Abstract
Background
Chronic kidney disease (CKD) has recently been recognized as a public health issue. Prognosis and risk stratification are fundamental for decision-making to implement patient-centered strategies in clinical practice. Different prognosis scales have been evaluated, such as the Charlson Comorbidity Index (CCI), surprise questions, functional and biochemical parameters, to stratify patients with CKD initiating dialysis. The aim of this study was to determine prognostic factors for mortality in patients with CKD and delayed initiation of hemodialysis (HD).
Methods
We performed a prospective cohort study based on data from a reference dialysis center in the northeastern region of Mexico. Individuals with CKD and delayed initiation of hemodialysis were stratified according to the CCI at admission. Additionally, sociodemographic, functional, and biochemical parameters were compared to assess all-cause mortality.
Results
A total of 218 patients were included, with a median follow-up of 45.5 weeks. An important proportion of all-cause mortality was associated with infections among all groups. At the end of follow-up, overall all-cause mortality was 40%. Patients stratified with a low CCI had a survival rate of 79.2%, whereas those with moderate, high and very high CCIs had survival rates of 66.7%, 56.6%, and 41%, respectively. After adjusting for clinical and biochemical characteristics, patients who answered that they would not be surprised if they died in the following 6 months had an increased risk of all-cause mortality regardless of the CCI category. Patients with a high CCI (HR: 2.52; 95% CI: 1.22–5.18) and very high CCI (HR: 3.73; 95% CI: 1.89–7.36) clearly had increased risk for all-cause mortality.
Conclusion
Individualized patient-centered care should be the goal of standard care. By integrating the CCI and the surprise question (would you be surprised if the patient died in the following 6 months), it is possible to guide decisions further therapeutic strategies in patients in resource-limited settings.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12882-025-04197-x.
Keywords: Chronic kidney disease, Hemodialysis, Charlson comorbidity index, Delayed hemodialysis
Background
Chronic kidney disease (CKD) has been recognized as a public health issue. In 2017, the global estimate of CKD cases was 697.5 million (9.1% of the world’s population), and the majority of cases were concentrated in low to middle-income countries [1]. Patients with Stage 5 CKD represent the group of patients with the greatest disease burden and lower quality of life scores and are worse among patients receiving hemodialysis [2]. Clinical practice should assess these needs and but not increase unnecessary burden. The presence of comorbidities and frailty at hemodialysis is associated with an increased risk of all-cause mortality [3]. This gives clinician-performed and patient-reported scales the value of being low burden interventions to assess patient prognosis. In patients receiving hemodialysis in particular, different scales such as the Index of Coexistent Diseases, the Wright-Khan score, the Davies indices, and the Charlson Comorbidity Index (CCI), differ slightly [4]. The differences were influenced mainly when albumin was adjusted, which could guide clinicians in combining clinical and biochemical characteristics to determine patient prognosis. The CCI in CKD patients has been shown to predict short and long-term outcomes such as unplanned readmission and mortality [5, 6]. Discussion of patient prognosis should be clear and reliable for clinicians before the initiation of hemodialysis. Therefore, we intend to evaluate the prognosis of patients with stage 5 CKD and late initiation of hemodialysis on the basis of their CCI stratification and after asking patients if they would be surprised if they died in the following 6 months.
