You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Background
==========

Chronic kidney disease (CKD) has been recognized as a worldwide health problem, and the associated morbidity and mortality in patients reaching end-stage renal disease (ESRD) is steeply increasing \[[@b1-medscimonit-23-4482]\]. Kidney transplantation is currently considered the best therapeutic option for ESRD patients, as it leads to a higher quality of life and longer survival than dialysis, together with considerable amelioration or resolution of the most common dialysis-related complications \[[@b2-medscimonit-23-4482]--[@b5-medscimonit-23-4482]\]. Furthermore, the cost-effectiveness of hemodialysis is significantly higher than transplantation \[[@b6-medscimonit-23-4482]\]. The growing gap between organ demand and availability and the increasing mortality rate among patients on kidney transplant waiting lists has amplified the efforts to increase the number of potential donors \[[@b7-medscimonit-23-4482],[@b8-medscimonit-23-4482]\]. Besides living donation, great attention has been devoted to the use of organs from incompatible AB0 donors \[[@b9-medscimonit-23-4482]\], donation after cardiac death (DCD) \[[@b10-medscimonit-23-4482]\], and expanded criteria donors (ECD) \[[@b11-medscimonit-23-4482],[@b12-medscimonit-23-4482]\]. The need to consider alternative strategies to enlarge the donor pool has led to greater utilization of elderly donors and to a consequent increase in average donor age and comorbidity rate \[[@b13-medscimonit-23-4482],[@b14-medscimonit-23-4482]\]. ECD are defined as any brain-dead donor aged \>60 years or a donor aged \>50 years with 2 of the following conditions: history of hypertension, terminal serum creatinine level ≥1.5 mg/dL, or death resulting from a cerebrovascular accident. This approach may be advantageous to overcome the organ shortage and to increase the chances to receive a transplant for some patients otherwise remaining under dialysis treatment \[[@b15-medscimonit-23-4482]\].

The aim of this study was to investigate changes in clinical parameters among potential deceased donors in the 15 years from 1999 to 2013 in the Emilia-Romagna region of Italy, and to evaluate their impact on transplantation procedure.

Material and Methods
====================

Study design
------------

This was an observational retrospective data analysis performed in the Nephrology, Dialysis, and Renal Transplant Unit, St Orsola Hospital, University of Bologna, Italy. A total of 1634 subjects identified as potential donors by the Intensive Therapy Units in Emilia-Romagna between January 1999 and December 2013 were included in the analysis. Exclusion criteria were age below 18 years and interruption of the donation procedure due to lack of consensus or death. The study population was divided into 2 groups: patients identified as potential donors in the period 1999 to 2005 (Group A) and patients identified between 2006 and 2013 (Group B). Donors were further divided into 3 categories: standard criteria donors (SCD), ECD, and unsuitable donors. We defined as SCD patients with clinical and anamnestic characteristics that did not require bioptic analysis before transplantation. ECD donors required biopsy before transplantation because of anamnesis, comorbidities (cardiovascular disease, diabetes, or hypertension), or age (\>60 years). The pre-implantation biopsies score resulted from a quantitative evaluation of lesions in different renal compartments, particularly vascular intimal sclerosis, tubular atrophy, interstitial fibrosis, and glomerular sclerosis as Karpinski's histological score \[[@b16-medscimonit-23-4482],[@b17-medscimonit-23-4482]\]. Unsuitable donors were considered not eligible for organ donation based on the following parameters: histology score (Karpinsky); comorbidities or severe renal dysfunction, anatomical, or vascular malformations that could affect surgical outcome of transplant; renal or other carcinomas; and increased risk of infections. Due to the retrospective nature of this study, registration or approval by the Ethics Committee was waived. The study was carried out in conformity with the Declaration of Helsinki.

General, biochemical, clinical, and transplant-related parameters
-----------------------------------------------------------------

For each potential donor included in the analysis, the following anthropometric parameters were collected: age, sex, and body mass index (BMI). To evaluate renal function, serum creatinine and glomerular filtration rate (GFR) calculated with CKD-EPI formula and Cockroft-Gault formula were recorded. Smoking, systemic arterial hypertension, diabetes, dyslipidemia, and cardiovascular diseases were considered as risk factors. Traumatic brain injury, cerebral hemorrhage, ictus, post-anoxic encephalopathy, bullet wounds in the head, and suicide were included among the causes of death. We also computed cold ischemia intervals of each single kidney (K1 and K2).

