Hospitalizations among adults with chronic kidney disease in the United States: A cohort study.

BACKGROUND: Adults with chronic kidney disease (CKD) are hospitalized more frequently than those without CKD, but the magnitude of this excess morbidity and the factors associated with hospitalizations are not well known. METHODS AND
FINDINGS: Data from 3,939 participants enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study between 2003 and 2008 at 7 clinical centers in the United States were used to estimate primary causes of hospitalizations, hospitalization rates, and baseline participant factors associated with all-cause, cardiovascular, and non-cardiovascular hospitalizations during a median follow up of 9.6 years. Multivariable-adjusted Poisson regression was used to identify factors associated with hospitalization rates, including demographics, blood pressure, estimated glomerular filtration rate (eGFR), and proteinuria. Hospitalization rates in CRIC were compared with rates in the Nationwide Inpatient Sample (NIS) from 2012. Of the 3,939 CRIC participants, 45.1% were female, and 41.9% identified as non-Hispanic black, with a mean age of 57.7 years, and the mean eGFR is 44.9 ml/min/1.73m2. CRIC participants had an unadjusted overall hospitalization rate of 35.0 per 100 person-years (PY) [95% CI: 34.3 to 35.6] and 11.1 per 100 PY [95% CI: 10.8 to 11.5] for cardiovascular-related causes. All-cause, non-cardiovascular, and cardiovascular hospitalizations were associated with older age (≥65 versus 45 to 64 years), more proteinuria (≥150 to <500 versus <150 mg/g), higher systolic blood pressure (≥140 versus 120 to <130 mmHg), diabetes (versus no diabetes), and lower eGFR (<60 versus ≥60 ml/min/1.73m2). Non-Hispanic black (versus non-Hispanic white) race/ethnicity was associated with higher risk for cardiovascular hospitalization [rate ratio (RR) 1.25, 95% CI: 1.16 to 1.35, p-value < 0.001], while risk among females was lower [RR 0.89, 95% CI: 0.83 to 0.96, p-value = 0.002]. Rates of cardiovascular hospitalizations were higher among those with ≥500 mg/g of proteinuria irrespective of eGFR. The most common causes of hospitalization were related to cardiovascular (31.8%), genitourinary (8.7%), digestive (8.3%), endocrine, nutritional or metabolic (8.3%), and respiratory (6.7%) causes. Hospitalization rates were higher in CRIC than the NIS, except for non-cardiovascular hospitalizations among individuals aged >65 years. Limitations of the study include possible misclassification by diagnostic codes, residual confounding, and potential bias from healthy volunteer effect due to its observational nature.
CONCLUSIONS: In this study, we observed that adults with CKD had a higher hospitalization rate than the general population that is hospitalized, and even moderate reductions in kidney function were associated with elevated rates of hospitalization. Causes of hospitalization were predominantly related to cardiovascular disease, but other causes contributed, particularly, genitourinary, digestive, and endocrine, nutritional, and metabolic illnesses. High levels of proteinuria were observed to have the largest association with hospitalizations across a wide range of kidney function levels.

Introduction

The prevalence of chronic kidney disease (CKD) is high, affecting up to 15% to 20% of the adult population in the United States (US) [1-3]. Numerous studies have provided evidence that declining kidney function is associated with increased hospitalization, with CKD as an important predictor of hospitalizations, even after accounting for comorbidities [3-8]. Hospitalization rates for individuals with CKD have been reported to be more than twice the rate of those without CKD [9]. Hospitalizations that occur in people with kidney disease are also more likely to lead to further complications, including greater re-hospitalization rates, higher mortality, longer lengths of stay, and worsening of kidney function, compared with patients without CKD [10-13]. However, incomplete information on comorbidities and inadequate study of underrepresented populations within the broader US CKD population limit inferences from these studies regarding the burden of hospitalization in the setting of CKD. As well, prior published reports in this population do not permit extensive characterization of the risk factors for hospitalization [4,11,14-18].

Cardiovascular disease has been identified as a major cause of hospitalization among those with advanced kidney disease, but little is known about the other causes contributing to the high burden of hospitalization among those with earlier stages of CKD [3,6,19,20]. Additionally, prior research on hospitalizations in populations with kidney disease has been limited by studying only 1 center [6], 1 region [10], certain age groups [11,14], 1 sex [15], or only those with end-stage kidney disease (ESKD) [7]. As well, prior studies have primarily used retrospective claims data [6,7,10,11,21] and cross-sectional data [4,16-18] or focused primarily on healthcare costs, without accounting for proteinuria [8,22-24]. Further research is needed to characterize the major causes of hospitalization in CKD, which could guide more intensive outpatient management strategies to reduce hospitalization and ultimately reduce the morbidity and healthcare costs in this high-risk population.

In this manuscript, we characterize the burden of hospitalizations within a diverse study population with mild-to-moderate CKD enrolled in the multicenter observational Chronic Renal Insufficiency Cohort (CRIC) Study. We also explore associations of demographic and kidney-specific factors with rates of hospitalization, characterize the leading causes of hospitalization, and compare the rates of hospitalizations in this population with rates in a sample representative of nearly all hospitalizations in the US general population.

Methods

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Table).

