practice is guided by novel robust evidence from randomized clinical trials (RCT). These studies usually follow a rigorous prospective design with pre-defined visits and planned exams during follow-up. In the context of Nephrology, RCTs have been mainly designed to test the efficacy of new therapies in reducing the risk for kidney failure, estimated Glomerular Filtration Rate decline and mortality or cardiovascular events. However, the progress of informatics is reflected in an improvement and expansion of technologies and innovations in present and future RCTs. We here present some innovations and future directions of RCTs in Chronic Kidney Disease patients.

Keywords: Randomized Clinical Trials, Chronic Kidney Disease, Informatics Innovation, Medical Devices, Albuminuria

Introduction

Randomized clinical trials (RCTs) represent, to date, the most robust research tools for generating evidence on the efficacy and safety of new therapies for a given clinical condition [1]. The reliability of randomized studies lies in their prospective, ad hoc design, in which one group of subjects (controls) is assigned to the standard of care for the condition under investigation, while another group (active treatment arm) receives the standard of care plus the experimental treatment. The assignment to the control or active arm is random and occurs through randomization, which aims to create two groups of patients with similar characteristics (e.g. age, sex, prevalence of patients with type 2 diabetes, or those treated with diuretics), with the only difference being their assignment to either the active treatment or the control group. This randomization tool therefore allows for the optimal isolation and estimation of the effect of the “new” treatment compared to the standard of care in reducing the incidence of a specific clinical outcome (e.g., reduction in proteinuria or admission to dialysis) or in improving the symptoms of a disease (e.g., improving blood pressure or reducing fatigue).

In the field of kidney diseases, numerous randomized studies have been conducted over the past two decades. They have proven the effectiveness of drugs targeting different mechanisms, such as antihypertensives, erythropoiesis-stimulating drugs, and mineralocorticoid receptor antagonists, in terms of nephroprotection, as well as in reducing mortality and cardiovascular (CV) events, across different patient groups (e.g., diabetic, non-diabetic chronic kidney disease, glomerulonephritis) [2].

Recently, there has been an explosive and rapid increase in drugs available to treat kidney diseases, which has simultaneously led to a rise in the number of randomized studies in Nephrology [3]. In this scenario, with the progress and refinement of research tools in association with digital technologies, the design of randomized studies is undergoing innovations that involve the type of patients included, the methods of their inclusion, the monitoring or choice of treatment, the randomization sequence, and the study outcomes (endpoints). In this article, we describe the essential points of randomized studies in Nephrology and the innovations to consider for their accurate and timely interpretation.

 

Principal aspects of a randomized trial in Nephrology

Inclusion criteria

One starting point when designing a randomized study is to establish which type of patients should be included. This leads to the definition of inclusion criteria. The first inclusion criterion should define the disease setting, for example patients with Chronic Kidney Disease (CKD) with or without diabetes, or both. Such a decision may lead to the estimation of a treatment effect in both types of CKD conditions rather than restrict to one of them. For example, canagliflozin, a SGLT2 inhibitor that protects the kidneys from chronic failure, was tested in patients with CKD and diabetes in the CREDENCE trial, whereas similar agents, empagliflozin and dapagliflozin, were tested in both diabetic and non-diabetic CKD in the EMPA-KIDNEY and DAPA-CKD trials [47]. The extremely positive results from these studies influenced the guidelines and clinical practice, leading to changes in drug reimbursement [8].

The other major inclusion criteria in CKD trials are the so called “kidney measures” namely estimated Glomerular Filtration Rate (eGFR) and albuminuria levels. Such a biomarkers-based enrichment in clinical trials is due to the fact that eGFR and albuminuria represent two main predictors of clinical outcomes in patients suffering from CKD [9, 10].  Moreover, the presence of abnormal levels of eGFR and albuminuria allows to reach a significant number of clinical endpoints throughout the study. In previous trials, such as the first studies testing the efficacy of angiotensin converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARBs), including the RENAAL and IDNT studies, serum creatinine was used instead of eGFR [11, 12]. For instance, in the RENAAL study, patients with a serum creatinine level of 1.3 to 3.0 mg/dL were included. Later on, serum creatinine was replaced by eGFR, that is considered a more accurate prognostic biomarker [13]. Another lesson from previous trials was the “flexibility” of inclusion criteria. In the DERIVE study, a phase 3 trial designed to test the efficacy and safety of dapagliflozin in early CKD, only patients with CKD stage 3A (eGFR between 45 and 60 mL/min/1.73 m2) on at least two visits were included. Such restriction, albeit of clinical relevance, was associated with a high percentage of screening failure and slowed the study completion. Similarly, the ALTITUDE study (Aliskiren in patients with CKD and diabetes) included patients with albuminuria > 200 mg/g in at least two out three first morning voids or eGFR between 30 and 60 mL/min [14].  A recent post-hoc analysis of the ALTITUDE database highlighted how adopting more flexible criteria, such as lowering thresholds for albuminuria (e.g. >160 mg/g at the second collection) and relaxing thresholds for eGFR (e.g. 25-75 mL/min), can significantly reduce screening failure rates. This approach not only increases the number of eligible patients participating in the study, but also maintains the integrity of the clinical endpoints, without compromising the statistical power of the study. In particular, the intrapatient variability of albuminuria and eGFR values, which can be influenced by factors such as diet, hydration and therapy adherence, suggests that more stringent screening criteria may not be necessary to predict clinical outcomes [15].

