Egfr and the Risk of Community-Acquired Infections

Article

eGFR and the Risk of Community-Acquired Infections

| | Hong Xu,*† Alessandro Gasparini,†‡ Junichi Ishigami,§ Khaled Mzayen, Guobin Su, ¶ Peter Barany,† Johan A¨rnlo¨v,**†† Bengt Lindholm,† Carl Gustaf Elinder,† Kunihiro Matsushita,§ and Juan Jesu´s Carrero*†

Abstract Background and objectives Community-acquired infections are common, contributing to adverse outcomes and increased health care costs. We hypothesized that, with lower eGFR, the incidence of community-acquired infections increases, whereas the pattern of site-specific infections varies. Departments of 6 *Medical Epidemiology Design,setting, participants,&measurementsAmong 1,139,470health careusers(meanage =52 18 years old, 53% and Biostatistics and | women) from the Stockholm CREAtinine Measurements Project, we quantified the associations of eGFR with the Public Health † risk of infections, overall and major types, over 12 months. Sciences and Division of Renal Medicine and Baxter Novum, ResultsA total of 106,807 counts of infections were recorded throughout 1,128,313 person-years. The incidence rate Department of Clinical of all infections increased with lower eGFR from 74/1000 person-years for individuals with eGFR=90–104 ml/min Science, Intervention per 1.73 m2 to 419/1000 person-years for individuals with eGFR,30 ml/min per 1.73 m2.ComparedwitheGFRof and Technology, 90–104 ml/min per 1.73 m2, the adjusted incidence rate ratios of community-acquired infections were 1.08 (95% Karolinska Institutet, fi – 2 fi Stockholm, Sweden; con dence interval, 1.01 to 1.14) for eGFR of 30 59 ml/min per 1.73 m and 1.53 (95% con dence interval, 1.39 to ‡Department of Health , 2 1.69) for eGFR 30 ml/min per 1.73 m . The relative proportions of lower respiratory tract infection, urinary tract Sciences, University of infection, and sepsis became increasingly higher along with lower eGFR strata (e.g., low respiratory tract infection Leicester, Leicester, , – 2 United Kingdom; accounting for 25% versus 15% of community-acquired infections in eGFR 30 versus 90 104 ml/min per 1.73 m , § respectively). Differences in incidence associated with eGFR were in general consistent for most infection types, Department of Epidemiology, Johns except for nervous system and upper respiratory tract infections, for which no association was observed. Hopkins Bloomberg School of Public Conclusions This region-representative health care study finds an excess community-acquired infections Health, Baltimore, incidence in individuals with mild to severe CKD. Lower respiratory tract infection, urinary tract infection, and Maryland; ¶Department of sepsis are major infections in CKD. Nephrology, Clin J Am Soc Nephrol 12: 1399–1408, 2017. doi: https://doi.org/10.2215/CJN.00250117 Guangdong Provincial Hospital of Chinese Medicine, Guangzhou University of Chinese Introduction would inform health care policymakers about appropri- Medicine, Guangzhou CKD is common, with a population prevalence of 5%– ate prevention strategies and health service planning in City, Guangdong 15% in most developed countries (1,2), and it is Province, China; the context of CKD. In this study, we hypothesized **School of Health and associated with a markedly increased risk of death that, with lower eGFR, the incidence of community- Social Studies, Dalarna and hospitalizations (3,4). Infections are probably the acquired infections increases, whereas the pattern of University, Falun, most significant and serious noncardiovascular compli- fi Sweden; and speci cinfectionsvaries. †† cations among persons with CKD (https://www.usrds. Department of Medical Sciences, org/adr.aspx) (5). Decreased kidney function leads to Uppsala University retention of metabolic waste products and alteration of Materials and Methods Hospital, Uppsala, multiple pathways, including the immune system (6). Study Population Sweden In patients undergoing dialysis, the risks of fatal We used data from the Stockholm CREAtinine and nonfatal infections are markedly high (7–9), and a Measurements (SCREAM) Project (2,19). Briefly, the Correspondence: few studies have shown that mildly to moderately SCREAM Project is a health care utilization cohort Dr. Juan Jesu´s Carrero, Department of Medical decreased kidney function is also associated with from the region of Stockholm, Sweden, and it includes Epidemiology and increased risk of infections (10–15). However, almost all residents who undertook at least one measurement Biostatistics (MEB), all of these studies focus on mortality or hospitaliza- of serum creatinine in ambulatory or hospital care Karolinska Institutet, tion due to infections, including both nosocomial and during 2006–2011. Creatinine and other laboratory Nobels va¨g 12A, Box community acquired (10–15). data were linked with regional and national admin- 281, 171 77 Stockholm, Sweden. Community-acquired infections account for consid- istrative databases for information on health care Email: juan.jesus. erable morbidity and mortality as well as substantial utilization, dispensed drugs, validated RRT end [email protected] health care costs worldwide (16–18), but a compre- points, and follow-up for death, with virtually no or hensive analysis on the risk of infections and possible minimal loss to follow-up. Given the commonness of differences in their proportions across the full spec- creatinine testing, the SCREAM Project captured 66% trum of kidney function is lacking. Such an analysis of the complete population census of the region, www.cjasn.org Vol 12 September, 2017 Copyright © 2017 by the American Society of Nephrology 1399 1400 Clinical Journal of the American Society of Nephrology