Study design and study populations

The CRIC Study enrolled a total of 3,939 men and women between 2003 and 2008 at 7 clinical centers (University of Pennsylvania, Johns Hopkins University, Case Western Reserve University, University of Michigan, University of Illinois at Chicago, Tulane University, and Kaiser Permanent of Northern California) across the US. Eligibility criteria have been previously described [25,26]. Age-specific estimated glomerular filtration rate (eGFR) criteria for eligibility were as follows: 20 to 70 mL/min/1.73m2 for individuals aged 21 to 44 years, 20 to 60 mL/min/1.73m2 for individuals aged 45 to 64 years, and 20 to 50 mL/min/1.73m2 for individuals aged 65 to 74 years. Major exclusion criteria included prior dialysis lasting more than 1 month, NYHA Class III/IV heart failure, polycystic kidney disease, or other primary renal diseases requiring active immunosuppression. Participants completed annual clinic visits at which data were obtained, and blood and urine specimens were collected.

The National Inpatient Sample (NIS) was utilized as a representation of US nationwide hospitalizations from the year 2012. The NIS is the largest available all-payer inpatient database in the public domain that includes hospital discharge data, reflecting approximately 95% of all hospital discharges within the US [27]. The year 2012 was meant to be illustrative in comparison with the CRIC cohort and was chosen because it represented a year when the entire original CRIC cohort was enrolled and hospitalization ascertainment was optimized. The NIS provides discharge-level demographic and clinical characteristics that are searchable using the International Classification of Diseases, ninth revision (ICD-9) or the Clinical Classification System (CCS) codes and is sponsored by the Agency for Healthcare Research and Quality (AHRQ) and the Healthcare Cost and Utilization Project [28].

Study variables

Outcomes

Hospitalizations between 2003 and 2018 among CRIC participants were ascertained through self-report and hospital queries and confirmed after review of medical records by study personnel. Any hospitalization that occurred during the follow-up period was accounted for. The unit of observation in the NIS was an inpatient stay record, and after applying discharge weights, the amount of discharges in the US was estimated [29].

The length of hospitalization was calculated as the date of discharge minus the date of admission, plus one. Hospitalizations that were longer than 1 calendar day are the primary focus of this paper; hospital stays with an admission and discharge on the same calendar day are classified as ≤1-day hospitalizations. To characterize the specific cause of each hospitalization, the primary ICD-9 or ICD-10 admission code was extracted from the hospitalization discharge record and classified into 1 of 18 categories using the CCS Software developed by the AHRQ [28]. The cause of each hospitalization was further classified as “cardiovascular” if it met the AHRQ-defined categorization of “diseases of the circulatory system,” such as hypertension, coronary atherosclerosis and other heart disease, valvular and conduction disorders, heart failure, cerebrovascular disease, and peripheral vascular disease, and if the primary cause did not meet the definition, the hospitalization was classified as “non-cardiovascular.” Please see S1 Text for further details regarding the CCS categories.

Covariates

All considered covariates for the CRIC Study were ascertained at the baseline study visit. We examined age, sex, education, race/ethnicity, diabetes status (based on a fasting glucose of ≥126 mg/dL, a non-fasting glucose of ≥200 mg/dL, or self-reported use of insulin or other medications for glycemic control), systolic blood pressure (SBP) calculated as an average of 3 standardized measurements, eGFR assessed with a CRIC-specific equation using serum creatinine and cystatin C (ml/min/1.73m2) [30], and urine protein-to-creatinine ratio (UPCR), calculated as urine total protein concentration divided by urine creatinine concentration (mg/g). The site of recruitment was also included to account for patterns of hospitalization potentially related to specific geographic regions and case mix. In the NIS data, age was ascertained from the discharge record, and the distribution of diabetes and race/ethnicity (black, white, Hispanic, and other) was estimated from US national prevalence reports [31,32].

Statistical analysis

The analyses closely followed the original planning document. Please see S2 Text for further details.

The CRIC Study population was described using means, standard deviations, percentages, medians, and interquartile ranges; differences in key characteristics across categories of eGFR were assessed using ANOVA, chi-squared test, and Kruskal–Wallis test as appropriate. The follow-up time used in the analyses included time until the onset of ESKD, withdrawal, death, or end of the current follow-up period (early 2018), whichever occurred first, for median follow-up of 9.6 years.

Unadjusted rates of all-cause, cardiovascular-related, and non-cardiovascular-related hospitalizations were calculated in the CRIC Study as the total number of hospitalizations that occurred during follow-up divided by the total number of observed person-years (PY) outside of the hospital for all participants and stratified by baseline demographic and kidney-specific characteristics (age, sex, race/ethnicity, education level, diabetes status, and categories of SBP, eGFR, and UPCR).

Hospitalization rates were adjusted for age, race, and diabetes using Poisson regression models with the length of follow-up included as an offset term to account for varying durations of follow-up. Models examined the multivariable-adjusted associations of key baseline demographic and kidney-specific covariates (age <45, 45 to 64, >64 years), race/ethnicity (non-Hispanic [NH] white, NH black, Hispanic, other), sex (male, female), education level (less than high school, high school, some college, college graduate or higher), clinical center (University of Pennsylvania, Johns Hopkins University, Case Western Reserve University, University of Michigan, University of Illinois at Chicago, Tulane University, and Kaiser Permanent of Northern California), diabetes (no, yes), SBP (<120, 120 to <130, 130 to <140, ≥140 mmHg), eGFR (<30, 30 to <45, 45 to <60, ≥60 ml/min/1.73m2), and UPCR (<150, 150 to <500, ≥500 mg/g) with hospitalization rates. We also modeled age, SBP, eGFR, and UPCR as continuous variables as per unit change in response to reviewers. Poisson regression modeling was utilized to estimate a relative risk, reported as rate ratios (RR) and 95% confidence intervals (CI). In addition to main effects, we explored an interaction between proteinuria (UPCR < 150, 150 to <500, ≥500 mg/g) and eGFR (<30, 30 to <45, 45 to <60, ≥60 ml/min/1.73m2).

