Abstract
Introduction:
The profile of patients referred from primary to tertiary nephrology care is unclear. Ethnic Malay patients have the highest incidence and prevalence of kidney failure in Singapore. We hypothesised that there is a Malay predominance among patients referred to nephrology due to a higher burden of metabolic disease in this ethnic group.
Methods:
This is a retrospective observational cohort study. From 2014 to 2018, a coordinator and physician triaged patients referred from primary care, and determined co-management and assignment to nephrology clinics. Key disease parameters were collated on triage and analysed.
Results:
A total of 6,017 patients were studied. The mean age of patients was 64 ± 16 years. They comprised 57% men; 67% were Chinese and 22% were Malay. The proportion of Malay patients is higher than the proportion of Malays in the general population (13.4%) and they were more likely than other ethnicities to have ≥3 comorbidities, including diabetes mellitus, hypertension, hyperlipidaemia, coronary artery disease and stroke (70% vs. 57%, P < 0.001). Malay and Indian patients had poorer control of diabetes mellitus compared to other ethnicities (glycated haemoglobin 7.8% vs. 7.4%, P < 0.001). Higher proportion of Malay patients compared to other ethnicities had worse kidney function with estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m2 on presentation (28% vs. 24%, P = 0.003). More ethnic Malay, Indian and younger patients missed appointments.
Conclusion:
A disproportionately large number of Malay patients are referred for kidney disease. These patients have higher metabolic disease burden, tend to miss appointments and are referred at lower eGFR. Reasons underpinning these associations should be identified to facilitate efforts for targeting this at-risk population, ensuring kidney health for all.
Keywords: Ethnicity, metabolic syndrome, primary health care, referral and consultation, renal insufficiency, chronic
INTRODUCTION
The prevalence of chronic kidney disease (CKD) is escalating worldwide, largely driven by the increased population burden of diabetes mellitus, hypertension and hyperlipidaemia.[1] Singapore ranks fourth in prevalence of end-stage kidney disease globally and the prevalence of CKD in Singapore in 2035 is projected to be double the rate in 2007.[2,3,4] Malay patients, who have the highest incidence and prevalence of kidney failure in Singapore, are particularly susceptible.[4] Progression of CKD may lead to serious complications, including fluid overload, anaemia, mineral bone disorder and accelerated cardiovascular disease.[5,6] These complications may be averted through early referrals to nephrology for co-management and comprehensive care.
The large volume of CKD patients places a strain on medical services.[6] Many models of care have attempted various ways to improve the care of CKD patients. Electronic medical records carry the promise of remote management by primary care doctors, and favourable results have been observed with an electronic consultation system.[7,8] In Singapore, publicly funded acute hospitals have access to funding programmes designed to optimally manage patients at primary care or specialist clinics and to integrate medical services across a geographical region. The Division of Nephrology, National University Hospital, formed a team of coordinators under the Nephrology Evaluation, Management and Optimisation programme, subsequently followed by the Holistic Approach in Lowering and Tracking Chronic Kidney Disease (HALT-CKD) programme, to provide care coordination and improved referrals from public primary care facilities (polyclinics) to nephrology.[9] To reduce unnecessary speciality visits, an electronic monitoring and communication system was established to assist primary care doctors in polyclinics. Algorithms developed by the Department of Nephrology, National University Hospital, assisted polyclinic doctors in decision-making thresholds for referrals. The team of coordinators ‘triaged’ all nephrology referrals from 2014 to 2018. The ‘triage’ programme was a dynamic one, with different priorities of CKD management emphasised based on available physician resource. In earlier years (2014–2016), we emphasised remote co-management with primary care doctors to avert unnecessary speciality visits, and in subsequent years (2016–2018), we focused on early referrals for moderately severe CKD for nephrology co-management.
The patient profile of patients referred to nephrology is unclear. We hypothesised that there is a Malay predominance among patients referred to nephrology due to a higher burden of metabolic disease in this ethnic group. Using data obtained upon triage, we analysed the profile of referrals to a hospital-based speciality nephrology service.