Statistical analysis
--------------------

All data were extracted from the database using FileMaker software and recorded in a Microsoft Excel datasheet. Continuous variables were compared with the Student t-test; categorical variables were analyzed with the chi-square test. To evaluate the sum of risk factors, we used the Mann-Whitney test. A p value lower than 0.05 was considered as statistically significant. All analyses were performed with the Statistical Package for the Social Sciences (SPSS).

Results
=======

Patients
--------

We identified 1845 potential donors in Emilia-Romagna region from 1999 to 2013. Among them, 211 did not complete the evaluation process and were not included in the analysis. Thus, 1634 were examined and divided into 2 groups based on the evaluation period: 707 potential donors were identified from 1999 to 2005 (Group A), and 927 from 2006 to 2013 (Group B). Donors in each group were further divided into 3 subgroups according to quality factors: there were 370 SCD in Group A and 369 in Group B; 173 ECD in Group A and 276 in Group B; and 164 unsuitable donors in Group A and 282 in Group B ([Figure 1](#f1-medscimonit-23-4482){ref-type="fig"}).

General, biochemical, clinical, and transplant-related features of the overall donor population
-----------------------------------------------------------------------------------------------

Donors characteristics evaluated in 1999--2005 (Group A) compared to those identified in 2006--2013 (Group B) are reported in [Table 1](#t1-medscimonit-23-4482){ref-type="table"}. The age of donors significantly increased over the years, with a concomitant rise in the proportion of elderly donors (aged more than 65 years). Other parameters, including sex, BMI, creatinine, GFR, and bioptic scores, were similar between the groups. In Group B (corresponding to the most recent years) we found a significant reduction of cold ischemia time for both transplanted kidneys ([Table 1](#t1-medscimonit-23-4482){ref-type="table"}).

[Table 2](#t2-medscimonit-23-4482){ref-type="table"} depicts the distribution in the 2 groups by cause of death, type and number of risk factors, and type of donors. We found that over the years there was a decrease among the potential donors of the deaths for traumatic brain injury and a rise in deaths due to post-anoxic encephalopathy. Concerning the risk factors, cardiovascular disease and dyslipidemia were found to be significantly more frequent in Group B than in Group A, and there were more donors with 1 risk factor and with 3 or more risk factors in the period 2006--2013. As expected, there was an increase in the percentage of ECD and unsuitable donors in Group B ([Table 2](#t2-medscimonit-23-4482){ref-type="table"}).

Considering the differences in the features of ECD alone in Group A *vs.* Group B, we noticed a significant rise in the proportion of elderly donors (aged \>65 years) (64.9% *vs.* 71.5%, p=0.004). On the other hand, no significant differences were observed in the frequency of the other risks factors included in the criteria used to define ECD (serum creatinine \>1.5 mg/dL: 5.1% *vs.* 5.6%, p=n.s.; arterial hypertension: 34.1% *vs.* 37.5%, p=n.s.; and cerebrovascular accident: 64.9% *vs.* 63.6%, p=n.s.).

Regarding the causes of the exclusion from donation of unsuitable donors, the comparison of Group A *vs.* Group B revealed that frequency of ineligibility due to bioptic score decreased over time (38.4% *vs.* 27.8%, p=0.044), while the rate of the other causes remained unchanged (nephrological unsuitability: 8.5% *vs.* 9.6%, p= n.s; surgical unsuitability: 2.4% *vs.* 6.8%, p=n.s.; other causes: 50.6% *vs.* 55.9%, p=n.s.).

Analysis of parameters after stratification by type of donor
------------------------------------------------------------

As [Tables 3](#t3-medscimonit-23-4482){ref-type="table"} and [4](#t4-medscimonit-23-4482){ref-type="table"} show, Group A (1999--2005) and Group B (2006--2013) were further analyzed according to the type of donor: SCD, ECD, and unsuitable donors.

