Prevalence and Correlates of Chronic Kidney Disease in Patients with hypertension in Rural Malawi

Prevalence and Correlates of Chronic Kidney Disease in Patients with hypertension in Rural Malawi | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Prevalence and Correlates of Chronic Kidney Disease in Patients with hypertension in Rural Malawi Chiyembekezo Kachimanga, Lawrence Nazimera, Enoch Ndarama, Richard Kamwezi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.2.10113/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2019 Read the published version in SN Comprehensive Clinical Medicine → Version 1 posted You are reading this latest preprint version Abstract Background The prevalence of chronic kidney disease (CKD) in patients with hypertension is very high in Africa. We investigated the prevalence and correlates of CKD in patients with hypertension attending longitudinal care in rural Malawi, where currently no data on prevalence of CKD in patients with hypertension exists. Methods We retrospectively reviewed medical records of all hypertensive patients who were screened for CKD between January 2018 and April 2019. Screening was done using serum creatinine and CKD epidemiology formula was used to estimate the glomerular filtration rate (eGFR). We used Kidney Disease: Improving Global Outcomes definitions of renal insufficiency and CKD. Logistic regression analysis was used to identify correlates of CKD. Results During the study duration, 1197 patients with hypertension were screened for CKD. The mean creatinine and eGFR was 0.90 mg/dl (Confidence Interval (CI) 0.85-0.94 mg/dl) and 84.1 ml/min/1.73m2 (CI 82.7-85.4 ml/min/1.73m2) respectively. About half of the patients had a normal eGFR (48.3%, n=578) and 36.3% (n=435) had mildly decreased eGFR. The prevalence of renal insufficiency was 15.4% (CI 13.4-17.5, 184/1197) and the prevalence of CKD was 7.1% (CI 5.7-8.7%). By eGFR category in the CKD patients, 41.2% (n=35), 31.8% (n=27), 24.7% (n=21) and 2.3% (n=2) had CKD stage 3a, 3b, 4 and 5 respectively. CKD was strongly associated with age and diabetes. Conclusions We found moderately high renal insufficiency and CKD in this cohort. We propose investing in screening for CKD in patients with hypertension in other clinics in Malawi. Urology & Nephrology Non-communicable diseases chronic kidney disease hypertension renal insufficiency Malawi Figures Figure 1 Background Globally, the prevalence of hypertension is high, affecting over 31% of adults (1). Low-and-middle-income countries, especially African countries, are severely affected with over 75% of patients with hypertension living in these countries (1,2). In Africa alone, hypertension affect over 30% of adults(3), and is the most common cause of chronic kidney disease (CKD) (4). CKD is a growing public health problem in Africa with recent increases in CKD incidence, prevalence and mortality (5). Whilst 15% of the general population have CKD, this is higher in patients with hypertension, with a recent systematic review reporting a prevalence of 36% (4). Additionally, the burden of CKD in patients with hypertension varies widely. For example, studies in Cameroon and one study in Ghana reported about 50% prevalence of CKD in patients with hypertension (6,7), but a study in Uganda and another study in Ghana report lower prevalence, at 38.5% and 13% respectively (8). The management of CKD and its complications is expensive, and most African countries cannot afford to manage the complications of CKD, particularly end stage renal disease (9,10). Additionally, these countries are struggling with a high burden of communicable diseases and maternal and child health conditions, with some of these diseases such as HIV also increasing the risk of CKD (11). Dialysis and renal replacement therapy is not affordable and feasible for all the patients that need these services in African countries (10,12). However, screening for CKD, which is feasible and affordable, provides an opportunity for early detection and management of CKD in Africa. Screening for CKD, especially in high risk groups like patients with hypertension, has many advantages. Screening allows for early detection as CKD may be asymptomatic in early stages. This enables early interventions to reduce progression of CKD and may help improve the quality of life and extend life expectancy in these patients (11). Additionally, screening saves patient and health care costs as it is cheaper than managing complications of CKD (9,13). Far more importantly, screening helps to improve awareness of CKD, as many patients in Africa are not aware that they have CKD (14). All of these advantages can be achieved by using relatively cheap screening tests such as urinary albumin and/or serum creatinine, which have been shown to be effective and feasible in many African countries (15). Situated in southern Africa, Malawi currently faces an increase in non-communicable diseases (NCDs), in addition to an existing burden of HIV (16). Up to 16% of adults aged 18 years and above have hypertension (17). Although screening for CKD is included in the National NCD Action Plan for Prevention and management of NCDs in Malawi, most facilities, especially district and rural health facilities, have minimal capacity to screen for CKD (18–20). Currently, limited data exists on the prevalence of CKD patients with hypertension, especially in rural Malawi where over 84% of the population resides (21). The majority of studies on CKD were done in the context of HIV and are all from urban facilities(22–25). We present a study on the prevalence and correlates of CKD in hypertensive patients aged 18 years and above attending Integrated Chronic Care Clinics in Neno District, Malawi. This study is relevant because 1) It estimates the prevalence of undiagnosed CKD in a cohort of patients with hypertension, a key high risk group in Malawi; 2) It investigates CKD in rural Malawi, where despite the majority of Malawians living in rural areas, no data exists on CKD; and 3) It measures context specific correlates of CKD. Methods Setting This is a cross-sectional study conducted between January 2018 and April 2019 in Neno District, Malawi. We retrospectively reviewed medical records of all patients with hypertension screened for CKD in Integrated Chronic Care Clinics. Neno District is a remote and rural south-western district of Malawi with an estimated population of 138,000 in 2018 (21).Within the Neno District’s 14 health facilities (2 hospitals and 12 primary health facilities), the Ministry of Health (MOH) in collaboration with a non-governmental organization called Partners In Health has been providing integrated HIV-NCD care since 2015 (26). In the Integrated Chronic Care Clinic, a team of mid-level providers, nurses, and other support staff provide longitudinal care to patients with HIV, hypertension, diabetes, epilepsy, chronic obstructive respiratory disease, mental illnesses, and other chronic NCDs. By April 2019, over 7850 HIV and 3400 NCD patients, of which 2184 were patients with hypertension, were receiving care in this clinic (Partners In Health internal data). The Integrated Chronic Care model has been described elsewhere (26,27). Hypertension Enrolment, CKD Screening and Laboratory Measurements In the Integrated Chronic Care clinics, patients were referred for treatment for hypertension from several places: inpatient wards, outpatient clinics, and community screening events (28). Patients were enrolled in the clinic for treatment for hypertension either if they were currently on hypertension treatment or based on a systolic blood pressure ≥ 160 mmHg and/or diastolic blood pressure ≥110 mmHg , procedures initiated in Neno District in order to capture the highest risk patients first and avoid early overcrowding of the Integrated Chronic Care Clinics. Beginning January 2018, Neno District introduced annual routine screening for CKD in patients with hypertension in the Integrated Chronic Care Clinics using two point of care chemistry analyzers. For eligible hypertensive patients, at least one milliliter of venous blood was collected in heparinized bottles by clinic nurses using aseptic techniques for the measurement of plasma creatinine (mg/dl). In order to provide immediate results and expedite patient care, the samples were analysed immediately in the clinic using an I-Stat analyzer and chem8+ cartridges (Abbot, USA). The analysis was done by support staff working in the clinic with support from a laboratory technician who provided a one day training followed by longitudinal mentorship at least once every 3 months. In the event an I-Stat analyzer was malfunctioning or not available in the clinic, the samples were transported to one of the two hospitals in Neno for same day analysis on I-Stat analyzers located in the laboratories. Towards the end