Owing to this evidence, recent RCTs have been designed by including CKD patients with lower levels of albuminuria and larger range of eGFR. For example, in the ROTATE-3 study, a 100 mg of 24h-albuminuria, stable on two visits, was considered sufficient to screen and randomize patients. The stability in this case was not interpreted as a value of at least 100 mg/day also on the second measurement, but rather as a difference in albuminuria from the first visit: less than 40 mg/24hr for subjects with albuminuria between 100 and 300 mg/24hr, and less than 20% difference for subjects with albuminuria between 300 and 3500 mg/24hr [16]. Such an implementation was used to warrant a reliable and appreciable evaluation of the treatment effect (eplerenone or dapagliflozin or the combination of both them) in reducing albuminuria after 4 weeks. A further element to take into consideration to enroll patients is the trajectory of the eGFR decline over time. A steeper decline means that patients are at increased risk for future kidney failure and thus are exactly those patients who need a more urgent treatment. A post-hoc analysis of the SONAR trial, a phase 3 trial showing the nephroprotection of atrasentan in CKD and diabetes, demonstrated that treatment effect of atrasentan in terms of kidney failure risk reduction was greater in patients with higher eGFR decline before the trial initiation [17]. Recent trials used the eGFR decline criterion (i.e. eGFR decline > 1 mL/min/year during the 2 years prior to entry in the study based on at least 3 eGFR measurements) to select patients [18].

Outcomes of clinical trials

The outcome or endpoint is defined as the event of interest on which a specific therapeutic intervention should act. In reference to the aforementioned RCTs, the randomization process creates two patient groups that are homogeneous with respect to factors such as gender, age, demographics and blood chemistry characteristics. Each group is assigned a therapeutic protocol to compare the effects of the experimental treatment with those of the standard of care or placebo. In some cases, depending on the study design, the same patient may receive different treatments over time, generally separated by washout periods (the so-called crossover design). What is observed at the end of the study period represents the primary outcome. The primary outcome is crucial in clinical trials because the entire study is designed around its incidence rate (in terms of sample size, duration). Other outcomes that can however be tested as pre-specified or post hoc analyses are the secondary outcomes. The difference in the incidence of the outcome between the treated group and the control group is generally expressed in the form of Relative Risk (RR), defined as the probability that a subject belonging to the group exposed to certain factors (i.e. experimental intervention) develops the outcome compared to the probability that a subject in the unexposed group (i.e. standard of care/placebo) develops the same outcome.

For years, the main hard endpoint evaluated in Nephrology trials was End Stage Kidney Disease (ESKD, that nowadays is also called Kidney Failure or KF), then differentiated into subgroups of persistence of CKD stage 5 (defined by the finding of eGFR < 15 ml/min confirmed on at least two occasions 15-30 days apart), entry into dialysis or arrival at kidney transplantation. The main trials in Nephrology, including the studies that demonstrated the effectiveness of ACEi and ARBs on slowing the progression of kidney damage, have adopted this primary endpoint.

However, it is known that the onset of ESKD may require a long observation period (even over 10 years), generally longer than the duration proposed in the study design [19]. This limit is added to the high rate of failures in the patient screening process, which is far higher than that of trials conducted in other areas of medicine and which, together, account for the important delay recorded in the panorama of scientific research in Nephrology.

To counter this trend, the identification of secondary endpoints or surrogate endpoints that could faithfully anticipate entry into dialysis was proposed as a workaround. Among these, we remember the main ones of reduction of eGFR and albuminuria. While on the first endpoint an eGFR decline of 57-40-30-20% within 3 years was tested, on proteinuria there is still no certain data on the expected reduction necessary to be able to speak of a good response to the treatment.

Despite the attempt by some authors to reduce the threshold of albuminuria among the inclusion criteria of a trial with the aim of increasing the sample of patients enrolled and reducing the study time, there was only a modest reduction in renal and cardiovascular events, contrary to what was expected.

This data was interpreted as a consequence of the variability in the measurement of albuminuria, confirming the importance of extending the inclusion criteria during the screening process to improve feasibility and efficiency of the study [20].

The use of eGFR and albuminuria as secondary outcomes could, on the other hand, reduce the generalizability of the trial results. One could, for example, incur the bias of excluding a given nosological entity such as non-albuminuric CKD, which is known to have an increased prevalence and a high risk of progression towards ESKD [21].

Regarding the reduction in eGFR, historically the main secondary endpoint identified has been an eGFR slope of -57%, generally represented in the form of a doubling of creatininemia. Also in this case, some authors have attempted to identify alternative endpoints with the aim of obtaining greater precision in estimating therapeutic efficacy at the cost, however, of recording a reduction thereof in the absence of a substantial increase in the statistical significance of the study [22]. In any case, the percentage reduction in filtration appears to be the best unit of measurement to estimate the temporal trend of renal function compared to the changes in eGFR in absolute value [19].