The frequency of each primary cause of hospitalization (using the 18 categories of primary cause generated by the CCS Software) during the follow-up period was calculated overall and by diabetes status. We compared the hospitalization rates (all-cause, cardiovascular-related, non-cardiovascular-related) among participants in the CRIC Study and the rates among those in the NIS sample within 4 age group categories (≤45 years, 46 to 55 years, 56 to 65 years, ≥66 years), adjusted for the national distribution of race and diabetes. In both data sets, the rate was ascertained by summing hospitalizations for a given age group over the person-time of the follow-up in that group. Hospitalization rates in the NIS were calculated by first applying discharge weights to the NIS discharge data to estimate the number of hospital discharges that occurred in the US in 2012 [29] and then divided this number by the US population determined by the census.

Ethics statement

The CRIC Study protocol was approved by the institutional review boards of all participating centers: the University of Pennsylvania Institutional Review Board (Federalwide Assurance # 00004028), Johns Hopkins Institutional Review Board (Study # NA_00044034 / CIR00004697), The University of Maryland, Baltimore Institutional Review Board, University Hospitals Cleveland Medical Center Institutional Review Board, MetroHealth Institutional Review Board, Cleveland Clinic Foundation Institutional Review Board (IRB #5969), University of Michigan Medical School Institutional Review Board (IRBMED), Wayne State University Institutional Review Board, University of Illinois at Chicago Institutional Review Board, Tulane Human Research Protection Office, Institutional Review Boards, Biomedical Social Behavioral (reference#: 140987), and Kaiser Permanente Northern California Institutional Review Board. The study is in accordance with the Declaration of Helsinki. All CRIC participants provided written informed consent.

Results

The analysis included data from 3,939 CRIC Study participants (mean age 57.7 years; 54.9% male). Baseline characteristics of the study population are reported overall and by eGFR category (Table 1). Age, sex, race, education, UPCR and SBP levels, and diabetes significantly differed across eGFR categories. A total of 11,603 hospitalizations occurred between 2003 and 2018, for a median follow-up of 9.6 (interquartile range [IQR] 4.2 to 12.5) years.

Table 1

Characteristics of CRIC participants at baseline overall and by eGFR (N = 3,939).

Characteristic	Overall(N = 3,939)	By eGFR (ml/min/1.73m2)
		<30(N = 806)	30 to <45(N = 1,340)	45 to <60(N = 1,091)	≥60(N = 702)	p-value*
Age, years	57.7 (11)	58.4 (10.9)	59.2 (10.9)	58.2 (10.6)	52.9 (10.6)	<0.001
Sex						<0.001
Male	2,161 (54.9%)	402 (49.9%)	712 (53.1%)	650 (59.6%)	397 (56.6%)
Female	1,778 (45.1%)	404 (50.1%)	628 (46.9%)	441 (40.4%)	305 (43.4%)
Race/ethnicity						<0.001
Non-Hispanic white	1,638 (41.6%)	255 (31.6%)	529 (39.5%)	490 (44.9%)	364 (51.9%)
Non-Hispanic black	1,650 (41.9%)	370 (45.9%)	583 (43.5%)	439 (40.2%)	258 (36.8%)
Hispanic	497 (12.6%)	153 (19%)	186 (13.9%)	117 (10.7%)	41 (5.8%)
Other	154 (3.9%)	28 (3.5%)	42 (3.1%)	45 (4.1%)	39 (5.6%)
Education						<0.001
Less than high school	828 (21%)	250 (31%)	341 (25.4%)	184 (16.9%)	53 (7.6%)
High school graduate	741 (18.8%)	175 (21.7%)	265 (19.8%)	202 (18.5%)	99 (14.1%)
Some college	1,146 (29.1%)	220 (27.3%)	383 (28.6%)	341 (31.3%)	202 (28.8%)
College graduate or higher	1,223 (31.1%)	161 (20%)	351 (26.2%)	364 (33.4%)	347 (49.5%)
Diabetes	1,908 (48.4%)	479 (59.4%)	737 (55%)	492 (45.1%)	200 (28.5%)	<0.001
Systolic blood pressure (mmHg)	129 (22)	133 (24)	130 (23)	127 (21)	122 (19)	<0.001
Urine protein-creatinine ratio (mg/g)	152.6 (57.5–777.5)	675.9 (138.3–2295.4)	236.2 (68.4–1096.5)	100.6 (50.6–364.3)	64.1 (41.6–135.4)	<0.001
eGFR (ml/min/1.73m2)	44.9 (16.7)	24.3 (3.8)	37.5 (4.2)	51.9 (4.4)	71.7 (10.3)	<0.001

For categorical variables, reported as number (%, percentage), and for continuous variables, mean and standard deviation is reported if normally distributed and mean and interquartile range if not normally distributed.

CRIC, Chronic Renal Insufficiency Cohort; eGFR, estimated glomerular filtration rate.

*χ2 test for categorical variables and the ANOVA test or the Kruskal–Wallis test for continuous variables.