of 2018, the MOH purchased a new chemistry analyzer, Mindray BS 120 chemistry analyzer, that was used when I-Stat cartridges were in short supply. Based on the initial serum creatinine, an estimated glomerular filtration rate (eGFR) was calculated. If the patients had renal insufficiency on their initial test, creatinine was checked again after at-least 3 months in order to confirm the diagnosis of CKD. Follow up was facilitated by reminders to clinic staff, using community health workers to inform patients to come to the clinic for follow up testing, and, if necessary, home visits were conducted by the clinical team to identify and escort patients to nearest clinic for CKD screening. Inclusion and Exclusion Criteria of the Study We retrospectively reviewed the electronic medical record (EMR) of patients with hypertension. All patients’ clinical information was recorded in the MOH standardized patient chart, and the data was also duplicated in the EMR. All hypertensive patients aged 18 years and above who were screened for CKD were included in the study. We excluded hypertensive patients with an existing diagnosis of CKD. Clinical Measurements and Outcomes CKD was the main outcome and was defined based on Kidney Disease: Improving Global Outcomes (KDIGO) definition: an estimated eGFR of less than 60ml/min/1.73m 2 persistent for at least 3 months (29). EGFR of less than 60ml/min/1.73m 2 on one occasion only was defined as renal insufficiency. In addition, we categorized eGFR in all patients based on KDIGO categories of eGFR. We derived an eGFR from plasma creatinine using CKD Epidemiology formula without the factor of race. The CKD Epidemiology formula without race was chosen as it has been shown to better predict eGFR in Malawians than CKD epidemiology formula with black as a race factor or other formulas of predicting eGFR (30). We extracted several other variables from the patients’ charts based on data that was routinely collected during clinical encounters and variables that have been shown to be associated with CKD in other studies. Therefore, we extracted the following independent for each patient with hypertension: Demographic variables: Age (years), gender (male or female) Clinical history of hypertension: Years since diagnosis of hypertension and duration of enrolment in the clinic (less than one year, 1-2 years, more than 2 years), and most recent systolic and diastolic blood pressures (mmHg) Risk factors of hypertension: Body mass index ( BMI) (kg/m 2 ), BMI over 25, BMI categories (<18.5, ≥18.5-<25, ≥25-<30, ≥30), proteinuria (negative and trace were coded as protein <1+, protein 1+, 2+ and 3+ were coded as protein ≥1+) Comorbidities (yes or no): Patients with concurrent diabetes and/or HIV. We planned to include variables that are representative of the hypertensive population. Therefore we opted to exclude from the final analysis any variable that had more than 90% of the participants’ data missing. This included history of smoking and alcohol and the following complications: stroke, cardiovascular disease, peripheral vascular disease and neuropathy. All included data were collected during routine clinical encounters, and protocols for their measurement is explained elsewhere (27). Data Management and Statistical Analysis All data used in this study were initially extracted from the EMR to Microsoft Excel version 2013. Data cleaning and analysis was performed using Stata 15 (Stata Corp, Texas). Descriptive statistics were used to describe the data. Depending on normality, mean and standard deviation were used for continuous variables and median and Interquartile range (IQR) were used non-normally distributed data. Depending on normality and type of variables, Chi 2 , t-test , Mann Whitney U test were used to explore the relationship between the independent variables and patients CKD. We performed logistic regression analysis to determine correlates of CKD. Initially, we performed single predictor models to identify significant correlates of CKD. We then performed multivariable logistic regression using identified significant factors from the single predictor models. Statistical significance was defined at a p value <0.05 at 95% confidence interval. Ethics Approval and Consent to Participate All data were collected as part of routine patient care and analysed retrospectively. As a result, we did not obtain informed consent from patients. Access to data was given only to investigators and data were kept securely. The study received ethical clearance from Malawi National Health Sciences Research Committee protocol number 1216. Results Baseline Characteristics of the Participants Between January 2018 and April 2019, 1197 out of 2184 (54.8%) patients with hypertension were screened for CKD (Figure 1). Seventy nine percent (n=942) were females and the median age was 61 years (IQR 51-70 years). About half of the patients had had been diagnosed and enrolled in Integrated Chronic Care Clinic for more than 2 years. The median body mass index (BMI) was 22.9 kg/m 2 (IQR 20.3-26.6 kg/m 2 ) and only 5% (n=46) had proteinuria >=1+. 5.3% (n=63) and 10.9% (n=112) had diabetes and HIV as comorbidities of hypertension respectively (Table 1). Figure 1. Screening Results for Patients with Hypertension Table 1. Characteristics of Patients with Hypertension Screened for CKD Among all hypertensive patients that were screened for CKD, the mean creatinine was 0.90 mg/dl (Confidence Interval (CI) 0.85-0.94 mg/dl) and the mean eGFR was 84.1 ml/min/1.73m 2 (CI 82.7-85.4 ml/min/1.73m 2) . About half of the patients had a normal eGFR (48.3%, n=578) and 36.3 % (n=435) had mildly decreased eGFR (Table 2).The prevalence of renal insufficiency was 15.4% (CI 13.4-17.5, 184/1197). Among the 184 hypertensive patients with renal insufficiency, 126 patients were successfully re-tested after a minimum of 3 months (Figure 1). Of these, 85 had persistent eGFR < 60 ml/min/1.73m 2 , hence the prevalence of CKD in this study was 7.1% (CI 5.7-8.7 %). By eGFR category in the CKD patients, 41.2% (n=35), 31.8% (n=27), 24.7 % (n=21) and 2.3% (n=2) had CKD stage 3a, 3b, 4 and 5 respectively. Table 2: Distribution of eGFR in all patients screened for renal insufficiency and CKD After univariate logistic regression model, increasing age (unadjusted odds ratio (OR) 1.06, CI 1.04-1.08, p<0.001), higher systolic blood pressure (unadjusted OR 1.01, CI 1.004 -1.02, p<0.001) and concurrent diagnosis of diabetes (unadjusted OR 2.32, CI 1.10-4.88, p=0.03) were all associated with CKD (Table 3). In multivariable logistic regression, older age (adjusted OR 1.06, CI 1.04-1.08, p<0.001) and a history of diabetes (adjusted OR 2.63, CI 1.21-5.69, p=0.01 were strongly associated with CKD. Most recent systolic blood pressure was weakly associated with CKD (adjusted OR 1.01 CI 1.00- 1.02, p=0.048) Table 3 Univariate Analysis of Correlates of CKD Table 4 Multivariable analysis of Correlates of CKD Discussion As far as we know, this is the first study to investigate CKD among patients with hypertension attending longitudinal care in rural Malawi. Approximately half of the patients with hypertension tested had abnormal eGFR, and at-least 15% of the patients had evidence of renal insufficiency. This demonstrates the clinical importance of routine screening for CKD in hypertension clinics. Most patients with renal insufficiency benefitted from follow up after three months for confirmation of CKD. The screening and dissemination of their results may also have helped with raising the awareness of CKD. The confirmation of CKD by KDIGO guidelines requires re-testing after at least 3 months, and in rural impoverished settings, more effort was needed to find these patients for repeat testing. This challenge has been reported in a previous study that used KDIGO guidelines (31). Despite our efforts to track the patients, we were not able to re-test all patients with renal insufficiency; 126 out of 184 (68%) patients with hypertension were reached and re-tested. It was impossible to track other patients; some had died or transferred outside the district, and others could not be located for testing within our study period. Therefore, we may have underestimated the prevalence of CKD. Other programs implementing screening for CKD should consider how to strengthen the ways used to track patients in this study, which also has benefits for patient care beyond screening, or try other strategies that can be used to effectively track patients for confirmation of CKD. We found a lower prevalence of CKD in patients with hypertension in comparison to most studies published in Africa (13,32). The prevalence may have been underestimated since we could not find all the patients who needed a repeat test. Additionally, we had used KDIGO guidelines, and therefore some people had an initially low eGFR which