PEW Impact on Healthcare Costs in CKD Patients

Current Medical Research and Opinion
ISSN: 0300-7995 (Print) 1473-4877 (Online) Journal homepage: http://www.tandfonline.com/loi/icmo20
Protein-energy wasting significantly increases
healthcare utilization and costs among patients
with chronic kidney disease: a propensity-score
matched cohort study
Chia-Ter Chao, Chao-Hsiun Tang, Rhoda Wen-Yi Cheng, Michael Yao-Hsien
Wang & Kuan-Yu Hung
To cite this article: Chia-Ter Chao, Chao-Hsiun Tang, Rhoda Wen-Yi Cheng, Michael Yao-Hsien
Wang & Kuan-Yu Hung (2017): Protein-energy wasting significantly increases healthcare utilization
and costs among patients with chronic kidney disease: a propensity-score matched cohort study,
Current Medical Research and Opinion, DOI: 10.1080/03007995.2017.1354823
To link to this article: http://dx.doi.org/10.1080/03007995.2017.1354823
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Date: 15 July 2017, At: 23:16
Protein-energy wasting significantly increases healthcare utilization and costs among patients with
chronic kidney disease: a propensity-score matched cohort study
Chia-Ter Chaoa,b, Chao-Hsiun Tangc, Rhoda Wen-Yi Chengd, Michael Yao-Hsien Wangd, Kuan-Yu Hungb,e
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Department of Medicine, National Taiwan University Hospital Jin-Shan branch, No.7, Yulu Rd., Wuhu
Village, Jinshan Dist., New Taipei City 20844, Taiwan. E-mail: [email protected]
b
Department of Internal Medicine, National Taiwan University Hospital, No. 7, Chung-Shan South Rd.,
Taipei 10002, Taiwan.
c
School of Health Care Administration, College of Management, Taipei Medical University, No. 250, Wuxing
St., Taipei 11031, Taiwan. E-mail: [email protected]
d
Medical Affairs, Abbott Nutrition, 6F, No. 51, Sec. 3, Min Sheng E. Rd, Taipei 10478 Taiwan. E-mail:
[email protected]; [email protected]
e
Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu branch, No. 25, Lane 442,
Sec.1, Jingguo Rd., Hsinchu City 300, Taiwan. E-mail: [email protected]
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Corresponding author: Kuan-Yu Hung, MD, PhD, Department of Internal Medicine, National Taiwan
University Hospital Hsin-Chu branch, No. 25, Lane 442, Sec. 1, Jingguo Road, Hsin-Chu City 300, Taiwan.
E-mail: [email protected]
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TRANSPARENCY
Declaration of Funding
Abbott provided funding to support manuscript publication; however, this research work did not receive
any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The funder has
no role in the analysis, the interpretation of results, and discussion of this study.
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Declaration of conflict of Interest
KYH has received grant funding and consultancy fees from Abbott Nutrition. RWYC and MYHW are
employees of Abbott Nutrition; however, the information presented in this article is based on clinical
evidence and is not influenced by any financial relationship. CHT and CTC, have no relevant financial or
non-financial competing interests to declare. CMRO peer reviewers on this manuscript have no relevant
financial or other relationships to disclose.
Author contributions
CHT and KYH designed and conducted the study, analyzed and interpreted the data, and drafted the
manuscript; CTC conducted the study and interpreted data. RWYC and MYHW coordinated and managed
the research project. All authors critically reviewed manuscript drafts for important intellectual content and
approved the final version submitted.
Acknowledgement
The authors thank Dr. David Neil, of Content Ed Net (Taiwan), for his English editing services, which were
funded by Abbott Laboratories Services Corp., Taiwan Branch.
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Abstract
Background: Disease-related malnutrition is highly prevalent and has prognostic implications for patients
with chronic kidney disease (CKD); however, few studies have investigated the impact of malnutrition, or
protein-energy wasting (PEW), on healthcare utilization and medical expenditure among CKD patients.
Methods: Using claim data from the National Health Insurance in Taiwan, we identified patients with CKD
between 2009 and 2013 and categorize into those with mild, moderate, or severe CKD. Cases with PEW
after CKD was diagnosed were propensity-score matched with controls in 1:4 ratio. We compared
healthcare resource utilization metrics including outpatient and emergency department visits, frequency
and duration of hospitalization, and the cumulative costs associated with different CKD severity.