All-cause, cardiovascular, and non-cardiovascular hospitalization rates

The unadjusted all-cause hospitalization rates were 35.0 per 100 PY (95% CI: 34.3 to 35.6), with non-cardiovascular rates 23.9 per 100 PY (95% CI: 23.3 to 24.4) and cardiovascular-specific rates less than one-third of the all-cause rates (11.1 per 100 PY, 95% CI: 10.8 to 11.5) (S2 Table). Age-, race-, and diabetes-adjusted all-cause, cardiovascular, and non-cardiovascular-related hospitalization rates were highest among older participants (≥65 years), NH black participants, and those with diabetes (see Fig 1 and S3 Table).

Fig 1

Age-, race-, and diabetes-adjusted rates for all-cause, non-cardiovascular, and cardiovascular hospitalizations in CRIC participants (N = 3,939).

Rates reported as per 100 PY. Error bars indicate 95% confidence intervals. CRIC, Chronic Renal Insufficiency Cohort; PY, person-years.

Association of baseline demographic and kidney-specific characteristics and hospitalization rates

Multivariable-adjusted associations of demographic and kidney-related characteristics and hospitalization rates, reported as RR and 95% CI using categorical versions of age, SBP, UPCR, and eGFR are shown in Fig 2 and S4 Table. When modeled continuously, these variables demonstrated a linear relationship with hospitalization rates (S1 Fig). The adjusted associations of non-cardiovascular hospitalizations largely aligned with those of all-cause hospitalizations. Age ≥65 years (versus 45 to 64 years), diabetes (versus no diabetes), SBP ≥140 mmHg (versus SBP 120 to <130 mmHg), UPCR categories ≥500 and 150 to <500 mg/g (versus UPCR <150 mg/g), and an eGFR categories <30, 30 to <45, 45 to <60 ml/min/1.73m2 (versus eGFR ≥60 ml/min/1.73m2), particularly at an eGFR <45 ml/min/1.73m2, were associated with higher rates of hospitalizations for all-causes, cardiovascular, and non-cardiovascular causes (please see Fig 2 and S4 Table). Adjusted associations of baseline characteristics with hospitalization rate ratios were similar in magnitude and direction across types (all-cause, cardiovascular, non-cardiovascular), except for sex, race/ethnicity, and SBP. Compared with males, female participants experienced higher rates of all-cause and non-cardiovascular hospitalizations, but lower rates of cardiovascular hospitalizations (all-cause: RR 1.08, 95% CI: 1.04 to 1.13, p-value < 0.001; non-cardiovascular: RR 1.18, 95% CI: 1.13 to 1.24, p-value < 0.001; cardiovascular: RR 0.89, 95% CI: 0.83 to 0.96, p-value = 0.002). Compared with NH whites, NH black participants experienced significantly higher cardiovascular hospitalization rates, but almost the same all-cause and non-cardiovascular hospitalizations (cardiovascular: RR 1.25, 95% CI: 1.16 to 1.35, p-value < 0.001; all-cause: RR 1.04, 95% CI: 1.00 to 1.09, p-value < 0.001; non-cardiovascular: RR 0.96, 95% CI: 0.91 to 1.01, p-value < 0.001). An SBP level of ≥140 mmHg (versus 120 to <130) was associated with more all-cause and non-cardiovascular hospitalizations (all-cause: RR 1.14, 95% CI: 1.08 to 1.21, p-value < 0.001; non-cardiovascular: RR 1.09, 95% CI: 1.02 to 1.17, p-value = 0.017), but an SBP level of 130 to <140 was associated with increased cardiovascular hospitalizations (RR 1.16, 95% CI: 1.04 to 1.30, p-value < 0.001). There was a consistent dose-dependent increase on all types of hospitalization as proteinuria increased and as eGFR decreased (Fig 2 and S4 Table).

Fig 2

Plot of adjusted all-cause, cardiovascular, and non-cardiovascular hospitalization rate ratios.

Error bars indicate 95% confidence intervals. Models adjusted for age, race, sex, clinical center, education, SBP, diabetes, UPCR, and CRIC baseline eGFR. CRIC, Chronic Renal Insufficiency Cohort; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; UPCR, urine protein-to-creatinine ratio.

An interaction between eGFR and proteinuria was detectable for all types of hospitalizations (p-value < 0.001) (Fig 3 and S5 Table). The observed effect of each factor on hospitalization rates was largely restricted to participants with relatively preserved kidney function as measured by the other factor. Participants with an eGFR <30 ml/min/1.73m2 experienced high hospitalization rates across UPCR categories of <150 mg/g and ≥500 mg/g and participants with moderate-heavy proteinuria (UPCR of ≥500 and 150 to <500 mg/g) experienced high hospitalization rates across eGFR levels.

Fig 3

Adjusted rates of hospitalization for (A) all-causes, (B) non-cardiovascular, and (C) cardiovascular, by level of eGFR and proteinuria. Models adjusted for age, race, sex, clinical center, education, SBP, diabetes, UPCR, eGFR, and interaction between UCPR and eGFR. eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; UPCR, urine protein-to-creatinine ratio.