normalized after repeat testing. Additionally, most of the patients in this study are females, which reflects health service utilization in Malawi, where more females utilize services than men. After controlling for multiple factors, only older age and diabetes were strongly associated with CKD in patients with hypertension, and this has also been shown in other studies in Africa (8,33) There are many clinical and research implications from these results. First, we concluded that all patients with hypertension should be screened for CKD, and Neno District continues to gradually screen all patients and plans to repeat CKD screening annually. Additionally, Neno District has created 2 Advanced NCD Clinics where patients with CKD stage 4 and 5 are routinely managed alongside patients with other severe chronic NCDs. Beyond Neno, hypertension clinics need to invest in CKD screening for early detection and management of kidney disease. Additionally, more research needs to be generated, particularly in urban areas, to compare with the results from this study. There are a few key limitations in this study. We used only eGFR only to define patients with CKD, and we were unable to measure albumin-creatinine ratio or other measures of CKD as defined by KDIGO guidelines. The study is also a facility based audit and was conducted in a rural area. As a result, the study may not be generalizable to populations outside Neno District. We also investigated CKD in patients with hypertension aged 18 years and above thereby excluding all patients less than 18 years old. In addition, to be enrolled for hypertension treatment in clinics in Neno, patients have blood pressures ≥160/110, so the results are not generalizable to patients with Stage I hypertension. Finally, we measured prevalence and not incidence of CKD, therefore we could not differentiate the temporal sequence of hypertension and CKD. Conclusions The study found moderately high prevalence of renal insufficiency and CKD in a rural cohort of hypertension patients, 15.4% and 7.1% respectively. Additionally, CKD was significantly associated with older age and a history of diabetes. We advocate for investing in CKD screening among patients with hypertension attending longitudinal care in order to optimize early diagnosis and management in this population. List of abbreviations CI confidence interval CKD Chronic kidney disease NCD Non-communicable disease EGFR Estimated glomerular filtration rate KDIGO Kidney Disease: Improving Global Outcomes BMI Body mass index IQR interquartile range MOH Ministry of Health EMR Electronic medical record OR Odds ratio Declarations Acknowledgements We acknowledge all of our patients who receives care at all the Integrated Chronic Care clinics in Neno District, Malawi. Special acknowledgment to all the staff working in Neno health facilities, their efforts continues to contribute to better quality care of the patients. Funding None Availability of data and materials The data and materials used for this study belongs to the Ministry of Health in Neno District therefore cannot be shared publicly. However, the data and materials can be shared upon reasonable request to the corresponding author. Authors' contributions CK, LN, EN, GCT and EBW conceptualized the study. LT, RK, CK and GCT performed data curation. CK performed data cleaning and analysis. CK wrote the first draft. All authors provided feedback and approved the final manuscript for publication. Ethics approval and consent to participate All data were collected as part of routine patient care and was analysed retrospectively. As a result, we did not obtain informed consent from patients. Access to data was given only to investigators and data were kept securely. The study received ethical clearance from Malawi National Health Sciences Research Committee protocol number 1216. Consent for publication Not applicable Competing interests The authors declare no competing interests References 1. Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global Disparities of Hypertension Prevalence and Control:a Systematic Analysis of Population-Based Studies from 90 Countries. Circulation. 2016;134:441–50. 2. NCD Countdown 2030 collaborators. 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KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int Suppl [Internet]. 2013;3(1):136–50. Available from: http://www.kdigo.org/clinical_practice_guidelines/pdf/CKD/KDIGO CKD-MBD GL KI Suppl 113.pdf%5Cnhttp://www.nature.com/doifinder/10.1038/kisup.2012.73%5Cnhttp://www.nature.com/doifinder/10.1038/kisup.2012.76 30. Glaser N, Deckert A, Phiri S, Rothenbacher D, Neuhann F. Comparison of various equations for estimating GFR in Malawi: How to determine renal function in resource limited settings? PLoS One. 2015;10(6):1–17. 31. Kaze FF, Meto DT, Halle M, Ngogang J, Kengne A. Prevalence and determinants of chronic kidney disease in rural and urban Cameroonians : a cross-sectional study. BMC Nephrol [Internet]. BMC Nephrology; 2015;16. Available from: http://dx.doi.org/10.1186/s12882-015-0111-8 32. Abd Elhafeez S, Bolignano D, D’Arrigo G, Dounousi E, Tripepi G, Zoccali C. Prevalence and burden of chronic kidney disease among the general population and high-risk groups in Africa: A systematic review. BMJ Open. 2018;8(1). 33. Ladi-Akinyemi TW, Ajayi I. Risk factors for chronic kidney disease among patients at Olabisi Onabanjo University Teaching Hospital in Sagamu, Nigeria: A retrospective cohort study. Malawi Med J. 2017;29(2):166–70. Tables Table 1. Characteristics of patients with hypertension screened for CKD Total hypertension patients No CKD CKD Total number of patients screened (%) 1197 1112 (92.9) 85 (7.1) Median age(IQR)-years * 61 (51-70) 60 (50-69) 70 (62-78) Female gender (%) 942 (78.7) 878(79.0) 64(75.3) Years since diagnosis of hypertension (%) Less than 1 290 (24.3) 275 (24.7) 15 (17.6) 1-2 274 (22.9) 252 (22.7) 22 (25.9) Over 2 632 (52.8) 584 (52.6) 48( 56.5) Duration in clinic (%)-years Less than 1 291(24.3) 276 (24.8) 15 (17.6) 1-2 276 (23.1) 254 (22.9) 22 (25.9) Over 2 630 (52.6) 582 (52.3) 48 (56.5) Median most recent systolic blood pressure(IQR)-mmHg* 135 (123-148) 135 (123-148) 140.5 (124.5-157) Median most recent diastolic blood pressure(IQR)-mmHg 84 (76.5-93) 84 (77-93) 83 (73-92) Risk factors Median BMI (IQR)-kg/m2 22.9 (20.3-26.6) 22.8 (20.3-26.5) 23.7 (20.3-27.4) BMI over 25 (median, IQR)-kg/m2 29.1(27.3-32.9) 27.8 (26.0-31.6) 29.3 (27.3-33.2) BMI category (%)-kg/m2 <18.5 115 (9.7) 105 (9.5) 10 (11.9) 18.5-<25 684 (57.4) 641(57.9) 43 (51.2) 25-<30 270 (22.7) 245 (22.1) 25 (29.8) 30 and above 122 (10.2) 116 (10.5) 6 (7.1) Proteinuria Protein =1 46 (4.6) 42 (4.5) 4 (6.0) Diabetes* No 1134 (94.7) 1058 (95.1) 76 (89.4) Yes 63 (5.3) 54 (4.9) 9 (10.6) HIV Negative 916 (89.1) 845 (88.6) 71 (95.9) Positive 112 (10.9) 109 (11.4) 3 (4.1) CKD Chronic Kidney Disease % Percentage Sd Standard deviation IQR interquartile range BMI Body mass index *significant differences between CKD patients and patients without CKD Table 2 Estimated GFR stages in all patients with hypertension screened for chronic kidney disease Stages of eGFR eGFR (ml/min/1.73m2) N (%) Normal EGFR ≥ 90 578 (48.3) G2 (Mildly decreased) 60-89 435 (36.3) G3a (Mildly to Moderately decreased) 45-59 85 (7.1) G3b ( Moderately to severely decreased) 30-44 57 (4.8) G4 (Severely decreased) 15-29 33 (2.8) G5 (Kidney failure) <15 9 (0.7) Total 1197 (100) eGFR Estimated GFR CKD Chronic kidney disease Table 3. Univariate Logistic Regression of Correlates of CKD Independent Variable Odds ratio (CI) P value Age (years) 1.06 (1.04-1.08) <0.001* Gender Female ref ref Male 1.23 (0.74-2.06) 0.43 Years since diagnosis <1 year ref 1-2 years 1.60 (0.81-3.15) 0.17 Over 2 years 1.51 (0.83-2.73) 0.19 Diagnosis duration <1 year ref 1-2 years 1.59 (0.81 -3.14) 0.19 Over 2 years 1.52 (0.83 -2.76) 0.17 Most recent systolic blood pressure 1.01 (1.004 -1.02) <0.001* Most recent diastolic blood pressure 0.99 (0.98-1.01) 0.60 BMI 1.02 (0.99 -1.06) 0.40 Overweight or obesity 1.01 (0.93-1.09) 0.82 BMI category <18.5 1.42 (0.69-2.91) 0.34 18.5-<25 Ref Ref 25-<30 1.52 (0.91-2.5) 0.11 30 and above 0.77 (0.32 -0.85) 0.56 Proteinuria Less than 1+ Ref 1+ and above 1.33 (0.46-3.80) 0.59 Diabetes diagnosis No Ref Yes 2.32 (1.10-4.88) 0.03* HIV diagnosis Negative Ref Positive 0.33 (0.10-1.06) 0.06 *significant associations Table 4 Multivariable analysis of Correlates of CKD Independent Variable Adjusted Odds ratio (CI) P value Age (years) 1.06 (1.04-1.08) <0.001 Most recent systolic blood pressure 1.01 (1.00- 1.02) 0.048 Diabetes diagnosis No Ref Yes 2.63 (1.21- 5.69) 0.01 CKD Chronic kidney disease Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2019 Read the published version in SN Comprehensive