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ISSN 1678-4464

38 nº.2

Rio de Janeiro, Fevereiro 2022


ARTIGO

Qualidade de atenção primária em doença renal crônica em um serviço público de um município do Estado de São Paulo, Brasil

Farid Samaan, Danilo Euclides Fernandes, Gianna Mastroianni Kirsztajn, Ricardo de Castro Cintra Sesso, Ana Maria Malik

http://dx.doi.org/10.1590/0102-311X00090821


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RESUMO
As complicações da doença renal crônica (DRC) podem ser evitadas quando a doença é diagnosticada e tratada oportunamente. O estudo teve como objetivo descrever a qualidade dos indicadores da detecção e assistência para a DRC no sistema púbico de saúde em um município do Estado de São Paulo, Brasil. O estudo retrospectivo analisou prontuários de pacientes que utilizaram serviços de atenção primária no sistema público entre novembro de 2019 e fevereiro de 2020. Selecionamos dez indicadores de qualidade com base na relevância científica e disponibilidade, a partir dos prontuários médicos. Calculamos o percentual de adequação com dados de 1.066 indivíduos que apresentavam ≥ 1 fatores de risco para DRC: hipertensão, diabetes ou idade > 60 anos. No total, 79,4% dos pacientes apresentavam informação sobre creatinina sérica, e 58,8% foram investigados para proteinúria. Dados de pressão arterial foram encontrados em 98,9% dos prontuários. As proporções de pacientes com pressão arterial < 140x90mmHg, hemoglobina glicada < 6,5% e LDL < 100mg/dL foram 79,2%, 49,2% e 33,3%, respectivamente. Os antagonistas do sistema renina-angiotensina foram prescritos em 82,8% dos pacientes com hipertensão e DRC. O potássio sérico foi medido em 35,7% dos pacientes em uso de antagonistas do sistema renina-angiotensina. Entre os indivíduos com DRC, 16,7% tinham esse diagnóstico registrado no prontuário médico. Entre os participantes com risco mais elevado de DRC, 31,6% foram encaminhados para um nefrologista. O estudo confirmou a falta de alguns indicadores de qualidade para DRC na assistência primária. Os resultados podem ajudar gestores a desenvolverem políticas públicas que melhorem a assistência para indivíduos com risco maior de DRC. O seguimento a longo prazo dos indicadores de saúde propostos aqui será útil para avaliar o impacto dessa política de intervenção.

Insuficiência Renal Crônica; Indicadores Básicos de Sáude; Atenção Primária à Saúde


 

Introduction

Chronic kidney disease (CKD) affects approximately 10% of adults and concerns the public health system worldwide 1,2. Obesity, hypertension, and diabetes account for the main reasons why CKD has become more prevalent recently 3. A report from the World Health Organization (WHO) defined kidney disorders as the most neglected non-communicable diseases of the world 4.

Diagnosing CKD is simple and cheap, indeed, and screening for CKD is highly cost-effective as its risk factors and natural course are widely known 5. Even though CKD remains asymptomatic for a long time, once CKD is diagnosed poor outcomes can be delayed or avoided 5. However, only 10% of the patients who were in the initial stages of CKD know they have kidney disorders and only 16-30% of the health care professionals can recognize a pre-dialytic CKD 6.

In 2014, the Brazilian Ministry of Health published the Clinical Guidelines for Chronic Kidney Disease Healthcare7. This document describes the role of the primary care in identifying people at high risk for CKD and in managing the initial stages of the disease, which aims to delay or preventing CKD from evolving into end-stage renal disease and dialysis. Previous international studies showed that treating CKD in primary care is not suitable 8,9. Moreover, relevant research in this field showed that some health indicators could be used to appraise health services in CKD 10.

The Brazilian Ministry of Health supports that states and cities decide which health indicators - among the ones the Brazilian Ministry of Health suggests - better fit their local problems 11. Therefore, this study aimed to describe quality indicators of CKD detection and healthcare in the primary care public service of a city in the State of São Paulo, Brazil.

