Different rates of progression and mortality in patients with chronic kidney disease at outpatient nephrology clinics across Europe
2018; Elsevier BV; Volume: 93; Issue: 6 Linguagem: Inglês
10.1016/j.kint.2018.01.008
ISSN1523-1755
AutoresKatharina Brück, Kitty J. Jager, Carmine Zoccali, Aminu K. Bello, Roberto Minutolo, Kyriakos Ioannou, Francis Verbeke, Henry Völzke, Johan Ärnlöv, Daniela Leonardis, Pietro Manuel Ferraro, Hermann Brenner, Ben Caplin, Philip A. Kalra, Christoph Wanner, Alberto Martínez Castelao, José Luis Górriz, Stein Hallan, Dietrich Rothenbacher, Dino Gibertoni, Luca De Nicola, Georg Heinze, Wim Van Biesen, Vianda S Stel,
Tópico(s)Healthcare cost, quality, practices
ResumoThe incidence of renal replacement therapy varies across countries. However, little is known about the epidemiology of chronic kidney disease (CKD) outcomes. Here we describe progression and mortality risk of patients with CKD but not on renal replacement therapy at outpatient nephrology clinics across Europe using individual data from nine CKD cohorts participating in the European CKD Burden Consortium. A joint model assessed the mean change in estimated glomerular filtration rate (eGFR) and mortality risk simultaneously, thereby accounting for mortality risk when estimating eGFR decline and vice versa, while also correcting for the measurement error in eGFR. Results were adjusted for important risk factors (baseline eGFR, age, sex, albuminuria, primary renal disease, diabetes, hypertension, obesity and smoking) in 27,771 patients from five countries. The adjusted mean annual eGFR decline varied from 0.77 (95% confidence interval 0.45, 1.08) ml/min/1.73m2 in the Belgium cohort to 2.43 (2.11, 2.75) ml/min/1.73m2 in the Spanish cohort. As compared to the Italian PIRP cohort, the adjusted mortality hazard ratio varied from 0.22 (0.11, 0.43) in the London LACKABO cohort to 1.30 (1.13, 1.49) in the English CRISIS cohort. These results suggest that the eGFR decline showed minor variation but mortality showed the most variation. Thus, different health care organization systems are potentially associated with differences in outcome of patients with CKD within Europe. These results can be used by policy makers to plan resources on a regional, national and European level. The incidence of renal replacement therapy varies across countries. However, little is known about the epidemiology of chronic kidney disease (CKD) outcomes. Here we describe progression and mortality risk of patients with CKD but not on renal replacement therapy at outpatient nephrology clinics across Europe using individual data from nine CKD cohorts participating in the European CKD Burden Consortium. A joint model assessed the mean change in estimated glomerular filtration rate (eGFR) and mortality risk simultaneously, thereby accounting for mortality risk when estimating eGFR decline and vice versa, while also correcting for the measurement error in eGFR. Results were adjusted for important risk factors (baseline eGFR, age, sex, albuminuria, primary renal disease, diabetes, hypertension, obesity and smoking) in 27,771 patients from five countries. The adjusted mean annual eGFR decline varied from 0.77 (95% confidence interval 0.45, 1.08) ml/min/1.73m2 in the Belgium cohort to 2.43 (2.11, 2.75) ml/min/1.73m2 in the Spanish cohort. As compared to the Italian PIRP cohort, the adjusted mortality hazard ratio varied from 0.22 (0.11, 0.43) in the London LACKABO cohort to 1.30 (1.13, 1.49) in the English CRISIS cohort. These results suggest that the eGFR decline showed minor variation but mortality showed the most variation. Thus, different health care organization systems are potentially associated with differences in outcome of patients with CKD within Europe. These results can be used by policy makers to plan resources on a regional, national and European level. Chronic kidney disease (CKD) is one of the fastest growing causes of death worldwide.1Collaborators GMCoDGlobal, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.Lancet. 