Furthermore, it was observed that reducing the percentage slope threshold of the eGFR from 57% to 40% increases the number of patients who develop the outcome, and this data also correlates with a high risk of evolution towards ESKD and mortality [23].

In light of what has been observed, the use of new potential surrogate outcomes such as an eGFR slope equal to – 30/40% on a sample of patients with baseline eGFR < 30 ml/min and a linear filtrate loss recorded in the run period equal to – 5 ml/min/year, could allow the achievement of the outcome and at the same time a reduction in study times.

Among the limitations identified, however, it should not be forgotten that some categories of patients, mainly elderly, non-proteinuric patients, with higher baseline eGFR, observe a non-linear trajectory of renal function which may reduce the predictability of the surrogate endpoints analyzed on the hard endpoints. Furthermore, the various formulas for estimating glomerular filtration take into account factors that can be modified over time, requiring optimization of the precision of the study with the integration of directly measured glomerular filtration values [19].

 

Innovations in the nephrology field of clinical trials

Role of the patient in treatment decisions

In recent decades, the role of the patient has undergone a significant transformation in decision-making processes within randomized clinical trials. In this context, a new doctor-patient relational model has gradually emerged, known as “Shared-Decision Making” (SDM), which involves the sharing of information and decisions between healthcare professionals and enrolled patients, empowering the latter with an increasingly active, informed, and autonomous role [24].

This model has found space since the end of the 1990s in the field of oncology, when various therapeutic options with uncertain outcomes emerged for terminally ill patients.

Thus, an increasingly broad exchange of communication began between the doctor and the patient, in order to involve them in decision-making and increase their awareness in the clinical field (Figure 1).

Figure 1. Innovations in the nephrology field of clinical trials and patients’ role.
Figure 1. Innovations in the nephrology field of clinical trials and patients’ role.

The SDM model is based on four fundamental pillars, as illustrated by some of its pioneers such as Charles and colleagues: 1) equal involvement of medical staff and patients; 2) sharing of information by both; 3) shared participation in drawing up consent to treatment; 4) reaching an agreement on the therapeutic path to follow [25].

The adoption of this model has allowed an ever-increasing participation of patients in trials, with better quality of the trials and construction of more solid evidence.

Greater therapeutic adherence and a reduction in losses during follow-up were also progressively documented, probably as a result of a greater degree of satistaction and psycho-physical well-being on the part of the patients enrolled in these types of trials.

The evidence shows that the use of SDM strategies also allows us to achieve better clinical, behavioral and psychological outcomes compared to those observed in trials with different strategies [26].

If the adoption of this model presents numerous positive implications on a scientific level, those on an ethical level are intuitive, as the priority of these trials is to place the will and needs of the patient at the centre.

However, guaranteeing greater information, awareness and decision-making autonomy implies a greater expenditure of energy and resources such as time and workload, giving more complexity to the decision-making process.

Conflicting circumstances can sometimes arise when the possibility of randomization in the control group or the blinding procedures do not reflect the patient’s wishes [27].

Patients with dementia, or any other type of cognitive dysfunction, also represent a real barrier [28].

Instead, in various circumstances, informing the patient about possible side effects and risks can lead to agitation and confusion, and it is easy to run into misunderstandings [29].

For these reasons, healthcare personnel have made use of Patients Decision Aids (PtDA), i.e. decision-making aids to support patients, consisting of educational materials or easy-to-consult media assistance. These come in the form of smartphone apps, web sources, videos or written documentation such as brochures. In some circumstances, healthcare personnel undergo professional training courses with the aim of acquiring greater communication skills [24].

An international cooperation, the International Patient Decision Aid Standards (IPDAS) Collaboration, was launched specifically to establish the criteria, on the basis of scientific evidence, according to which PtDAs should be drawn up and evaluated, guaranteeing the best possible information and supporting the sharing of decision-making processes [30].

Additionally, the use of questionnaires to assess patients’ preferences for different treatments in a crossover clinical trial is becoming more widespread, and collecting this information will assist clinicians in making personalized treatment decisions. An example is the upcoming FINESSE trial (EU trial number 2023-506434-69-00), in which digital questionnaires will be used to assess patients’ preferences of finerenone or semaglutide, as well as their perspectives on the feasibility of participation in a home-based trial.

New outcomes in current and future RCTs

In addition to monitoring treatment effect with eGFR and albuminuria changes over time (after treatment initiation), novel outcomes are gaining momentum in Nephrology nowadays. To this aim, the International Society of Nephrology is publishing a bi-monthly volume called Global Trial Focus (GTF) reporting the new RCTs related to kidney diseases (herein the link: https://www.theisn.org/in-action/research/clinical-trials-isn-act/). In the GTF, a number of studies tested the effect of treatment on new endpoints, such as quality of life, chronic pain, glycemic control, change in pruritus in different stages of CKD, and others.  The combination of an increasing number of RCTs (mirroring a more feasibility of these studies compared to the past) and more comprehensive endpoints may allow for deeper insights and improve the care of kidney diseases in the future.