Clinical Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1193","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":93352,"identity":"49c49cce-e360-4025-bf8a-69cd0f3b933d","order_by":1,"name":"Chiyembekezo Kachimanga","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-3507-9591","institution":"Partners In Health","correspondingAuthor":true,"prefix":"","firstName":"Chiyembekezo","middleName":"","lastName":"Kachimanga","suffix":""},{"id":93353,"identity":"0c23a2e4-11f2-425e-a139-b747b34d3b3a","order_by":2,"name":"Lawrence Nazimera","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"","lastName":"Nazimera","suffix":""},{"id":93354,"identity":"4d9e84e0-a398-4aa4-8e76-f649e3eeada9","order_by":3,"name":"Enoch Ndarama","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Enoch","middleName":"","lastName":"Ndarama","suffix":""},{"id":93355,"identity":"bd917f09-c24f-4bfa-a6eb-4068ac1dc551","order_by":4,"name":"Richard Kamwezi","email":"","orcid":"","institution":"Partners In Health","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Kamwezi","suffix":""},{"id":93356,"identity":"34b1de86-e307-41f4-a8bc-6591c4ff2fdb","order_by":5,"name":"Limbani Thengo","email":"","orcid":"","institution":"Partners In Health","correspondingAuthor":false,"prefix":"","firstName":"Limbani","middleName":"","lastName":"Thengo","suffix":""},{"id":93357,"identity":"4161fe56-9572-4a56-82d9-eda4931db746","order_by":6,"name":"Emily B Wroe","email":"","orcid":"","institution":"Partners In Health","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"B","lastName":"Wroe","suffix":""},{"id":93358,"identity":"9ae02e56-82fa-41d2-b040-80bc55267f69","order_by":7,"name":"George C Talama","email":"","orcid":"","institution":"Partners In Health","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"C","lastName":"Talama","suffix":""}],"badges":[],"createdAt":"2019-06-03 15:41:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.2.10113/v1","doiUrl":"https://doi.org/10.21203/rs.2.10113/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42399-019-00154-6","type":"published","date":"2019-10-24T02:40:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2613757,"identity":"1be5f7a4-dfa2-47f6-90b1-c253a0136995","added_by":"b0e95e7b-bbe0-4bfd-bf12-a325b7db0c3e","created_at":"2020-09-25 20:53:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46081,"visible":true,"origin":"","legend":"Screening Results for Patients with Hypertension \nCKD Chronic kidney disease\neGFR Estimated glomerular filtration rate","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1193/v1/figure_1.jpg"},{"id":13467719,"identity":"f2695898-2f89-4708-9a21-be5ef15b8378","added_by":"auto","created_at":"2021-09-16 20:56:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":575343,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1193/v1/f3ceccd6-ab02-4d7d-ae00-bc4d9a9039b4.pdf"}],"financialInterests":"","formattedTitle":"Prevalence and Correlates of Chronic Kidney Disease in Patients with hypertension in Rural Malawi","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobally, the prevalence of hypertension is high, affecting over 31% of adults (1).\n Low-and-middle-income countries, especially African countries, are severely affected\n with over 75% of patients with hypertension living in these countries (1,2). In Africa\n alone, hypertension affect over 30% of adults(3), and is the most common cause of\n chronic kidney disease (CKD) (4).\u003c/p\u003e\n \n\n \n\u003cp\u003eCKD is a growing public health problem in Africa with recent increases in CKD incidence,\n prevalence and mortality (5). Whilst 15% of the general population have CKD, this\n is higher in patients with hypertension, with a recent systematic review reporting\n a prevalence of 36% (4). Additionally, the burden of CKD in patients with hypertension\n varies widely. For example, studies in Cameroon and one study in Ghana reported about\n 50% prevalence of CKD in patients with hypertension (6,7), but a study in Uganda and\n another study in Ghana report lower prevalence, at 38.5% and 13% respectively (8).\u003c/p\u003e\n \n\n \n\u003cp\u003eThe management of CKD and its complications is expensive, and most African countries\n cannot afford to manage the complications of CKD, particularly end stage renal disease\n (9,10). Additionally, these countries are struggling with a high burden of communicable\n diseases and maternal and child health conditions, with some of these diseases such\n as HIV also increasing the risk of CKD (11). Dialysis and renal replacement therapy\n is not affordable and feasible for all the patients that need these services in African\n countries (10,12). However, screening for CKD, which is feasible and affordable, provides\n an opportunity for early detection and management of CKD in Africa. \u003c/p\u003e\n \n\n \n\u003cp\u003eScreening for CKD, especially in high risk groups like patients with hypertension,\n has many advantages. Screening allows for early detection as CKD may be asymptomatic\n in early stages. This enables early interventions to reduce progression of CKD and\n may help improve the quality of life and extend life expectancy in these patients\n (11). Additionally, screening saves patient and health care costs as it is cheaper\n than managing complications of CKD (9,13). Far more importantly, screening helps to\n improve awareness of CKD, as many patients in Africa are not aware that they have\n CKD (14). All of these advantages can be achieved by using relatively cheap screening\n tests such as urinary albumin and/or serum creatinine, which have been shown to be\n effective and feasible in many African countries (15).\u003c/p\u003e\n \n\n \n\u003cp\u003eSituated in southern Africa, Malawi currently faces an increase in non-communicable\n diseases (NCDs), in addition to an existing burden of HIV (16). Up to 16% of adults\n aged 18 years and above have hypertension (17). Although screening for CKD is included\n in the National NCD Action Plan for Prevention and management of NCDs in Malawi, most\n facilities, especially district and rural health facilities, have minimal capacity\n to screen for CKD (18–20). Currently, limited data exists on the prevalence of CKD\n patients with hypertension, especially in rural Malawi where over 84% of the population\n resides (21). The majority of studies on CKD were done in the context of HIV and are\n all from urban facilities(22–25). \u003c/p\u003e\n \n\u003cp\u003eWe present a study on the prevalence and correlates of CKD in hypertensive patients\n aged 18 years and above attending Integrated Chronic Care Clinics in Neno District,\n Malawi. This study is relevant because 1) It estimates the prevalence of undiagnosed\n CKD in a cohort of patients with hypertension, a key high risk group in Malawi; 2)\n It investigates CKD in rural Malawi, where despite the majority of Malawians living\n in rural areas, no data exists on CKD; and 3) It measures context specific correlates\n of CKD. \u003c/p\u003e"},{"header":"Methods","content":"\u003ch2 data-xsweet-outline-level=\"1\"\u003eSetting\u003c/h2\u003e\n \n\n \n\u003cp\u003eThis is a cross-sectional study conducted between January 2018 and April 2019 in Neno\n District, Malawi. We retrospectively reviewed medical records of all patients with\n hypertension screened for CKD in Integrated Chronic Care Clinics.\u003c/p\u003e\n \n\u003cp\u003eNeno District is a remote and rural south-western district of Malawi with an estimated\n population of 138,000 in 2018 (21).Within the Neno District’s 14 health facilities (2 hospitals and 12 primary\n health facilities), the Ministry of Health (MOH) in collaboration with a non-governmental\n organization called Partners In Health has been providing integrated HIV-NCD care\n since 2015 (26). In the Integrated Chronic Care Clinic, a team of mid-level providers,\n nurses, and other support staff provide longitudinal care to patients with HIV, hypertension,\n diabetes, epilepsy, chronic obstructive respiratory disease, mental illnesses, and\n other chronic NCDs. By April 2019, over 7850 HIV and 3400 NCD patients, of which 2184\n were patients with hypertension, were receiving care in this clinic (Partners In Health\n internal data). The Integrated Chronic Care model has been described elsewhere (26,27).