METHODS
This is a retrospective observational cohort study of adults referred from primary and tertiary care providers to a hospital-based nephrology service. Ethics approval from the Domain-Specific Review Board (DSRB) was obtained (2018/01013-AMD0001). From 2014 to 2018, the Division of Nephrology deployed a coordinator for the triage and active management of patients referred by the polyclinics. Some private cases and intrahospital interdepartmental referrals were managed under this programme if directed to the coordinator. Patient profile and disease parameters collected upon triage included patient demographics, reason for referral (extracted verbatim from consultation letters), pre-existing medical conditions (coronary artery disease, stroke, hypertension, hyperlipidaemia and diabetes mellitus) and laboratory/imaging results (kidney ultrasonography, serum creatinine, urine protein to creatinine ratio (UPCR), serum potassium, serum albumin). The 2009 Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation was used to estimate the glomerular filtration rate (eGFR, mL/min/1.73 m2). The reasons for referral were adjudicated by two authors (TBW, MYLG). For referrals with multiple reasons, the order of classification is listed in Table S1 [see Supplemental Digital Appendix]. All referrals had an initial automatic follow-up date scheduled under 60 days from the date of referral and included ‘right-siting’ of patients into various speciality clinics. An overseeing nephrologist deferred the appointment if further tests were required to ascertain kidney disease or brought forward the appointment for urgent management in cases of suspected acute kidney injury and rapidly progressive kidney disease.
Table S1.
Order of classification of reasons for referral
| Reason | Keywords in letter of referral |
|---|---|
| Hematuria | Gross hematuria |
| Microscopic hematuria | |
| Proteinuria | Microalbuminuria |
| Macroalbuminuria | |
| Increased UPCR | |
| Increased UACR | |
| Kidney dysfunction (Of uncertain acuity/timeline) | Elevated creatinine |
| Increased creatinine | |
| Raised creatinine | |
| High creatinine | |
| Low eGFR | |
| Kidney impairment | |
| Fluctuating trends | |
| Increase in creatinine | |
| Only creatinine values stated | |
| Worsening kidney function (Suggestive of chronic progression) | eGFR decline |
| Rising creatinine | |
| Worsening creatinine | |
| Worsening eGFR | |
| Declining/decreasing GFR | |
| Drop in GFR | |
| Deteriorating renal function | |
| Creatinine rise | |
| Creatinine upward trend | |
| Acute kidney injury (Suggestive of rapidly changing kidney function over days to weeks) | Acute on chronic kidney disease |
| Acute kidney injury | |
| Acute renal impairment | |
| Acute renal dysfunction | |
| Acute renal failure | |
| Sudden deterioration | |
| Rapidly declining function | |
| Abnormal kidney imaging | Tumor |
| Cysts | |
| Chronic kidney disease (CKD) | Referred for CKD |
| End-stage renal failure | |
| Chronic renal failure | |
| Dialysis | |
| Chronic renal impairment | |
| End-stage kidney disease | |
| Non diabetic mellitus nephropathy | |
| Only UACR values stated! | |
| Diabetic kidney disease | Chronic kidney disease due to diabetes |
| Diabetic nephropathy | |
| DKD with proteinuria/macroalbuminuria | |
| Transfer of care | |
| Low hemoglobin | |
| Anemia | Drop in Hb |
| Hyperkalemia | High serum potassium values |
| Nephrotic Syndrome | |
| Uncontrolled Hypertension | |
| Renal Artery Stenosis | |
| Others | Glycosuria |
| Glomerulonephritis | |
| IgA Nephropathy | |
| Hypokalemia | |
| Hypouricemia | |
| Hypomagnesemia | |
| Urinary tract infection Second opinion | Hypophosphatemia |
†UACR: Urine albumin to creatinine ratio. ‡UPCR: Urine protein to creatinine ratio.
The triage protocol captured 100% of all regularly referred cases from the polyclinics. Some private cases and intrahospital interdepartmental referrals were recorded if directed to the coordinator. Direct doctor-to-doctor private referrals were not routinely captured.
The patient profile and disease parameters collected upon triage were analysed. For this analysis, all laboratory data refers to results within 365 days before the date of referral unless otherwise specified. Trace results for dipstick protein and blood were ignored. To capture the CKD status of patients, all available data on serum creatinine, eGFR and UPCR were used. UPCR data with results beyond detection limits were excluded. Late referrals are defined by eGFR <30 mL/min/1.73 m2.
Statistical analyses were performed using JMP® version 15 (SAS Institute Inc., Cary, NC, USA), IBM SPSS Statistics version 25 (IBM Corp, Armonk, NY, USA) and Microsoft Excel version 2010 (Microsoft Corp, Redmond, WA, USA). Data is reported as mean ± standard deviation, median (interquartile range [IQR]: 25th percentile, 75th percentile), frequency and percentages. Continuous variables were compared using independent t-test, while categorical variables were compared using chi-square tests. A P value <0.05 was considered statistically significant.