Apps that remotely monitor blood pressure

Hypertension affects over a billion people in the world and constitutes a preponderant risk factor for the onset of various diseases, including cardiovascular and renal diseases [31].

It is estimated that approximately 12.8% of global deaths are attributable to this pathology, the treatment of which is based on lifestyle changes and medication.

However, despite the initiation of therapeutic and behavioral measures, hypertension control rates remain suboptimal, with only about half of patients achieving adequate blood pressure control [3133].

Self-monitoring has allowed us to obtain some clinical benefits over time, favoring greater therapeutic adherence and the reduction of some problems such as masked hypertension or “white coat” hypertension [33].

However, further measures are necessary. The demand for health tools to support the problem has grown considerably, due to the significant impact it has on public health and economic resources. In recent years, there has been an ever-increasing interest in digital innovations, as an inexpensive method within everyone’s reach [31, 34].

The Health App market is constantly growing, and thousands of new ones are produced every year. The main fields of interest are, in fact, represented by chronic diseases such as arterial hypertension and diabetes [33].  The invention of sensors and wearable devices, such as Bluetooth bracelets or smartwatches, has made it possible to detect vital parameters such as heart rate, blood pressure, sleep quality and physical activity [35].

Apps that detect blood pressure also offer various additional functions, such as maintaining a daily blood pressure log, organizing records, and providing reminders for measurements and medication. They also provide the patient with basic information on disease, treatment, lifestyle management and how to self-monitor.

There are available also analysis tools that offer an overview of the trend in blood pressure over time, using graphs or tables. Readings and other recorded data can be exported, allowing healthcare professionals to view and analyze them remotely. Most of these apps are available for free [33, 36].

However, sufficient data is not yet available to evaluate the quality and accuracy of these applications. The results obtained so far must be interpreted with caution due to the variance between the sizes of the samples on which the studies were conducted (very often too small and with subjects coming from different populations), due to the heterogeneity of the studies themselves and the use of self-reported scales to measure adherence to treatments. Therefore, research should establish standardized protocols for measuring parameters and reporting adherence. But it is important not to overlook the potential of this type of intervention to implement knowledge of patients’ health status and support self-management towards a healthy lifestyle and adherence to therapy, especially given the ever-increasing role that artificial intelligence is acquiring, which could guarantee significant support [37].

Remote collection of blood and urine samples

One of the challenges in patient participation in clinical trials is the frequency of visits required for monitoring the progress of the study and for collecting biological samples (blood and urine) for analysis. The introduction of new technologies that allow patients to collect blood and urine samples without needing to visit the study center opens up new possibilities for remote protocol monitoring. Examples of validated devices for at-home blood and urine sampling are Hem-Col® and PeeSpot®, respectively. The Hem-Col® tool is an innovative blood collection microtube designed to facilitate capillary blood sampling through a finger prick; the presence of an anticoagulant and a preservation buffer enhances analyte stability in whole blood, allowing the device to be sent to the laboratory for analysis via regular post.  The PeeSpot® is a validated tool for collecting small amounts of urine (about 1.2 mL) through absorption onto a pad, which is held inside a tube. By adding various preservatives, the urine in the PeeSpot® can be preserved for up to 4 days in the refrigerator before being sent to the laboratory for the measurement of urinary albumin and creatinine. These devices represent a practical and efficient solution for both patients and healthcare professionals involved in clinical trials, lowering costs associated with frequent in-patient visits and optimizing the workflow. The FINESSE study (EU trial number 2023-506434-69-00), which will soon be launched, will use both devices for the remote monitoring of enrolled patients.

 

Conclusions

Many randomized studies have been published around kidney disease in the past few years. Taken together, results of randomized studies have allowed to improve guidelines and, at the end, to improve patients’ survival, time free from dialysis and to reduce the number of CV events. Significant progresses have also been made over the past decades in terms of inclusion criteria, outcomes, and patients’role, which will aid the future conduction of randomized studies worldwide.

 

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Review and Practical Excursus of the Propensity Score: Low Protein Diet Compared to Mediterranean Diet in Patients With Chronic Kidney Disease

Abstract

Although Randomized clinical trials (RCT) represent the gold standard to compare two or more treatments, the impact of observational studies cannot be ignored. Obviously, these latter are performed on unbalanced sample, and differences among the compared groups could be detected. These differences could have an impact on the estimated association between our allocation and our outcome. To avoid it, some methods should be applied in the analysis of observational cohort.
Propensity score (PS) can be considered as a value which sums up and balances the known variables. It aims to adjust or balance the probability of receiving a specific allocation group, and could be used to match, stratify, weight, and perform a covariate adjustment. PS is calculated with a logistic regression, using allocation groups as the outcome. Thanks to PS, we compute the probability of being allocated to one group and we can match patients obtaining two balanced groups. It avoids computing analysis in unbalanced groups.
We compared low protein diet (LPD) and the Mediterranean diet in CKD patients and analysed them using the PS methods. Nutritional therapy is fundamental for the prevention, progression and treatment of Chronic Kidney Disease (CKD) and its complications. An individualized, stepwise approach is essential to guarantee high adherence to nutritional patterns and to reach therapeutic goals. The best dietary regimen is still a matter of discussion.  In our example, unbalanced analysis showed a significant renal function preservation in LPD, but this correlation was denied after the PS analysis.
In conclusion, although unmatched analysis showed differences between the two diets, after propensity analysis no differences were detected. If RCT cannot be performed, balancing the PS score allows to balance the sample and avoids biased results.