Methods
This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [7]. From March 2017 to November 2019, we included adult patients (> 18 years old) with CKD stage 5 and delayed initiation of Hemodialysis [8]. After consent to participate in the study, patients were recruited from the nephrology consult or the emergency room which a nephrologist had contact within its admission. Recruitment was stopped in 2019 due to SARS-Cov-2 public health measures. Baseline characteristics were obtained before patients initiated their first hemodialysis session and their co-morbidities controlled by multidisciplinary care. Delayed initiation of hemodialysis was defined as starting dialysis when the estimated glomerular filtration rate (eGFR) was ≤ 7 mL/min/1.73 m2 [9]. The eGFR was calculated with the CDK-EPI formula [10]. Additional indications for hemodialysis were refractory severe hyperkalemia (K⁺ >6.5 mmol/L), refractory severe metabolic acidosis (pH < 7.1), and severe uremic syndrome (accumulation of uremic toxins leading to multi-organ dysfunction) symptoms. Patients with CKD stage were prospectively and consecutively identified from the Dialysis & Hemodialysis Center Registry and confirmed through patient records. Pregnant or lactating women, initiation hemodialysis due to acute kidney injury, and patients with invasive respiratory support were excluded from the study. The University Hospital “José Eleuterio González” from the Autonomous University of Nuevo León is a tertiary hospital that is the center of reference for various diseases, including CKD. The center serves as a focal point for healthcare in northeastern Mexico, primarily concentrating on patients from underserved populations, including low-income families and minority groups. The hospital employs a systematic approach to patient care by coding diagnoses according to the International Classification of Diseases, 9th Revision (ICD-9), with specific designations such as Chronic Kidney Disease Stage V (CKD Stage V: 585.5) and End-Stage Renal Disease (ESRD: 585.6) to ensure accurate tracking and treatment of conditions [10]. An institutional review board and ethics committee reviews and approved this study. We adhered to the principles of the Helsinki 2013 Declaration [11]. Informed consent was obtained from all participants. All personal identifiable information was encrypted to protect patient privacy. The primary outcome of interest was the all-cause mortality rate across CCI categories. The secondary outcome was the association of the surprise question (Would you be surprised if the patient died in the following 6 months?) and all-cause mortality.
Statistical analysis
The prevalence of the CCI category and all-cause mortality were calculated for all patients. Demographic, clinical, and biochemical characteristics were described. After complete CCI categorization, the patients were stratified into three groups: low score (≤ 3), moderate score (4–5), high score (6–7), and very high score (≥ 8). The surprise question was posed to first-year nephrology fellows after they conducted a clinical and biochemical evaluation of patients, either during their entire emergency department stay or on their first day in the internal medicine ward, as well as following their initial hemodialysis session. Parametric quantitative variables presented as the means and standard deviations. The distribution of continuous variables was summarized as medians and interquartile ranges for non-parametric samples. Categorical variables were described as frequencies and proportions. Qualitative variables were compared via Fisher’s exact test or the Pearson x2 test. For quantitative variables, the Kolmogorov Smirnov test was used to test the data distribution, unpaired Student’s t tests or Mann-Whitney U tests were used. In the case of more than two variables, the data were analyzed via one-way ANOVA or Kruskal-Wallis test. To assess differences in mortality rates across the CCI strata (20%, 30%, 40%, 60%, respectively) using a chi-square test for multiple proportions formula, a sample size of 55 patients per group was determined to detect a minimum difference of 10% with 90% power and a two-sided α of 0.05, based on an effect size (Cohen’s w) of 0.50 [12]. Cox proportional hazards regression was used to evaluate the associations between clinical and biochemical characteristics. The proportional hazards assumption was tested via Schoenfeld residuals. The model included covariates such as age, sex, and baseline measurements of biochemical markers. Interactions between key variables were also tested. Hazard ratios with 95% confidence intervals and p-values were reported for each covariate. All analyses were performed in SPSS program version 24 (SPSS, Inc., Armonk, NY) considering only p values < 0.05 as significant [13].