RESULTS
From 2014 to 2018, a total of 6,017 patients were referred to the nephrology service [Table 1]. The average age of the patients was 64.0 ± 15.6 years, and 57.3% were men. The mean age of patients referred increased over the years [Table 1]. Majority of the patients were Chinese (n = 4,050, 67.3%), followed by Malay (n = 1,327, 22.1%). The proportion of Malay patients was higher than that in the general population (13.4%) [Figure 1].[10] Most of the patients had a diagnosis of hypertension (n = 4,944, 82.2%), hyperlipidaemia (n = 4,491, 74.6%) and diabetes mellitus (n = 3,578, 59.5%), but fewer patients had coronary artery disease (n = 1,430, 23.8%) and stroke (n = 568, 9.4%). More Malay patients had hypertension and hyperlipidaemia, and along with Indian patients, they were more likely to have diabetes mellitus [Table 2]. Correspondingly, Malay patients were more likely than other ethnicities to have ≥3 comorbidities, including diabetes mellitus, hypertension, hyperlipidaemia, coronary artery disease or stroke (70% vs. 57%, P < 0.001). The median serum creatinine was 134 μmol/L (IQR: 97, 173), and the median eGFR was 42 mL/min/1.73 m2 (IQR: 30, 64). Table 3 shows the proportion of patients referred each year by eGFR. A higher proportion of Malay patients compared to other ethnic groups had worse kidney function with eGFR <30 mL/min/1.73 m2 on presentation (28% vs. 24%, P = 0.003).
Table 1.
Patient characteristics by year (N=6,017).
| Characteristic | Year | ||||
|---|---|---|---|---|---|
|
| |||||
| 2014 | 2015 | 2016 | 2017 | 2018 | |
| No. of patientsa | 1,019 (16.9) | 1,095 (18.2) | 958 (15.9) | 1,151 (19.1) | 1,794 (29.8) |
|
| |||||
| Ageb (yr) | 62.1±16.4 | 62.8±16.1 | 63.6±15.7 | 63.9±15.4 | 66.0±14.6 |
|
| |||||
| Malea | 584 (57.3) | 649 (59.3) | 558 (58.3) | 671 (58.3) | 988 (55.1) |
|
| |||||
| Ethnicitya | |||||
|
| |||||
| Chinese | 670 (65.7) | 749 (68.4) | 615 (64.2) | 773 (67.2) | 1,243 (69.3) |
|
| |||||
| Indian | 59 (5.8) | 68 (6.2) | 57 (5.9) | 87 (7.6) | 124 (6.9) |
|
| |||||
| Malay | 263 (25.8) | 255 (23.3) | 231 (24.1) | 229 (19.9) | 349 (19.5) |
|
| |||||
| Others | 27 (2.6) | 23 (2.1) | 55 (5.7) | 62 (5.4) | 78 (4.3) |
|
| |||||
| Serum creatininec (µmol/L) | 141 (94, 185) | 134 (96, 175) | 137.5 (98.3, 181) | 135 (96.5, 177) | 129 (99, 160) |
|
| |||||
| eGFRc,d (mL/min/1.73 m2) | 39.2 (27.7, 68.8) | 43.0 (30.3, 66.1) | 39.7 (28.7, 63.6) | 42.6 (29.4, 63.8) | 42.8 (32.9, 61.0) |
|
| |||||
| UPCRc (mg/mmol) | 181 (70.0, 372.3) | 162 (61, 358.5) | 130 (44.8, 288.8) | 128 (45, 322) | 133 (50, 273) |
|
| |||||
| Comorbiditiesa | |||||
|
| |||||
| Diabetes mellitus | 611 (60.0) | 644 (58.8) | 559 (58.4) | 662 (57.5) | 1,102 (61.4) |
|
| |||||
| Hypertension | 826 (81.1) | 884 (80.7) | 783 (81.7) | 926 (80.5) | 1,525 (85.0) |
|
| |||||
| Hyperlipidaemia | 739 (72.5) | 820 (74.9) | 676 (70.6) | 842 (73.2) | 1,414 (78.8) |
|
| |||||
| Coronary artery disease | 242 (23.8) | 259 (23.7) | 232 (24.2) | 300 (26.1) | 397 (22.1) |
|
| |||||
| Stroke | 100 (9.8) | 82 (7.5) | 85 (8.9) | 125 (10.9) | 176 (9.8) |
|
| |||||
| Serum albuminc (g/L) | 38 (34, 41) | 38 (32.3, 41) | 39 (34, 43) | 39 (34, 42) | 39 (35, 43) |
Data presented as an (%), bmean±standard deviation and cmedian (interquartile range). dEstimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation. UPCR: urine protein to creatinine ratio.
