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 {#Sec1}
==========

End-stage kidney disease (ESKD) is a leading cause of morbidity and mortality worldwide \[[@CR1]\]. Compared with patients with chronic diseases but without ESKD \[[@CR2]\], patients receiving maintenance hemodialysis (HD) and peritoneal dialysis (PD) tend to have a shorter life expectancy, as well as a higher rate of hospitalization and readmission. In the United States, it was observed that 35.2% of patients on HD and PD were readmitted within 30 days in 2012, which drew the attention of the US Centers for Medicare and Medicaid Services \[[@CR3]\]. Starting in 2017, the 30-day readmission has been included in the ESKD Quality Incentive Program as an important move in payment reform \[[@CR4]\]. A subsequent 2% reduction of overall payment of dialysis patients was observed after the implementation of the program \[[@CR5]\], which indicated that 30-day readmission is an important indicator for the quality of healthcare among HD and PD patients.

Previous studies revealed that multiple comorbidities were associated with the risk of 30-day readmission for maintenance HD patients \[[@CR6], [@CR7]\]; though it is difficult to quantitatively or semi- quantitatively use those results in clinical practice for risk-stratification. The Charlson Comorbidity Index (CCI) is the most frequently used tool to measure co-existing diseases \[[@CR8]\], and it has been validated for predicting the risk of mortality, disability, hospitalization and length of hospital stay in various clinical settings \[[@CR9]\]. In the field of nephrology, CCI has been used to predict mortality of patients with acute kidney injury (AKI), diabetic kidney disease \[[@CR10], [@CR11]\]. As for patients with ESKD, several studies \[[@CR12], [@CR13]\] have validated that the CCI was an effective tool for comorbidity assessment and it could be used for survival prediction. To the best of our knowledge, there are few studies investigating the association between CCI and 30-day readmission in HD and PD patients. Therefore, we aimed to explore the association between CCI and the risk of 30-day readmission in patients receiving maintenance HD and PD based on a national administrative database in China.

Methods {#Sec2}
=======

Data sources and population setting {#Sec3}
-----------------------------------

Patients on HD and PD therapy were extracted from the database of Hospital Quality Monitoring System (HQMS), which is a mandatory patient-level national database for hospital accreditation, under the authority of the Bureau of Medical Administration and Medical Service Supervision, National Health Commission of the People's Republic of China. Details of the HQMS were described in detail elsewhere \[[@CR14]\]. In brief, standardized electronic inpatient discharge summaries, which were completed within 48 h after hospital discharge, were uploaded on a daily basis to HQMS by most Class 3 hospitals (similar to tertiary hospitals) in China. The database collects 346 patient-level variables, including demographic characteristics, diagnoses, procedures, pathology diagnoses, and expenses. The discharge diagnoses were coded by professional medical coders at each hospital using the International Classification of Diseases-10 (ICD-10) coding system, and the procedures were coded using the International Classification of Diseases-9, Clinical Modification Volume 3 (ICD-9-CM-3).

The system was relatively well-established in 2013, and thus the data used in this study were from 1 January 2013 to 31 December 2015. The inclusion criteria for our study population included (1) aged ≥18 years old and ≤ 100 years old and (2) identified as receiving maintenance HD and PD by ICD-10 combined with ICD-9-CM-3. The ICD codes used for identifying dialysis patients are provided in Additional file [1](#MOESM1){ref-type="media"}: Table S1. The exclusion criteria included (1) patients without identification number and therefore readmissions could not be identified; (2) patients diagnosed as AKI, or chronic kidney disease (CKD) stage G1--4; (3) receipt of kidney transplantation during the index hospitalization; (4) patients died during the index hospitalization; and (5) patients' records identified as having errors, e.g., the discharge date was earlier than admission date. Here we excluded those patients died during the index hospitalization because death is a competing risk for rehospitalization. There are specific diagnosis or procedure codes for PD, HD, AKI, CKD stages G1--4 and kidney transplantation respectively, and patients with all these diseases can be identified using corresponding ICD codes from the HQMS database. The ICD codes used for identifying patients with AKI, CKD stages G1--4 and kidney transplantation are provided in Additional file [1](#MOESM1){ref-type="media"}: Table S2.