including .75% of individuals above the age of 45 years old this follow-up to reduce possible misclassification bias and (19). For this study, index date was determined by the first assumed that eGFR remained stable during this short period. available serum creatinine measurement of any adult (.18 The primary outcome was the overall incidence of community- years old) (Supplemental Figure 1). Exclusion criteria were acquired infections, includingupperandlowerrespiratory creatinine measurement during a hospital stay, pregnancy tract infections, gastrointestinal tract infections, urinary tract (defined by the presence of an International Classification of infections (UTIs), skin/soft tissue infections, nervous system Disease, 10th Revision, Clinical Modification [ICD-10] infections, sepsis, musculoskeletal system infections, and code among Z321, Z33–Z38, and any O code in the cardiovascular system infections (Supplemental Table 1) di- preceding 6 months), presence of chronic infections (in- agnosed in health care (at primary care, outpatient specialist cluding HIV; ICD-10 codes B15–B19, B20–B24, and A15– consultation, or primary hospitalization diagnosis). The sec- A19), or undergoing RRT (dialysis or history of kidney ondary outcome was the incidence of type-specificinfections. transplantation as ascertained by linkage with the Swedish To avoid overestimation from repeated attendance for the Renal Registry (http://www.medscinet.net/snr/) (Supplemental same infection, repeated diagnostic codes recorded within 28 Material). To avoid selecting creatinine values that may be days of one another were attributed to a single episode of determined by preexisting infections, we excluded serum infection, and the date of appearance of the first code was creatinine measurements with a diagnosis of infection during selected as the event date. We further excluded infections the preceding 3 months (definitions of infection are likely to be acquired in hospital, which encompassed post- in Supplemental Table 1). surgical infections, central line–associated bloodstream infections, catheter-associated UTIs, and ventilator-associated Exposure and Covariates pneumonia ICD-10 diagnoses (a list of excluded codes is The exposure was eGFR calculated from serum creati- detailed in Supplemental Table 3); in addition, infections nine using the 2009 Chronic Kidney Disease Epidemiology diagnosed in the 14 days after a hospital discharge were also Collaboration equation (20). All creatinine measurements considered hospital-acquired infections and excluded. were standardized to isotope dilution mass spectrometry standards. Although data for ethnicity were not available Data Analyses fi by law, misclassi cation of eGFR is expected to be minimal, We present descriptive values as mean and SD or count because the vast majority of the residents of the Stockholm with proportion. We calculated crude incidence rates with region are of white origin (21). Five categories of eGFR 95% confidence intervals (95% CIs) using the exact method $ – – – , were studied: eGFR 105, 90 104, 60 89, 30 59, and 30 and adjusted incidence rates and incidence rates ratios (IRRs) 2 – ml/min per 1.73 m , with eGFR of 90 104 ml/min per using a zero-inflated negative binomial model to account for 1.73 m2 serving as the reference group for consistency overdispersion and excess zero counts. We also included an with a previous publication in a comparable health care offset term in the model to account for time at risk. Covariates extraction from Canada (11) and because this range included in the model were age in categories; sex; and use of showed the lowest risk of the study outcome. immunosuppressive medication, antibiotics, antimycotics, fi fi Other covariates were de ned at index date of the rst and antivirals as well as the abovementioned comorbidities. recorded serum creatinine measurement and included age, As sensitivity analyses, we (1) modified the definition of the sex, and comorbidities on the basis of ICD-10 codes (car- timeframe for counting infections to up to 3 months from diovascular disease [composite of myocardial infarction, inclusion date and to 3–12 months from inclusion date and (2) congestive heart failure, peripheral vascular disease, and repeated the main analysis in specific subgroups defined by cerebrovascular disease], dementia, chronic pulmonary dis- age ( $75, 65–74, 45–64, and ,45 years old), sex, diabetes ease, rheumatic disease, peptic ulcer disease, liver disease, (yes/no), and history of cancer (yes/no). We performed the hemiplegia or paraplegia, cancer, and diabetes according to the sensitivity analysis to explore whether unknown disease not domains of the Charlson comorbidity score [22,23] as well as captured by our ICD-10–based definitions may have influ- the presence of hypertension). Comorbidities, such as diabetes enced the serum creatinine levels (hence biasing the resulting and hypertension, were enriched with the current purchase of GFR estimation) and at the same time, may be responsible related medication (purchase of oral antidiabetics: Anatomic for the infection risk observed. In that case, the pattern of 3 Therapeutic Chemical [ATC] code A10; or antihypertensives: versus 3–12 months infection risk prediction would differ. All ATC codes C03, C07, C08, and C09) up to 6 months before analyses were performed using R (https://www.r-project. index date. We also included information on recent/current org) and Stata, version 14.0 (StataCorp, College Station, TX). use of immunosuppressive drugs (ATC code L04), antibiotics (ATC codes J01, D06AA, and D06AX), antimycotic (ATC code J02), or antivirals (ATC code J05) in the 6 months preceding Results study start (i.e., precedes serum creatinine measurement/ Baseline Characteristics index date). Of note, the dispensation of these drugs in The study cohort consisted of 1,139,470 participants Swedish pharmacies is exclusively done via medical prescrip- (Supplemental Figure 1) (53% women), with a mean age tion (Supplemental Table 2). of 52618 years old (Table 1). Mean eGFR was 94621 ml/ min per 1.73 m2, 31% of individuals had eGFR$105 ml/ Study Outcome min per 1.73 m2, 30% had eGFR of 90–104 ml/min per Participants were followed from index date to the end of 1.73 m2,32%hadeGFRof60–89 ml/min per 1.73 m2,6% follow-up (12 months, death, RRT, or migration from the county, had eGFR of 30–59 ml/min per 1.73 m2, and 1% had whichever occurred first), and these events were taken into eGFR,30 ml/min per 1.73 m2. The most common comor- account when calculating individual time at risk. We chose bidities were hypertension (present in 25% of the lnJA o eho 2 3910,Spebr 2017 September, 1399–1408, 12: Nephrol Soc Am J Clin

Table 1. Baseline characteristics of the study cohort overall and by eGFR

eGFR, ml/min per 1.73 m2 Total, Characteristic eGFR$105, eGFR=90–104, eGFR=60–89, eGFR=30–59, eGFR,30, n=1,139,470 n=357,687 n=346,623 n=365,491 n=63,805 n=5864 eGFR, ml/min per 1.73 m2,mean6SD 11769976479685068236694621 Age, yr, mean6SD 34610 51613 63615 78611 79614 52618 Women, no. (%) 189,998 (53) 170,910 (49) 200,540 (55) 39,151 (61) 3219 (55) 603,818 (53) Recent or current use of medications, no. (%) Immunosuppressives 2120 (0.6) 2244 (0.7) 2604 (0.7) 618 (1) 61 (1) 7647 (0.7) Antibiotics 63,236 (18) 59,525 (17) 64,699 (18) 12,447 (20) 1336 (23) 201,243 (18) Antimycotics 5439 (2) 3443 (1) 2918 (0.8) 307 (0.5) 25 (0.4) 12,132 (1) Antivirals 4827 (1) 4620 (1) 4246 (1) 436 (0.7) 31 (0.5) 14,160 (1) Medical history, no. (%) Hypertension 21,235 (6) 75,650 (22) 141,170 (39) 46,093 (72) 4885 (83) 289,033 (25) Cardiovascular disease 3168 (0.9) 12,992 (4) 37,358 (10) 20,968 (33) 2928 (50) 77,414 (7) Myocardial infarction 717 (0.2) 4030 (1) 10,238 (3) 5812 (9) 980 (17) 21,777 (2) Congestive heart failure 652 (0.2) 2940 (0.9) 13,334 (4) 11,779 (19) 2061 (35) 30,766 (3) Peripheral vascular disease 555 (0.2) 2223 (0.6) 6581 (2) 3928 (6) 600 (10) 13,887 (1)