Results: From among 347,501 CKD patients, we selected eligible cohorts of 66,872 with mild CKD (49.2%),
27,122 with moderate CKD (19.9%), and 42,013 with severe CKD (30.9%). Malnourished CKD patients had
significantly higher rates of hospitalization (p < 0.001 for all severities) and re-admission (p = 0.015 for mild
CKD, p = 0.002 for severe CKD) than non-malnourished controls. Cumulative medical costs for outpatient
and emergency visits, and hospitalization, were significantly higher among all malnourished CKD patients
than non-malnourished ones (p < 0.001); total medical costs were also higher among malnourished patients
with mild (62.9%), moderate (59.6%), or severe (43.6%) CKD compared to non-malnourished patients (p <
0.001).
Conclusions: In a nationally-representative cohort, CKD patients with PEW had significantly more
healthcare resource utilization and higher aggregate medical costs than those without, across the spectrum
of CKD: preventing PEW in CKD patients should receive high priority if we would like to reduce medical
costs.
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Key words: Chronic kidney disease; Emergency department; End-stage renal disease; Healthcare utilization;
Medical costs; Protein-energy wasting; Re-admission.
Running Title: Malnutrition and healthcare utilization in CKD patients
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Abbreviations: BMI, body mass index; CCI, Charlson Comorbidity Index; CKD, chronic kidney disease; ESRD,
end-stage renal disease; ICD, International Classification of Disease; US, United States.
Introduction
Malnutrition denotes a state of deficient nutrition due to inadequate or imbalanced nutrient intake
(general or specific), which results in altered body composition and detriments clinical outcomes.1
Malnutrition has a tremendous global impact; compared to well-nourished patients, malnourished ones are
more likely to be hospitalized, have procedure-related complications, and higher mortality.2,3 Besides
children and the famine-stricken, in whom the adverse consequences of malnutrition are well-known, it has
become apparent that aged and disease-affected individuals are also highly susceptible to malnutrition.
Disease-related malnutrition is particularly exigent because of its high prevalence in various clinical
settings, its contribution to poor functional recovery and survival, and low awareness about this problem
due to inconsistent terminology and definitions.4-6 International nutritional societies advocate diagnosing
malnutrition based on etiologic factors including inflammatory status, with definitive diagnosis by either
low body-mass index (BMI), or combined weight loss and reduced BMI or fat-free mass index.1,5
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Disease-related malnutrition is very common among patients with chronic kidney disease (CKD) or
end-stage renal disease (ESRD), now termed protein-energy wasting (PEW), with estimated prevalence of
40% to 89%7-9, depending upon the assessment methods used. Despite considerable progress in treating
ESRD, high prevalence of PEW has been augmented by rising incidence over time.10 Factors presumed to
play a pathogenic role in PEW among CKD/ESRD patients include: coexisting gastrointestinal and/or
psychiatric illnesses; chronic low grade inflammation with cytokine influences; a uremic central nervous
system milieu with nausea and appetite alterations; and defective taste sensing.10,11 The complex interplay
between such factors further contributes to the significant detrimental effect of PEW on CKD/ESRD patient
outcomes.
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Besides compromising outcomes, malnutrition also incurs considerable costs; researchers in the
Netherlands reported that managing malnutrition accounted for 2% of annual national healthcare
expenditure.12 In general, malnourished patients with medical and surgical illnesses have 50% higher
medical costs than well-nourished ones, as a consequence of relatively longer hospital stays and higher
requirements for medical attention.13,14 However, published reports on the economic impact of
malnutrition are mostly based on data from single institutes or regional registries, and involve cohorts such
as hospitalized patients or those with cancer2,15,16 – rarely CKD/ESRD patients. Hence, we used a
well-validated health insurance claims database in Taiwan to investigate whether PEW significantly
increases healthcare utilization and medical costs among patients with CKD/ESRD.
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Methods
Study design
Analytic data were excerpted from the research database provided by the National Health Insurance
Administration in Taiwan, which has reimbursed nearly 100% of national healthcare services since 1995.
The broad array of reimbursed services, which include emergency, inpatient and outpatient care, surgery,
and medication prescriptions, permits precise estimation of healthcare resource utilization and medical
costs among patients with any specific illness – in this instance, CKD. All clinical data relevant to CKD were
excerpted, including demographic profiles, comorbidities and in-hospital diagnosis, drugs, and procedures.