The study outcome was the unplanned readmission following the index hospital discharge within 30 days. The planned readmission was identified by the specific question of "Is there any plan to readmit the patients within 30 days?" in the HQMS database, and those with positive answers were excluded. Furthermore, for those with a time interval of less than 2 days between two admissions were also excluded, because it might be a way to avoid a long length of stay, which is an indicator for healthcare quality evaluation. The reasons for readmission were considered in our analysis. We identified those readmission reasons using the medical procedure codes recorded during the rehospitalization first. The procedure codes were given the highest priority because the most important treatments for hospitalized patients can be identified through procedure codes. For those patients without recorded medical procedures during the rehospitalizations, we then used the first diagnosis as the causes of readmission. The recorded procedure and diagnosis codes of all included patients were classified into three categories: building dialysis access, dialysis comorbidity and other reasons, among which the first two are main causes of readmission with frequency \> 10% respectively, and the third one consists of all those readmission reasons with frequency lower than 10%. We have listed the ICD codes of the readmission reasons in Additional file [1](#MOESM1){ref-type="media"}: Table S3.

For patients with multiple hospitalizations, we randomly selected one hospitalization as the index hospitalization. Generally, compared with the first hospitalization, patients would have worse conditions in the later hospitalizations. To randomize the severity of patients' illness, randomly selecting one hospitalization is a common approach to ensure the uniform distribution of the severity of patients' conditions \[[@CR6], [@CR7]\]. The flowchart of the process for the patients' inclusion is shown in Fig. [1](#Fig1){ref-type="fig"}. Fig. 1The procedure of study population selection

Comorbidities {#Sec4}
-------------

Comorbidities were identified by ICD-10 coding (Additional file [1](#MOESM1){ref-type="media"}: Table S4). The first edition of CCI contains 19 comorbidities in which these comorbid diseases are weighted according to the severities of the corresponding chronic conditions. The second edition of CCI is a combined age-comorbidity score that adds the factor of age such that scores based on the second edition CCI are higher for older people \[[@CR15]\] (The CCI with combined age-comorbidity is shown in Additional file [1](#MOESM1){ref-type="media"}: Table S5). In our study, the age-adjusted CCI was used because of its better predictive ability. More comorbidities and older age could result in a higher CCI score, which implies a higher disease burden. The CCI scores were classified into four groups based on the distribution in our study population: 2 (scored by ESKD per se), 3--4, 5--6, and \> 6.

Statistical analyses {#Sec5}
--------------------

The characteristics of patients included in the study were described. Data were reported as counts and percentages for categorical variables, and the mean (standard deviation, SD) or median (interquartile range, IQR) for continuous variables. Chi-square tests or Wilcoxon rank sum tests were used for comparison.

We calculated the 30-day readmission rate in different subgroups, grouped by the modality of dialysis and gender among dialysis patients with CCI score 2, 3--4, 5--6, and \> 6. Patients were classified into different groups according to the causes of rehospitalization and the distribution of readmission reasons among patients in different CCI score categories were presented. A cause-specific Cox proportional hazards model was used to assess the risk of unplanned 30-day readmission. In addition to CCI, potential confounders or variables predictive of readmission were included in the multivariable models, including: sex (male or female), the length of hospital stay (continuous), mechanical ventilation (yes or no), resuscitation (yes or no), admission through emergency department (yes or no), modality of dialysis (hemodialysis or peritoneal dialysis), and admission to intensive care unit (ICU, yes or no). All the confounders in our analysis are clinically important variables. We considered that patients with longer length of hospital stay, mechanical ventilation, resuscitation, admission through emergency department, admission to ICU during their index hospitalizations are more likely to have severe condition and all these factors might have association with the risk of readmission \[[@CR6], [@CR16]--[@CR18]\]. Since age was included in CCI, it was excluded from the proportional hazards model to avoid collinearity. Hazard ratios (HR) and 95% confidence intervals (CI) were reported. The proportional hazard assumptions were ascertained before fitting the Cox proportional hazard model.

All of the *P* values are 2-tailed. Python version 3.6 (Python Software Foundation, Delaware, United States) and R version 3.5.0 (R Foundation for Statistical Computing, Vienna, Austria) were used for analyses.