Cerebrovascular disease 1558 (0.4) 5835 (2) 16,139 (4) 7762 (12) 954 (16) 32,248 (3) 1401 al. et Xu Infections, Community-Acquired and CKD Diabetes 10,336 (3) 18,705 (5) 27,590 (8) 10,296 (16) 1468 (25) 68,395 (6) Cancer 4880 (1) 14,414 (4) 27,576 (8) 8168 (13) 870 (15) 55,908 (5) Chronic obstructive pulmonary disease 7507 (2) 10,461 (3) 16,483 (5) 5425 (9) 632 (11) 40,508 (4) Rheumatoid disease 1719 (0.5) 3284 (1) 6989 (2) 2858 (5) 284 (5) 15,134 (1) Dementia 86 (0.02) 880 (0.3) 6051 (2) 3530 (6) 401 (7) 10,948 (1) Peptic ulcer 1223 (0.3) 1965 (0.6) 3063 (0.8) 1389 (2) 250 (4) 7890 (0.7) Liver disease 1120 (0.3) 1550 (0.5) 1679 (0.5) 528 (0.8) 62 (1) 4939 (0.4) Hemiplegia/paraplegia 1119 (0.3) 742 (0.2) 794 (0.2) 235 (0.4) 42 (0.7) 2932 (0.3)

Recent or current use of medications considered any drug purchase at the time of or within the 6 mo proceeding the study start (index serum creatinine). 1402 Clinical Journal of the American Society of Nephrology

population), cardiovascular disease (7%), diabetes mellitus (6%), cancer (5%), and chronic obstructive pulmonary disease a (4%). The remaining comorbidities (rheumatoid disease, dementia, peptic ulcer, liver disease, and hemiplegia paraplegia)

— were present in 1% or less of participants. Age, prevalence of (95% CI) Adjusted Incidence comorbidities, and use of immunosuppressive and antibiotic Rate Ratio medications were higher in lower eGFR categories; antimycotic and antiviral medications were more common in higher eGFR categories, and the proportion of women was similar (Table 1).

Crude Incidence Rates and Proportion of Type-Speciﬁc Infections per 1000

a As many as 17,950 (1.6%) participants died, 211 (0.02%) (95% CI) person-yr initiated RRT, and 3900 (0.34%) migrated from the Rate region before the end of follow-up. The remaining individ- Adjusted Incidence uals (98.04%) completed 12 months of follow-up. Overall, 106,807 infectious events were recorded throughout 1,128,313 person-years. The majority of codes (68%) were identiﬁed in ambulatory care (primary care–issued codes, 25%; outpatient specialist–issued codes, 43%). The crude incidence rate of (95% CI) person-yr infections (any type) was 95/1000 person-years. Incidence Rate per 1000

Crude Incidence rates were progressively higher across lower eGFR strata: from 74/1000 person-years for individuals with eGFR=90– 104 ml/min per 1.73 m2 to 419/1000 person-years for individuals with eGFR,30 ml/min per 1.73 m2 (Table 2). Years

at Risk The most common infections observed were skin and soft Person- tissue related (crude incidence rate of 28/1000 person-years) followed by UTIs (21/1000 person-years), lower respiratory tract infections (15/1000 person-years), upper respiratory tract infections (11/1000 person-years), and infections of the Total Infections Counts of gastrointestinal tract (10/1000 person-years). The incidence rates of all single type–speciﬁc infections, with the exception of upper respiratory tract infections, also increased across

3 lower eGFR categories (Figure 1, Supplemental Table 4). $ The pattern of type-speciﬁc infections, however, varied across eGFR categories (Figure 2A). Speciﬁcally, the proportions of UTIs and lower respiratory tract infections were higher along with lower eGFR categories (from 16% and 15% in individuals with eGFR=90–104 ml/min per 1.73 m2 to 38% and 25% in individuals with eGFR,30 ml/min per 1.73 m2, respectively), and skin/soft tissue–related and upper respiratory tract infections were less common in lower eGFR categories (from 34% and 14% in individuals with eGFR=90– 104 ml/min per 1.73 m2 to 15% and 2% in individuals with eGFR,30 ml/min per 1.73 m2, respectively). For other less

Infections, % in Row frequent infections (Figure 2B), a similar increasing pattern was observed for sepsis, and a decreasing proportion of nervous system infections emerged across lower eGFR strata. No. of Participants with Multiple

012 Adjusted Incidence Rates and IRRs The majority of individuals did not acquire any infection (93%), 5% of individuals acquired one infection (any type), and the remaining 2% acquired two or more infections during follow-up (Table 2). After segregating by eGFR categories, the proportion of individuals with one infection dence interval. 2 ﬁ increased from 4.5% to 15% across higher to lower eGFR

=346,623) 327,215 (94.4) 15,452 (4.5) 2756 (0.8) 1200 (0.3) 25,362 344,800strata, 74 (73 to 74) and the 80 (79 to 82) proportion 1 (Reference) of individuals with two or more =365,491)=63,805) 338,166 (92.5) 20,585 (5.6) 54,510 (85) 4641 (1.3) 6355 (10) 2099 (0.6) 1935 (3) 37,394 1005 (2) 361,369 103 (102 to 105) 13,850 78 (76 to 79) 61,141 227 (223 to 230) 0.93 (0.89 to 0.98) 106 (102 to 110) 1.08 (1.01 to 1.14) n =357,687) 335,708 (93.9) 17,825 (5) 2974 (0.8) 1180 (0.3) 28,097 355,975 79 (78 to 80) 92 (90 to 94) 1.08 (1.03 to 1.14)

=1,139,470) 1,060,120 (93) 61,082 (5.4) 12,610 (1.1) 5658 (0.5) 106,807 1,128,313 95 (94 to 95) 85 (84 to 86) , n n =5864) 4521 (77) 865 (15) 304 (5) 174 (3) 2104 5026 419 (401 to 436) 211 (194 to 229) 1.53 (1.39 to 1.69) n infections likewise increased from 1% to 5%. We ob- n n served an overall adjusted incidence rate of 85 infections 104 ( 89 ( 59 ( – – – per 1.73 m 30 ( 105 ( Characteristic per 1000 person-years, which ranged from 78 to 211 60 30 , $ 90

The model was adjusted for age, sex, use of immunosuppressive drugs, prior anti-infection drugs (antibiotics, antimycotics, and antivirals), comorbidities (cardiovascular disease, dementia, infections per 1000 person-years when stratifying by Table 2. Numbers of infections (any type), incidence rates, and incident rate ratios by eGFR categories within 12 mo of follow-up 95% CI, 95%a con chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, hemiplegia/paraplegia, cancer, diabetes, and hypertension), and exposure time. eGFR, ml/min Overall ( eGFR strata. Clin J Am Soc Nephrol 12: 1399–1408, September, 2017 CKD and Community-Acquired Infections, Xu et al. 1403

Figure 1. | Increased incidence rate of type-speciﬁc infections across eGFR categories within 12 months of follow-up.