Results
Baseline characteristics & laboratory characteristics
A total of 218 patients with stage 5 CKD who were initiating late hemodialysis were included. Only 33% of patients with a known diagnosis of chronic kidney disease (CKD) were aware of the need for renal replacement therapy (RRT) initiation, but they did not begin treatment until they arrived at the emergency room. After CCI classification, 24.3% of patients were classified as having a low CCI, 23.4% as having a moderate CCI, 24.3% having a high CCI, and 28% as having a very high CCI (Table 1). Patient age significantly differed across groups. The main reason for admission was uremic syndrome from the internal medicine ward. The Karnofsky score was lower in groups with high or very high CCI. Patients were asked if they would be surprised if they died in the following 6 months and 69.8% of low CCI patients answered yes, whereas 49%, 30.2%, and 13.1% of moderate, high, and very high CCI patients, respectively, did. Most patients did not have access to social security. The main cause of death was infection, followed by cardiovascular causes. Albumin, bicarbonate, blood urea nitrogen, calcium, creatinine, estimated glomerular filtration rate, hematocrit, hemoglobin, and phosphorous were significantly different across the CCI stratifications (Table 2). No patient received a kidney transplant during follow-up. Patient´s co-morbidities and cause for CKD are presented on supplemental Tables 1–2.
Table 1.
Baseline characteristics
| Low CCI (n = 53) | Moderate CCI (n = 51) | High CCI (n = 53) | Very high CCI (n = 61) | p-value | |
|---|---|---|---|---|---|
| Age | 35 (42.5–26) | 49 (59 − 36) | 55 (61.5–50.5) | 64 (73.5–60.5) | < 0.0001 |
| Sex | |||||
| Female | 27 (50.9) | 25 (49) | 22 (41.5) | 29 (47.5) | 0.787 |
| Male | 26 (49.1) | 26 (51) | 31 (58.5) | 32 (52.5) | |
| Hypertension | |||||
| Yes | 23 (43.4) | 42 (82.4) | 46 (86.8) | 50 (82) | < 0.0001 |
| No | 30 (56.6) | 9 (17.6) | 7 (13.2) | 11 (18) | |
| Admission Diagnosis | |||||
| Uremic Syndrome | 41 (77.4) | 30 (58.8) | 36 (67.9) | 31 (50.8) | 0.02 |
| CHF | 3 (5.7) | 5 (9.8) | 4 (7.5) | 13 (21.3) | |
| CV | 0 | 1 (2) | 0 | 3 (4.9) | |
| Infectious | 1 (1.9) | 8 (15.7) | 9 (17) | 8 (13.1) | |
| Other | 8 (15.1) | 7 (13.7) | 4 (7.5) | 6 (9.8) | |
| Hospitalization Area | |||||
| Ward | 36 (67.9) | 41 (80.4) | 41 (77.4) | 50 (82) | 0.301 |
| ICU | 17 (32.1) | 10 (19.6) | 12 (22.6) | 11 (18) | |
| Social Security | |||||
| Yes | 10 (18.9) | 8 (15.7) | 10 (18.9) | 19 (31.1) | 0.186 |
| No | 43 (81.1) | 43 (84.3) | 43 (81.1) | 42 (68.9) | |
| Surprise Question | |||||
| Surprised | 37 (69.8) | 25 (49) | 16 (30.2) | 8 (13.1) | < 0.0001 |
| Not Surprised | 16 (30.2) | 26 (51) | 37 (69.8) | 53 (86.9) | |
| Karnofsky Score | 60 (70 − 40) | 50 (60 − 40) | 40 (50 − 25) | 40 (50 − 20) | < 0.0001 |
| Cause of Death | |||||
| CV | 1 (1.9) | 5 (9.8) | 5 (9.4) | 14 (23) | 0.005 |
| Treatment Interruption | 3 (5.7) | 4 (7.8) | 5 (9.4) | 12 (19.7) | |
| Infection | 5 (9.4) | 7 (13.7) | 13 (24.5) | 12 (19.7) | |
| Neoplasia | 0 | 1 (2) | 0 | 3 (4.9) | |
| Other | 2 (3.8) | 0 | 0 | 1 (1.6) | |
| Survived | 42 (79.2) | 34 (66.7) | 30 (56.6) | 25 (41) |
CCI: Charlson comorbidity index; CHF: Congestive heart failure; CV: Cardiovascular; ICU: Intensive care unit
Table 2.