Considering the subgroup of SCD, no significant differences between the groups were observed in terms of sex, age, BMI, serum creatinine, or GFR calculated by CKD-EPI equation. The proportion of elderly donors grew in the latest period (Group B). Renal function estimated by Cockcroft-Gault formula was better in Group B than in Group A. The cold ischemia time was found to be significantly lower for both kidneys in Group B than in Group A ([Table 3](#t3-medscimonit-23-4482){ref-type="table"}). The causes of death were fairly unchanged during the years, with the exception of brain traumatic injury, which was less common in the period 2006--2013. Among the risk factors, the frequency of dyslipidemia showed a highly significant rise in Group B ([Table 4](#t4-medscimonit-23-4482){ref-type="table"}).

In the subgroup of ECD, we found that the ones in the Group B were older, and the percentage of donors aged more than 65 years grew over the years. No differences were observed in terms of sex distribution, BMI, serum creatinine levels, GFR, or bioptic scores. Again, the cold ischemia time was significantly decreased in 2006--2013 for both kidneys ([Table 3](#t3-medscimonit-23-4482){ref-type="table"}). Death from cerebral hemorrhage was significantly more frequent in Group A *vs.* Group B, but the prevalences of cardiovascular diseases and dyslipidemia were lower ([Table 4](#t4-medscimonit-23-4482){ref-type="table"}).

In unsuitable donors, sex distribution, age, BMI, serum creatinine levels, and bioptic score remained unchanged over the years; the frequency of elderly donors was greater in Group B, and GFR was also significantly increased ([Table 3](#t3-medscimonit-23-4482){ref-type="table"}). Similar to the other subgroups, death from cerebral hemorrhage showed a significant decline over time, while the prevalence of stroke increased. Concerning risk factors, among unsuitable donors we observed an increase from the period 1999--2005 (Group A) to the period 2006--2013 (Group B) in the rate of arterial hypertension and dyslipidemia ([Table 4](#t4-medscimonit-23-4482){ref-type="table"}).

Discussion
==========

This observational retrospective study was undertaken to compare the main features of kidney transplant donors during the periods 1999--2005 and 2006--2013, with the aim to identify those parameters that have changed over the last 15 years. A total of 1634 patients were included in the analysis and clustered according to the evaluation period into Group A (period 1999--2005, 707 potential donors) and Group B (period 2006--2013, 927 potential donors). In line with the general trend, our donor population became older over the years, with an increasing proportion of donors older than 65 years, and presented more comorbidities; in particular, we found a growth in the rate of cardiovascular disease and dyslipidemia. Moreover, there was a decreased frequency of traumatic causes of death, and the procedure for organ management after procurement was progressively improved as indicated by the decreased cold ischemia interval for both kidneys, consistent with previous reports \[[@b18-medscimonit-23-4482]\]. This finding is suggestive of a progressive improvement in organizational policies and procedures at our Regional Transplant Center, with acceleration of the alert phase when a potential donor becomes available, and the successive organ allocation. The reduction of cold ischemia time, together with the optimization of preservation techniques, represent essential strategies to limit the severity of tissue damage caused by hypothermic preservation, especially in expanded criteria donors \[[@b19-medscimonit-23-4482],[@b20-medscimonit-23-4482]\]. There is a large body of evidence indicating that the cost-effectiveness associated with renal transplantation from ECD is consistently better than chronic dialysis treatment. In addition, the survival and quality of life of patients transplanted with marginal organs has improved. In the attempt to enlarge the pool of organs available for transplantation, many ECDs have been evaluated for donation; thus, unsurprisingly, the proportion of SCD has been decreasing in the last years, and this effect is partly due to the older average age of the general population \[[@b21-medscimonit-23-4482],[@b22-medscimonit-23-4482]\]. In our analysis, we noticed that renal function parameters and bioptic scores did not change significantly over time when considering the overall donor population. However, after stratification by type of donors, eGFR was found to be improved in the donors of the latest period for SCD and unsuitable donors (nearly significant for SCD when using Cockcroft-Gault formula).