Establishment of CKD cohorts
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Fig. 1 shows the patient selection process. CKD was defined by three consecutive outpatient visits or a
single hospitalization with an International Classification of Diseases 9th Version – Clinical Modification
(ICD-9-CM) diagnostic code denoting CKD (ICD-9-CM 585.1–585.9 and/or V45.11)17, from 2009 to 2013.
Enrolled CKD patients were divided into groups with mild, moderate, or severe CKD, according to specific
criteria. Severe CKD was defined as having procedural codes for hemodialysis > 26 times in 90 days, or
peritoneal dialysis > 3 times in 90 days, a catastrophic illness certificate for ESRD, and documented
evidence of ESRD persisting > 3 months.18,19 Procedure codes are codes used by healthcare facilities to
specify reimbursable operations or interventions and to report to National Health Insurance Bureau in
Taiwan, while catastrophic illness certificates are documents that confirm the presence of
government-recognized serious illnesses, and are issued only after a formal review by expert panels of
discipline-specific specialists.20 Since patients who possess these certificates do not need to make
copayment for the medical care they receive, the review process is very stringent and carries high
credibility. The use of catastrophic illness certificates and procedure codes to increase the accuracy of
identifying patients with ESRD has been utilized repetitively in the literature and exhibits high
specificity.18,21
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The criteria defining moderate CKD were having received erythropoiesis-stimulating agents to treat CKD
within 12 months before the index date22 (according to Taiwan National Health Insurance policy, only those
with CKD and a serum creatinine > 6 mg/dL or estimated glomerular filtration rate < 15 ml/min/1.73 m2 are
eligible for reimbursed erythropoiesis-stimulating agents), but having no ESRD catastrophic illness
certificate or dialysis codes; in addition, such patients had to be free of cancer or hematologic illnesses that
might also indicate erythropoiesis-stimulating agents. Mild CKD was defined as having received
angiotensin-converting enzyme inhibitors or angiotensin receptor blockers for ≥ 3 months during the index
enrollment year, but not having a catastrophic illness certificate, dialysis procedure codes, or receiving
erythropoiesis-stimulating agents during the year preceding the index date.
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Patients assigned procedural codes during 2009 were excluded to ensure that clinical features, including
comorbidities, had existed for at least one year preceding the enrollment index date, which was defined as
the date participants were first given the ICD-9-CM code for CKD and/or dialysis. In addition, patients with
moderate or mild CKD who had received dialysis for non-standard acute treatment, as well as some with
mild CKD who had received erythropoiesis-stimulating agents for a short period before the index date were
excluded, as were patients with presumed diagnoses of PEW during the year preceding the index date
(based on ICD-9-CM codes including: 260–263, 263.0, 263.2, 263.8, 263.9, 265, 265.0–265.2, 266, 266.0–
266.2, 266.9, 268, 268.0–268.2, 268.9, 799, 799.0–799.02, 799.1, 799.2, 799.21–799.25, 799.29, 799.3–
799.5, 799.51–799.55, 799.59, 799.8, 799.81, 799.82, 799.89, and 799.9).23 Since the National Health
Insurance database we used did not record laboratory parameters including serum creatinine, albumin, or
anthropometric variables (body mass index or body composition data), we could only use the diagnostic
codes of PEW to capture malnourished CKD cases. Charlson Comorbidity Index (CCI) scores, excluding the
contribution of CKD, were calculated based on the comorbidity profiles recorded during the year preceding
the index date.
From among each CKD severity group, patients with presumed PEW after the index date, based on the
aforementioned ICD-9-CM codes, constituted cases. Logistic regression was used to model the risk of
mortality in different CKD groups as a function of participants’ sociodemographic characteristics (age, sex,
residential location), comorbidity profile and CCI scores. Based on the regression results, a propensity-score
matched group was identified using an established procedure24, with a balanced distribution of covariates
between CKD patients of differing severity and with and without PEW, to serve as controls. CKD patients
defined as having PEW were matched 1:4, without replacement, to those without malnutrition, based on a
nearest neighbor approach. Both cases and controls in mild, moderate, and severe CKD groups were
prospectively followed up from the index date until death or 31 December 2013, whichever occurred first.
Outcome of interest
The primary outcome was extent of healthcare resource utilization, which included: the annual frequency
of outpatient and emergency department visits; the annual frequency and duration of hospitalization; and
the cumulative costs associated with different categories of medical care during the period between index
date and the end of follow-up, converted to United States (US) dollars at the time of analysis.