Biochemical characteristics
| Low CCI (n = 53) |
Moderate CCI (n = 51) |
High CCI (n = 53) |
Very High CCI (n = 61) |
p-value | |
|---|---|---|---|---|---|
| Albumin (g/L) | 2.9 (3.2–2.2) | 2.7 (3.1-2.0) | 2.6 (3.2–1.9) | 2.4 (2.8-2.0) | 0.018 |
| Bicarbonate (mEq/L) | 9.7 (12.7–6.7) | 12.7 (16.1–9.6) | 12.7 (16.3–8.6) | 13.6 (16.6–10.2) | 0.001 |
| Blood Urea Nitrogen (mg/dL) | 137.0 (46.5) | 113 (51.0) | 124.1 (49.4) | 113 (46.5) | 0.03 |
| Calcium (mg/dL) | 7.7 (8.7–6.3) | 8.8 (9.4–7.9) | 8.5 (9.1-8.0) | 8.6 (9.4-8.0) | 0.001 |
| Creatinine (mg/dL) | 17.8 (24.2–11.5) | 10.9 (17.8-7.0) | 11.1 (16.8–7.3) | 8.9 (13.5–6.3) | < 0.0001 |
| eGFR (ml/min/1.73m2) | 3 (4 − 2) | 5 (7 − 2) | 4 (6 − 3) | 5 (7 − 3) | < 0.0001 |
| Hematocrit (%) | 22.7 (25.7–15.8) | 25.3 (30.0-22.8) | 26.0 (31.1–22.3) | 26.4 (30.0-23.8) | < 0.0001 |
| Hemoglobin (g/dL) | 7.0 (2.4) | 8.3 (1.7) | 8.7 (2.4) | 8.4 (1.7) | < 0.0001 |
| Leukocytes (K/µL) | 10.7 (15.7–7.3) | 9.5 (12.4–6.8) | 9.7 (13.5–7.4) | 10.6 (15.1–7.7) | 0.131 |
| Lymphocytes (K/µL) | 9.1 (12.7–6.1) | 9.2 (15.5–5.87) | 6.7 (11.7-4.0) | 6.95 (12.5–4.9) | 0.365 |
| Platelets (K/ul) | 196 (298 − 146) | 234 (293 − 141) | 230 (309 − 152) | 254 (313 − 174) | 0.365 |
| Phosphorous (mg/dL) | 9.6 (2.9) | 7.0 (2.9) | 8.0 (2.6) | 7.3 (2.9) | < 0.0001 |
| Potassium (mEq/L) | 4.9 (6.0-4.4) | 5.2 (5.9–4.4) | 5.5 (6.5–4.3) | 5.4 (6.1–4.5) | 0.349 |
CCI: Charlson Comorbidity Index
Primary and secondary outcomes
Overall survival for all patients was 60%. Patients with low CCI had 79.2% overall survival at the end of follow-up compared to 66.7%, 56.6%, and 41% in patients with moderate CCI, high CCI, and very high CCI, respectively, did (Table 3). After adjusting for clinical and biochemical variables, patients stratified by high CCI (HR: 2.52; 95% CI: 1.22–5.18) and very high CCI (HR: 3.73; 95% CI: 1.89–7.36) had an increased risk for all-cause mortality. Patients with a moderate CCI (HR: 1.69; 95%CI: 0.79–3.61) had an unclear effect estimate. These effects were also observed in the univariate analysis, which was adjusted for clinical and biochemical variables. Higher levels of the Karnofsky score (HR: 0.95; 95%CI: 0.93–0.96) were associated with a lower risk of all-cause mortality (Table 4). Patients with a low CCI presented the lowest proportion of patients who would be surprised if they died. Patients who were classified as if they would not die within the following six months had an increased risk of all-cause mortality independent of the CCI category. The proportional hazards assumption was tested via Schoenfeld residuals, and no significant violations were found. Post-hoc analysis was performed adjusting for serum potassium (Supplemental Table 3). In all patients, main cause of infections was bacterial pneumonia followed by sepsis related to vascular access.