2015; 385: 117-171Abstract Full Text Full Text PDF PubMed Scopus (5380) Google Scholar In stark contrast is the lack of novel treatment options for the management of CKD.2Ortiz A. Translational nephrology: what translational research is and a bird's-eye view on translational research in nephrology.Clin Kidney J. 2015; 8: 14-22Crossref PubMed Scopus (17) Google Scholar Current predialysis care can slow the progression in patients with CKD and reduce mortality in ESRD patients.3Powe N.R. Early referral in chronic kidney disease: an enormous opportunity for prevention.Am J Kidney Dis. 2003; 41: 505-507Abstract Full Text PDF PubMed Scopus (31) Google Scholar In addition, national health care system characteristics may influence outcomes in patients with CKD.4Hallan S.I. Ovrehus M.A. Romundstad S. et al.Long-term trends in the prevalence of chronic kidney disease and the influence of cardiovascular risk factors in Norway.Kidney Int. 2016; Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar Describing outcomes in CKD patients across regions and countries may identify regions with overall slow CKD progression and/or low rates of mortality. Such a comparison may help to identify health care system characteristics that are associated with improved population health. Moreover, information regarding the decline of mean estimated glomerular filtration rate (eGFR) over time can be used by policy makers to plan resources at the regional, national, and European level. Up to the present, little is known about the epidemiology of CKD progression. Studies from individual countries describing CKD progression in referred CKD patients have reported declines in the rates of eGFR varying from 0.35 to 5.16 ml/min per 1.73 m2 per year.5Jones C. Roderick P. Harris S. Rogerson M. Decline in kidney function before and after nephrology referral and the effect on survival in moderate to advanced chronic kidney disease.Nephrol Dial Transplant. 2006; 21: 2133-2143Crossref PubMed Scopus (130) Google Scholar, 6de Goeij M.C. Liem M. de Jager D.J. et al.Proteinuria as a risk marker for the progression of chronic kidney disease in patients on predialysis care and the role of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker treatment.Nephron Clin Pract. 2012; 121: c73-c82Crossref PubMed Scopus (13) Google Scholar Next to differences in the way progression is being expressed, comparisons of these studies is complicated by differences in baseline eGFR, albuminuria, primary renal disease (PRD), and presence of comorbidities, all factors that independently may influence the rate of CKD progression.7Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work GroupKDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.Kidney Int Suppl. 2013; 3: 1-150Abstract Full Text Full Text PDF Scopus (1578) Google Scholar Importantly, as the rate of change in eGFR influences mortality risk,8Naimark D.M. Grams M.E. Matsushita K. et al.Past decline versus current eGFR and subsequent mortality risk.J Am Soc Nephrol. 2016; 27: 2456-2466Crossref PubMed Scopus (32) Google Scholar mortality risk needs to be taken into account when describing eGFR change in CKD patients. A relatively new statistical method, which enables simultaneous analysis of longitudinal and survival data, is the joint model.9Crowther M.J. Abrams K.R. Lambert P.C. Flexible parametric joint modelling of longitudinal and survival data.Stat Med. 2012; 31: 4456-4471Crossref PubMed Scopus (43) Google Scholar, 10Wulfsohn M.S. Tsiatis A.A. A joint model for survival and longitudinal data measured with error.Biometrics. 1997; 53: 330-339Crossref PubMed Scopus (636) Google Scholar The main advantage of this model, in the context of CKD progression, is its ability to correct for the measurement error in repeated eGFRs.10Wulfsohn M.S. Tsiatis A.A. A joint model for survival and longitudinal data measured with error.Biometrics. 1997; 53: 330-339Crossref PubMed Scopus (636) Google Scholar, 11Asar O. Ritchie J. Kalra P.A. Diggle P.J. Joint modelling of repeated measurement and time-to-event data: an introductory tutorial.In J Epidemiol. 