Primary causes of hospitalizations

The 5 most common causes of hospitalization were cardiovascular-related (31.8%); genitourinary (GU)-related (8.7%); digestive/gastrointestinal (GI)-related (8.3%); endocrine, nutritional, or metabolic and immunity-related (8.3%); and respiratory-related (6.7%) (Fig 4). The patterns among participants with diabetes mirror the overall pattern just described. Participants without diabetes experienced fewer admissions due to endocrine, nutritional, or metabolic and immune causes (4.2% versus 11.6%) and more admissions due to musculoskeletal/connective tissue causes (8.1 versus 4.9%) (S2 Fig).

Fig 4

Prevalence of primary cause of each hospitalization in the CRIC Study follow-up period.

CRIC, Chronic Renal Insufficiency Cohort.

Age-specific hospitalization rates in the CRIC study and national inpatient sample

All-cause adjusted hospitalization rates were observed to be higher in the CRIC cohort than the NIS for each age category (≤45 years: 19.1 versus 7.7; 46 to ≤55 years: 24.1 versus 9.3; 56 to ≤65 years: 26.3 versus 13.1; >65 years: 32.3 versus 31.5 per 100 PY) (Fig 5). Cardiovascular hospitalization rates were also higher in the CRIC cohort than in the NIS across each age category (≤45 years: 4.1 versus 0.2; 46 to ≤55 years: 6.9 versus 1.6; 56 to ≤65 years: 7.8 versus 2.9; >65 years: 11.3 versus 8.2 per 100 PY). Non-cardiovascular hospitalization rates remained higher in the CRIC Study for individuals aged ≤65 years (≤45 years: 15.0 versus 7.4; 46 to ≤55 years: 17.2 versus 7.6; 56 to ≤65 years: 18.4 versus 10.3) and were similar to the NIS for those aged >65 years (20.8 versus 23.3 per 100 PY). In general, discrepancies between hospitalization rates in the CRIC and NIS samples were greatest in the younger age groups; for individuals over 65 years of age, all-cause hospitalization rates in the 2 samples were more similar.

Fig 5

Hospitalization rates (per 100 person-years) across age categories in the CRIC Study and national inpatient sample.

CRIC, Chronic Renal Insufficiency Cohort.

Unadjusted and adjusted hospitalization rates of hospital stays ≤1-day are displayed in S6–S9 Tables and S3 Fig.

Discussion

In the current investigation of CRIC Study participants, a well-characterized cohort of individuals with mild-to-moderate CKD, we observed that while the most common reason for hospitalization was related to cardiovascular disease, the majority of hospitalizations were due to non-cardiovascular causes, particularly GI-; GU-; and endocrine, nutrition, and metabolic-related issues. We also identified several characteristics associated with increased hospitalization, including a relatively preserved eGFR (<60 ml/min/1.73m2), a blood pressure level of SBP of >130 mmHg, and the presence of moderate-to-heavy proteinuria. Hospitalization rates in the CRIC cohort were consistently higher among individuals younger than 65 years than among similarly aged individuals in the NIS, a representative US national sample of hospitalizations. These findings shed new light on how the patterns of and factors related to hospitalizations in the setting of CKD reflect the high burden of diverse morbidities in this population.

Previous studies have reported that older age, female sex, concomitant cardiovascular disease, reduced eGFR, and diabetes may predict hospitalization in patients with CKD [6,7,21,23]. However, many of these previous reports were limited to retrospective cohort studies of individuals with advanced CKD, including individuals with incident ESKD. Similar to previous studies, we observed that older age and diabetes had significant associations with hospitalizations, but further extended these findings to individuals with earlier stages of CKD and to the characterization of types of hospitalization, including all-cause, cardiovascular, and non-cardiovascular hospitalizations.

The eGFR level at which hospitalization rates increase in the setting of CKD is not well characterized. In a community-based cohort study of elderly adults, the eGFR level associated with more hospitalizations was reported to be less than 30 ml/min/1.73m2, but this study was limited to adults over 75 years of age [11]. In the current study, comprising a much younger cohort, hospitalization rates were higher at even at moderate reductions in kidney function (eGFR level of <60 ml/min/1.73m2), and consistent with previous reports [4], we observed that individuals with eGFR of 30 to <45 ml/min/1.73m2 (CKD stage 3b) had higher rates of cardiovascular-related hospitalizations than those with CKD stage 3a (eGFR of 45 to <60 ml/min/1.73m2), even after adjusting for proteinuria and other comorbidities. Together, these findings suggest that patients with stage 3 CKD, who represent the majority of North Americans with CKD [1], are at an increased risk for hospitalizations. Since fewer than 10% of individuals with eGFR of <60 ml/min/1.73m2 in the US are under the care of a nephrologist, awareness should be raised among primary healthcare providers of the increased hospitalization risk among this group of individuals [33,34].

The impact of proteinuria on hospitalization risk has not been well described as many of the previous studies in kidney disease populations did not account for proteinuria [6,8,10,19,21,35-38]. However, in 1 large community-based longitudinal study of patients with CKD, Hemmelgarn and colleagues reported that the risks of hospitalization for myocardial infarction independently increased in patients with higher levels of proteinuria at a given level of eGFR [39]. Extending these findings, we observed that participants with the heaviest proteinuria experienced higher rates of all types of hospitalization across a wide range of eGFR levels, similar to the rates observed among participants with eGFR of <30 ml/min/1.73m2 at any proteinuria level. Even in the presence of moderate proteinuria (150 to <500 mg/g), hospitalization rates were particularly high among those with eGFR of <45 ml/min/1.73m2. Together, these findings support the need to characterize the risk of hospitalization based on both eGFR and severity of proteinuria, which reinforces the utility of the Kidney Disease Improving Global Outcomes (KDIGO) staging system for CKD, which accounts for both eGFR and proteinuria [40]. Doing so could more effectively identify individuals at highest risk for being hospitalized.