Material and methods

Healthcare indicators selection

We revised the healthcare quality indicators for CKD identification and pre-dialytic CKD managing on PubMed, Google Scholar, and EBSCO (Business Source Complete) from October to December 2019. The descriptors we used were “chronic kidney disease”, “quality indicators”, “performance measures”, and “key performance indicators”. We selected the indicators that met the following criteria: (1) identification or management of CKD in primary care; (2) stronger evidence of their association with renal and non-renal endpoints (CKD progression, renal replacement therapy incidence, cardiovascular outcomes, hospitalization, and death) 8,9,10,12; (3) feasibility of recovering such information from the patients' medical records. We excluded the health indicators associated with anemia treatment, bone and mineral disorder, and metabolic acidosis because they are included in the secondary health care 7. We also excluded missing indicators in the medical records, such as the percentage of patients with CKD and inappropriate prescription of nonsteroidal anti-inflammatory drugs, and the vaccination rate for influenza, pneumococcus, and hepatitis B virus in patients with CKD. The final list of health indicators is shown in Box 1.

 

 

Box 1 Selected healthcare quality indicators 8,9,10,12.

 

Study design, setting and population

This is a retrospective study. All the data showed here were obtained from the patients' medical records. The appointments evaluated occurred between November 2019 and February 2020, and data were collected between July 8th, 2020, and October 21st, 2021.

We included patients who had ≥ 1 risk factor for CKD: hypertension, diabetes, or > 60 years old 13. We excluded the individuals who were younger than 18 years old, adults with no information on hypertension or diabetes on their medical records, and follow-up < 12 months. We analyzed medical records from all 10 basic health units (UBS) from Santana do Parnaíba city, a metropolitan area of São Paulo with 138,132 inhabitants (12.02% ≥ 60 years old, per capita income of BRL 1,507.55, and human development index of 0.814 14. The mean number of public primary care appointments was 3.14/inhabitant, and private healthcare coverage was 40.5% in 2017 15. Santana do Parnaíba public health system also has a secondary healthcare clinic where a nephrologist is available 12 hours a week. In 2019, this nephrologist performed 1.295 medical appointments.

Extracted data and variables definitions

We collected information on age, gender, ethnicity, and chronic diseases (hypertension, diabetes, dyslipidemia, heart failure, coronary insufficiency, stroke, and CKD). Besides being explicitly described in the records, additional definitions were adopted for identification of the diseases, as follows: (1) anti-hypertensive drugs use for hypertension; (2) oral anti-diabetics or insulin use for diabetes; (3) statins or fibrates use for dyslipidemia according to U.S. National Cholesterol Education Program (NCEP) 16; (4) angina pectoris, acute myocardial infarction, coronary stent surgery, or myocardial revascularization defined coronary insufficiency; and (5) estimated glomerular filtration ratio (eGFR) < 60mL/min/1.73m2 or proteinuria defined CKD according to Kidney Disease Outcomes Quality Initiative (KDOQI) 17,18.

If the blood pressure was described, we used the last three measures to estimate the mean blood pressure we show in this study. We looked for information on the two anti-hypertensive drugs that are freely provided by the Brazilian Unified National Health System (SUS) and undoubtedly slow CKD progression: the renin-angiotensin system (RAS) blockers, the angiotensin-receptor blockers (ARBs), and the angiotensin-converting enzyme inhibitor (ACEs).

The laboratory results we collected were serum creatinine, LDL-cholesterol, potassium, glycosylated hemoglobin, and proteinuria. We defined blood pressure < 140x90mmHg, glycosylated hemoglobin (HbA1c) < 6.5% and LDL-cholesterol < 100mg/dL as targets for considering controlled hypertension, diabetes, and dyslipidemia, respectively 7,10,17,18. We estimated the eGFR using CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation 18.