2015; 44: 334-344Google Scholar Another advantage is that it accounts for mortality risk when estimating GFR decline.9Crowther M.J. Abrams K.R. Lambert P.C. Flexible parametric joint modelling of longitudinal and survival data.Stat Med. 2012; 31: 4456-4471Crossref PubMed Scopus (43) Google Scholar, 12Vonesh E.F. Greene T. Schluchter M.D. Shared parameter models for the joint analysis of longitudinal data and event times.Stat Med. 2006; 25: 143-163Crossref PubMed Scopus (119) Google Scholar Despite these clear advantages for studies investigating outcomes in CKD patients, joint models are currently underused within the nephrology research.11Asar O. Ritchie J. Kalra P.A. Diggle P.J. Joint modelling of repeated measurement and time-to-event data: an introductory tutorial.In J Epidemiol. 2015; 44: 334-344Google Scholar, 13Boucquemont J. Heinze G. Jager K.J. Oberbauer R. Leffondre K. Regression methods for investigating risk factors of chronic kidney disease outcomes: the state of the art.BMC Nephrol. 2014; 15: 45Crossref PubMed Scopus (27) Google Scholar The objective of this study was to describe CKD progression and mortality outcomes in patients attending outpatient nephrology clinics. We used individual patient data from 9 CKD cohorts in 5 European countries taking part in the European CKD Burden Consortium.14Bruck K. Jager K.J. Dounousi E. et al.Methodology used in studies reporting chronic kidney disease prevalence: a systematic literature review.Nephrol Dial Transplant. 2016; 31: 680Crossref PubMed Scopus (3) Google Scholar, 15Bruck K. Stel V.S. Gambaro G. et al.CKD prevalence varies across the European general population.J Am Soc Nephrol. 2016; 27: 2135-2147Crossref PubMed Scopus (312) Google Scholar Using a joint model, we combined a linear mixed model to estimate mean annual eGFR changes and a Weibull survival model to estimate all-cause mortality risk. Additionally, we determined mean annual eGFR changes for subgroups based on age, sex, and the presence of diabetes mellitus. We obtained data from 9 cohort studies,16D'Hoore E. Neirynck N. Schepers E. et al.Chronic kidney disease progression is mainly associated with non-recovery of acute kidney injury.J Nephrol. 2015; 28: 709-716Crossref PubMed Scopus (21) Google Scholar, 17Stel V.S. Ioannou K. Bruck K. et al.Longitudinal association of body mass index and waist circumference with left ventricular mass in hypertensive predialysis chronic kidney disease patients.Nephrol Dial Transplant. 2013; 28: iv136-iv145Crossref PubMed Scopus (7) Google Scholar, 18Leonardis D. Mallamaci F. Enia G. et al.The MAURO study: baseline characteristics and compliance with guidelines targets.J Nephrol. 2012; 25: 1081-1090Crossref PubMed Scopus (15) Google Scholar, 19Gibertoni D. Mandreoli M. Rucci P. et al.Excess mortality attributable to chronic kidney disease. Results from the PIRP project.J Nephrol. 2016; 29: 663-671Crossref PubMed Scopus (12) Google Scholar, 20De Nicola L. Minutolo R. Chiodini P. et al.The effect of increasing age on the prognosis of non-dialysis patients with chronic kidney disease receiving stable nephrology care.Kidney Int. 2012; 82: 482-488Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar, 21Hoefield R.A. Kalra P.A. Baker P. et al.Factors associated with kidney disease progression and mortality in a referred CKD population.Am J Kidney Dis. 2010; 56: 1072-1081Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar, 22Caplin B. Nitsch D. Gill H. et al.Circulating methylarginine levels and the decline in renal function in patients with chronic kidney disease are modulated by DDAH1 polymorphisms.Kidney Int. 2010; 77: 459-467Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar followed in 5 European countries, including a total of 27,771 CKD patients not on renal replacement therapy (RRT), of which 25,702 patients (93%) had a baseline eGFR below 60 ml/min per 1.73 m2. Of these patients, 18,126 had at least 2 creatinine measurements and were included in the main analysis. Inclusion and exclusion criteria for the cohorts are listed in