IRRs were calculated against the reference category of of infections of the musculoskeletal and cardiovascular eGFR=90–104 ml/min per 1.73 m2.Comparedwiththose systems were not addressed. Compared with the category individuals, individuals with eGFR$105 ml/min per of eGFR=90–104 ml/min per 1.73 m2,participantswith 1.73 m2 had an 8% higher adjusted IRR (1.08; 95% CI, eGFR$105 ml/min per 1.73 m2 were at a significantly increased 1.03 to 1.14). Individuals with eGFR of 60–89 ml/min per rate of skin/soft tissue infections. For the other eGFR categories, 1.73 m2 did not have a significantly higher adjusted IRR, we observed, in general, increasedIRRsacrosslowereGFR and those with eGFRs of 30–59 and ,30 ml/min per strata for the risk of skin/soft tissue infections, sepsis, gastro- 1.73 m2 had 8% (IRR, 1.08; 95% CI, 1.01 to 1.14) and 53% intestinal infections, UTIs, and lower respiratory tract infections, (IRR, 1.53; 95% CI, 1.39 to 1.69) higher IRRs of infections. particularly among individuals with eGFR,30 ml/min per IRRs for single type–specific infections are shown in 1.73 m2. No association was observed between eGFR strata and Figure 3. Because of low numbers of events, the categories the risk of upper respiratory tract or nervous system infections. 1404 Clinical Journal of the American Society of Nephrology

Figure 2. | Different relative percentage for incidence rates of type-speciﬁc infections by eGFR categories within 12 months of follow-up. A shows the ﬁve most common infection types (those with an incidence rate $10/1000 person-years), and the remaining minority types were grouped under the category other. B expands the relative percentage of the less common infection types (those with an incidence rate ,10/1000 person-years) grouped as other in A. Clin J Am Soc Nephrol 12: 1399–1408, September, 2017 CKD and Community-Acquired Infections, Xu et al. 1405

Figure 3. | Increased incidence rate ratio (IRR) and 95% conﬁdence intervals (CI) for type-speciﬁc community-acquired infections across eGFR categories within 12 months of follow-up. IRRs were adjusted for age, sex, use of immunosuppressive drugs, prior anti-infection drugs (antibiotics, antimycotics, and antivirals), comorbidities (cardiovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, hemiplegia or paraplegia, cancer, diabetes, and hypertension), and exposure time. 1406 Clinical Journal of the American Society of Nephrology

Sensitivity Analyses For CNS infections, the crude incidence rate is higher in Time sequential analyses showed a similar pattern lower eGFR strata, but these differences were attenuated in within the short (3 months of follow-up) and long term multivariable analysis. CNS infections are rare but the most (between 3 and 12 months of follow-up) (Supplemental severe form of infections, and it is possible that our short Figure 2). Age-, sex-, diabetes-, and cancer status–stratified observation period may have limited our power to observe analyses are shown in Supplemental Figure 3. Incidence an association. The risk of gastrointestinal tract infections rates were similar for both men and women but higher washighinmagnitudebutdidnotreachstatistical among those with (compared with without) diabetes or significance, something consistent with a previous study cancer. In all strata considered, a similar U-shape trend was from the United States (11). noted across the broad eGFR spectrum. A higher incidence We do not observe a straightforward “dose relationship” rate of infections was noted among the elderly, particularly between the risk of lower respiratory tract infections and among those in the highest eGFR category ($105 ml/min the intermediate eGFR strata, which is at odds with two per 1.73 m2). previous reports showing strong predictions for pneumonia hospitalizations (11,12). Differences in the severity of the event predicted, clinical practices, participant charac- Discussion teristics, and notably, follow-up (1 year in our study General Summary versus a median of 2.5 [11] and 4.6 [12] years of observa- Community-acquired infections are common and carry a tion) may explain this discrepancy. large burden in terms of morbidity and health care costs. Finally, the associations between levels of eGFR of 105 ml/ In a large health care utilization cohort of .1million min per 1.73 m2 or greater and higher risk of some types of Swedes, we report that participants with reduced eGFR infections are also interesting and have been reported in experienced increased risk of infections during 12 months previous studies (10–12). They are likely a consequence of of follow-up. The incidence rate of major infection types inaccurate estimation of true GFR in these individuals due to increased with more severe CKD, but the leading types of low serum creatinine generation as a result of reduced muscle infections varied. Overall, this large and comprehensive mass accompanying chronic illness. This is supported by our description of changing infection patterns across eGFR can sensitivity analyses showing a markedly increased infection serve to increase patient and provider awareness, leading risk among the elderly in the highest eGFR category, pre- toward improved patient management and more effective sumably denoting frailty. vaccination strategies and health service planning. Differential Pattern of Infections and Clinical Implications Increased Incidence Rates and IRRs Disaggregating infection types across eGFR strata is Preceding literature suggests an overall direct relation- potentially useful from a clinical perspective, because these ship between reduced kidney function and infections subcategories may require different interventions and requiring hospitalization (10–15). An analysis from the prevention strategies. We observed that UTIs, sepsis, and United Kingdom showed increased incidence of community- lower respiratory tract infections became increasingly acquired infections for selected types of infections (lower common among individuals with more severe CKD, respiratory tract infections and sepsis) among elderly whereas the proportions of skin/soft tissue and upper patients with diabetes identified in primary care (12). Our respiratory tract infections were lower. This is a novel study expands that evidence to the greater community by aspect of our report that may have clinical implications. analyzing the whole spectrum of infections in ambulatory Given the fact that CKD remains underdiagnosed and consultations. unrecognized in most societies, including our society (2), We observe that the crude incidence of most infection our findings may help clinicians become more aware of types increased across lower eGFR strata. Compared with CKD and its complications. This in turn may be useful to eGFR values of 90–104 ml/min per 1.73 m2, individuals identify patients at increased risk of infection and inform with eGFR,30 ml/min per 1.73 m2 were at higher risk of discussions about infection risk and vaccination strategies. most infections. This increased risk was observed for most Infection prevention programs usually target patients on infection types, with the exception of upper respiratory dialysis and individuals with severe kidney dysfunction tract and central nervous system (CNS) infections, which (26–28), whereas our results suggest that these programs did not clearly associate with eGFR. These are novel perhaps should be expanded to also include persons with observations for which there is no comparison in the less severe kidney dysfunction. Furthermore, because CKD literature and warrant further confirmation. associates with an increased risk of a number of adverse The lack of association with upper respiratory tract health outcomes, characterizing the relative distribution of infections may be due to patients with CKD being less specific infection types by eGFR category provides a prone to getting upper respiratory infections or being comprehensive description of the type-specific infection diagnosed with upper respiratory infections. For the burden of disease in CKD. This information may be helpful former, high influenza vaccination rates among patients for prioritizing resource allocation to future interventions. with CKD in Sweden may be indeed preventing upper respiratory infection (24). For the latter, it is possible that Plausible Mechanisms patients with CKD are more likely to present with a Several health care–related factors and plausible mech- complex of symptoms and signs, and thus, the diagnosis by anisms could explain our findings. It could be postulated health care providers may lean toward lower respiratory that individuals with CKD might be monitored more infection (25). closely and thus, are more likely to be diagnosed with Clin J Am Soc Nephrol 12: 1399–1408, September, 2017 CKD and Community-Acquired Infections, Xu et al. 1407