We observed that women with mild-to-moderate CKD experienced more all-cause and non-cardiovascular hospitalizations but fewer cardiovascular-related hospitalizations than men. Potentially, these observations may be related to a greater rate and severity of complications of kidney disease in women, leading to increased hospitalizations for non-cardiovascular causes, such as electrolyte disorders or underestimation of kidney function, compared with men [7]. Additionally, there may be differences in the prevention, recognition, and care of cardiovascular disease between men and women. Previous literature has reported that physicians are less likely to discuss with women cardiovascular disease risk and provide quality preventative care or refer for cardiac catheterization when presenting with chest pain compared to men, and historically, women have been excluded from cardiovascular clinical trials [41-43]. Additionally, participants who identified as NH black or Hispanic experienced more cardiovascular hospitalizations than NH white participants. This finding could be attributed to the rising rate of cardiovascular disease and its risk factors among these minority groups compared to NH whites [44].

Individuals with CKD have been reported to be hospitalized more than twice as frequently in the preceding year as individuals without CKD [9]. In the current study, we expanded on these findings and report actual rates of hospitalization in a diverse cohort of individuals with known CKD, comparing with rates in a nationally representative sample of all hospitalized Americans. We observed that the rates for all-cause and cardiovascular-related hospitalizations were approximately 2-fold higher in CRIC than in the NIS, while non-cardiovascular hospitalization rates among the group older than 65 years were similar. These findings further demonstrate that individuals with CKD are hospitalized for a diverse set of causes more frequently at all ages. They further highlight the higher rate hospitalization in the setting of CKD compared with the rest of the US population.

In the literature, the main burden of hospitalizations in CKD has been attributed to cardiovascular causes, estimated to be 21% to 32% of all hospitalizations [3,10,20]. Several authors have also reported that after cardiovascular causes, the next largest contributor of hospitalizations is due either to infections (22%) [3] or digestive diseases (9% to 11%) [10,20], which leaves uncharacterized the majority of hospitalizations that occur in the CKD population not yet characterized. In the current study, we also identified that cardiovascular-related and digestive/GI-related causes were responsible for a large amount of hospitalizations (40.1%) occurring during the CRIC follow-up. This study has also pinpointed 2 other large contributors to the burden of hospitalization that have not yet been described in the CKD population, namely, GU-related causes and endocrine, nutrition, metabolic-related causes. The GU-related CCS category includes conditions related to kidney disease itself, such as acute kidney injury and chronic renal failure, as well as disorders of the urinary tract, including hydronephrosis, calculi, and infections (refer to S1 Text for more details). The broad category of endocrine, nutrition, and metabolic-related hospitalizations includes not only many categories clearly related to endocrine and nutrition issues, such as diabetes, thyroid disorders, and nutritional deficiencies, but also causes that could be attributed to kidney disease, including gout, fluid and electrolyte disorders, and disorders of mineral metabolism. These hospitalizations were particularly common among CRIC participants with diabetes compared with those without diabetes (11.6% versus 4.2%, respectively), which is especially important since almost half of individuals with CKD have diabetes [45]. Overall, close to 70% of hospitalizations in CKD are not due to cardiovascular disease, which is often thought of as the principal morbidity in CKD, and our findings support that recognizing CKD as a risk factor for acute kidney injury, and providing guideline-based management for CKD care could be a very important strategy to reduce hospital admissions among those with CKD. Future efforts directed at the underlying causes of endocrine, nutrition, and metabolic-related admissions, such as managing and/or preventing electrolyte abnormalities, polypharmacy, and ensuring correct medication dose adjustments, could further reduce these hospitalizations.

Our study has several potential limitations that should be considered when interpreting its findings. First, diagnostic codes and the CCS categorizations are subject to misclassification. We suspect that the low number of hospitalizations attributed to infections is due to the CCS defined categories, since infections could also be categorized into individual organ systems in addition to the infection category. Given the large number of hospitalizations across various regions of the US, however, we expect this to not invalidate our aggregate results. Also, as with all observational studies, residual confounding is possible. Additionally, the CRIC Study enrolled volunteers, which may limit generalizability of these findings. In particular, enrollment criteria excluded those with advanced heart disease, which may have biased our results toward more non-cardiovascular hospitalizations. Our study also has several important strengths including the large prospective cohort of well-characterized participants with mild-to-moderate CKD from 7 clinical centers across the US whose hospitalizations are captured through multiple approaches of ascertainment. Further, we were able to adjust hospitalization rates for proteinuria, an important outcome prediction marker, which has been a prior limitation of many previous studies related to hospitalization in the CKD population. Additionally, the NIS is estimated to represent approximately 95% of all hospital discharges in the US. Finally, the CRIC Study populations’ age distribution is notably broader than was the distribution of other prospective observational cohorts with CKD that have principally examined hospitalization data in older individuals.

Conclusion

The observed hospitalization rate among those with CKD was much higher than the rate in the general population ≤65 years of age that is hospitalized in the US, and the hospitalizations typically occurred at kidney function levels not requiring nephrology care. Although cardiovascular illness is the most common cause of hospitalization, a majority of hospital stays are due to non-cardiovascular illnesses, including GI-; GU-; and endocrine, nutrition, and metabolic-related causes. Heavy proteinuria is a potent risk factor for hospitalization. These findings highlight the need for developing better approaches to identifying patients at risk for severe complications of CKD and to developing mitigation strategies to improve outcomes in CKD.