Keywords: Chronic Kidney Disease, Low Protein Diet, Matching, Mediterranean Diet, Nutritional Therapy, Propensity Score, Randomized Clinical Trials

Introduction

Clinical investigations are mainly categorized in observational and interventional studies, the latter including randomized controlled trials (RCT) [1]. Comparative effectiveness studies belong to the family of observational studies and aim to compare two active treatments to identify which one is more efficient in improving the time course of a disease or reducing the risk of a given condition in real life (i.e., in a context different from an RCT) [2]. From this perspective, this type of study design differs from RCTs because the latter specifically contemplate ‘no intervention’ (i.e., the placebo arm).

Treatments are candidates to be investigated by a study of comparative effectiveness only when the same treatment was proved to be effective versus a control in an RCT. The main reason why these studies are considered with caution by the scientific community is the lack of randomization, which implies that the results of these studies are prone to a peculiar type of bias called ‘confounding by indication’ [3]. In a given treatment-outcome pathway, a confounder is a variable that is associated with the treatment (i.e., it differs between the study arms). It is not an effect of the treatment, does not lie in the causal pathway between the treatment and the outcome, and represents a risk factor for the outcome. In real life, a confounder can increase, reduce, or definitely obscure the true effect of treatment on an outcome. Despite these challenges, observational studies of effectiveness offer opportunities to examine questions impossible to be investigated by RCTs [4]. First, they can be used to examine the effectiveness of medication that has already obtained marketing authorization and for which funding for further trials may be limited. Second, they can allow the examination of effectiveness for rare treatment indications. Third, a large observational study can be more representative of a clinical population and less prone to selection bias than a trial.

In observational studies of effectiveness, common methods used to adjust to confounding are multiple regression models [5], the use of instrumental variables [6], and the propensity score (PS) [7]. Briefly, multiple regression analyses are performed by including in the model all variables that meet the criteria to be considered as confounders. An instrument is a variable that predicts exposure, but conditional on exposure shows no independent association with the study outcome. As an example, we can consider an observational multicenter study that evaluates how different treatments can affect a clinical outcome. The facility allocation can be considered as the result of a ‘natural experiment’ by simulating a randomization. In this manuscript, we describe an efficient statistical technique used by researchers to mitigate the problem of confounding in observational studies of effectiveness.

 

Propensity score

The propensity score (PS) was described in 1983. This method allows adjusting or balancing for the probability to receive a specific allocation group, an estimation of the likelihood of being in one or in another group in relation to a set of covariates [8]. PS could be used to match, stratify, weight, and perform a covariate adjustment. If the outcome is a binary variable, matching has less bias than stratification or covariate adjustment, as in a time-dependent outcome both matching and Inverse Probability of Treatment Weighting (IPTW) are less biased than stratification or covariate adjustment. PS is calculated with a logistic regression, using allocation groups as outcome. Thanks to this method, we can compute the probability of confounder variables to be allocated in one group. Since PS has no limits of variables, it can be used in small samples and for rare diseases [9], unlike multivariate regression.

Matching

Matching with PS methods allows us to compare one or more patients with the same allocation probability, so it follows that matched patients have similar features, decreasing bias. This method consists of matching cases of two or more groups on the basis of similar predicted PS, thus allowing the comparison of groups with an equal distribution of confounders (covariate balance) [10, 11]. Imaging having two groups of patients, at first, we need to compute PS, corresponding to the probability of receiving allocation in group A, for each one of them [12]. By doing this, a binomial logistic regression is performed to select, among the study variables, those associated with the allocation variable. Patients with the same PS value are thus compared. Minimizing the differences between patients, and comparing homogeneous groups, confounding is reduced.

Stratification

The stratification by PS follows the matching methods. Strata will be created between subjects with similar PS of treatments. The Stratification method removes about 90% of bias due to covariate imbalance [13].

Formally, stratification by PS can be resumed as follows:

  • choosing variables included in the PS model among personal data, comorbidities, laboratory data, and variables clinically related to outcome
  • estimating PS value for each subject, with logistic regression using allocation as the dependent variable
  • calculating the Cumulative Distribution Function for each subject, able to define the distribution also in a discrete and binomial variable
  • ranking population based on PS value, dividing the whole sample into quartiles, tertiles, deciles, etc., based on PS values
  • assessing balance for each of the K (K is the indicator of the treatment group), analyzing the baseline features
  • retaining the PS value ordering that creates strata with the best covariate balance and conducting a stratified outcome analysis to estimate ATE or ATT [14].