Table 1. One cohort (Complesso Integrato Columbus [CIC]) did not have any exclusion criteria, 3 cohorts (Prevention of Renal Insufficiency Progression [PIRP], Chronic Renal Insufficiency Standards Implementation Study [CRISIS], London Arterial Calcification, Kidney and Bone Outcomes [LACKABO]) solely excluded patients with acute kidney injury or with RRT at first presentation, and the remaining cohorts had additional exclusion criteria in place. Table 1 additionally shows the type of access to nephrology care by cohort. Four cohorts applied an open access system (i.e., patients could visit a nephrologist without a referral from their general practitioner). In the other 5 cohorts, patients required a referral from their general practitioner prior to visiting the nephrologist (i.e., gatekeeper system).Table 1Inclusion and exclusion criteria according to study and access to specialist nephrology careStudyCountryRegionNInclusion criteriaExclusion criteriaInclusion periodAccess to nephrologistBelgiumGhent557All patients aged ≥18 yrWilling to participate in biobankingRecent AKI (<3 mo)Recent acute CV event (<3 mo)Infection2008–2012Open accessCyprusNicosia104CKD patients (≥3 mo)MalignancyInflammation (<3 mo)Major CV event (i.e., stroke/MI/acute IHD) ( 1.5 and 1.3 and 30 mg/24 h≥ 2 consecutive visitsAKI or rapidly evolving renal disease;transplant, pregnancy, cancer ordisease in a terminal phase2005–2008Open accessPIRPItalyEmiliaRomagna18,244All consecutive patients referred to nephrologist by primary care physiciansSubjects with RRT or AKI2005–2015Gatekeeper systemTABLEItalyMultiplebTABLE patients included in 25 centers: most of these centers were located in southern Italy, surrounding Naples and further south, 1 from Verona, 1 from Pisa, 1 from Chieti, and 3 from Sicily.1184All consecutive patients with eGFR 3 mos)Patients with acute kidney injury(<6 mo before first visit)Patients with first visit < 1 year2000–2005Gatekeeper systemPECERASpainValencia995CKD stage 4–5 not receiving dialysisLife expectancy >1 yrInformed consentKidney transplant, AKI, wasting disease, malignancy, incapacitating disease, oractive infection/inflammation2006–2009Gatekeeper systemCRISISUKManchester264910 < eGFR ≤60 ml/min per 1.73 m2Able to give written consentAKIPrevious RRT2002–2013Gatekeeper systemLACKABOUKLondon271serum creatinine >150 μmol/l (men) or >130 μmol/l (women)Able to give consentSubjects with RRT or AKI2006–2008Gatekeeper systemAKI, acute kidney injury; CIC, Complesso Integrato Columbus; CKD, chronic kidney disease; CRISIS, Chronic Renal Insufficiency Standards Implementation Study; CV, cardiovascular; eGFR, estimated glomerular filtration rate; Gatekeeper, referral by general practitioner required; LACKABO, London Arterial Calcification, Kidney and Bone Outcomes; MAURO, multiple intervention and audit in renal diseases to optimize care; MI, myocardial infarction; N, total number of patients included in study; Open access, no referral by general practitioner required; PECERA, Proyecto de Estudio Colaborativo En pacientes con insuficiencia Renal Avanzada; PIRP, prevention of renal insufficiency progression; RRT, renal replacement therapy; TABLE, target blood pressure levels in CKD.a MAURO patients included in 21 centers: 17 in Calabria, 3 in Sicily, 1 in Puglia, and 1 in Sardinia.b TABLE patients included in 25 centers: most of these centers were located in southern Italy, surrounding Naples and further south, 1 from Verona, 1 from Pisa, 1 from Chieti, and 3 from Sicily. Open table in a new tab AKI, acute kidney injury; CIC, Complesso Integrato Columbus; CKD, chronic kidney disease; CRISIS, Chronic Renal Insufficiency Standards Implementation Study; CV, cardiovascular; eGFR, estimated glomerular filtration rate; Gatekeeper, referral by general practitioner required; LACKABO, London Arterial Calcification, Kidney and Bone Outcomes; MAURO, multiple intervention and audit in renal diseases to optimize care; MI, myocardial infarction; N, total number of patients included in study; Open access, no referral by general practitioner