Curves from model-based associations of age, eGFR, UPCR, and SBP modeled continuously with hospitalization rates.

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The prevalence of the primary cause of each hospitalization in CRIC by diabetes status.

(TIF)

Click here for additional data file.

The prevalence of the primary cause of each ≤1-day hospitalization in CRIC by diabetes status.

(TIF)

Click here for additional data file.

Modified STROBE Statement.

(DOCX)

Click here for additional data file.

Unadjusted all-cause hospitalization rates.

(DOCX)

Click here for additional data file.

Age-, race-, and diabetes-adjusted all-cause, cardiovascular-, and non-cardiovascular-related hospitalization rates.

(DOCX)

Click here for additional data file.

Multivariable-adjusted associations of demographic and kidney-related characteristics and hospitalization rates.

(DOCX)

Click here for additional data file.

Multivariable-adjusted all-cause, cardiovascular, and non-cardiovascular hospitalization rates in CRIC participants by proteinuria and eGFR level during the follow-up period.

(DOCX)

Click here for additional data file.

Unadjusted rate of all-cause, cardiovascular, and non-cardiovascular ≤1-day hospitalizations by key baseline characteristics of CRIC participants (N = 3,939).

(DOCX)

Click here for additional data file.

Multivariable adjusted rates of all-cause, cardiovascular, and non-cardiovascular ≤1-day hospitalizations by age, race/ethnicity, and diabetes of CRIC participants (N = 3,939).

Rates reported as per 100 person-years.

(DOCX)

Click here for additional data file.

Multivariable adjusted rate ratios of all-cause, cardiovascular, and non-cardiovascular ≤1-day hospitalizations by key baseline characteristics of CRIC participants (N = 3,939).

(DOCX)

Click here for additional data file.

Multivariable-adjusted all-cause, cardiovascular, and non-cardiovascular ≤1-day hospitalization rates in CRIC participants by proteinuria and eGFR level during the follow-up period.

(DOCX)

Click here for additional data file.

Categories of CCS.

(DOCX)

Click here for additional data file.

Analytical Planning Document.

(DOCX)

Click here for additional data file.

11 May 2020

Dear Dr Schrauben,

Thank you for submitting your manuscript entitled "Hospitalization Experience of Adults with Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by .

Login to Editorial Manager here: https://www.editorialmanager.com/pmedicine

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Helen Howard, for Clare Stone PhD

Acting Editor-in-Chief

PLOS Medicine

plosmedicine.org

18 Jun 2020

Dear Dr. Schrauben,

Thank you very much for submitting your manuscript "Hospitalization Experience of Adults with Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study" (PMEDICINE-D-20-01995R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Jul 09 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

* Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions).

* Please combine the Methods and Findings sections into one section, “Methods and findings”.

Abstract Methods and Findings:

* Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

* Please include the study design, population and setting, number of participants, years during which the study took place, length of follow up, and main outcome measures.

* Please quantify the main results (with 95% CIs and p values).

* Please include the important dependent variables that are adjusted for in the analyses.

Abstract Conclusions:

* Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful.

* Please interpret the study based on the results presented in the abstract, emphasizing what is new without overstating your conclusions.

* Please avoid vague statements such as "these results have major implications for policy/clinical care". Mention only specific implications substantiated by the results.

* Please avoid assertions of primacy ("We report for the first time....")

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

References- please use square brackets throughout and use Vancouver style for bibliography

Methods- please briefly state where the participants were recruited in the US

On page 8- please remove the information regarding the funding source as this is pulled from the article meta-data only

Please provide 95% confidence intervals and p values throughout as needed. Please note we need exact p values unless p<0.001

Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

STROBE- please specify where individual items on the checklist can be found using paragraphs and sections.

The Data Availability Statement (DAS) requires revision. For each data source used in your study:

a) If the data are freely or publicly available, note this and state the location of the data: within the paper, in Supporting Information files, or in a public repository (include the DOI or accession number).

b) If the data are owned by a third party but freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). Note that a study author cannot be the contact person for the data.

c) If the data are not freely available, please describe briefly the ethical, legal, or contractual restriction that prevents you from sharing it. Please also include an appropriate contact (web or email address) for inquiries (again, this cannot be a study author).

Comments from the reviewers:

Reviewer #1: I confine my remarks to statistical aspects of this paper. The general approach is fine but I have a couple issues to resolve before i can recommend publication.

The biggest one is that continuous independent variables should not be categorized. In *Regression Modeling Strategies* Frank Harrell lists 11 problems that this causes and sums up "nothing could be more disastrous". I wrote a blog post taking a graphical look at what can happen: https://medium.com/@peterflom/what-happens-when-we-categorize-an-independent-variable-in-regression-77d4c5862b6c?source=friends_link&sk=1428cd15968e218268121dc507ce8025

Other issues: p. 7 Please operationalize all variables.

p 9 Give the SD for age

Fig 1: Use a line plot for age and do not use "dynamite plots" (see e.g http://biostat.mc.vanderbilt.edu/wiki/Main/DynamitePlots)

Fig 3: Do not use 3 dimensional plots. William S. Cleveland has shown that these cannot be accurately read. But the variables should be continuous, so this plot will change a lot.