Results {#Sec6}
=======

General characteristics {#Sec7}
-----------------------

Overall, 124,721 patients were included in the analysis. The characteristics of the dataset were described in Table [1](#Tab1){ref-type="table"}. The mean age was 54.6 years old, and 41.5% were women. Regarding the modality of dialysis, 73.7% of patients were on hemodialysis. Table 1Characteristics of populations with and without 30-day readmissionCharacteristicTotal\
*n* = 124,721With 30-d Readmission\
*n* = 19,893Without 30-d Readmission\
*n* = 104,828*P*-valueAge (mean, SD)^b^54.6 (15.6)54.7 (15.4)54.5 (15.7)0.004Women n(%)^a^51,813 (41.5)8166 (41.1)43,647 (41.6)0.125Length of hospital stay (median, IQR)^b^11 (11)13 (14)11 (12)\< 0.001Mechanical ventilation n(%)^a^584 (0.5)41 (0.2)543 (0.5)\< 0.001ICU n(%)^a^4043 (3.2)496 (2.5)3547 (3.4)\< 0.001Rescue n(%)^a^8088 (6.5)1099 (5.5)6989 (6.7)\< 0.001Emergency n(%)^a^17,904 (14.4)2917 (14.7)14,987 (14.3)0.180MI n(%)^a^1123 (0.9)130 (0.7)993 (1.0)\< 0.001Heart failure n(%)^a^23,645 (19.0)3899 (19.6)19,746 (18.8)0.012Peripheral Vascular Disease n(%)^a^1921 (1.5)262 (1.3)1659 (1.6)0.006Cerebrovascular disease n(%)^a^10,376 (8.3)1582 (7.6)8794 (8.4)0.042Dementia n(%)^a^149 (0.1)27 (0.1)122 (0.1)0.540Chronic Pulmonary Disease n(%)^a^3355 (2.7)489 (2.5)2866 (2.7)0.029Connective Tissue Disease n(%)^a^3547 (2.8)617 (3.1)2930 (2.8)0.018Peptic Ulcer Disease n(%)^a^1178 (0.9)195 (1.0)983 (0.9)0.593Diabetes Mellitus uncomplicated n(%)^a^20,963 (16.8)3583 (18.0)17,380 (16.6)\< 0.001Mild Liver Disease n(%)^a^7318 (5.9)1069 (5.4)6249 (6.0)0.001Hemiplegia n(%)^a^153 (0.1)23 (0.1)130 (0.1)0.842Solid Tumor including leukemia, Lymphoma n(%)^a^2726 (2.2)360 (1.8)2366 (2.3)\< 0.001Diabetes Mellitus complicated n(%)^a^20,724 (16.6)3575 (18.0)17,149 (16.4)\< 0.001Moderate or Severe Liver Disease n(%)^a^1733 (1.4)250 (1.3)1483 (1.4)0.087CharacteristicTotal n = 124,721With 30-d Readmission, n = 19,893Without 30-d Readmission, n = 104,828P-valueSolid Tumor with metastatic n(%)^a^499 (0.4)53 (0.3)446 (0.4)0.001AIDS n(%)^a^28 (\< 0.1)5 (\< 0.1)23 (\< 0.1)0.986CCI (mean, SD)^b^4.3 (2.0)4.3 (2.0)4.2 (2.0)\< 0.001Modality of dialysis^a^ Hemodialysis n(%)91,901 (73.7)12,760 (64.1)79,141 (75.5)\< 0.001 Peritoneal dialysis n(%)25,333 (20.3)5493 (27.6)19,840 (18.9) Unknown dialysis n(%)7487 (6.0)1640 (8.2)5847 (5.6)Charlson Comorbidity Index^a^ CCI = 230,413 (24.4)4798 (24.1)25,615 (24.4)\< 0.001 3 \< =CCI \< =442,051 (33.7)6526 (32.8)35,525 (33.9) 5 \< =CCI \< =634,895 (28.0)5674 (28.5)29,221 (27.9) CCI \> 617,371 (13.9)2904 (14.6)14,467 (13.8)*Abbreviations*: *AIDS* Acquired Immune Deficiency Syndrome, *CCI* Charlson Comorbidity Index, *ICU* intensive care unit, *MI* myocardial infarction, *SD* standard deviation, *IQR* interquartile range^a^significance was assessed by Chi-squared test^b^significance was assessed by Wilcoxon rank sum test

There were 19,893 patients identified as having unplanned 30-day readmission (16.0%). The median intervals between the date of discharge and the readmission was 20 days (IQR 14--21 days) among patients with 30-day readmission. Among the comorbidities included in CCI, heart failure, diabetes mellitus, and cerebrovascular disease were the three most frequent diseases in the study population. Compared with patients without readmission, a larger proportion of patients readmitted within 30 day were with higher CCI score, and the contrast was 28.5% vs. 27.9% (readmitted vs. non-readmitted, 5 \< =CCI \< =6) and 14.6% vs. 13.8% (readmitted vs. non-readmitted, CCI \> 6), respectively. Furthermore, the percentage of patients receiving peritoneal dialysis was relatively higher among patients with readmission compared with those without (27.6% vs. 18.9%).