Statistical analysis
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Statistical analyses were performed using R-2.11.1 for Windows software (R Foundation for Statistical
Computing, Vienna, Austria)25, run on Microsoft Windows 7 Operating System. For clinical features
including sociodemographic and comorbidity data, and all outcomes, continuous variables were expressed
as mean and standard deviation (SD), compared using Student’s t-test. Categorical variables were
expressed as percentages, and compared using the Chi-square text. We constructed survival curves based
on the absence or presence of malnutrition in groups with differing CKD severity using Kaplan-Meier
analysis, and compared results with a log-rank test. Cox proportional hazard regression analysis,
incorporating sociodemographic variables, CCI, and PEW or not, was applied to assess the relationship
between malnutrition and mortality, as well as developing ESRD after follow-up, in each CKD subgroup. We
compared the extent of healthcare resource utilization and cumulative medical costs between patients with
mild, moderate, or severe CKD, with or without PEW, to identify the economic impact of PEW on each CKD
category.
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Results
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Ethics statement
The National Taiwan University Hospital Ethics Committee approved the study, and waived the
requirement for informed consent as the National Health Insurance Research Database used enciphers
patient identification numbers to ensure anonymity and privacy.
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Between 2009 and 2013 the Taiwan National Health Insurance database registered 347,501 patients with
ICD-9-CM codes of 585.1–585.9 (CKD) and/or V45.11 (dependence on renal dialysis), from among whom
66,782 with mild CKD (49.2%), 27,122 with moderate CKD (19.9%), and 42,013 with severe CKD (30.9%)
were selected according to the inclusion/exclusion criteria used in this study (Fig. 1) for 1:4
propensity-score matching22 of those with or without PEW; subsequent case-control analysis included 1087
patients with mild CKD, 408 with moderate CKD, and 1433 with severe CKD.
In general, CKD patients with and without PEW had similar sociodemographic and comorbidity profiles
(Table 1). However, some differences were statistically significant; patients with mild CKD and PEW had
lower prevalence of heart failure, while those with moderate CKD and PEW had proportionally less
peripheral vascular disease and moderate to severe hepatic disease, and higher prevalence of dementia.
After an average of 4 years’ follow-up, patients with CKD of any severity and PEW had significantly higher
mortality rates than those without PEW (Table 2 and supplementary Fig. 1). Cox proportional hazard
regression analyses showed that patients with PEW were twice as likely to die, irrespective of CKD severity,
and that those with mild or moderate CKD had higher risk of developing ESRD (Table 3).
Compared with CKD patients without PEW, PEW-positive counterparts with mild, moderate or severe CKD
also had significantly higher rates of hospitalization and re-admission within 30 days of a previous
hospitalization (Table 2, Fig. 2), as well as significantly higher frequencies of emergency and outpatient
visits, and days hospitalized per year. Consequently, healthcare expenditure of patients with CKD and PEW
was significantly higher than on those without PEW, with severe CKD group incurring the highest inpatient,
medication, and total costs (Fig. 3).
Discussion
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This study shows that PEW significantly detriments survival and renal prognosis of patients with mild to
severe CKD. Moreover, PEW also increases healthcare utilization by CKD patients, including more frequent
outpatient visits, up to twice as many emergency department visits, and two- to four-fold higher
hospitalization rates, with consequently increased healthcare costs, especially among patients with severe
CKD – a rarely-reported finding.
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In this study, we found that patients with moderate CKD and PEW exhibited numerically higher mortality
than those with mild or severe CKD and PEW. It is expectable that those with moderate CKD and PEW had
higher mortality than those with mild CKD and malnutrition, but the finding that those with moderate CKD
and PEW had higher mortality than those with severe CKD and PEW is unexpected. Several reasons might
be responsible for this phenomenon. First of all, patients with moderate CKD and PEW had higher
prevalence of peripheral vascular disease, dementia, moderate to severe liver disease, and any cancer
(Table 1) than those with severe CKD and PEW. These comorbidities have been reported to increase the risk
of mortality among CKD patients, and this higher comorbidity prevalence may correlate with worse
outcomes.26-28 Alternatively, the higher mortality in patients with moderate CKD and PEW than those with
severe CKD might be explained by a relatively decreased mortality in the latter group. It has been widely
recognized that patients with ESRD, especially those of advanced age, with frailty, or with poor functional
status, tend to be offered conservative management including dialysis withhold and palliative care, and this
is also the case in Taiwan.29,30 It is possible that the prevalent ESRD patients we enrolled to form the severe
CKD group, were of younger age and had comparatively better functional status than those with mild or
moderate CKD. This is supported by our finding that the age of patients with severe CKD was lower than
that of those with mild or moderate (Table 1). Further studies are needed in this regard.