\u003c/p\u003e\n \n\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eHypertension Enrolment, CKD Screening and Laboratory Measurements \u003c/h2\u003e\n \n\n \n\u003cp\u003eIn the Integrated Chronic Care clinics, patients were referred for treatment for hypertension\n from several places: inpatient wards, outpatient clinics, and community screening\n events (28). Patients were enrolled in the clinic for treatment for hypertension either\n if they were currently on hypertension treatment or based on a systolic blood pressure\n ≥ 160 mmHg and/or diastolic blood pressure ≥110 mmHg , procedures initiated in Neno\n District in order to capture the highest risk patients first and avoid early overcrowding\n of the Integrated Chronic Care Clinics. \u003c/p\u003e\n \n\u003cp\u003eBeginning January 2018, Neno District introduced annual routine screening for CKD\n in patients with hypertension in the Integrated Chronic Care Clinics using two point\n of care chemistry analyzers. For eligible hypertensive patients, at least one milliliter\n of venous blood was collected in heparinized bottles by clinic nurses using aseptic\n techniques for the measurement of plasma creatinine (mg/dl). In order to provide immediate\n results and expedite patient care, the samples were analysed immediately in the clinic\n using an I-Stat analyzer and chem8+ cartridges (Abbot, USA). The analysis was done\n by support staff working in the clinic with support from a laboratory technician who\n provided a one day training followed by longitudinal mentorship at least once every\n 3 months.\u003c/p\u003e\n \n\u003cp\u003eIn the event an I-Stat analyzer was malfunctioning or not available in the clinic,\n the samples were transported to one of the two hospitals in Neno for same day analysis\n on I-Stat analyzers located in the laboratories. Towards the end of 2018, the MOH\n purchased a new chemistry analyzer, Mindray BS 120 chemistry analyzer, that was used\n when I-Stat cartridges were in short supply.\u003c/p\u003e\n \n\u003cp\u003eBased on the initial serum creatinine, an estimated glomerular filtration rate (eGFR)\n was calculated. If the patients had renal insufficiency on their initial test, creatinine\n was checked again after at-least 3 months in order to confirm the diagnosis of CKD.\n Follow up was facilitated by reminders to clinic staff, using community health workers\n to inform patients to come to the clinic for follow up testing, and, if necessary,\n home visits were conducted by the clinical team to identify and escort patients to\n nearest clinic for CKD screening.\u003c/p\u003e\n \n\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eInclusion and Exclusion Criteria of the Study\u003c/h2\u003e\n \n\n \n\u003cp\u003eWe retrospectively reviewed the electronic medical record (EMR) of patients with hypertension.\n All patients’ clinical information was recorded in the MOH standardized patient chart,\n and the data was also duplicated in the EMR. All hypertensive patients aged 18 years\n and above who were screened for CKD were included in the study. We excluded hypertensive\n patients with an existing diagnosis of CKD.\u003c/p\u003e\n \n\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eClinical Measurements and Outcomes\u003c/h2\u003e\n \n\n \n\u003cp\u003eCKD was the main outcome and was defined based on Kidney Disease: Improving Global\n Outcomes (KDIGO) definition: an estimated eGFR of less than 60ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e persistent for at least 3 months (29). EGFR of less than 60ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e on one occasion only was defined as renal insufficiency. In addition, we categorized\n eGFR in all patients based on KDIGO categories of eGFR.\u003c/p\u003e\n \n\u003cp\u003eWe derived an eGFR from plasma creatinine using CKD Epidemiology formula without the\n factor of race. The CKD Epidemiology formula without race was chosen as it has been\n shown to better predict eGFR in Malawians than CKD epidemiology formula with black\n as a race factor or other formulas of predicting eGFR (30).\u003c/p\u003e\n \n\u003cp\u003eWe extracted several other variables from the patients’ charts based on data that\n was routinely collected during clinical encounters and variables that have been shown\n to be associated with CKD in other studies. Therefore, we extracted the following\n independent for each patient with hypertension:\u003c/p\u003e\n \n \n \n\u003ch2\u003eDemographic variables: Age (years), gender (male or female)\u003c/h2\u003e\n \n \n \n\u003cp data-xsweet-list-level=\"0\"\u003eClinical history of hypertension: Years since diagnosis of hypertension and duration of enrolment in the clinic (less\n than one year, 1-2 years, more than 2 years), and most recent systolic and diastolic\n blood pressures (mmHg)\u003c/p\u003e\n \n \n \n\u003cp data-xsweet-list-level=\"0\"\u003eRisk factors of hypertension: Body mass index ( BMI) (kg/m\u003csup\u003e2\u003c/sup\u003e), BMI over 25, BMI categories (\u0026lt;18.5, ≥18.5-\u0026lt;25, ≥25-\u0026lt;30, ≥30), proteinuria (negative\n and trace were coded as protein \u0026lt;1+, protein 1+, 2+ and 3+ were coded as protein ≥1+)\u003c/p\u003e\n \n \n \n\u003ch2\u003eComorbidities (yes or no): Patients with concurrent diabetes and/or HIV.\u003c/h2\u003e\n \n \n \n\u003cp\u003eWe planned to include variables that are representative of the hypertensive population.\n Therefore we opted to exclude from the final analysis any variable that had more than\n 90% of the participants’ data missing. This included history of smoking and alcohol\n and the following complications: stroke, cardiovascular disease, peripheral vascular\n disease and neuropathy. All included data were collected during routine clinical encounters,\n and protocols for their measurement is explained elsewhere (27).\u003c/p\u003e\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eData Management and Statistical Analysis \u003c/h2\u003e\n \n\n \n\u003cp\u003eAll data used in this study were initially extracted from the EMR to Microsoft Excel\n version 2013. Data cleaning and analysis was performed using Stata 15 (Stata Corp,\n Texas). Descriptive statistics were used to describe the data. Depending on normality,\n mean and standard deviation were used for continuous variables and median and Interquartile\n range (IQR) were used non-normally distributed data. Depending on normality and type\n of variables, Chi\u003csup\u003e2\u003c/sup\u003e, t-test , Mann Whitney U test were used to explore the relationship between the independent\n variables and patients CKD.\u003c/p\u003e\n \n\u003cp\u003eWe performed logistic regression analysis to determine correlates of CKD. Initially,\n we performed single predictor models to identify significant correlates of CKD. We\n then performed multivariable logistic regression using identified significant factors\n from the single predictor models. Statistical significance was defined at a p value\n \u0026lt;0.05 at 95% confidence interval.\u003c/p\u003e\n \n\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eEthics Approval and Consent to Participate \u003c/h2\u003e\n \n\n \n\u003cp\u003eAll data were collected as part of routine patient care and analysed retrospectively.\n As a result, we did not obtain informed consent from patients. Access to data was\n given only to investigators and data were kept securely. The study received ethical\n clearance from Malawi National Health Sciences Research Committee protocol number\n 1216.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2 data-xsweet-outline-level=\"1\"\u003eBaseline Characteristics of the Participants \u003c/h2\u003e\n \n\n \n\u003cp\u003eBetween January 2018 and April 2019, 1197 out of 2184 (54.8%) patients with hypertension\n were screened for CKD (Figure 1). Seventy nine percent (n=942) were females and the\n median age was 61 years (IQR 51-70 years). About half of the patients had had been\n diagnosed and enrolled in Integrated Chronic Care Clinic for more than 2 years. The\n median body mass index (BMI) was 22.9 kg/m\u003csup\u003e2\u003c/sup\u003e (IQR 20.3-26.6 kg/m\u003csup\u003e2\u003c/sup\u003e) and only 5% (n=46) had proteinuria \u0026gt;=1+. 5.3% (n=63) and 10.9% (n=112) had diabetes\n and HIV as comorbidities of hypertension respectively (Table 1). \u003c/p\u003e\n \n\u003ch2\u003eFigure 1. Screening Results for Patients with Hypertension \u003c/h2\u003e\n \n\n\u003ch2\u003eTable 1. Characteristics of Patients with Hypertension Screened for CKD\u003c/h2\u003e\n\u003cp\u003eAmong all hypertensive patients that were screened for CKD, the mean creatinine was\n 0.90 mg/dl (Confidence Interval (CI) 0.85-0.94 mg/dl) and the mean eGFR was 84.1 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e (CI 82.7-85.4 ml/min/1.73m\u003csup\u003e2)\u003c/sup\u003e. About half of the patients had a normal eGFR (48.3%, n=578) and 36.3 % (n=435) had\n mildly decreased eGFR (Table 2).The prevalence of renal insufficiency was 15.4% (CI\n 13.4-17.5, 184/1197).\u003c/p\u003e\n \n\u003cp\u003eAmong the 184 hypertensive patients with renal insufficiency, 126 patients were successfully\n re-tested after a minimum of 3 months (Figure 1). Of these, 85 had persistent eGFR\n \u0026lt; 60 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e, hence the prevalence of CKD in this study was 7.1% (CI 5.7-8.7 %). By eGFR category\n in the CKD patients, 41.2% (n=35), 31.8% (n=27), 24.7 % (n=21) and 2.3% (n=2) had\n CKD stage 3a, 3b, 4 and 5 respectively.