infections compared with those without CKD. However, Among 1.1 million community-dwelling adults access- infections are, in general, acute conditions with unique ing health care in the region of Stockholm, an independent symptoms and signs, and thus, they are less prone to such graded association was observed between reduced kidney an ascertainment bias (15). Also, it is important that associ- function and overall incidence of community-acquired ations remained strong, despite adjustment for age, sex, and infections. Although associations were consistently seen multiple comorbidities. More important is perhaps the fact for most separate infections, lower respiratory tract infec- that there is a biologic plausibility for the causality in the tions, UTIs, and sepsis were the infection types most associations reported given the well known effect of uremic markedly linked to a reduced kidney function, exhibiting toxicity on T lymphocyte and antigen-presenting cells (6) and the greatest difference in incidence rates and their relative the generation of oxidative stress (29), factors that alter both proportions, in individuals with advanced CKD. Increasing cellular and humoral immunity. Because it is possible that patient and health care provider awareness of this differ- additional mechanisms operate in the setting of moderate ential pattern of risk could have beneﬁts for patient kidney disease, it may be worthwhile to pursue additional management, prevention strategies, and health service experimental studies in the search of underlying reasons. planning.

Strengths and Limitations Acknowledgments The results should be interpreted in view of some unique We thank the team from the London School of Hygiene & Tropical strengths and limitations. The most important limitation is Medicine (Dorothea Nitsch, Sara Thomas, Elizabeth Millet, Jennifer the reliance on ICD-10 diagnoses. The reliability of phy- Quint, and Helen McDonald) for sharing with us their International sician diagnosis codes may vary across different sites and ClassificationofDisease,10th Revision, ClinicalModification–based severity of infection, raising a particular concern regarding definitions of community-acquired infections. the ascertainment of upper respiratory tract infections. We This work was supported by the Stockholm County Council and are not aware of any study validating outpatient infection the Swedish Heart and Lung Foundation. H.X. is partially supported diagnoses in Sweden. However, a previous study reported by the Karolinska Institutet program for postgraduate education. the validity of national inpatient diagnoses in Sweden to be Baxter Novum is the result of a grant from the Baxter Healthcare 85%–95% (30). The validity of selected inpatient infections, Corporation to Karolinska Institutet. namely infection, pneumonia, community-acquired sepsis, The funders of this study did not have a role in study design, and tuberculosis codes, was found to be of low sensitivity collection, analysis, and interpretation of data; writing the report; or (,50%) but excellent specificity (.95% in all of them) in decision to submit the report for publication. Sweden (31). This is the largest material so far addressing this topic in Disclosures material with a large census population coverage of our B.L. is employed by Baxter Healthcare Corporation. None of the region (19). However, this is a population accessing health other authors declare any conflict of interest. care, and reasons for testing creatinine could also be confounders. Although the reasons for measurement are unknown and selected patients may differ from patients References 1. Eckardt KU, Coresh J, Devuyst O, Johnson RJ, Ko¨ttgen A, Levey without creatinine measurements, this is unlikely to invalidate AS, Levin A: Evolving importance of kidney disease: From our findings, which are on the basis of a large proportion of subspecialty to global health burden. Lancet 382: 158–169, patients from our source population and reflect standard 2013 outpatient clinical practices in a large health care region with 2. Gasparini A, Evans M, Coresh J, Grams ME, Norin O, Qureshi AR, ¨ individuals having universal access to health care. Addition- Runesson B, Barany P, Arnlo¨v J, Jernberg T, Wettermark B, Elinder fi CG, Carrero JJ: Prevalence and recognition of chronic kidney ally, our analytic design provides a careful quanti cation of disease in Stockholm healthcare. Nephrol Dial Transplant 31: community-acquired infections by excluding hospitalization- 2086–2094, 2016 related codes and avoiding the overestimation from repeated 3. Fried LF, Katz R, Sarnak MJ, Shlipak MG, Chaves PH, Jenny NS, attendances. We also studied a short 1-year follow-up, which Stehman-Breen C, Gillen D, Bleyer AJ, Hirsch C, Siscovick D, Newman AB: Kidney function as a predictor of noncardiovascular allows us to assume that eGFR hasbeenratherconstantinthat fi mortality. J Am Soc Nephrol 16: 3728–3735, 2005 period. That said, our ndings are restricted to the region of 4. Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, Jafar TH, Stockholm, Sweden and may not be generalizable to other Heerspink HJ, Mann JF, Matsushita K, Wen CP: Chronic kidney populations. Although we excluded participants with recent disease and cardiovascular risk: Epidemiology, mechanisms, and infections and controlled for the use of immunosuppressive prevention. Lancet 382: 339–352, 2013 5. Cheikh Hassan HI, Tang M, Djurdjev O, Langsford D, Sood MM, and anti-infection medication, our records do not have reliable Levin A: Infection in advanced chronic kidney disease leads to information on vaccination status or vaccine purchases, increased risk of cardiovascular events, end-stage kidney disease because they can be done in private practices/centers. In and mortality. Kidney Int 90: 897–904, 2016 favor of our findings, however, Scandinavian countries have 6. Eleftheriadis T, Antoniadi G, Liakopoulos V,Kartsios C, Stefanidis the lowest rates of antibiotic consumption and antibiotic I: Disturbances of acquired immunity in hemodialysis patients. Semin Dial 20: 440–451, 2007 resistance worldwide (32). Because information on protein- 7. Allon M, Radeva M, Bailey J, Beddhu S, Butterly D, Coyne DW, uria was lacking in most patients and we only used one serum Depner TA, Gassman JJ, Kaufman AM, Kaysen GA, Lewis JA, creatinine to estimate GFR, we could not accurately stage Schwab SJ; HEMO Study Group: The spectrum of infection- CKD severity and therefore, refer to eGFR strata. Finally, related morbidity in hospitalized haemodialysis patients. Nephrol Dial Transplant 20: 1180–1186, 2005 because of the study design, we cannot assume causality in 8. Slinin Y, Foley RN, Collins AJ: Clinical epidemiology of pneu- the associations reported, and residual confounding is in- monia in hemodialysis patients: The USRDS waves 1, 3, and 4 herent to all observational studies. study. Kidney Int 70: 1135–1141, 2006 1408 Clinical Journal of the American Society of Nephrology