Taken together, these data suggest that the increasing age and presence of comorbidities might not be necessarily associated with impaired graft function or poor histological features. Radhawa et al. described a glomerulosclerosis rate ranging from 0% to 10% in patients aged 60--75 years and bioptic abnormalities in younger patients, showing that donor age alone may be a misleading criterion for organ classification and allocation \[[@b23-medscimonit-23-4482]\]. In spite of the belief that the use of organs from elderly or marginal donors results in poorer post-transplant renal function and outcome \[[@b8-medscimonit-23-4482],[@b24-medscimonit-23-4482],[@b25-medscimonit-23-4482]\], McGlynn et al. proposed that the performance of an organ is more strictly correlated to its level of senescence rather than donor age itself. Some organs from older donors have proven to function adequately for many years, similarly to those from younger donors, and the explication might lie in the fact that biological age of the organ is a more effective predictor of graft outcome than its chronological age. Hence, telomere shortening and the expression of biomarkers of cellular damage and senescence are likely to be more suitable criteria to assess the quality of an organ \[[@b26-medscimonit-23-4482]\].

Among unsuitable donors, a particular finding of our study was the similar rates of discharge due to bioptic score between the potential donors of the less recent period (1999--2005) and the ones of the latest years (2006--2013). Despite the contrast with OPTN data, which report a higher rate of discard due to the degree of glomerulosclerosis \[[@b27-medscimonit-23-4482]\], our finding might be explained by increased expertise of the transplant pathologists in our transplant center. Currently, there is no shared consensus regarding the policies for discard based on histology findings. Since our first kidney transplant in 1967, we have performed more than 2700 renal transplants, resulting in a higher percentage of patients on our waiting list needing retransplantation and longer average waiting times. A very recent study that focused on identification of the most relevant predictors in the selection of deceased donor kidneys in the USA reported a weak correlation between kidney biopsy performance and discard rate \[[@b28-medscimonit-23-4482]\].

Our analysis has the limitation of considering only donors features to assess the effectiveness of potential donors based on the number of performed transplants, without examining transplant outcomes. Nevertheless, previous evidence has suggested that histology does not correlate with 1-year patient and allograft survival \[[@b29-medscimonit-23-4482]\]. Further studies are needed to better address the contribution of ECD to long-term patient survival and graft outcomes in kidney transplantation.

Conclusions
===========

The increasing organ shortage requires a great effort to promote and optimize novel strategies and specific allocation policies for ECD transplantation. Our analysis provides encouraging data that support enlarging criteria to broaden the pool of potential donors eligible for organ donation, especially among elderly and marginal donors. We confirmed that donor age alone may be not an independent and predictive factor in decision-making about organ classification and allocation. In particular, better organs should be allocated to patients with predicted longer life expectancy to prevent or minimize the risk of retransplantation in case of kidney allograft failure.

**Conflict of interest statements**

The authors have no financial conflicts of interest.

**Source of support:** Departmental sources

BMI

:   body mass index

CKD

:   chronic kidney disease

DCD

:   donation after circulatory death

ECD

:   expanded criteria donors

ESRD

:   end-stage renal disease

GFR

:   glomerular filtration rate

SCD

:   standard criteria donors

SPSS

:   Statistical Package for Social Science

![Scheme used for donor classification.](medscimonit-23-4482-g001){#f1-medscimonit-23-4482}

###### 

General, biochemical, clinical and transplant-related characteristics of the donor population divided into Group A (years from 1999 to 2005) and Group B (years from 2006 to 2013). Continuous variable are presented as mean ± standard deviation (SD), categorical as number and percentages (%).

                                  Group A (N=707)   Group B (N=927)   p
  ------------------------------- ----------------- ----------------- ---------
  Sex (F)                         308 (43.6%)       405 (43.7%)       n.s.
  Age (\>65 years)                223 (31.5%)       388 (41.9%)       \<0.001
  Age (years)                     54.6±17.1         58.8±16.3         \<0.001
  BMI (kg/m^2^)                   25.4±3.9          25.7±3.8          n.s.
  Serum creatinine (mg/dL)        0.98±0.54         0.94±0.49         n.s.
  GFR Cockcroft-Gault (mL/min)    92.2±34.2         94.5±39.9         n.s.
  GFR CKD-EPI (mL/min)            84.8±23.7         85.7±23.9         n.s.
  Cold ischemia time K1 (hours)   15.6±4.6          13.5±4.4          \<0.001
  Cold ischemia time K2 (hours)   17.5±5.5          14.6±4.8          \<0.001
  Bioptic score right kidney      3.62±1.52         3.6±1.62          n.s.
  Bioptic score left kidney       3.94±1.55         3.90±1.64         n.s.