required; PECERA, Proyecto de Estudio Colaborativo En pacientes con insuficiencia Renal Avanzada; PIRP, prevention of renal insufficiency progression; RRT, renal replacement therapy; TABLE, target blood pressure levels in CKD. All cohorts provided data for serum creatinine concentration, age, and sex. Eight cohorts provided data for the presence of comorbidities, baseline albuminuria, and PRD. Of the patients included in the main analysis, 34% had data available for either albuminuria or proteinuria. Tables 2 and 3 show baseline characteristics, and the availability of follow-up measurements of patients included in the main analysis (i.e., CKD stages 3 to 5 and ≥2 creatinine measurements). Supplementary Table S1 shows the characteristics of all included patients compared to those with only 1 creatinine measurement. Eight studies (89% of included studies) used isotope dilution mass spectrometry (IDMS) standardized creatinine measurements, of which 1 study used IDMS standardized creatinine methods in 79% of included patients.Table 2Population characteristics by studyCountriesBelgiumCyprusItalySpainUKStudiesUZGhentNicosiaCICMAUROPIRPTABLEPECERACRISISLACKABON4037014207191,12771,0319392,049218Median age yr (range)69 (61–77)72 (68–76)74 (66–80)65 (57–70)74 (67–80)69 (58–76)73 (61–79)67 (56–75)61 (51–70)% of Males61.071.458.659.164.657.360.461.672.0% with Diabetes35.760.036.634.936.626.835.932.320.2% of Missing DM0.00.00.00.00.00.00.03.80.0% with HypertensionaHypertension in the UZGhent cohort is based on blood pressure alone.48.498.6NA94.497.897.191.495.983.9% of Missing HT0.00.0100.00.00.00.00.00.00.0% with Obesity34.861.4NA31.924.025.730.9NA26.4% with Missing BMI0.20.0100.00.30.00.00.1100.07.8Current smokers, %11.924.3NA12.59.59.511.312.613.8% of Ex–smokers40.525.7NA37.141.722.934.053.430.7% of Missing smokers2.00.0100.00.029.10.00.04.10.0% using ACEiNA48.6NA65.740.852.633.043.450.9% using ARBsNA75.7NA41.237.525.255.026.540.8% Missing medication100.00.0100.05.60.00.00.00.90.0% of PRDVascular27.722.912.059.725.040.925.36.1Diabetic nephropathy19.560.08.012.014.613.517.212.6Glomerulonephritis10.510.08.04.612.66.716.714.5Tubule-interstitial9.24.37.75.810.810.620.36.5Polycystic kidney3.07.43.25.54.65.29.8Congenital6.70.61.20.00.5Other12.03.50.610.212.215.331.8Unknown11.52.952.912.921.211.418.2Missing PRD data0.50.0100.00.40.00.00.00.11.8BMI, body mass index; DM, diabetes mellitus; glomerulonephritis, glomerulonephritis + membranous nephropathy + IgA nephropathy; NA, not applicable; obesity, BMI >30 kg/m2; tubule-interstitial, pyelonephritis + interstitial + post renal; vascular, hypertensive + renovascular.Median is presented with interquartile range in brackets.a Hypertension in the UZGhent cohort is based on blood pressure alone. Open table in a new tab Table 3Population characteristics by study; kidney function/damage and follow-up dataCountriesBelgiumCyprusItalySpainUKStudiesUZGhentNicosiaCICMAUROPIRPTABLEPECERACRISISLACKABOBaseline eGFR in ml/min per 1.73m2Mean (±SD) CKD-EPI37.7 (11.5)41.2 (11.3)33.8 (12.3)33.6 (12.0)30.2 (11.9)29.8 (13.8)19.2 (5.4)29.0 (13.3)33.5 (13.5)% of Baseline eGFR categories45–5929.341.421.819.912.917.4NA15.224.330–4443.240.035.739.535.628.32.028.033.915–2925.315.740.834.941.538.172.940.933.0<152.22.91.85.710.016.225.015.98.7% with Albuminuria dataNormoalbuminuria51.339.1NA18.341.022.214.137.822.3Microalbuminuria22.733.3NA28.636.624.528.729.828.9Macroalbuminuria26.027.5NA53.122.453.257.232.448.8Missing4.71.4100.09.592.80.05.67.944.5Follow-up dataMedian (quartile range) creatinine measurements16 (11–26)4 (4–4)3 (2–5)7 (6–7)4 (2–7)4 (2–5)5 (3–5)4 (2–5)5 (3–10)Median duration follow-up, yr5.7 (4.0–7.6)3.0 (3.0–3.0)0.5 (0.0–1.9)3.0 (3.0–3.0)2.4 (1.2–4.3)4.2 (2.2–5.1)2.5 (1.3–3.0)3.2 (1.9–5.8)5.2 (4.6–5.4)Rate per 1,000 person yr at 1 year follow-upMortality rate7.514.4NA9.822.54.627.18.44.2RRT rate2.500.00NA5.633.563.3159.453.78.4% Missing follow-up7.42.9NA0.02.70.022.90.04.1NA, not available; normoalbuminuria, albumin creatinine ratio (ACR)<30 mg/g or protein creatinine ratio (PCR) <150 mg/g or proteinuria 300 mg/g; PCR >500 mg/g or proteinuria >500 mg/24 h.Means are presented with SDs; medians are presented with interquartile ranges. Open table in a new tab BMI, body mass index; DM, diabetes mellitus; glomerulonephritis, glomerulonephritis + membranous nephropathy + IgA nephropathy; NA, not applicable; obesity, BMI >30 kg/m2; tubule-interstitial, pyelonephritis + interstitial + post renal; vascular, hypertensive + renovascular. Median is presented with interquartile range in brackets. NA, not available; normoalbuminuria, albumin creatinine ratio (ACR)<30 mg/g or protein creatinine ratio (PCR) <150 mg/g or proteinuria 300 mg/g; PCR >500 mg/g or proteinuria >500 mg/24 h. Means are presented with SDs; medians are presented with interquartile ranges. We assessed CKD progression by using a joint model, simultaneously analyzing repeated measures of eGFR and mortality risk. As such, mortality risk was taken into account for the calculation of the mean annual eGFR decline, and conversely, eGFR decline was taken into account for calculating the mortality risk. Both crude results and results adjusted for baseline eGFR, age, sex, PRD, diabetes mellitus, hypertension, obesity, and smoking are presented. Adjustment for the presence of albuminuria and angiotensin-receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEi's) are presented in the Supplementary Tables S2, S3, S4, and S5. Figure 1 and Table 4 show the crude and adjusted mortality hazard ratios (HR) and their 95% confidence intervals (95% CI). The PIRP cohort served as the reference, based on population size. The crude HR varied from 0.08 (95% CI, 0.04 to 0.16) in the English LACKABO cohort to 1.0 in the reference population. The adjusted HR varied from 0.22 (95% CI, 0.11 to 0.43) in the LACKABO cohort to 1.30 (95% CI, 1.13 to 1.49) in the CRISIS cohort. Supplementary Table S2 presents the HR additionally adjusted for use of ACEi and ARB, indicating the impact of ACEi and ARB use in the causal pathway between cohort and CKD outcome. This HR ranged from 0.21 (95% CI, 0.11 to 0.41) in the LACKABO cohort to 1.11 (95% CI, 0.96 to 1.27) in the CRISIS cohort.Table 4Hazard ratio (95% CI) for mortality, with PIRP cohort as reference groupCountryBelgiumCyprusItalySpainUKStudyGhentNicosiaCICMAUROPIRPTABLEPECERACRISISLACKABON323701,4207191,1277103,1009392,049218Model 10.20 (0.14–0.30)0.52 (0.19–1.44)NA0.30 (0.21–0.43)ref.0.42 (0.35–0.50)0.76 (0.63–0.93)0.77 (0.70–0.85)0.08 (0.04–0.16)Model 20.22 (0.15–0.32)0.55 (0.20–1.52)NA0.74 (0.52–1.07)ref.0.63 (0.52–0.75)0.93 (0.76–1.14)1.21 (1.09–1.34)0.20 (0.10–0.38)Model 3<Events0.41 (0.15–1.10)NA0.73 (0.51–1.04)ref.0.68 (0.57–0.82)1.34 (1.12–1.61)1.29 (1.17–1.43)0.20 (0.10–0.39)Model 4<Events0.53 (0.19–1.45)NA0.75 (0.52–1.07)ref.0.66 (0.55–0.80)0.99 (0.82–1.21)1.34 (1.18–1.52)0.21 (0.11–0.42)Model 5 65 yr0.84 (0.28–1.39)1.20 (+1.92– 4.33)NA1.40 (0.86–1.94)1.50 (1.07–1.58)1.94 (1.42–2.46)2.21 (1.52–2.90)1.76 (1.37–2.14)2.48 (1.15–3.80)Female0.26 (+0.10– 0.61)1.55 (+0.29– 3.39)+0.12 (+0.62– 0.38)0.78 (0.36–1.20)1.07 (0.92–1.22)1.41 (1.02–1.80)1.75 (1.27–2.23)0.89 (0.56–1.21)0.10 (+1.03– 1.23)Male1.00 (0.58–1.42)1.49 (+0.67– 3.64)0.57 (+0.08– 1.22)1.21 (0.69–1.74)1.23 (1.05–1.40)1.92 (1.43–2.42)2.29 (1.69–2.89)1.03 (0.66–1.39)2.47 (1.11–3.84)Non-DM0.60 (0.27–0.93)1.29 (+0.20– 2.78)NA0.84 (0.51–1.17)1.03 (0.91–1.15)1.65 (1.36–2.44)2.06 (1.67–2.44)0.97 (0.70–1.24)1.54 (0.81–2.27)DM1.07 (0.55–1.58)1.63 (+0.32– 3.61)NA1.37 (0.82–1.92)1.40 (1.20–1.59)1.78 (1.22–2.34)2.08 (1.46–2.69)0.94 (0.54–1.33)2.07 (0.40–3.74)+eGFR, indicates increase instead of decline; CI, confidence interval; CIC, Complesso Integrato Columbus; CRISIS, Chronic Renal Insufficiency Standards Implementation Study; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; LACKABO, London Arterial Calcification, Kidney and Bone Outcomes; MAURO, multiple intervention and audit in renal diseases to optimize care; NA, not applicable; PECERA, Proyecto de Estudio Colaborativo En pacientes con insuficiencia Renal Avanzada; PIRP, prevent
Referência(s)