With the increase of CCI score, the 30-day readmission rates of patients grouped by the modality of dialysis and gender were shown in Table [2](#Tab2){ref-type="table"}. For patients receiving hemodialysis, the readmission rate increased with elevated CCI scores. However, the trend was not observed for patients receiving peritoneal dialysis, who had relatively higher readmission rates compared with patients receiving hemodialysis. Table 2The 30-day readmission rate stratified by the Dialysis modality and gender in different CCI score groupsSubgroupCCI = 23 \< =CCI \< =45 \< =CCI \< =6CCI \> 6TotalDialysis modality Hemodialysis12.7%13.3%14.7%15.3%13.9% Peritoneal22.2%21.2%22.1%20.9%21.7%Gender Male15.8%15.5%16.5%17.3%16.1% Female15.7%15.6%16.0%15.9%15.8% Total15.8%15.5%16.3%16.7%16.0%*Abbreviations*: *CCI* Charlson Comorbidity Index

The distribution of readmission reasons among different CCI score groups was shown in Table [3](#Tab3){ref-type="table"}. Most patients (68.8%) were readmitted for other reasons instead of causes related to dialysis. For those patients readmitted because of dialysis related reasons, dialysis comorbidity is a more frequent cause than building dialysis access (20.1% vs. 11.1%). The distribution of readmission reasons among patients in different CCI score groups has no significant difference (*P* \> 0.05). Table 3The distribution of readmission reasons among patients in different CCI score groupsReason of rehospitalizationCCI = 2\
n (%)3 = \<CCI \< =4\
n (%)5 = \<CCI \< =6\
n (%)CCI \> 6\
n (%)Total\
n (%)*P*-value^a^Dialysis access531 (11.1)698 (10.7)659 (11.6)313 (10.8)2201 (11.1)0.3Dialysis complications920 (19.2)1355 (20.8)1145 (20.2)580 (20.0)4000 (20.1)Others3338 (69.7)4473 (68.5)3870 (68.2)2011 (69.3)13692 (68.8)*Abbreviations*: *CCI* Charlson Comorbidity Index^a^significance was assessed by Chi-squared test

CCI as the predictor of rehospitalization within 30 days {#Sec8}
--------------------------------------------------------

Categories of CCI were independently associated with the risk of unplanned 30-day readmission (Table [4](#Tab4){ref-type="table"}). In the fully adjusted model, compared with patients with CCI of 2, the HR for those with CCI 3--4, 5--6 and \> 6 was 1.01 (95% CI 0.98--1.05), 1.09 (95% CI 1.05--1.14), and 1.14 (95% CI 1.09--1.20), respectively. Table 4Association between CCI and 30-day readmission adjusted for gender, dialysis modality and other variables, respectivelyCCI SubgroupGender-adjusted HRGender and dialysis modality-adjusted HRMultivariable-adjusted HR^a^CCI = 21113 \< =CCI \< =40.99 (0.95,1.01)1.01 (0.97,1.05)1.01 (0.98,1.05)5 \< =CCI \< =61.04 (1.00,1.08)1.09 (1.05,1.14)1.09 (1.05,1.14)CCI \> 61.07 (1.02,1.12)1.14 (1.09,1.19)1.14 (1.09,1.20)*Abbreviations*: *CCI* Charlson Comorbidity Index, *HR* hazard ratio^a^Adjusted for gender, the length of hospital stay, the receipt of mechanical ventilation, rescue, admission through emergency, the modality of dialysis and admission to intensive care unit (ICU)

Discussion {#Sec9}
==========

To the best of our knowledge, this study is the first describing the association between CCI and unplanned 30-day readmission among patients receiving maintenance dialysis based on a large nationwide database. The unplanned 30-day readmission rate in our study was 16.0%. Furthermore, CCI was independently associated with the risk of 30-day readmission and could therefore be used for risk stratification for hospitalized dialysis patients. Besides, we randomly selected one hospitalization as the index hospitalization, and the CCI score of patients in the index hospitalization could be useful to stratify a patient's risk of readmission according to our result. Patients on dialysis may have uniform distribution of illness severity in their randomly selected index hospitalizations and CCI score can play as an important predictor for readmission.