9. Berman SJ, Johnson EW, Nakatsu C, Alkan M, Chen R, LeDuc J: 23. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Burden of infection in patients with end-stage renal disease re- Saunders LD, Beck CA, Feasby TE, Ghali WA: Coding algorithms quiring long-term dialysis. Clin Infect Dis 39: 1747–1753, 2004 for defining comorbidities in ICD-9-CM and ICD-10 adminis- 10. Dalrymple LS, Katz R, Kestenbaum B, de Boer IH, Fried L, Sarnak trative data. Med Care 43: 1130–1139, 2005 MJ, Shlipak MG: The risk of infection-related hospitalization with 24. Ethgen O, Cornier M, Chriv E, Baron-Papillon F: The cost of decreased kidney function. Am J Kidney Dis 59: 356–363, 2012 vaccination throughout life: A western European overview. Hum 11. James MT, Quan H, Tonelli M, Manns BJ, Faris P, Laupland KB, Vaccin Immunother 12: 2029–2037, 2016 Hemmelgarn BR; Alberta Kidney Disease Network: CKD and risk 25. Stevens PE, Levin A; Kidney Disease: Improving Global of hospitalization and death with pneumonia. Am J Kidney Dis 54: Outcomes Chronic Kidney Disease Guideline Development 24–32, 2009 Work Group Members: Evaluation and management of chronic 12. McDonald HI, Thomas SL, Millett ER, Nitsch D: CKD and the risk kidney disease: Synopsis of the kidney disease: Improving global of acute, community-acquired infections among older people outcomes 2012 clinical practice guideline. Ann Intern Med 158: with diabetes mellitus: A retrospective cohort study using elec- 825–830, 2013 tronic health records. Am J Kidney Dis 66: 60–68, 2015 26. Centers for Disease Control: Recommendations for preventing 13. Wang HE, Gamboa C, Warnock DG, Muntner P: Chronic kidney transmission of infections among chronic hemodialysis patients. disease and risk of death from infection. Am J Nephrol 34: MMWR Recomm Rep 50: 1–43, 2001 330–336, 2011 27. Hooton TM, Bradley SF, Cardenas DD, Colgan R, Geerlings SE, 14. James MT, Laupland KB, Tonelli M, Manns BJ, Culleton BF, Rice JC, Saint S, Schaeffer AJ, Tambayh PA, Tenke P, Nicolle LE; Hemmelgarn BR; Alberta Kidney Disease Network: Risk of Infectious Diseases Society of America: Diagnosis, prevention, bloodstream infection in patients with chronic kidney disease not and treatment of catheter-associated urinary tract infection in treated with dialysis. Arch Intern Med 168: 2333–2339, 2008 adults: 2009 International clinical practice guidelines from the 15. Ishigami J, Grams ME, Chang AR, Carrero JJ, Coresh J, Matsushita Infectious Diseases Society of America. Clin Infect Dis 50: 625– K: CKD and risk for hospitalization with infection: The Athero- 663, 2010 sclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis 69: 28. Ford DW, Goodwin AJ, Simpson AN, Johnson E, Nadig N, 752–761, 2017 SimpsonKN:A severe sepsis mortalityprediction model andscore 16. Feldman C, Anderson R: Community-acquired pneumonia: Still a for use with administrativedata. Crit Care Med 44: 319–327, 2016 major burden of disease. Curr Opin Crit Care 22: 477–484, 2016 29. Himmelfarb J: Uremic toxicity, oxidative stress, and hemodialysis 17. Tandogdu Z, Wagenlehner FM: Global epidemiology of urinary as renal replacement therapy. Semin Dial 22: 636–643, 2009 tract infections. Curr Opin Infect Dis 29: 73–79, 2016 30. Ludvigsson JF, Andersson E, Ekbom A, Feychting M, Kim JL, 18. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Reuterwall C, Heurgren M, Olausson PO: External review and Pinsky MR: Epidemiology of severe sepsis in the United States: validation of the Swedish national inpatient register. BMC Public Analysis of incidence, outcome, and associated costs of care. Crit Health 11: 450, 2011 Care Med 29: 1303–1310, 2001 31. Gedeborg R, Furebring M, Michae¨lsson K: Diagnosis-dependent 19. Runesson B, Gasparini A, Qureshi AR, Norin O, Evans M, Barany misclassification of infections using administrative data variably P, Wettermark B, Elinder CG, Carrero JJ: The Stockholm affected incidence and mortality estimates in ICU patients. J Clin CREAtinine Measurements (SCREAM) project: Protocol Epidemiol 60: 155–162, 2007 overview and regional representativeness. Clin Kidney J 9: 32. Hellen G, Molly M-P, Suraj P, Sumanth G, Jordan L, Devra B, 119–127, 2016 Andrea W, Ramanan L: State of the World’s Antibiotics, Center for 20. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Disease Dynamics, Economics & Policy: Washington, DC, 2015 Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration): Received: January 9, 2017 Accepted: May 26, 2017 A new equation to estimate glomerular filtration rate. Ann Intern Med 150: 604–612, 2009 H.X. and A.G. contributed equally to this work. 21. Gasparini A, Evans M, Coresh J, Grams ME, Norin O, Qureshi AR, Runesson B, Barany P, A¨ rnlo¨v J, Jernberg T, Wettermark B, Elinder CG, Carrero JJ: Prevalence and recognition of chronic kidney Published online ahead of print. Publication date available at www. disease in Stockholm healthcare. Nephrol Dial Transplant 31: cjasn.org. 2086–2094, 2016 22. Charlson ME, Pompei P,Ales KL, MacKenzie CR: A new method of This article contains supplemental material online at http://cjasn. classifying prognostic comorbidity in longitudinal studies: De- asnjournals.org/lookup/suppl/doi:10.2215/CJN.00250117/-/ velopment and validation. J Chronic Dis 40: 373–383, 1987 DCSupplemental. Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

SUPPLEMENTAL MATERIALS

KIDNEY FUNCTION AND THE RISK OF COMMUNITY ACQUIRED INFECTIONS. Hong Xu, Alessandro Gasparini, Junichi Ishigami, Khaled Mzayen, Guobin Su, Peter Barany, Johan Ärnlöv, Bengt Lindholm, Carl Gustaf Elinder, Kunihiro Matsushita, Juan Jesús Carrero.