Identification of CKD was defined if one of the following terms were observed in the medical records: “CKD”, “chronic kidney disease”, “renal failure”, and “renal insufficiency”. We considered that the patients were correctly referred to a nephrologist if some of these criteria were met: severe proteinuria regardless of CKD stage, CKD stages 4 or 5 regardless of proteinuria, or CKD stage 3b with mild proteinuria, according to the Brazilian Ministry of Health's or the KDIGO's recommendations 7,13. We classified CKD into five stages according to the current guidelines: stage 1 (eGFR ≥ 90mL/min/1.73m2 and any level of proteinuria), stage 2 (eGFR between 60-89mL/min/1.73m2 and any level of proteinuria), stage 3a (eGFR between 45-59mL/min/1.73m2), stage 3b (eGFR between 30-44mL/min/1.73m2), stage 4 (eGFR between 15-29mL/min/1.73m2), and stage 5 (eGFR < 15mL/min/1.73m2) 13,17. The methods for measuring proteinuria were urinalysis, albumin-to-creatinine ratio (ACR), 24h-albuminuria, and 24h-proteinuria. We categorized proteinuria into: (1) absent of mild (urinalysis < 1+, ACR < 30mg/g, 24h-albuminuria < 30mg, or 24h-proteinuria < 150mg); (2) moderate (urinalysis = 1+, ACR between 30-300mg/g, 24h-albuminuria between 30-300mg, or 24h-proteinuria between 150-1,000mg); and (3) severe (urinalysis = 2+ or 3+, ACR > 300mg/g, 24h-albuminuria > 300mg, or 24h-proteinuria > 1,000mg) 13.

Sample size and statistical analysis

We estimated our sample size based on previous data that described the investigation rate of serum creatinine and proteinuria (70% and 20%, respectively) 8,9,12,19,20, blood pressure (50%) 21,22, and diagnosing CKD (10-27%) 23,24,25,26,27,28,29. We set a 95% confidence interval, with an error of 20%, which led us to a minimal sample of 857 patients. The number of medical records evaluated for inclusion was 2,450. After applying the inclusion criteria, we selected 1,066 patients, being 107 to 152 from the bigger UBS (Álvaro Ribeiro, Fazendinha, São Pedro, Parque Santana, and Colinas) and 45 to 95 from the smaller ones (Limério, Jaguari, Ingaí, Cururuquara, and Alphaville). Figure 1 shows our enrollment flow chart.

 

 

Figure 1 Enrollment flow diagram.

 

We used the SPSS software, version 18 (https://www.ibm.com/), to analyze our data. The categorical variables were described in frequencies and compared by chi-square test. The numerical variables were shown in median and interquartile, given their non-normal distribution. The indicators' performances are shown in percentages.

Our protocol was approved by the Research Ethical Committee of Federal University of São Paulo (document n. 4.055.532), and it was performed according to the Declaration of Helsinki and Resolution n. 466/2012 of the Brazilian National Healtth Council.

Results

Altogether (n = 1,066), our sample was mostly of women (61.5%) whose mean age was 61.2 years old. Hypertension was found in 77.3% of our sample, while diabetes was found in 43.3%. The main comorbidities were dyslipidemia (45.9%), CKD (12.9%), and heart failure (2.4%) Table 1. The CKD prevalence among the hypertensive, diabetic, and > 60 years old reached 14.1%, 15.7%, and 18%, respectively. Compared to individuals without CKD, the ones with CKD were mostly male (73.9% vs. 38.7%, p = 0.001), with hypertension (95.7% vs. 76.9%, p = 0.04), dyslipidemia (69.6% vs. 45.3%, p = 0.03), coronary disease (13% vs. 2%, p = 0.01) and gout (13% vs. 3.1%, p = 0.04). No significant difference between the groups regarding ethnicity, smoking status, obesity, diabetes, heart failure, stroke history, and nephrolithiasis was found.

 

 

Tab.: 1
Table 1 Sample characteristics (N = 1,066).