BMI -- body mass index; CKD -- chronic kidney disease; GFR -- glomerular filtration rate.

###### 

Distribution of cause of death, type and number of risk factors, and type of donors in the donor population divided into Group A (years from 1999 to 2005) and Group B (years from 2006 to 2013). Data (all categorical variables) are presented as number and percentages (%).

                               Group A (N=707)   Group B (N=927)   p
  ---------------------------- ----------------- ----------------- ---------
  **Cause of death**                                               
   Cerebral hemorrhage         407 (57.6%)       501 (54.0%)       n.s.
   Ictus                       52 (7.4%)         89 (9.6%)         n.s.
   Traumatic brain injury      198 (27.0%)       207 (22.3%)       0.008
   Post-anoxic encefalopathy   27 (3.8%)         57 (6.1%)         0.035
   Bullet wounds at the head   11 (1.6%)         10 (1.1%)         n.s.
   Suicide                     3 (0.4%)          7 (0.8%)          n.s.
  **Risk factors**                                                 
   Arterial hypertension       241 (34.1%)       348 (37.5%)       n.s.
   Cardiovascular disease      85 (12.0%)        191 (20.6%)       \<0.001
   Smoking                     143 (20.2%)       172 (18.6%)       n.s.
   Dyslipidemia                18 (2.5%)         91 (9.8%)         \<0.001
   Diabetes                    50 (7.1%)         72 (7.8%)         n.s.
  **Number of risk factors**                                       
   0                           340 (48.1%)       433 (46.7%)       n.s.
   1                           231 (32.7%)       228 (24.6%)       0.008
   2                           108 (15.3%)       180 (19.4%)       n.s.
   3 or more                   28 (4.0%)         86 (9.3%)         \<0.001
  **Type of donors**                                               
   SCD                         370 (52.3%)       369 (39.8%)       0.002
   ECD                         173 (24.5%)       276 (29.8%)       0.047
   Unsuitable                  164 (23.2%)       282 (30.4%)       0.016

ECD -- expanded criteria donors; n.s. -- not significant; SCD -- standard criteria donors.

###### 

General, biochemical, clinical and transplant-related characteristics of the donor population stratified by type of donors (SCD *vs.* ECD and unsuitable donors) in Group A (years from 1999 to 2005) and Group B (years from 2006 to 2013). Continuous variable are presented as mean ± standard deviation (SD), categorical as number and percentages (%).

                                  SCD (N=739)                 ECD (N=449)                Unsuitable (N=446)                                        
  ------------------------------- ------------- ------------- ------------- ------------ -------------------- --------- ------------ ------------- ---------
  Sex (F)                         155 (41.9%)   160 (43.4%)   n.s.          75 (43.3%)   110 (39.9%)          n.s.      66 (40.2%)   140 (49.6%)   n.s.
  Age (\>65 years)                111 (30.0%)   149 (37.9%)   0.048         47 (27.2%)   118 (42.8%)          0.027     50 (30.5%)   112 (39.7%)   \<0.001
  Age (years)                     44.9±15.7     46.6±13.6     n.s.          64.8±9.6     68.5±8.8             \<0.001   65.9±12.9    65.3±15.1     n.s.
  BMI (kg/m^2^)                   24.8± 3.8     25.2±3.9      n.s.          26.3±3.8     26.2±3.6             n.s.      25.7±3.9     25.9±3.8      n.s.
  Serum creatinine (mg/dL)        0.91±0.35     0.87±0.33     n.s.          0.93±0.37    0.93±0.47            n.s.      1.23±0.90    1.08±0.71     n.s.
  GFR Cockcroft-Gault (mL/min)    104.8±33.9    111.4±40.1    0.018         83.9±26.8    81.9±31.2            n.s.      70.5±29.0    79.2±38.4     0.024
  GFR CKD-EPI (mL/min)            93.9±22.2     95.6±23.5     n.s.          78.1±18.1    78.0±20.7            n.s.      71.5±23.9    78.0±22.9     \<0.001
  Cold ischemia time K1 (hours)   15.5±4.7      13.0±4.5      \<0.001       16.0±4.3     14.1±4.3             \<0.001   /            /             /
  Cold ischemia time K2 (hours)   17.3±5.6      13.9±4.9      \<0.001       18.0±5.1     15.5±4.5             \<0.001   /            /             /
  Bioptic score right kidney      n.a.          n.a.          /             3.03±1.18    3.09±1.35            n.s.      5.26±1.15    5.31±1.26     n.s.
  Bioptic score left kidney       n.a.          n.a.          /             3.45±1.23    3.41±1.39            n.s.      5.40±1.38    5.55±1.34     n.s.