Peter Flom

Reviewer #2: Schrauben et al. explored hospitalization rates among persons with CKD in the CRIC observational study and contrasted with hospitalization rates in the Nationwide Inpatient Sample (NIS) for 2012. They found higher hospitalization rate in the CRIS than NIS, predominantly related to diverse illnesses other than cardiovascular disease.

I offer the following suggestions for consideration:

1. The concluding statement in the abstract "Adults with CKD appear to have a substantially higher hospitalization rate than those without CKD" is not supported by the results.

2. On page 8, under statistical analysis, please specify the start of follow-up in the CRIC cohort and the median follow-up time. Also, describe how the hospitalization rates were calculated for the NIS.

3. Please indicate the reasoning for selecting the year 2012 for NIS data.

4. NIS data represent the number of discharge diagnoses per hospital stay, not per patient, so the incidence of any hospitalization is overestimated, an aspect that should be discussed as a limitation. Along this line, it is not clear if the numerator for the CRIC data included the first (incident) hospitalization during the follow-up period, or any hospitalizations. If the former is true, the authors may consider discussing how this aspect affects the comparison with NIS rates. The authors ought to specify how the numerator was counted in each of the two data sources.

Reviewer #3: This is an interesting paper and highlights a major issue for patients with even fairly mild chronic kidney disease. The data presented genuinely contribute to novel understanding of this issue and in my opinion will be useful for research, healthcare policy and resource allocation. The CRIC cohort is well known, but this looks like a completely new and more detailed analysis compared to any of their other work.

To my reading, this is well presented, the analyses look sound and the tables and figures are informative. I have little to suggest to this and I think this could be a very useful paper for others working in this field.

My main comments are around the grammar, or messaging in the paper

Abstract.

1.I presume they mean 500mg/g of proteinuria? - there is no explanation as to what this measurement refers to. Also why present this threshold in the abstract when most of the thresholds for uPCR in the paper are 150mg/g. Is this correct? I presume this is as 500mg/g is the upper category - it could be clarified as to why this value appears where it does

2. Although I accept that the majority of illnesses are not cardiovascular, the single biggest disease by far affecting these patients is cardiovascular disease as their analysis shows (31.8% compared to 8.7% for the next condition down). I think the message should be slightly rephrased to make sure that the message about CVD is not diminished too much

Main text

They should go through this carefully for units etc. I found a few instances where the units had dropped off inappropriately e.g. for GFR on p10. There are probably others. I don't view it as the role of reviewer to be a proof reader!

Any attachments provided with reviews can be seen via the following link:

[LINK]

30 Sep 2020

Submitted filename: PLOS_Medicine_Response_to_Reviewers.docx

Click here for additional data file.

26 Oct 2020

Dear Dr. Schrauben,

Thank you very much for re-submitting your manuscript "Description of Hospitalizations Among Adults with Chronic Kidney Disease: An Observational Longitudinal Cohort Study" (PMEDICINE-D-20-01995R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Nov 02 2020 11:59PM.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Title- I suggest revising to “ Hospitalizations Among Adults with Chronic Kidney Disease in the United States: A Cohort Study" or similar.

Abstract

Please provide brief participant demographics and where the cohort is located

Please include 2-3 limitations of your study in the last sentence of the methods and findings section

Please revise to ""had" in the first line of the conclusions

Throughout- please place reference brackets before punctuation, providing a space in between.

Please provide p-values up to three decimal places throughout.

Page 16 first paragraph third sentence contains an additional “to”

STROBE checklist- please remove page numbers as these are likely to change during publication. Please use paragraphs and sections instead. The STROBE and protocol documents can be uploaded as separate attachments, referred to in the methods

Please provide access details for ref 19, 36. Reference 1 needs 6 author names (+ page numbers?)

At the end of the ms, the acknowledgements are mainly funding, I think, which is in the submission form and can be removed.

Please ensure all p-values provided are up to three decimal places. All iterations of p<0.0001 should be changed to p<0.001

Comments from Reviewers:

Reviewer #1: The authors have addressed my concerns and I now recommend publication

Peter Flom

Reviewer #2: The authors' responses are satisfactory and i have no further comments. Thank you.

Reviewer #3: I have no further comments

Any attachments provided with reviews can be seen via the following link:

[LINK]

1 Nov 2020

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

5 Nov 2020

Dear Dr. Schrauben,

On behalf of my colleagues and the academic editor, Dr. Meda E Pavkov, I am delighted to inform you that your manuscript entitled "Hospitalizations Among Adults with Chronic Kidney Disease in the United States: A Cohort Study" (PMEDICINE-D-20-01995R3) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (within 5 business days) and a PDF proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors. Please return the copyedited file within 2 business days in order to ensure timely delivery of the PDF proof.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point. Given the disruptions resulting from the ongoing COVID-19 pandemic, there may be delays in the production process. We apologise in advance for any inconvenience caused and will do our best to minimize impact as far as possible.

PRESS

A selection of our articles each week are press released by the journal. You will be contacted nearer the time if we are press releasing your article in order to approve the content and check the contact information for journalists is correct. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact.

PROFILE INFORMATION

Now that your manuscript has been accepted, please log into EM and update your profile. Go to https://www.editorialmanager.com/pmedicine, log in, and click on the "Update My Information" link at the top of the page. Please update your user information to ensure an efficient production and billing process.

Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org