 

We found information on blood pressure measurement in 98.7% of the records. Among diabetic patients, 81.5% of them had their glycosylated hemoglobin registered in the medical records. LDL-cholesterol was described in 87.7% of the patients with dyslipidemia. Serum creatinine and proteinuria were found, respectively, in 79.4% and 58.8% of the records. Regarding serum creatinine measurements, the diabetic patients overcame the hypertensive ones (84.6% vs. 76%, p = 0.04). Proteinuria investigation was similar among those with diabetes and hypertension (62.6% vs. 57.4%, p = 0.34). ACR, 24h-albuminuria or 24h-proteinuria was found in 16.2% and 9.1% of the patients with diabetes and hypertension, respectively (p = 0.01). Blood pressure was adequately managed in 79.2% of the patients, as well as diabetes (49.2%) and dyslipidemia (33.3%). RAS blockers were prescribed to 82.8% of the patients who had both diabetes and hypertension, and 35.7% were investigated for serum potassium. Among those with CKD (n = 137), 16.8% had their diagnosis written down on the record. Among the 19 patients (13.9%) who met the criteria for a nephrologist referral, 6 (31.6%) reached this specialist, eventually. The healthcare performance indicators are summed up in Table 2.

 

 

Tab.: 2
Table 2 Healthcare performance indicators.

 

We classified the patients who were investigated for their serum creatinine and proteinuria (n = 611) into the risk map for CKD. We found 83.9% (n = 513) of them at low risk, 10.6% (n = 65) at moderate risk, 3.6% (n = 22) at high risk, and 1.8% (n = 11) at very high risk Table 3.

 

 

Tab.: 3
Table 3 Patients distribution according to risk categories for chronic kidney disease (CKD) outcomes (n = 611 *).

 

Discussion

This study showed that healthcare performance indicators could demonstrate how CKD has been managed in the public health system of a city in the State of São Paulo. Up to 20-50% of the individuals - who were at high risk - were not screened for CKD with serum creatinine and proteinuria. Except hypertension, no other comorbidity reached the targets to be considered under control.

For those at risk for CKD, screening for kidney disorders by serum creatinine varied from 32% to 73.5% in previous reports. Proteinuria investigation (no method mentioned) was described in 2.5% to 40% of the hypertensive, diabetic and/or older patients 8,9,10,12,19,20. Similarly, our results show that healthcare professionals still poorly understand the role of proteinuria over the serum creatinine investigation among those at high risk for CKD. KDIGO recommends screening non-diabetic patients for CKD executing urinalysis or P/C in a random urine sample. Those with diabetes need to be screened for CKD by a random urine sample albuminuria 13. In 2016, Medicare disclosed that the albuminuria investigation rate reached 40% (diabetic patients) and 18% (hypertensive patients) 20. Our results show that, even among diabetic patients, the albuminuria investigation was extremely infrequent, which reinforces that further medical training programs should approach methods of screening for CKD.

The rate at which CKD is acknowledged varies between 12-38% in international reports, and it increases as the disease becomes more severe (stage 3 = 6-8%, stage 4 = 12-31%, and stage 5 = 50-87%) 23,24,25,26,27,28. Our study also shows that only 16.8% of patients with CKD were identified (stage 3 = 13.5% and stage 4 = 42.9%), which confirms that general practitioners and non-nephrologists physicians are unaware of the definition and classification of CKD 29,30,31. We could not explore if the participants knew about their CKD condition, but we investigated if those with eGFR < 60mL/min/1.73m2 had this diagnosis registered in medical records. At Santana de Parnaíba, the laboratories that perform serum creatinine measurement do not describe the corresponding eGFR, an important analysis which could help physicians deal with the initial stages of CKD. The description of the eGFR and medical trainings may improve the concern of both health professionals and patients regarding the CKD 32,33. It was reported that the referrals to a nephrologist reached 36% (CKD stage 4-5) and 35% (severe proteinuria) in Stockholm 27. Similarly, one-third of our patients met a nephrologist.

Some of the limitations of this study should be mentioned. First, the patients' medical records missing information may not have been associated with the healthcare professional unawareness of the CKD diagnosis or laboratory results, as we presumed. Some studies have suggested that diagnosis annotations overcome looking for the International Classification of Diseases (ICD) 22,23,27. Second, some variables we did not include in this study may have interfered with the health indicators we studied - weight, height, and body surface can affect eGFR; age, tobacco use, cardiovascular diseases determine LDL-cholesterol target. Additionally, the cross-sectional design prevented us from establishing causal relationships between failures in quality indicators and clinical outcomes. Third, we could not recover other causes of CKD, such as glomerulonephritis, polycystic kidney disease, and urinary obstruction. Still, diabetes and hypertension remain the pivotal causes of CKD in Brazil and worldwide 34,35. Finally, the results of only one city, even if they covered 100% of its UBS, lack external validity. However, they may work as a comparator to other similar places and may contribute to evaluating further health interventions - such as medical training - in the city where this investigation occurred. This study is probably one of the largest and the most detailed that investigated quality indicators of CKD assistance in primary healthcare of the SUS, which may contribute to planning health interventions in São Paulo and Brazil.