The 30-day readmission rate for patients with kidney disease varied across different countries. The proportion of 30-day readmission in ESKD patients was 35.4% reported by the US Renal Data System (USRDS), while it was 17.0% of patients receiving hemodialysis from 157 acute care hospitals in Canada, which is similar to our results \[[@CR6], [@CR19]\]. Possible reasons for the variation might include differences in the pattern of dialysis service and patients' characteristics. Furthermore, the 30-day readmission rate in the general patient populations might contribute substantially to the variation. For example, the readmission rate within 30 days was 15.3% among Medicare beneficiaries without CKD in the US, and it was 8.5% among overall hospitalized patients in Canada \[[@CR6], [@CR19]\]. In China, the readmission rate among ESKD patients was also two times that of all in-patients (10% versus 5.18%) in 2015\[[@CR20]\], and was much higher compared with patients with diabetes (2.4--4.2% during the year 2008--2013) \[[@CR21]\] or patients with acute exacerbation for chronic obstructive pulmonary disease (COPD) (6.8%) \[[@CR22]\]. Therefore, although varied, high readmission rates among the ESKD population were common and imposed increased burdens on health systems.

Furthermore, our results revealed that the readmission rate was higher among patients on peritoneal dialysis compared to those on hemodialysis, which is consistent with previous reports. Perl et al.\[[@CR23]\] demonstrated that patients on peritoneal dialysis had a 19% higher readmission risk than patients receiving hemodialysis therapy. Lafrance et al. \[[@CR24]\] found that the modality of peritoneal dialysis was associated with an increased risk of infection-related hospitalizations compared with the use of hemodialysis \[[@CR24]\]. For patients on peritoneal dialysis, peritonitis and bacteremia were common complications, which may have increased the risk of infection-related disease, and fewer physician visits in the dialysis unit than in hemodialysis patients probably resulted in the higher readmission rate.

Previous studies investigated the association between CCI and mortality among various types of kidney disease, and all of these studies suggested that the CCI could be used as a predictor of adverse outcome for patients with kidney disease \[[@CR10], [@CR11], [@CR13]\]. Some studies developed modified CCIs \[[@CR12], [@CR25], [@CR26]\] for mortality analysis of dialysis patients without counting kidney disease, but the performance of a modified CCI is almost identical to the original CCI in terms of c-statistics. In our study, although the overall CCI score is systematically deviated towards a higher score as kidney disease is scored for patients receiving dialysis. The deviated CCI score of patients on dialysis would not affect the relationship between CCI and unplanned readmission because we transformed a numerical CCI score to a categorical one by classifying patients into four different groups. Furthermore, there were other comorbidity indexes developed for patients receiving maintenance dialysis. For example, Wright-Khan \[[@CR27]\] proposed a comorbidity index based on data of ESKD patient. Davies \[[@CR28]\] developed a comorbidity index to predict the mortality of patients on ambulatory peritoneal dialysis. Several studies compared the above two indexes with CCI, and found that the CCI had the best performance regarding prediction of mortality \[[@CR29], [@CR30]\]. However, there are few studies investigating the association between comorbidity-indexes and the risk of 30-day readmission among patients with ESKD. CCI was initially designed to predict 1-year mortality for all hospitalized patients, but it has never been used to perform 30-day readmission prediction in dialysis patients. Previous studies have explored the association between CCI and readmission in the hip fracture population \[[@CR31]\] and in patients after orthopedic surgery \[[@CR32]\]. However, the predictive abilities of CCI for readmission in different populations were inconsistent. Our study indicated that a higher CCI score may indicate a higher risk of 30-day readmission rate, and it could therefore be used for risk stratification in clinical practice.