Table 3.
All-cause mortality and survival according to Charlson comorbidity index
| N (%) | Deaths | Survived | p-value | Follow-up (weeks) | |
|---|---|---|---|---|---|
| Low CCI | 53 (24.3) | 11 (20.8) | 42 (79.2) | 0.0003 | 61 (98.5–61) |
| Moderate CCI | 51 (23.4) | 17 (33.3) | 34 (66.7) | 54 (90 − 25) | |
| High CCI | 53 (24.3) | 23 (43.4) | 30 (56.6) | 38 (64.5–6.5) | |
| Very high CCI | 61 (28) | 36 (59) | 25 (41) | 29 (57 − 5) | |
| Total | 218 (100) | 87 (40) | 131 (60) | 45.5 (78-16.7) |
CCI: Charlson Comorbidiy Index
Table 4.
Charlson comorbidity index and the risk of All-cause mortality
| Model 1 | Model 2 | Model 3 | Model 4 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B | HR (95% CI) | p-value | B | HR (95% CI) | p-value | B | HR (95% CI) | p-value | B | HR (95% CI) | p-value | |
| Low CCI | Reference | Reference | Reference | Reference | ||||||||
| Moderate CCI | 0.529 | 1.69 (0.79–3.62) | 0.172 | 0.531 | 1.70 (0.79–3.63) | 0.170 | 0.504 | 1.65 (0.77–3.53) | 0.194 | 0.526 | 1.69 (0.79–3.61) | 0.175 |
| High CCI | 0.956 | 2.6 (1.26–5.34) | 0.009 | 0.942 | 2.56 (1.24–5.28) | 0.011 | 1.012 | 2.75 (1.33–5.65) | 0.006 | 0.925 | 2.52 (1.22–5.18) | 0.012 |
| Very High CCI | 1.331 | 3.78 (1.92–7.46) | 0.0001 | 1.331 | 3.78 (1.92–7.45) | 0.0001 | 1.268 | 3.55 (1.80–7.02) | 0.0002 | 1.318 | 3.73 (1.89–7.36) | 0.0001 |
| Surprise Question (yes vs. no) | 1.153 | 3.16 (1.88–5.33) | < 0.0001 | 1.157 | 3.18 (1.89–5.35) | < 0.0001 | 1.182 | 3.26 (1.91–5.56) | < 0.0001 | 1.201 | 3.32 (1.94–5.67) | < 0.0001 |
Model 1: Univariate analysis; Model 2: Adjusted for age (continuous), sex (female vs. male), hypertension (yes vs. no), Karnofsky score (continuous), surprise question (surprised vs. not surprised); Model 3: Adjusted for albumin (continuous), bicarbonate (continuous), blood urea nitrogen (continuous), corrected calcium (continuous), creatinine (continuous), estimated glomerular filtration rate (continuous), hematocrit (continuous), hemoglobin (continuous), phosphorous (continuous); Model 4: Clinical and biochemical variables
CCI: Charlson Comorbidity Index; B: beta-coefficient; HR: hazard ratio; CI: confidence interval
Discussion
In this study, we observed that integrating the Charlson Comorbidity Index (CCI) with the surprise question enhances prognostic accuracy for all-cause mortality in patients with stage 5 CKD initiating late hemodialysis, particularly in resource-limited settings. By stratifying patients into low, moderate, high, and very high CCI categories, we identified a clear gradient of mortality risk, with high (HR: 2.52; 95% CI: 1.22–5.18) and very high CCI (HR: 3.73; 95% CI: 1.89–7.36) groups exhibiting significantly increased all-cause mortality. The surprise question further refined prognosis, as patients for whom clinicians would not be surprised if they died within 6 months faced higher mortality risk across all CCI categories. These findings underscore the combined approach’s potential to guide tailored, patient-centered care, optimizing resource allocation and facilitating early palliative care discussions for high-risk patients.