Conclusion

This study revealed some missed quality indicators of identifying CKD and treatment of its main risk factors. Our results show data that may help managers develop public policies that improve health care for those at high risk for CKD. Long-term follow-up of the quality health indicators we proposed here will be helpful to assess the impact of policy intervention.

Acknowledgments

The authors would like to thank the institutional support of the Santana de Parnaíba City Hall, the Municipal Health Secretary José Carlos Misorelli, and the primary care coordinators Roseli Borós, Marcos Fernando Rosalen Lima, and Thais Cardoso Benedetti. We also thank all the directors and physicians of the basic health units we studied.

References

1.   Glassock RJ, Warnock DG, Delanaye P. The global burden of chronic kidney disease: estimates, variability and pitfalls. Nat Rev Nephrol 2017; 13:104-14.
2.   Wetmore JB, Collins AJ. Global challenges posed by the growth of end-stage renal disease. Ren Replace Ther 2016; 2:15.
3.   Hill NR, Fatoba ST, Oke JL, Hirst JA, O'Callaghan CA, Lasserson DS, et al. Global prevalence of chronic kidney disease - a systematic review and meta-analysis. PLoS One 2016; 11:e0158765.
4.   Luyckx VA, Tonelli M, Stanifer JW. The global burden of kidney disease and the sustainable development goals. Bull World Health Organ 2018; 96:414-22D.
5.   Bochud M. On the rationale of population screening for chronic kidney disease: a public health perspective. Public Health Rev 2015; 36:11.
6.   Ene-Iordache B, Perico N, Bikbov B, Carminati S, Remuzzi A, Perna A, et al. Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. Lancet Glob Health 2016; 4:e307-19.
7.   Departamento de Atenção Especializada e Temática, Secretaria de Atenção à Saúde, Ministério da Saúde. Diretrizes clínicas para o cuidado ao paciente com doença renal crônica - DRC no Sistema Único de Saúde. Brasília: Ministério da Saúde; 2014.
8.   Tu K, Bevan L, Hunter K, Rogers J, Young J, Nesrallah G. Quality indicators for the detection and management of chronic kidney disease in primary care in Canada derived from a modified Delphi panel approach. CMAJ Open 2017; 5:E74-81.
9.   Arora P. An observational study of the quality of care for chronic kidney disease: a Buffalo and Albany, New York metropolitan area study. BMC Nephrol 2015; 16:199.
10.   Smits KP, Sidorenkov G, Bilo HJ, Bouma M, Navis GJ, Denig P. Process quality indicators for chronic kidney disease risk management: a systematic literature review. Int J Clin Pract 2016; 70:861-9.
11.   Departamento de Articulação Interfederativa, Secretaria de Gestão Estratégica e Participativa, Ministério da Saúde. Caderno de diretrizes, objetivos, metas e indicadores: 2013-2015. Brasília: Ministério da Saúde; 2013. (Série Articulação Interfederativa, 1).
12.   Fukuma S. Development of quality indicators for care of chronic kidney disease in the primary care setting using electronic health data: a RAND-modified Delphi method. Clin Exp Nephrol 2016; 21:247-56.
13.   Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int 2013; 100(4 Suppl):S1-276 .
14.   Fundação Sistema Estadual de Análise de Dados. Plataforma interativa de consulta de dados. https://painel.seade.gov.br/ (accessed on 08/Mar/2021).
15.   Departamento de Informática do SUS. Informações de saúde. http://www2.datasus.gov.br/DATASUS/index.php?area=0 (accessed on 07/Dec/2020).
16.   National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002; 106:3143-421.