\u003c/p\u003e\n\u003ch2\u003eTable 2: Distribution of eGFR in all patients screened for renal insufficiency and\n CKD\u003c/h2\u003e\n\u003cp\u003eAfter univariate logistic regression model, increasing age (unadjusted odds ratio\n (OR) 1.06, CI 1.04-1.08, p\u0026lt;0.001), higher systolic blood pressure (unadjusted OR 1.01,\n CI 1.004 -1.02, p\u0026lt;0.001) and concurrent diagnosis of diabetes (unadjusted OR 2.32,\n CI 1.10-4.88, p=0.03) were all associated with CKD (Table 3). In multivariable logistic\n regression, older age (adjusted OR 1.06, CI 1.04-1.08, p\u0026lt;0.001) and a history of diabetes\n (adjusted OR 2.63, CI 1.21-5.69, p=0.01 were strongly associated with CKD. Most recent\n systolic blood pressure was weakly associated with CKD (adjusted OR 1.01 CI 1.00-\n 1.02, p=0.048)\u003c/p\u003e\n \n\u003ch2\u003eTable 3 Univariate Analysis of Correlates of CKD \u003c/h2\u003e\n \n\u003ch2\u003eTable 4 Multivariable analysis of Correlates of CKD \u003c/h2\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs far as we know, this is the first study to investigate CKD among patients with\n hypertension attending longitudinal care in rural Malawi. Approximately half of the\n patients with hypertension tested had abnormal eGFR, and at-least 15% of the patients\n had evidence of renal insufficiency. This demonstrates the clinical importance of\n routine screening for CKD in hypertension clinics. Most patients with renal insufficiency\n benefitted from follow up after three months for confirmation of CKD. The screening\n and dissemination of their results may also have helped with raising the awareness\n of CKD.\u003c/p\u003e\n \n\u003cp\u003eThe confirmation of CKD by KDIGO guidelines requires re-testing after at least 3 months,\n and in rural impoverished settings, more effort was needed to find these patients\n for repeat testing. This challenge has been reported in a previous study that used\n KDIGO guidelines (31). Despite our efforts to track the patients, we were not able\n to re-test all patients with renal insufficiency; 126 out of 184 (68%) patients with\n hypertension were reached and re-tested. It was impossible to track other patients;\n some had died or transferred outside the district, and others could not be located\n for testing within our study period. Therefore, we may have underestimated the prevalence\n of CKD. Other programs implementing screening for CKD should consider how to strengthen\n the ways used to track patients in this study, which also has benefits for patient\n care beyond screening, or try other strategies that can be used to effectively track\n patients for confirmation of CKD.\u003c/p\u003e\n \n\u003cp\u003eWe found a lower prevalence of CKD in patients with hypertension in comparison to\n most studies published in Africa (13,32). The prevalence may have been underestimated\n since we could not find all the patients who needed a repeat test. Additionally, we\n had used KDIGO guidelines, and therefore some people had an initially low eGFR which\n normalized after repeat testing. \u003c/p\u003e\n \n\u003cp\u003eAdditionally, most of the patients in this study are females, which reflects health\n service utilization in Malawi, where more females utilize services than men. After\n controlling for multiple factors, only older age and diabetes were strongly associated\n with CKD in patients with hypertension, and this has also been shown in other studies\n in Africa (8,33) \u003c/p\u003e\n \n\u003cp\u003eThere are many clinical and research implications from these results. First, we concluded\n that all patients with hypertension should be screened for CKD, and Neno District\n continues to gradually screen all patients and plans to repeat CKD screening annually.\n Additionally, Neno District has created 2 Advanced NCD Clinics where patients with\n CKD stage 4 and 5 are routinely managed alongside patients with other severe chronic\n NCDs. Beyond Neno, hypertension clinics need to invest in CKD screening for early\n detection and management of kidney disease. Additionally, more research needs to be\n generated, particularly in urban areas, to compare with the results from this study.\u003c/p\u003e\n \n\u003cp\u003eThere are a few key limitations in this study. We used only eGFR only to define patients\n with CKD, and we were unable to measure albumin-creatinine ratio or other measures\n of CKD as defined by KDIGO guidelines. The study is also a facility based audit and\n was conducted in a rural area. As a result, the study may not be generalizable to\n populations outside Neno District. We also investigated CKD in patients with hypertension\n aged 18 years and above thereby excluding all patients less than 18 years old. In\n addition, to be enrolled for hypertension treatment in clinics in Neno, patients have\n blood pressures ≥160/110, so the results are not generalizable to patients with Stage\n I hypertension. Finally, we measured prevalence and not incidence of CKD, therefore\n we could not differentiate the temporal sequence of hypertension and CKD.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe study found moderately high prevalence of renal insufficiency and CKD in a rural\n cohort of hypertension patients, 15.4% and 7.1% respectively. Additionally, CKD was\n significantly associated with older age and a history of diabetes. We advocate for\n investing in CKD screening among patients with hypertension attending longitudinal\n care in order to optimize early diagnosis and management in this population.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eCI confidence interval \u003c/p\u003e\n \n\u003cp\u003eCKD Chronic kidney disease \u003c/p\u003e\n \n\u003cp\u003eNCD Non-communicable disease\u003c/p\u003e\n \n\u003cp\u003eEGFR Estimated glomerular filtration rate \u003c/p\u003e\n \n\u003cp\u003eKDIGO Kidney Disease: Improving Global Outcomes\u003c/p\u003e\n \n\u003cp\u003eBMI Body mass index\u003c/p\u003e\n \n\u003cp\u003eIQR interquartile range\u003c/p\u003e\n \n\u003cp\u003eMOH Ministry of Health\u003c/p\u003e\n \n\u003cp\u003eEMR Electronic medical record\u003c/p\u003e\n \n\u003cp\u003eOR Odds ratio \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2 data-xsweet-outline-level=\"1\"\u003eAcknowledgements\u003c/h2\u003e\n \n\n \n\u003cp\u003eWe acknowledge all of our patients who receives care at all the Integrated Chronic\n Care clinics in Neno District, Malawi. Special acknowledgment to all the staff working\n in Neno health facilities, their efforts continues to contribute to better quality\n care of the patients.\u003c/p\u003e\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eFunding\u003c/h2\u003e\n \n\n \n\u003cp\u003eNone\u003c/p\u003e\n \n\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eAvailability of data and materials\u003c/h2\u003e\n \n\n \n\u003cp\u003eThe data and materials used for this study belongs to the Ministry of Health in Neno\n District therefore cannot be shared publicly. However, the data and materials can\n be shared upon reasonable request to the corresponding author. \u003c/p\u003e\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eAuthors' contributions\u003c/h2\u003e\n \n\n \n\u003cp\u003eCK, LN, EN, GCT and EBW conceptualized the study. LT, RK, CK and GCT performed data\n curation. CK performed data cleaning and analysis. CK wrote the first draft. All authors\n provided feedback and approved the final manuscript for publication.\u003c/p\u003e\n \n\n \n\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eEthics approval and consent to participate\u003c/h2\u003e\n \n\n \n\u003cp\u003eAll data were collected as part of routine patient care and was analysed retrospectively.\n As a result, we did not obtain informed consent from patients. Access to data was\n given only to investigators and data were kept securely. The study received ethical\n clearance from Malawi National Health Sciences Research Committee protocol number\n 1216.\u003c/p\u003e\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eConsent for publication\u003c/h2\u003e\n \n\n \n\u003cp\u003eNot applicable \u003c/p\u003e\n \n\n \n\u003ch2 data-xsweet-outline-level=\"1\"\u003eCompeting interests\u003c/h2\u003e\n \n\n \n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1. \n Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global Disparities\n of Hypertension Prevalence and Control:a Systematic Analysis of Population-Based Studies\n from 90 Countries. Circulation. 2016;134:441–50. \u003c/p\u003e\n \n\u003cp\u003e2. \n NCD Countdown 2030 collaborators. 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Leveraging\n HIV platforms to work toward comprehensive primary care in rural Malawi: The Integrated\n Chronic Care Clinic. Healthcare [Internet]. Elsevier; 2015;3(4):270–6. Available from:\n \u003ca href=\"http://dx.doi.org/10.1016/j.hjdsi.2015.08.002\"\u003ehttp://dx.doi.org/10.1016/j.hjdsi.2015.08.002\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e27. \n Partners In Health. Integrated Care Cascade Toolkit:An Implementation to Guide Screening,\n Treatment \u0026amp; Follow-up for HIV and NCDs [Internet]. 