The number of strata can be evaluated based on the number of covariates (2×covariates – 1) with groups of more than ten subjects [15]. In a large observational study, Cernaro V. et al. [16], on behalf of the Workgroup of the Sicilian Registry of Nephrology, analyzed the impact of convective dialysis on mortality and cardiovascular mortality. They performed Cox Regression analysis with incremental multivariate models but, although the independent impact of convective dialysis on mortality, many other variables were related to the outcome.

Thus, as highlighted in their methods section, PS stratification was computed to perform a sensitivity analysis [17]. PS was computed through a multivariate logistic regression model including age, gender, ethnicity, arterial hypertension, diabetes mellitus, and cardiac diseases. Then, the whole sample was divided into quartiles (based on PS value) and survival analyses computed in the whole sample were repeated. These latter results confirmed the independent impact of the treatment, but in subsamples that are theoretically more homogenous because PS value was computed on the bases of the possible confounding.

Inverse Probability of Treatment Weighting (IPTW) Estimation

IPTW analyses aimed to create a weighted sample in which the distribution of each confounding variable was the same between the compared groups [18]. Patients will be allocated the reciprocal of the PS value: each patient of the treated group receives the weight of 1/PS and each patient of the untreated group receives the weight of 1/(1-PS). A treated patient with a low PS value enters in the analysis with a high weighting because he is considered likely an untreated patient in terms of comorbidities, so a valid comparison can be made between the two [19]. Practically, in the analysis, each patient is evaluated as many times as their IPTW is.  A treated patient with a PS of 0.1 will weigh 1/0.1=10 and will be considered in the analysis ten times. Similarly, a treated patient with high PS, for example 0.8, will weigh 1/0.8=1.25 and it will be considered in the analysis 1.25 times. Moreover, IPTW was at the basis of the Marginal Structured Models, a multistep estimation procedure designed to control confounding variables at different time points in longitudinal studies [20]. IPTW method is not robust against the outliers.

Covariate adjustment

This method uses the PS values as a covariate in a linear regression analysis. Even if there is no significant association between the covariates used to compute PS value and the outcome, the use of PS value as a covariate allows us to approximate the effect of each of the aforementioned covariates [21].

 

Practical example

To explain these methods, we will use a dataset containing 75 non-randomized patients with CKD stage III-IV.

All the remaining patients gave written consent to data processing for research purposes in respect of privacy. Ethical approval was not necessary according to National Code on Clinical Trials declaration and according to Italian ministerial rules of September 6, 2002 n°6, because our observation derives from a real-life retrospective study.

Patients were followed up for one year.  40 patients followed an LPD, defined by a protein intake of 0.6 g/kg/day (Group A), and 35 patients were subjected to the Mediterranean diet (Group B). The allocation, according to the real-life observation design, was based on dietician suggestions, patient’ habits, and adherence abilities, which were evaluated during the baseline visit.  Supplementary Table 1 and Table 2 summarized the details about the quantity and the nature of both diet regimens. Laboratory data were collected at the baseline visit (T0) and the annual follow-up (T1), as follows: serum urea (mg/dl), serum creatinine(mg/dl), serum phosphorous (mg/dl), serum sodium (mmol/l), serum potassium (mmol/l), white blood cells (WBC) (cc/mmc).

The groups had significant differences in BMI (28.7 [25.0, 34.7] vs 26.4 [24.0, 28.0], p=0.02), age (68 ± 9 vs 74 + 13, p=0.04), and basal creatinine clearance (33 [25, 44] vs 27 [21, 36], p=0.03). Baseline features were summed up in Table 1.

Variable Whole sample Group A (n= 40) Group B (n= 35) p
Age (years) 71 ± 11 68 ± 9 74 + 13 0.04
Sex (M/F) 45/55 40/60 49/51 0.32
BMI (kg/mq) 27.4 [24.2 – 30] 28.7 [25.0 – 34.7] 26.4 [24.0 – 28.0] 0.02
Clearance (ml/min) 31 [23 – 41] 33 [25 – 44] 27 [21- 36] 0.03
Serum Urea (mg/dl) 73 [64 -102] 75 [65 -99] 73 [60 -121] 0.84
Serum creatinine (mg/dl) 1.8 [1.5 – 2.5] 1.7 [1.4 – 2.4] 2.0 [1.6 – 2.7] 0.03
Serum sodium (mmol/L) 141 ±3.3 4.7 [4.5 – 5.0] 4.4 [4.9 – 5.2] 0.40
Serum Potassium (mmol/l) 4.74 ± 0.58 4.72 ± 0.53 4.76 ± 0.64 0.68
Serum phosphorous (mg/dl) 3.8 [3.6 – 4.1] 3.7 [3.5 – 4.0] 3.8 [3.7 – 4.3] 0.35
WBC (cc/mmc) 7744 ± 1824 7575 ± 1947 7932 ±1683 0.46
Delta_Clearance -3.50/ 0.00/ 4.00 -0.25/ 1.00/ 7.25 -5.50/-2.00/ 2.00 0.001
Table 1. Baseline features of whole sample and into the two groups. Body mass index (BMI); White blood cells (WBC).