Supplemental Material S1 Construction of ICD-10-CM algorithms for site-specific community- acquired infections...... 2 Supplemental Table S1. List of ICD-10 codes and corresponding family considered Community- Acquired-Infections ...... Error! Bookmark not defined. Supplemental Table S2. List of ICD-10 and ATC codes of covariates ...... 4 Supplemental Table S3. List of ICD-10 codes considered hospital-acquired-infections ...... 4 Supplemental Table S4. Number of type-specific infections and incidence rates overall and by eGFR (ml/min/1.73 m2) categories within 12 months of follow up...... 5 Supplemental Figure S1. Overall study design and analytical cohort selection...... 6 Supplemental Figure S2. Incidence rate of infections (any type) across eGFR strata within 3 months, 3-12 months and 12 months of follow up...... 7 Supplemental Figure S3. Incidence rate of infections (any type) across eGFR strata within 12 months stratified by A) age, B) sex, C) diabetes, and D) cancer status...... 8

Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Material S1: Construction of ICD-10-CM algorithms for site-specific community- acquired infections.

The specific algorithms of ICD-10-CM codes for the families of lower respiratory tract infections and sepsis were kindly provided by the authors of recent validation analyses (PLoS One. 2013 Sep 11; 8 (9):e75131; and Diabet Med. 2014 May; 31(5):606-14). For other infection families, we inspected previously-published algorithms and whenever necessary utilized online conversion calculators for previous versions into ICD-10-CM coding. Verification and/or possible expansion of the considered codes were done by Dr. Khaled Mzayen, and the resulting list was reviewed cross-checked again by Dr. Guobin Su. When discrepancies between both assessments were found, a third reviewer, Dr. Hong Xu, was involved for final composition of the codes listed in Supplementary Table S1.

Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplementary Table S1: List of ICD-10 codes and corresponding family considered Community- Acquired-Infections Infection categories ICD-10 codes Upper respiratory A360, A361, A362, A368, A369, B002, B085, B442, J00, J010, J011, J012, tract infections J013, J014, J018, J019, J020, J028, J029, J030, J038, J039, J040, J050, J051, J060, J068, J069, J340, J390, J391 Lower respiratory A065, A202, A212, A221, A310, A37, A420, A430, A481, B012, B052, tract infections B250, B334, B371, B380, B382, B392, B402, B410, B420, B440, B441, B450, B460, B481, B960, J041, J042, J09, J10, J100, J101, J108, J11, J110, J111, J118, J12, J120, J121, J122, J128, J129, J13,J14, J15, J151, J152, J153, J154, J155, J156, J157, J158, J159, J16, J160, J168, J17, J18, J181, J182, J188, J189, J200, J201, J202, J203, J204, J205, J206, J207, J208, J209, J210, J218, J219, J22, J40, J411, J440, J47, J850, J851, J852, J853, J860, J869, U04, U049 Gastro-intestinal tract A000, A001, A009, A010, A011, A012, A013, A014, A020, A030, A031, infections A032, A033, A038, A039, A040, A041, A042, A043, A044, A045, A046, A047, A048, A049, A050, A051, A052, A053, A054, A058, A059, A060, A062, A063, A064, A070, A071, A072, A073, A078, A079, A080, A081, A082, A083, A084, A085, A090, A099, A213, A222, A421, B054, B150, B159, B160, B161, B162, B169, B170, B171, B172, B178, B179, B190, B199, B251, B258, B462, K350, K351, K359, K36, K37, K570, K572, K574, K578, K610, K611, K612, K613, K614, K630, K650, K659, K750, K810, K818, K819, K521, K658, K830 Urinary tract infections N10, N12, N136, N151, N159, N160, N300, N303, N308, N309, N340, N341, N342, N343, N390 Skin and soft A067, A201, A220, A260, A268, A269, A311, A320, A363, A431, A441, tissues infections A46, A480, B000, B001, B029, B07, B088, B09, B350, B351, B352, B353, B354, B355, B356, B358, B359, B360, B361, B362, B363, B368, B369, B372, B383, B403, B421, B430, B432, B452, B463, B480, L00, L010, L011, L020, L021, L022, L023, L024, L028, L029, L030, L031, L032, L033, L038, L039, L040, L041, L042, L043, L048, L049, L050, L059, L080, L081, L088, L089, L303, L700, L702, L732, L401, L403, L84, L88, L980, R02 Nervous system A066, A203, A321, A390, A83, A830, A84, A85, A86, A87, A870, A871, infections A872, A878, A879, A888, A89, B003, B004, B010, B011, B020, B021, B022, B050, B051, B060, B261, B262, B375, B384, B431, B451, B461, G000, G001, G002, G003, G008, G009, G040, G042, G060, G061, G062, G937, F059, G039, G048, G049, G629 Sepsis A021, A207, A227, A241, A267, A327, A391, A392, A394, A400, A401, A402, A403, A408, A409, A410, A411, A412, A413, A414, A415, A418, A419, A427, A483, B007, B377, R651, R650, R652, U049 Musculoskeletal M000, M001, M002, M008, M009, M462, M463, M465, M600, M650, system infections M651, M710, M711, M860, M861, M862, M868, M869, M468 Cardio-vascular A395, B332, B376, I010, I011, I012, I018, I019, I301, I32, I330, I39, I400, system infections I410, I411, I412, I430

Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplemental Table S2. List of ICD-10 and ATC codes of covariates. Covariates ICD-10 codes ATC codes Recent or current use of medications Use of immunosuppressive drugs L04 Antibiotics J01,D06AA, D06AX Antimycotic J02 Antivirals J05 Medical history Hypertension I10-15 C03, C07, C08, C09 Cardiovascular disease Myocardial infarction I21, I22, I252 Congestive heart failure I099, I110, I130, I132, I255, I420, I425- 429, I43, I50, P290 Peripheral vascular disease I70, I71, I731, I738, I739, I771, I790, I792, K551, K558, K559, Z958, Z959 Cerebrovascular disease G45-46, H340, I60-69 Diabetes mellitus E10-14 A10A, A10B Cancer Any malignancy, including lymphoma and leukemia (C00-C26, C30-C34, C37- C41, C43, C45-C58, C60-C76, C81-C85, C88, C90-C97) OR Metastatic solid tumor (C77-C80) Chronic obstructive pulmonary disease I278, I279, J40-47, J60-67, J684, J701, J703 Rheumatic disease M05, M06, M315, M32-M34, M351, M353, M360 Dementia F00-F03, F051, G30, G311 Peptic ulcer disease K25-K28 Liver disease Mild (B18, K700-K703, K709, K713- K715, K717, K73, K74, K760, K762- K764, K768, K769, Z944) OR moderate/severe (I850, I859, I864, I982, K704, K711, K721, K729, K765, K766, K767) Hemiplegia or paraplegia G041, G114, G801, G802, G81, G82, G830-G834, G839

Supplementary Table S3: List of ICD-10 codes considered hospital-acquired-infections Hospital-acquired-infections ICD-10 codes Central Line-associated Bloodstream Infection T80.2 T80.21 T80.211 T80.212 T80.218 T80.219 Catheter-associated Urinary Tract Infections T82.6 T82.7 T83.5 T83.6 T84.5 T84.6 T84.7 T85.7 Ventilator-associated Pneumonia J95.851 Surgical site infections T81.4 T87.4 T79.3 T88.0 O85 O86

Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

Supplementary Table S4: Number of type-specific infections and incidence rates overall and by eGFR (ml/min/1.73 m2) categories within 12 months of follow up. Counts of infections eGFR ≥ 105 eGFR 90-104 eGFR 60-89 eGFR 30-59 eGFR < 30 Total [incidence rate /1000 person-years (95% CI)] (n = 357,687) (n = 346,623) (n = 365,491) (n = 63,805) (n = 5,864) (n = 1,139,470) All-type infections 28,097 25,362 37,394 13,850 2,104 106,807 [79 (78-80)] [74(73-74)] [103(102-105)] [227(223-230)] [419(401-436)] [95(94-95)] Type-specific infections Respiratory tract 8,025 7,095 10,088 3,673 553 29,434 [23(22-23)] [21(20-21)] [28(27-28)] [60(58-62)] [110(101-119)] [26(25-26)] Upper respiratory tract 5,246 3,309 2,822 454 36 11,867 [15(14-15)] [10(9-10)] [8(7-8)] [7(7-8)] [7(5-10)] [11(10-11)] Lower respiratory tract 2,779 3,786 7,266 3,219 517 17,567 [8(7-8)] [11(10-11)] [20(20-21)] [53(51-54)] [103(94-112)] [15(15-16)] Gastro-intestinal tract 3,551 2,863 3,540 1,231 180 11,365 [10(9-10)] [8(8-9)] [10(9-10)] [20(19-21)] [36(31-41)] [10(9-10)] Urinary tract 4,762 4,072 8,904 4,635 794 23,167 [13(13-14)] [12(11-12)] [25(24-25)] [76(74-78)] [158(147-169)] [21(20-21)] Skin and soft tissue 9,818 8,526 10,229 2,629 310 31,512 [28(27-28)] [25(24-25)] [28(28-29)] [43(41-45)] [62(55-69)] [28(27-28)] Nervous system 650 1260 2136 624 58 4,728 [2(1-2)] [4(3-4)] [6(5-6)] [10(9-11)] [12(9-15)] [4(4-5)] Sepsis 341 703 1,513 737 155 3,449 [1(0-1)] [2(1-2)] [4(3-4)] [12(11-13)] [31(26-36)] [3(2-3)] Musculoskeletal system 878 784 889 291 50 2,892 [3(2-3)] [2(2-3)] [3(2-3)] [5(4-5)] [10(7-13)] [2.6 (2-3)] Cardiovascular system 72 59 95 30 4 260 [0.2(0.1-0.2)] [0.2(0.1-0.2)] [0.3(0.2-0.3)] [0.5(0.3-0.7)] [0.8(0.1-1.6)] [0.2(0.2-0.3)]

Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

SCREAM n=1,344,190

Study participants excluded due to: 1. Creatinine measured during a hospital stay (n=19,107), implausible values (n=145) 2. Undergoing renal replacement therapy (dialysis or history of renal transplantation) (n=1822)

3. Pediatric participants (n= 140,023) 4. Pregnancies (n=18,642) 5. Chronic infections (n=9,825) 6. Acute infection in the preceding 3 months (n=15,156)

Study participants included n=1,139,470

eGFR >105 eGFR 90‐104 eGFR 60‐89 eGFR 30‐59 eGFR <30 n=357,687 n=346,623 n=365,491 n=63,805 n=5,864

Supplementary Figure S1: Overall study design and analytical cohort selection. Abbreviations: SCREAM, Stockholm CREAtinine Measurements; eGFR, estimated glomerular filtration rate, expressed as ml/min/1.73 m2.

Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

450 400 years

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person 300

250

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per

150 rate

100

Incidence 0 <30 30‐44 45‐59 60‐74 75‐89 90‐104 ≥105

eGFR category

<3 m 3‐12m 0‐12m

Supplementary Figure S2: Incidence rate of infections (any type) across eGFR strata within 3 months, 3-12 months and 12 months of follow up.

s 600 Supplemental material is neither peer-reviewed nor thoroughly edited by CJASN. The authors alone are responsible for the accuracy and presentation of the material.

A B 500 500 age<45 age 45‐64 450 450 male female years years

400 400 age 65‐74 age 75+ 350 350 300 300 person person

250 250 200 200 1000 1000

150 150 per per

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rate 50 rate 50

0 0 <30 30‐44 45‐59 60‐74 75‐89 90‐104 ≥105 <30 30‐44 45‐59 60‐74 75‐89 90‐104 ≥105 eGFR starata eGFR categorytle Incidence Incidence

C D 600 600 non‐DM DM cancer free with cancer

years 500 500

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200 200 per per

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0 0 <30 30‐44 45‐59 60‐74 75‐89 90‐104 ≥105 <30 30‐44 45‐59 60‐74 75‐89 90‐104 ≥105

Incidence eGFR categories eGFR categories Incidence

Supplementary Figure S3: Incidence rate of infections (any type) across eGFR strata within 12 months stratified by A) age, B) sex, C) diabetes, and D) cancer status. 8