Infection was the primary cause of death in the low, moderate, and high CCI groups, while cardiovascular causes predominated in the very high CCI group, potentially due to older age and higher hypertension prevalence. Notably, neither the low nor moderate CCI groups had hypercalcemia as an indication for hemodialysis, which prior studies have linked to higher infection-related mortality [14]. The moderate CCI group displayed heterogeneous survival outcomes, with survival rates approximately 10% lower than the low CCI group but 10% higher than the high CCI group. This variability likely reflects the group’s intermediate comorbidity burden, complicating prognosis prediction. Residents’ uncertainty regarding the prognosis of moderate CCI patients underscores the need for individualized assessment in this subgroup to optimize care planning.
A Canadian cohort reported that CCI scores ≥ 12 conferred the greatest mortality risk in end-stage kidney disease, though single-point increases had minimal impact [15]. By categorizing CCI scores rather than using continuous values, our study improved specificity for predicting all-cause mortality, consistent with findings from an American cohort with mixed insurance status [16]. These results highlight CCI’s utility across diverse socioeconomic settings. Furthermore, the CCI can be calculated using administrative data, making it an accessible tool in resource-limited settings where resources must be allocated to those who may benefit most [17]. Additionally, the CCI can be complemented by the surprise question, which requires minimal resources or technology [18].
The “surprise question” posed to residents—whether they would be surprised if a patient died within 6 months—demonstrated strong clinical intuition, consistent with prior studies [19]. Identifying which clinical or biochemical factors residents prioritized could inform the development of standardized prognostic tools to complement or replace the surprise question. Such tools could guide decisions about hemodialysis parameters (e.g., access route, frequency, ultrafiltration rate) and facilitate early palliative care or advance care planning [20, 21]. These discussions should respect patient autonomy and beneficence, emphasizing quality of life while incorporating cultural, spiritual, and familial contexts with empathic communication [22]. We recommend a private setting conducive to serious conversations, involving family members if the patient prefers, to create a space where the patient feels comfortable expressing emotions and asking questions [23]. Clinicians should explore the patient’s understanding of their condition and expectations about healthcare services at the beginning of the conversation. This helps tailor the prognosis discussion to the patient’s level of knowledge and emotional readiness [24]. In the context of CKD, some patients may prioritize independence and less interaction with healthcare systems over survival time [25]. This underscores the importance of nephrologists initiating prognosis conversations early during hemodialysis initiation to involve patients in treatment decisions and prepare them for potential setbacks. Similarly, initiating hemodialysis assumes that the patient understands the therapy’s risks and benefits, providing an ideal opportunity to discuss prognosis using clinical tools like those proposed in our study, aligning with patients’ overall goals and values [26]. To reduce psychological distress, clinicians could use the NURSE (naming, understanding, respecting, supporting, exploring) framework to ensure empathy [27]. Strategies such as the Ask-Tell-Ask method have proven effective in the CKD context to ensure patient understanding [28]. After sharing prognostic information, the conversation should shift toward achieving goals based on patients’ values, cultural beliefs, and preferences [29]. We suggest exploring these by asking what is important to patients, whether they prioritize quantity or quality of life. Clinicians may encounter the truth-versus-hope dilemma. Patients have the right to know the truth, and providing false hope is considered unethical. However, if a patient prefers not to discuss their prognosis, their preference should be respected. Clinicians are also obligated to communicate the limitations of offered therapies and reassess goals throughout the disease’s natural history [30]. While clinicians should consider patients’ socioeconomic contexts, decisions should not be guided solely by economic factors; best practices should be offered regardless of economic status or cultural beliefs. Tailoring care to patient values, informed by tools like the surprise question, is particularly critical in resource-limited settings where shared decision-making can reduce healthcare disparities.