2017. Available from: \u003ca href=\"https://www.pih.org/practitioner-resource/integrated-care-cascade-toolkit\"\u003ehttps://www.pih.org/practitioner-resource/integrated-care-cascade-toolkit\u003c/a\u003e \u003c/p\u003e\n \n\u003cp\u003e28. \n Kachimanga C, Cundale K, Wroe E, Nazimera L, Jumbe A, Dunbar E, et al. Novel approaches\n to screening for noncommunicable diseases: Lessons from Neno, Malawi. 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Malawi Med J. 2017;29(2):166–70. \u003c/p\u003e"},{"header":"Tables","content":"\u003cp class=\"standard\"\u003e\u003cb\u003eTable 1. Characteristics of patients with hypertension screened for CKD \u003c/b\u003e\u003c/p\u003e\n\u003ctable class=table\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eTotal hypertension patients\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eNo CKD\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eTotal number of patients screened (%) \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e1197 \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e1112 (92.9)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e 85 (7.1)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eMedian age(IQR)-years *\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e61 (51-70)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e60 (50-69)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e70 (62-78)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eFemale gender (%)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e942 (78.7)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e878(79.0)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e64(75.3)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eYears since diagnosis of hypertension (%)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eLess than 1\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e290 (24.3)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e275 (24.7)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e15 (17.6)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e1-2 \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e274 (22.9)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e252 (22.7)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e22 (25.9)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eOver 2 \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e632 (52.8)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e584 (52.6)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e48( 56.5)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eDuration in clinic (%)-years \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eLess than 1 \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e291(24.3)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e276 (24.8)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e15 (17.6)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e1-2 \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e276 (23.1)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e254 (22.9)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e22 (25.9)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eOver 2 \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e630 (52.6)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e582 (52.3)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e48 (56.5)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eMedian most recent systolic blood pressure(IQR)-mmHg*\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e135 (123-148)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e135 (123-148)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e140.5 (124.5-157)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eMedian most recent diastolic blood pressure(IQR)-mmHg\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e84 (76.5-93)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e84 (77-93)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e83 (73-92)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eRisk factors\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eMedian BMI (IQR)-kg/m2\u003c/p\u003e\n\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e22.9 (20.3-26.6)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e22.8 (20.3-26.5)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e23.7 (20.3-27.4)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eBMI over 25\u003c/p\u003e\n\u003cp class=\"standard\"\u003e(median, IQR)-kg/m2\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e29.1(27.3-32.9)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e27.8 (26.0-31.6)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e29.3 (27.3-33.2)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eBMI category (%)-kg/m2\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u0026lt;18.5\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e115 (9.7)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e105 (9.5)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e10 (11.9)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e18.5-\u0026lt;25\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e684 (57.4)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e641(57.9)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e43 (51.2)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e25-\u0026lt;30\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e270 (22.7)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e245 (22.1)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e25 (29.8)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e30 and above \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e122 (10.2)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e116 (10.5)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e6 (7.1)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eProteinuria\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eProtein \u0026lt;1+\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e946 (95.4)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e883 (95.5)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e63 (94.0)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eProtein \u0026gt;=1 \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e46 (4.6)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e42 (4.5)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e4 (6.0)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eDiabetes*\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e No\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e1134 (94.7)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e1058 (95.1)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e76 (89.4)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eYes \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e63 (5.3)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e54 (4.9)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e9 (10.6)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eHIV \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e916 (89.1)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e845 (88.6)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e71 (95.9)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003ePositive \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e112 (10.9)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e109 (11.4)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e3 (4.1)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp class=\"standard\"\u003e CKD Chronic Kidney Disease \u003c/p\u003e\n\u003cp class=\"standard\"\u003e% Percentage \u003c/p\u003e\n\u003cp class=\"standard\"\u003eSd Standard deviation\u003c/p\u003e\n\u003cp class=\"standard\"\u003eIQR interquartile range \u003c/p\u003e\n\u003cp class=\"standard\"\u003eBMI Body mass index\u003c/p\u003e\n\u003cp class=\"standard\"\u003e*significant differences between CKD patients and patients without CKD \u003c/p\u003e\n\n\n\u003cp class=\"standard\"\u003e\u003cb\u003eTable 2 Estimated GFR stages in all patients with hypertension screened for chronic kidney disease\u003c/b\u003e\u003c/p\u003e\n\u003ctable class=table\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eStages of eGFR\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/p\u003e\n\u003cp class=\"standard\"\u003e\u003cb\u003e(ml/min/1.73m2)\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e Normal EGFR\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e≥ 90\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e578 (48.3)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eG2 (Mildly decreased)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e60-89\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e435 (36.3)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eG3a (Mildly to Moderately decreased)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e45-59\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e85 (7.1)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eG3b ( Moderately to severely decreased)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e30-44\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e57 (4.8)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eG4 (Severely decreased)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e15-29\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e33 (2.8)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003eG5 (Kidney failure)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u0026lt;15\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e9 (0.7)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp class=\"standard\"\u003e\u003cb\u003e1197 (100)\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp class=\"standard\"/\u003e\n\u003cp class=\"standard\"\u003eeGFR Estimated GFR\u003c/p\u003e\n\u003cp class=\"standard\"\u003eCKD Chronic kidney disease\u003c/p\u003e\n\n\n\u003cp\u003e\u003cb\u003eTable 3. Univariate Logistic Regression of Correlates of CKD \u003c/b\u003e\u003c/p\u003e\n\u003ctable class=table\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eIndependent Variable \u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eOdds ratio (CI)\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.06 (1.04-1.08)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eGender \u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eFemale \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.23 (0.74-2.06)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eYears since diagnosis \u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u0026lt;1 year \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e1-2 years \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.60 (0.81-3.15)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eOver 2 years \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.51 (0.83-2.73)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eDiagnosis duration \u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u0026lt;1 year \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e1-2 years \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.59 (0.81 -3.14)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eOver 2 years \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.52 (0.83 -2.76)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.17 \u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eMost recent systolic blood pressure\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.01 (1.004 -1.02)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eMost recent diastolic blood pressure \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.99 (0.98-1.01)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.60 \u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eBMI \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.02 (0.99 -1.06)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eOverweight or obesity \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.01 (0.93-1.09)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eBMI category \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.42 (0.69-2.91)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e18.5-\u0026lt;25\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eRef \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eRef \u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e25-\u0026lt;30\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.52 (0.91-2.5)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e30 and above \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.77 (0.32 -0.85)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eProteinuria\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eLess than 1+\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eRef \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e1+ and above \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.33 (0.46-3.80)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDiabetes diagnosis \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eRef \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e2.32 (1.10-4.88)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.03*\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eHIV diagnosis \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eNegative \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eRef \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003ePositive \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.33 (0.10-1.06)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp/\u003e\n\u003cp\u003e *significant associations\u003c/p\u003e\n\n\n\u003cp\u003e\u003cb\u003eTable 4 Multivariable analysis of Correlates of CKD \u003c/b\u003e\u003c/p\u003e\n\u003ctable class=table\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eIndependent Variable \u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eAdjusted Odds ratio (CI)\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.06 (1.04-1.08)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eMost recent systolic blood pressure\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.01 (1.00- 1.02)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDiabetes diagnosis \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eRef \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e2.63 (1.21- 5.69)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp/\u003e\n\u003cp\u003eCKD Chronic kidney disease\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-communicable diseases; chronic kidney disease; hypertension; renal insufficiency; Malawi","lastPublishedDoi":"10.21203/rs.2.10113/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.2.10113/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background\n\nThe prevalence of chronic kidney disease (CKD) in patients with hypertension is very high in Africa. We investigated the prevalence and correlates of CKD in patients with hypertension attending longitudinal care in rural Malawi, where currently no data on prevalence of CKD in patients with hypertension exists.\n\nMethods\n\nWe retrospectively reviewed medical records of all hypertensive patients who were screened for CKD between January 2018 and April 2019. Screening was done using serum creatinine and CKD epidemiology formula was used to estimate the glomerular filtration rate (eGFR). We used Kidney Disease: Improving Global Outcomes definitions of renal insufficiency and CKD. Logistic regression analysis was used to identify correlates of CKD.\n\nResults\n\nDuring the study duration, 1197 patients with hypertension were screened for CKD. The mean creatinine and eGFR was 0.90 mg/dl (Confidence Interval (CI) 0.85-0.94 mg/dl) and 84.1 ml/min/1.73m2 (CI 82.7-85.4 ml/min/1.73m2) respectively. About half of the patients had a normal eGFR (48.3%, n=578) and 36.3% (n=435) had mildly decreased eGFR. The prevalence of renal insufficiency was 15.4% (CI 13.4-17.5, 184/1197) and the prevalence of CKD was 7.1% (CI 5.7-8.7%). By eGFR category in the CKD patients, 41.2% (n=35), 31.8% (n=27), 24.7% (n=21) and 2.3% (n=2) had CKD stage 3a, 3b, 4 and 5 respectively. CKD was strongly associated with age and diabetes.\n\nConclusions\n\nWe found moderately high renal insufficiency and CKD in this cohort. We propose investing in screening for CKD in patients with hypertension in other clinics in Malawi.","manuscriptTitle":"Prevalence and Correlates of Chronic Kidney Disease in Patients with hypertension in Rural Malawi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2019-06-07 21:55:25","doi":"10.21203/rs.2.10113/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9bbd985-4d00-4d17-9f97-29a42cd6fe96","owner":[],"postedDate":"June 7th, 2019","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":13176,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2021-07-22T02:40:28+00:00","versionOfRecord":{"articleIdentity":"rs-1193","link":"https://doi.org/10.1007/s42399-019-00154-6","journal":{"identity":"sn-comprehensive-clinical-medicine","isVorOnly":false,"title":"SN Comprehensive Clinical Medicine"},"publishedOn":"2019-10-24 02:40:28","publishedOnDateReadable":"October 24th, 2019"},"versionCreatedAt":"2019-06-07 21:55:25","video":"","vorDoi":"10.1007/s42399-019-00154-6","vorDoiUrl":"https://doi.org/10.1007/s42399-019-00154-6","workflowStages":[]},"version":"v1","identity":"rs-1193","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-1193","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}