An unadjusted model with GLM for repeated measures showed a significant effect on creatinine clearance of the Mediterranean diet compared to LPD, with an estimate marginal mean of -9.98 ml/min [95% CI], 15.6/, 4.3]. Adjusted model for age, BMI and sex (Table 2) appeared to confirm this significance in the between-group mean in the joint mean difference (‒9.34, 95%CI ‒15.44/ ‒3.24) (Table 2).

Variable F p 2
Mediterranean diet vs low protein diet ‒9.34 0.003 0.119
Sex (Male vs female) 2.71 0.104 0.038
Age (years) 0.08 0.780 0.001
BMI (kg/m2) 0.04 0.947 0.000
Table 2. Between-group mean in the joint mean differences: Adjusted GLM model for repeated measures. Body mass index (BMI).

Due to the non-randomized study design and the unbalanced groups, we decided to implement the analysis with the PS matching. We computed PS value using the treatment as dependent variable of the logistic regression, and graphically evaluated it (Figure 1). The PS values were not equally distributed between the two groups. Carrying on with the matching, choosing a caliper of 0.2, 20 patients from group A were paired with 20 patients from group B (Table 3). Unmatched patients are excluded from the analysis, reducing sample’s size. This reduction of the patients admitted in the analysis is one of the major limitations of the matching.

Analyzing the standardized means of the baseline features before and after the matching, a better balance between the two groups could be shown (Figures 2a and 2b).

GLM for repeated measures performed in the matched sample did not show significant differences between the two groups (2.737, 95%CI –4.328/9.803). Also using the covariate adjustment, that uses the whole sample, the not significant relationship between the two treatments and the clearance progression was confirmed in the GLM for repeated measures including treatment and ps-value (-3.314, 95%CI -8.524/1.897).

Figure 1. Propensity score distribution before the matching.
Figure 1. Propensity score distribution before the matching.
Group A Group B PS value group A PS value group B
1 48 0.5728 0.5990
2 56 0.5029 0.4885
3 43 0.7979 0.8133
4 53 0.2244 0.2236
5 41 0.8256 0.8244
6 65 0.2370 0.2436
8 49 0.7872 0.7496
9 47 0.7313 0.7068
10 52 0.2709 0.2670
11 66 0.5662 0.5339
12 68 0.6588 0.6768
14 71 0.6731 0.6888
15 75 0.1971 0.2084
16 39 0.6640 0.6990
18 63 0.3849 0.3833
19 55 0.4595 0.6256
21 67 0.6014 0.6256
26 45 0.4350 0.4386
27 42 0.2674 0.2947
31 60 0.4544 0.4280
Table 3. Groups composition based on Propensity Score Matching.
Figure 2a. Balance of the covariate before and after the Matching.
Figure 2a. Balance of the covariate before and after the Matching.
Figure 2b. Propensity score distribution after the Matching.
Figure 2b. Propensity score distribution after the Matching.

 

Usefulness of propensity score

A few RCTs were conducted on ERSD patients due to high costs and their difficult organization. In these cases, a well-structured comparative effectiveness study could be done to generate hypothesis or to add results to existing RCT. For Example, Chan KE et al. conducted a large observational study including more than 10000 patients, the study’s population and structure were modeled on 4D study’s methods, using the same eligibility criteria, endpoints, and similar timeline. To reduce bias caused by known and unknown variables, patients were initially matched in statin-group and control-group based on similar lipid profiles and years of dialytic treatment. Subsequently, a logistic model was performed to compute the probability of receiving the therapy, also all Cox analyses were weighted using the IPTW methods. Differently from the unmatched baseline analysis, the baseline characteristic computed after propensity scoring showed two well-balanced groups. At the outcome analysis, all HRs computed in this observational study were compared with the HRs showed in the 4D Study, and no significant differences were found between these two studies (Figure 1). Furthermore, RCTs are often smaller than observational studies, due to the stronger inclusion criteria and the higher costs than observational design. As shown in Figure 1, PS methods computed in a big sample, allowed to find a smaller confidence interval compared to 4D RCT, without significant differences in anyone outcome.

Through these comparisons, although RCTs were the lowest-biased studies, we can speculate about the effective validity of observational comparative studies using PS methods to reduce biases.

 

Limitation of propensity score methods

PS is applicable when the treatment assignment is neglectable, with unknown and unmeasured confounders. Furthermore, PS value > 0 is necessary. According to G. et Lepeyre-Mestre M. [22], propensity score methods is not very able to reduce selection bias, information bias and instrumental bias. Despite PS reducing inhomogeneity between groups, some unconsidered variables can exist, hence residual bias should be taken into account in the interpretation of results and in the critical appraisal of the study [23].