This study has the advantage of utilizing a large nationwide database with strict quality-control processes. However, the study has some limitations worth mentioning. First, although a national database was used to explore the association between CCI and the risk of 30-day readmission, it included only class 3 hospitals and lacked hospitalization records of dialysis patients from the primary and secondary hospitals. Selection bias might have occurred because the patients in class 3 hospitals tend to have severe situations which probably overestimated the readmission rate. However, the class 3 hospitals included in the HQMS could provide healthcare services to nationwide patients due to the lack of a standard referral system. Hospitalizations for all causes were considered in our study, as not all dialysis patients were admitted through nephrology in China. Second, we selected the patients on maintenance dialysis based on the diagnosis code of ICD-10 and the procedure code of ICD-9-CM-3, which may have ignored those patients with conditions of dialysis but having no records of the dialysis diagnosis or procedure in the database. However, the use of ICD codes to extract patients from an administrative or claims dataset is a common method for observational study. In addition, a previous study of HQMS \[[@CR14]\] has shown that ICD codes in the database had relatively low sensitivity and high specificity. According to the results, the non-dialysis patients were less likely to be misclassified as dialysis patients, which ensured the homogeneity of our population. Finally, as an administrative database, the database of HQMS lacked the variables of vital signs, results of laboratory tests and clinical medications, which implied that there may be residual confounding. Considering that these variables were not recorded in inpatient discharge summaries, and that we aimed to predict the 30-day readmission of dialysis patients from routinely available data, the use of HQMS was the best choice at present. The HQMS database has been used in previous studies \[[@CR14], [@CR33]\] to analyze the distribution and trend of CKD in China and the utilization of this dataset for rehospitalization research is also feasible.

Conclusions {#Sec10}
===========

Our study indicated that CCI was a predictor for the risk of 30-day readmission among patients with ESKD, and could be used for clinical risk prediction and patient management. For hospitalized dialysis patients with a CCI of 5 or higher, relatively intense follow-up is needed, especially during the first month after discharge. Whether the effective risk prediction and corresponding intervention could lead to the reduction of 30-day readmission, as well as medical expenditures, warrants further interventional research.

Additional file
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 {#Sec11}

Additional file 1:**Table S1.** The ICD-10 diagnosis codes and ICD-9-CM-3 procedure codes for identifying dialysis patients. **Table S2.** The ICD-10 diagnosis codes and ICD-9-CM-3 procedure codes for excluding patients with AKI, kidney transplantation and CKD stages G1--4. **Table S3.** The ICD-10 diagnosis codes and ICD-9-CM-3 procedure codes for identifying causes of hospitalization. **Table S4.** The ICD-10 diagnosis codes for identifying the diseases in CCI. **Table S5.** Comorbidity component of CCI and weighted score. (DOCX 19 kb)

AKI

:   Acute Kidney Injury

CCI

:   Charlson Comorbidity Index

CI

:   Confidence interval

CKD

:   Chronic kidney disease

ESKD

:   End-stage kidney disease

HQMS

:   Hospital Quality Monitoring System

HR

:   Hazard ratios

ICD

:   The International Classification of Diseases

ICD-10

:   The International Classification of Diseases-10 coding system

ICD-9-CM-3

:   The International Classification of Diseases-9, Clinical Modification Volume 3

ICU

:   Intensive care unit

IQR

:   Interquartile range

SD

:   Standard deviation

USRDS

:   US Renal Data System

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The authors thank the Bureau of Medical Administration and Medical Service Supervision, National Health Commission of China for the support of this study.

Research idea and study design: YL, LXZ, GLK; data acquisition and pre-processing: YS, HBW; data analysis and statistical analysis: YL, CY, JYW, KL; the methodology for extracting patients and identifying diseases: HC; supervision or mentorship: LXZ, GLK; Manuscript writing: YL, LXZ, GLK. Each author contributed important intellectual content during manuscript drafting and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. All the authors have read and given final approval of the version to be published.

This study was supported by Grants from the National Natural Science Foundation of China (Grant Nos. 81771938, 91846101, 81301296), from Peking University (Grant Nos. BMU2018MX020, PKU2017LCX05), the National Key Technology R&D Program of the Ministry of Science and Technology of the People's Republic of China (2016YFC1305400), and the University of Michigan Health System-Peking University Health Science Center Joint Institute for Translational and Clinical Research (BMU20160466, BMU2018JI012, BMU2019JI005). The funding body had no role in study design; collection, analysis, interpretation of data; writing the report; or the decision to submit the report for publication.

The data that support the findings of this study are available from the Bureau of Medical Administration and Medical Service Supervision, National Health Commission of China but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Bureau of Medical Administration and Medical Service Supervision, National Health Commission of China.

The Ethics Committee of Peking University First Hospital reviewed and approved the study. Acquisition of informed consent was exempted.

Not applicable.

Luxia Zhang received research funding from AstraZeneca. The remaining authors declare that they have no competing interests.