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CKD patients are at higher risk of PEW due to gastrointestinal, psychiatric, and central nervous systems
perturbation, and being malnourished detriments patient outcomes31; PEW in patients with CKD has been
associated with up to three-fold higher risk of mortality32,33 or ESRD.34-36 However, few have addressed the
question of whether or how PEW is related to deteriorating renal function in CKD; furthermore, published
results are controversial, owing to being from a single center or having limited cohort sizes.37,38 In this
national cohort from Taiwan, we found that CKD patients with PEW had two-to-three-fold higher risk of
ESRD than those without; this finding highlights the prognostic significance of PEW in CKD in addition to its
well-known impact on overall survival.
Malnutrition not only detriments health-related patient outcomes, but also imposes an economic burden
on the healthcare system; the correlation between malnutrition and increased use of healthcare resources
has been recognized previously. Mosquera et al reported recently that malnourished patients receiving
major surgery were more likely than those without malnutrition to receive emergency procedures, had
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longer postoperative hospital stays, and higher rates of complications and readmission, all of which use
more resources and incur greater spending.39 It has been estimated that hospitalized patients with
malnutrition cost 20% to 38% extra to treat.13,14 A similar relationship is evident in disease-associated
malnutrition; for example, Hoong et al reported that patients with chronic obstructive pulmonary disease
patients were admitted more frequently if they had malnutrition, and also had longer hospital stays,
doubling total medical costs.40 In another large US study, the direct costs of disease-associated malnutrition
exceeded 15 billion US dollars, with malnourished patients with dementia and depression accounting for
two-thirds.41 This issue has rarely been addressed in patients with CKD/ESRD, among whom available
reports focused on the costs associated with the use of vitamin D, phosphate-binder, and intra-dialytic
supplementation.42,43 We found that malnutrition in CKD/ESRD patients, or PEW, likewise, increased their
healthcare utilization and costs. Interestingly, the extent of increased total cost relating to PEW in CKD
(20% to 30%; Fig. 3) appears lower than that in patients hospitalized for surgical (50% or higher) or other
medical illnesses (50% to three-fold)2,3,13,39,44; this might be because the risk of developing PEW among CKD
patients is high, and some patients in the propensity-matched control group might have clinical
malnutrition not detectable by administrative coding. On the other hand, patients with CKD tend to use
more healthcare resources than those without45, which would potentially lessen the discrepancy between
those with and without PEW, by augmenting the denominator. These issues should be taken into account
when we evaluate the economic burdens of malnutrition associated with different diseases.
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Accurate identification of malnutrition in clinical practice is an imperative, though controversial, issue.
Professional societies recommend screening using validated tools (Mini-Nutritional Assessment, Subjective
Global Assessment, Nutritional Risk Screening), to identify individuals at-risk for malnutrition, followed by
definitive diagnosis based on BMI and body composition parameters.1,46 However, studies to date have had
serious methodological shortcomings, including inadequate statistical power, preferential selection of
those amenable to receiving assessment, and inconsistent ascertainment methods. Accordingly,
researchers have begun to identify malnourished patients through electronic medical records of
administrative codes for malnutrition, to mitigate heterogeneity across institutes, patient populations, and
healthcare settings.23 Marco et al estimated the prevalence and risk factors for malnutrition in hospitalized
patients using ICD-9-CM codes in a nationwide database, and found that malnutrition was commonly linked
to dementia, cancer, and CKD.47 Corkins et al, using a composite set of malnutrition coding based on
ICD-9-CM, determined that 3.2% of all hospitalizations in the US had a diagnosis of malnutrition, and that
malnourished patients had significantly longer hospital stays and higher total medical costs.44 These results
indicate that malnutrition identified by administrative codes has similar prognostic implications as
malnutrition that is assessed objectively. We extracted clinical and reimbursement data from a claim-based
database, and defined malnutrition, or PEW by ICD-9-CM codes, which was similarly associated with higher
mortality and worse renal deterioration. More importantly, this approach facilitated a more comprehensive
analysis of healthcare expenditure in patients with CKD, an ever-increasing population that warrants
heightened attention. We contend that estimating healthcare utilization and costs using a
population-based database provides a more realistic and balanced view of the negative impact of
malnutrition.