BMI -- body mass index; CKD -- chronic kidney disease; ECD -- expanded criteria donors; GFR -- glomerular filtration rate; n.a. -- not applicable; n.s. -- not significant; SCD -- standard criteria donors.

###### 

Distribution of cause of death, type and number of risk factors, and type of donors in the donor population stratified by type of donors (SCD *vs.* ECD and unsuitable donors) in Group A (years from 1999 to 2005) and Group B (years from 2006 to 2013). Data (all categorical variables) are presented as number and percentages (%).

                              SCD (N=739)                 ECD (N=449)                 Unsuitable (N=446)                                       
  --------------------------- ------------- ------------- ------------- ------------- -------------------- ------- ------------- ------------- -------
  Cause of death                                                                                                                               
  Cerebral hemorrhage         167 (45.1%)   176 (47.7%)   n.s.          123 (71.1%)   170 (61.6%)          0.042   117 (73.6%)   155 (63.8%)   0.001
  Ictus                       26 (7.0%)     25 (6.8%)     n.s.          22 (12.7%)    42 (15.2%)           n.s.    4 (2.5%)      22 (9.1%)     0.021
  Traumatic brain injury      148 (40.0%)   118 (32.0%)   0.023         21 (12.1%)    44 (15.9%)           n.s.    29 (18.2%)    45 (18.5%)    n.s.
  Post-anoxic encefalopathy   16 (4.3%)     27 (7.3%)     n.s.          4 (2.3%)      12 (4.3%)            n.s.    7 (4.4%)      18 (7.8%)     n.s.
  Bullet wounds at the head   8 (2.2%)      8 (2.2%)      n.s.          2 (1.2%)      1 (0.4%)             n.s.    1 (1.2%)      1 (0.4%)      n.s.
  Suicide                     1 (0.3%)      4 (1.1%)      n.s.          1 (0.6%)      2 (0.7%)             n.s.    1 (0.6%)      2 (0.4%)      n.s.
  Risk factors                                                                                                                                 
  Arterial hypertension       64 (17.3%)    79 (21.4%)    n.s.          95 (54.9%)    163 (59.1%)          n.s.    82 (50.0%)    106 (37.6%)   0.012
  Cardiovascular disease      13 (3.5%)     20 (5.4%)     n.s.          38 (22.0%)    101 (36.6%)          0.012   34 (20.7%)    70 (24.8%)    n.s.
  Smoking                     85 (23.0%)    91 (24.7%)    n.s.          36 (20.8%)    39 (14.1%)           n.s.    22 (13.4%)    42 (14.9%)    n.s.
  Dyslipidemia                3 (0.8%)      20 (5.4%)     \<0.001       11 (6.4%)     46 (16.7%)           0.001   4 (2.4%)      25 (8.9%)     0.009
  Diabetes                    5 (1.4%)      4 (1.1%)      n.s.          21 (12.1%)    32 (11.6%)           n.s.    24 (14.6%)    36 (12.8%)    n.s.

ECD -- expanded criteria donors; n.s. -- not significant; SCD -- standard criteria donors.

[^1]: Study Design

[^2]: Data Collection

[^3]: Statistical Analysis

[^4]: Data Interpretation

[^5]: Manuscript Preparation

[^6]: Literature Search

[^7]: Funds Collection