Combining the CCI and the surprise question in resource-limited settings could guide clinicians in recommending treatment options. By integrating patients’ comorbidities and clinicians’ experience to assess prognosis, these tools, which have been individually validated in CKD patients, demonstrate good predictive ability with low disease burden [31]. Explaining these factors to patients could improve their decision-making. For instance, patients recommended for conservative management but who chose dialysis therapy gained only two months of life compared to those who initially pursued conservative therapy [32]. Better decision-making stems from well-informed patients, allowing clinicians and patients to redirect saved resources toward the patients’ goals. Financial barriers exacerbate healthcare inequities [33]. Advance care planning can optimize resource allocation, directing efforts toward high-impact interventions such as nutritional support. Malnutrition, prevalent in CKD, compromises immune function, increasing infection risk—the leading cause of death in our cohort [34, 35]. Adjusting hemodialysis regimens (e.g., reducing session frequency while optimizing duration or ultrafiltration rate) may improve adherence and reduce economic burden, though this approach carries risks of complications and requires careful monitoring [36]. Public health strategies should prioritize expanding hemodialysis access in rural areas and improving nephrology follow-up to manage comorbidities effectively, potentially reducing CKD exacerbations [37, 38]. Since the CCI accounts for comorbidities, its integration into routine care could enhance risk stratification and resource allocation.
Patients with stage 5 CKD requiring renal replacement therapy face significant disease burdens. Prognostic tools like the CCI can guide advance care planning, identifying those who may benefit most from intensive interventions versus palliative approaches. Future research should explore patient-reported outcomes and adaptive clinical tools to enhance inclusive decision-making. Our study’s strengths include its focus on a resource-limited population, highlighting real-world challenges, and the novel application of the CCI in late hemodialysis initiation, a context where its prognostic value is underexplored. The inclusion of clinician-reported outcomes further strengthens its clinical relevance with minimal patient burden. Limitations include early recruitment cessation due to the SARS-CoV-2 pandemic, which restricted follow-up duration, and the lack of planned delayed hemodialysis initiation, limiting generalizability. Additionally, we could not consistently record individual hemodialysis session parameters, potentially missing nuanced treatment effects. Despite these constraints, our findings underscore the prognostic utility of the CCI and the surprise question and the need for tailored care in CKD management.
Conclusion
The integration of clinical characteristics, the CCI, and clinicians’ expertise, as exemplified by the Surprise Question, is pivotal in determining prognosis and formulating individualized therapeutic strategies, particularly in resource-limited settings. Initiating prognosis discussions presents ethical challenges for clinicians; however, prioritizing patients’ values and preferences is essential to delivering optimal patient-centered care. While economic constraints may influence patients’ decision-making, clinicians have a duty to uphold the highest standards of medical practice, ensuring that treatment decisions are not dictated by cost considerations but are guided by clinical excellence and compassion.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We express our sincere gratitude to all patients and their families who participated in this study.
Abbreviations
- CCI
Charlson Comorbidity Index
- CI
Confidence intervals
- CKD
Chronic kidney disease
- eGFR
Estimated glomerular filtration rate
- HR
Hazard ratio
- MACE
Major adverse cardiovascular events
Author contributions
APFM: Conceptualization, methodology, data curation, formal analysis, writing-review, project administration. MGC: Methodology, data curation, formal analysis, original draft preparation. CSM: Conceptualization, data curation, revision of the manuscript. EGG Conceptualization, data curation, revision of the manuscript. LMRT: Methodology, data curation, original draft preparation, revision of the manuscript. MCOG: Methodology, resources, revision of the manuscript, supervision.
Funding
No funding.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Our institution review board and ethics committee reviewed and approved this study (University Hospital, “Dr. José Eleuterio González” Ethics Committee). This study adhered to the Declaration of Helsinki. Informed consent was obtained from all participants.
Consent for publication
All authors have reviewed and consented to the publication of this manuscript.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.