Limitations
Our results should be interpreted carefully, in light of several limitations. First, CKD severity was not
categorized according to the ICD-9-CM diagnostic codes qualifying for entry into the study, but by using
subsequent procedural codes as surrogates; therefore, the proportions designated mild, moderate, and
severe, may not correspond to clinically determined diagnoses. Likewise, cases with PEW based on
administrative data might under-estimate the true prevalence in clinical practice, since under-reporting for
hospitalized patients is common.48 Nevertheless, we suppose that if statistical significance is achieved
based on analyzing the CKD population with PEW, it follows that the difference in healthcare utilization and
costs will be even greater after analyzing the entire CKD population. In addition, expert consensus
recommends that PEW be used to describe typical CKD-associated body mass and fuel reserve loss instead
of conventional terms including uremic malnutrition, protein-energy malnutrition, or
malnutrition-inflammation complex syndrome49; the term malnutrition consists of under-nutrition and
over-nutrition in other disease status, and thus we used PEW in this study to avoid confusion. To define
PEW, it would be best to have serum biochemical data such as albumin, prealbumin, or anthropometric
parameters, body composition indices, or dietary recordings49, but these data are not available in
claim-based administrative database we used in this study. Since we had no objective measures of PEW
severity, we could not analyze how the extent of PEW impacted analyzed variables. Finally, the Taiwan
National Health Insurance Administration database does not record laboratory data values such as albumin
or pre-albumin levels.
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Conclusion
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Malnutrition detriments patient outcomes and we have broadened evidence of its influence to encompass
a pharmaco-economic burden; our findings affirm that malnutrition, or PEW also incurs considerably
increased in healthcare resource utilization and medical costs in patients with CKD/ESRD, who are an
expanding population due to population aging, rising prevalence of the metabolic syndrome, and the
emergence of multimorbidity. To ameliorate these burdens, high priority should be given to preventing
patients with CKD from developing PEW.
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Figure legends
Fig. 1. Selection of cases and propensity-score matched controls
ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; ESA,
erythropoiesis-stimulating agents; ESRD, end-stage renal disease; HD, hemodialysis; NHIRD, Taiwan
National Health Insurance Research Database; PD, peritoneal dialysis; ICD-9-CM, International Classification
of Diseases 9th Version – Clinical Modification.
a
Three consecutive outpatient visits or one hospitalization.
b
ACEIs or ARBs for ≥ 3 months during the index enrollment year.
c
Concurrent ESA before the index date, with no cancer or hematologic illness.
d
Catastrophic illness certificate for ESRD, documentary evidence of ESRD persisting longer than 3 months,
and procedural codes of HD or PD.
e
Based on ICD-9-CM codes listed in the method section
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Fig. 2. Healthcare resource utilization among malnutrition/PEW positive and malnutrition/PEW negative
patients with CKD of different severity.
* p < 0.001
CKD, chronic kidney disease; PEW, protein-energy malnutrition
Supplementary figure legends
ed
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Fig. 3. Healthcare expenditure incurred by malnutrition/PEW positive and malnutrition/PEW negative
patients with CKD of different severity.
* p < 0.001
CKD, chronic kidney disease; PEW, protein-energy malnutrition
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Supplementary Fig. 1. Survival rates of CKD patients with versus without PEW.
Data analyzed by Kaplan-Meier method and compared by the log-rank test.
CKD, chronic kidney disease; PEW, protein-energy malnutrition.