Leisman D.E. et al., resumed ten “Pearls and Pitfalls” about the use of matching method [24]. They highlighted problems regarding the reduction of sample size: the number of cases does not represent the whole sample because every unpaired subject is excluded from the analysis.  This can impair the external validity of the study, reducing its applicability. Consequently, the power of the study should be computed on the balanced sample, excluding the unmatched patients. Indeed, the analysis reflects the matched sample, losing information about the excluded cases. However, no patients were excluded by the analysis using the covariate adjustment and the IPTW. We highlighted that, similarly to our sample, no significant differences between matching and covariate adjustment were found. However, can be useful performing more PS methods, to compare the results. Furthermore, machine learning methods can be used to compute PS, and they reduce the variability of the PS.

Last but not least, a limitation of these methods is the inability to detect interaction variables. In correlated subgroup effects, these variables could indeed invalidate the PS model and should be excluded from it [25].

 

Discussion

Our analysis seemed to show a slow CKD progression in patients treated with LPD compared to patients treated with Mediterranean diet. However, the unbalanced covariate distribution between the two groups must be highlighted. Conversely to classic analysis, our result showed no difference between the two groups in matched sample, where the two groups were well balanced.

Healthy dietary habits are essential to contrast the progression of chronic diseases such as CKD and the risk factors related to its development. A tailored diet that follows patients’ eating habits can enhance compliance with nutritional therapy, improving the conservative management of CKD patients.

In patients with renal impairment, optimal eating is crucial, representing a high-impact modifiable lifestyle factor for the primary prevention of CKD progression [26], and it avoids the dysregulation of fluid status, pH, electrolytes [27, 29], chronic metabolic acidosis [30], all factors that should be corrected by an adequate dietary regimen and balanced supplementation of the missing nutrients.

Nutritional therapy can be useful to slow CKD progression and delay ESRD with a consistent improvement of the patient’s quality of life [31]. LPD should be started from GFR <30 ml/min, with a protein intake below 0.8 mg/kg/die, and it shown slower CKD progression and reduction of the mortality [32]. Rhee et al. (2018) [33] in their meta-analysis of randomized controlled trials (RCTs) found that the risk of progression to ESRD was significantly lower in patients with LPD regimens than those with higher‐protein diets, with serum bicarbonate augmentation. Notwithstanding its restrictions, LPD does not seem to impair the quality of life of CKD patients. The study of Piccoli et al. (2020) [34] on 422 CKD patients with stages III-V demonstrated that moderately protein-restricted diets (0.6 g/kg/day) guaranteed good compliance to therapy, with a median dietary satisfaction of 4 on a 1-5 scale with a minimal dropout.

The Mediterranean diet is a nutritious regimen first proposed by Keys in the mid-1980s that has been demonstrated to exert a favourable action on inflammation, CKD, cardiovascular health, and overall mortality [35, 37]. Different studies demonstrated a tight link between CKD prevention and Mediterranean diet regimen [38, 39]. How the Mediterranean diet exerts kidney protection is still under debate, and the anti-inflammatory and antioxidant effects were suggested [40, 41]. Moreover, tighter adherence to a healthy plant-based diet was associated with a slower eGFR decrease [42].

Asghari et al. (2017) [43] showed, in a six-year follow-up study, that adherence to the Mediterranean diet is associated with a reduced risk of 50% of incident CKD. These results are in line with the ones from the Northern Manhattan Study. In this cohort of patients, the patients with relatively preserved renal function and high adherence to the Mediterranean diet experienced an approximate 50% decreased odds for incidence of eGFR<60 ml/min/1.73m2.

The effectiveness of LPD compared to the Mediterranean diet is still a matter of debate. Mediterranean diet is characterized by free fat, abundant vegetables, legumes, fresh fruits, cereals, moderate wine consumption, low milk and milk products, low meat/animal products, and frequent fish. Moreover, both the Mediterranean diet and LPD are effective in the modulation of gut microbiota, reducing protein-bound uremic toxins levels, especially in patients suffering from moderate to advanced CKD.

Davis et al. (2015) [44] tried to define nutrient content and range of servings for the Mediterranean diet, analysing the variations in the quantity of this diet components in recent literature. The Mediterranean diet’s positive effects are not only limited to metabolic influence, but the conviviality, culinary and physical activity exerts a beneficial effect on mental health, ameliorating body homeostasis and reactivity to the chronic disease [45].

A diet regimen feasible in different settings is essential for adherence to nutritional therapy. Different dietetic strategies have been investigated over the years, but which is the best nutritional regimen remains controversial. Kim et al. analysed the data of 4343 incident CKD patients, during a median follow-up of 24 years and showed that higher adherence to a balanced diet was linked to a lower risk of CKD progression.

In conclusion, although our previous analysis showed differences between the two diets, after propensity match no differences were detected, as well as after the covariate adjustment methods. In the study of Hu et al. (2021) [46] adherence to healthy nutritional patterns was associated with lower risk for renal impairment progression and all-cause mortality in CKD patients. Thus, based on our results and according to the literature, the Mediterranean diet should be a good choice for patients who are not compliant with a low-protein diet, without a significant increase of CKD progression risk [47].

 

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