17.   National Kidney Foundation. K/DOQI clinical practice guidelines for bone metabolism and disease in chronic kidney disease. Am J Kidney Dis 2003; 42(4 Suppl 3):S1-201.
18.   Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro 3rd AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150:604-12.
19.   Nash DM. Quality of care for patients with chronic kidney disease in the primary care setting: a retrospective cohort study from Ontario, Canada. Can J Kidney Health Dis 2017; 4:2054358117703059.
20.   United States Renal Data System. Chapter 2: identification and care of patients with chronic kidney disease. https://www.usrds.org/media/1510/v1_ch_02_care-and-id-of-ckd-patients.pdf (accessed on 08/Mar/2021).
21.   Nobre F, Ribeiro AB, Mion Jr. D. Controle da pressão arterial em pacientes sob tratamento anti-hipertensivo no Brasil: Controlar Brasil. Arq Bras Cardiol 2010; 94:663-70.
22.   NCD Risk Factor Collaboration. Long-term and recent trends in hypertension awareness, treatment, and control in 12 high-income countries: an analysis of 123 nationally representative surveys. Lancet 2019; 394:639-51.
23.   Plantinga LC, Tuot DS, Powe NR. Awareness of chronic kidney disease among patients and providers. Adv Chronic Kidney Dis 2010; 17:225-36.
24.   Kern EFO, Maney M, Miller DR, Tseng C-L, Tiwari A, Rajan M, et al. Failure of icd-9-cm codes to identify patients with comorbid chronic kidney disease in diabetes. Health Serv Res 2006; 41:564-80.
25.   Ouseph R, Hendricks P, Hollon JA, Bhimani BD, Lederer ED. Under-recognition of chronic kidney disease in elderly outpatients. Clin Nephrol 2007; 68:373-8.
26.   Boulware LE, Troll MU, Jaar BG, Myers DI, Powe NR. Identification and referral of patients with progressive CKD: a national study. Am J Kidney Dis 2006; 48:192-204.
27.   Gasparini A, Evans M, Coresh J, Grams ME, Norin O, Qureshi AR, et al. Prevalence and recognition of chronic kidney disease in Stockholm healthcare. Nephrol Dial Transplant 2016; 31:2086-94.
28.   Rothberg MB, Kehoe ED, Courtemanche AL, Grams ME, Norin O, Qureshi AR, et al. Recognition and management of chronic kidney disease in an elderly ambulatory population. J Gen Intern Med 2008; 23:1125-30.
29.   Tamizuddin S, Ahmed W. Knowledge, attitude and practices regarding chronic kidney disease and estimated GFR in a tertiary care hospital in Pakistan. J Pak Med Assoc 2010; 60:342-6.
30.   Agaba E, Agaba P, Dankyau M, Akanbi M, Daniyam C, Okeke E, et al. Specialist physician knowledge of chronic kidney disease: a comparison of internists and family physicians in West Africa. Afr J Prim Health Care Fam Med 2012; 4:319.
31.   Agrawal V, Agarwal M, Ghosh AK, Barnes MA, McCullough PA. Identification and management of chronic kidney disease complications by internal medicine residents: a national survey. Am J Ther 2011; 18:e40-7.
32.   Noble E, Johnson DW, Gray N, Hollett P, Hawley CM, Campbell SB, et al. The impact of automated eGFR reporting and education on nephrology service referrals. Nephrol Dial Transplant 2008; 23:3845-50.
33.   Kagoma YK, Weir MA, Iansavichus AV, Hemmelgarn BR, Akbari A, Patel UD, et al. Impact of Estimated GFR reporting on patients, clinicians, and health-care systems: a systematic review. Am J Kidney Dis 2011; 57:592-601.
34.   Thomé FS, Sesso RC, Lopes AA, Lugon JR, Martins CT. Brazilian chronic dialysis survey 2017. Braz J Nephrol 2019; 41:208-14.
35.   United States Renal Data System. Chapter 1: incidence, prevalence, patient characteristics, and treatment modalities. https://www.usrds.org/media/1552/vol2_01_incidenceandprevalence_15.pdf (accessed on 10/Jan/2021).

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