pt
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ip
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ip
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ip
Table 1. Demographic and clinical characteristics of CKD patients with and without protein-energy wasting, after
propensity-score matching
CKD severity
PEW status
Mild
Moderate
Severe
Case
Control
p-value
Case
Control
p-value
Case
Control
p-value
74.1 ±
73.6 ±
0.244
71.9
71.4 ±
0.463
70.5
70.3 ±
12.6
12.2
±
13.3
±
13.7
0.620
76.8
1.000
Demographic data
Mean age (years ± SD)
13.9
86.5
86.9
80.9
80.8
76.7
ip
t
Proportion > 60 vs ≤ 60 (%)
14.0
57.1
57.0
57.1
1.000
Urban (%)
56.5
56.6
59.3
Rural (%)
43.5
43.4
0.9
0.71
Charlson Comorbidity Index
0.744
ed
scores
M
Comorbidity profile
88.5
89.1
pt
0–2
3–5
57.1
0.935
57.0
0.999
an
Urban vs. rural location
57.0
us
Male (%)
0.935
cr
0.935
Male vs. female sex
59.4
1.000
63.4
63.5
36.6
36.5
1.50
1.38
40.7
40.6
1.25
1.14
80.9
81.5
75.4
76.3
0.543
0.085
9.4
9.1
16.4
16.7
21.1
21.3
2.1
1.8
2.7
1.8
3.6
2.5
0.16
0.61
0.875
0.59
2.21
0.840
0.68
2.65
0.844
Congestive heart failure (%)
0.88
2.72
0.020
3.14
11.7
0.597
3.20
13.9
0.204
Peripheral vascular disease (%)
0.08
0.28
0.593
0.44
0.59
0.023
0.25
0.64
0.103
Cerebrovascular disease (%)
0.67
2.38
0.352
1.32
4.71
0.577
1.70
5.76
0.092
Dementia (%)
0.06
0.34
0.347
0.39
0.34
0.004
0.21
0.59
0.231
COPD (%)
0.48
1.86
0.856
1.08
3.82
0.608
1.14
4.12
0.383
Rheumatologic disease (%)
0.05
0.13
0.393
0.10
0.34
1.000
0.22
0.71
0.425
Peptic ulcer (%)
0.60
2.29
0.703
1.62
7.40
0.463
2.16
7.61
0.136
ce
≥6
Ac
Acute myocardial infarction (%)
Hepatic
disease
Mild
0.14
0.59
0.914
0.78
1.81
0.060
0.81
2.48
0.074
0.03
0.18
0.452
0.29
0.34
0.030
0.14
0.66
0.642
1.79
7.18
0.962
4.51
19.17
0.549
5.81
21.49
0.1
0.73
2.98
0.828
2.60
13.28
0.074
4.23
18.00
0.268
0.05
0.25
0.617
0.10
0.29
0.664
0.17
0.31
0.025
Any
0.48
1.81
0.737
1.47
3.97
0.057
1.31
4.19
0.049
Metastatic
0.15
0.38
0.103
0.29
0.54
0.127
0.33
0.64
0.003
Moderate/severe
(%)
Diabetes
Any
mellitus (%)
With end-organ
damage
Hemiplegia (%)
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Cancer (%)
CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; PEW, protein-energy
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wasting
Table 2. Hospitalization, readmission, and mortality rates among CKD patients with and without protein-energy
wasting
CKD severity
Case
Control
Case
Control
1078
3357
(50.30
(29.1)
(29.2)
(8.1)
(75.2)
(58.6)
t
132
0.658
390
41
42
(36.8)
(30.9)
(34.5)
(31.8)
<0.001
447
(41.5)
us
201
ip
0.015
(36.3)
<0.001
<0.001
665
222
467
753
1894
2217.9
9728.0
551.5
2833.1
2603.0
11520.1
0.19
0.07
0.16
0.29
0.16
ed
(person-years)
Ac
ce
pt
CKD, chronic kidney disease; PEW, protein-energy wasting.
0.40
0.002
1217
427
Mortality rates
p-value
<0.001
119
an
Person years
p-value
1263
Mortality rates
Deaths
Control
<0.001
M
30 days (%)
Case
Severe
547
30-day readmission rates
Number readmitted within
p-value
<0.001
Hospitalization rates
Number hospitalized (%)
Moderate
cr
PEW status
Mild
Table 3. Cox proportional hazard regression analysis of mortality and ESRD depending upon the presence of
PEW or not
Moderate
Severe
HR (95% CI)
PEW (+)
2.86 (2.53–3.23)
PEW (-)
1.00 (reference)
PEW (+)
2.48 (2.11–2.91)
PEW (-)
1.00 (reference)
PEW (+)
1.67 (1.54–1.82)
PEW (-)
1.00 (reference)
p-value
< 0.001
HR (95% CI)
p-value
3.07 (2.69–3.49)
< 0.001
1.00 (reference)
< 0.001
1.85 (1.53–2.24)
1.00 (reference)
< 0.001
1.84 (1.67–2.03)
< 0.001
< 0.001
1.00 (reference)
us
Mild
End-stage renal disease
t
status
Mortality
ip
Nutrition
cr
CKD severity
CKD, chronic kidney disease; ESRD, end-stage renal disease; HR, hazard ratio; CI, confidence interval; PEW,
Ac
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protein-energy wasting.