Figure 1.
Charts show the ethnicity in general population versus referred patients. Ethnicity in general population was obtained from Department of Statistics, Ministry of Trade and Industry, Republic of Singapore.[10]
Table 2.
Characteristics of patients by ethnicity (N=6,017).
| Characteristic | n (%) | P | |||
|---|---|---|---|---|---|
|
| |||||
| Chinese | Malay | Indian | Others | ||
| No. of patients | 4,050 (67.3) | 1,327 (22.1) | 395 (6.6) | 245 (4.1) | |
|
| |||||
| Comorbiditiesa | |||||
|
| |||||
| Diabetes mellitus | 2,221 (54.8) | 940 (70.8) | 279 (70.6) | 138 (56.3) | <0.001 |
|
| |||||
| Hypertension | 3,297 (81.4) | 1,148 (86.5) | 311 (78.7) | 188 (76.7) | <0.001 |
|
| |||||
| Hyperlipidaemia | 2,951 (72.9) | 1,064 (80.2) | 302 (76.5) | 174 (71.0) | <0.001 |
|
| |||||
| Coronary artery disease | 896 (22.1) | 329 (24.8) | 130 (32.9) | 75 (30.6) | <0.001 |
|
| |||||
| Stroke | 403 (0.10) | 118 (8.8) | 33 (8.4) | 15 (6.1) | <0.001 |
|
| |||||
| Serum creatininea (µmol/L) | 140 (95, 169) | 150 (104, 181) | 139 (93, 166) | 151 (99.3, 174.5) | <0.001 |
|
| |||||
| eGFRa,b (mL/min/1.73 m2) | 51.1 (30.4, 66.2) | 47.4 (28.7, 57.6) | 53.1 (33.1, 69.5) | 50.8 (30.3, 58.8) | <0.001 |
aData presented as median (interquartile range). bEstimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation.
Table 3.
Estimated glomerular filtration rate (eGFR) by year (N=5,777).
| eGFR (mL/min/1.73 m2) | n (%) | Total | ||||
|---|---|---|---|---|---|---|
|
| ||||||
| 2014 (n=962) | 2015 (n=1,062) | 2016 (n=933) | 2017 (n=1,093) | 2018 (n=1,727) | ||
| >90 | 132 (13.7) | 134 (12.6) | 120 (12.9) | 138 (12.6) | 193 (11.2) | 717 (12.4) |
|
| ||||||
| 60–89 | 144 (15.0) | 179 (16.9) | 133 (14.3) | 166 (15.2) | 253 (14.7) | 875 (15.1) |
|
| ||||||
| 45–59 | 135 (14.0) | 186 (17.5) | 151 (16.2) | 194 (17.8) | 325 (18.8) | 991 (17.2) |
|
| ||||||
| 30–44 | 259 (26.9) | 310 (29.2) | 263 (28.2) | 300 (27.5) | 631 (36.5) | 1,763 (30.5) |
|
| ||||||
| 15–29 | 256 (26.6) | 222 (20.9) | 245 (26.3) | 275 (25.2) | 291 (16.9) | 1,289 (22.3) |
|
| ||||||
| <15 | 36 (3.7) | 31 (2.9) | 21 (2.3) | 20 (1.8) | 34 (2.0) | 142 (2.5) |
The top three reasons for referral were proteinuria (n = 1,371, 22.8%), CKD (n = 1,328, 22.1%) and worsening kidney function (n = 988, 16.4%). There was an increase in the number of patients seen for CKD from 2014 to 2018 due to the implementation of the HALT-CKD programme [Table 4]. Most of the referrals were from primary care outpatient clinics (private and polyclinics, 56.9%), while the rest were from hospitals (internal and external). From 2014 to 2017, the proportion of intrahospital referrals ranged from 38.9% to 44.8%. However, in 2018, due to reorganisation of the polyclinic system, which resulted in a greater ‘catchment area’ of National University Polyclinics, the polyclinics were the main source of referrals (n = 1,144, 63.8%).
Table 4.
Reasons for referral by year (N=6,017).
| Reason | n (%) | ||||
|---|---|---|---|---|---|
|
| |||||
| 2014 (n=1,019) | 2015 (n=1,095) | 2016 (n=958) | 2017 (n=1,151) | 2018 (n=1,794) | |
| Proteinuria | 243 (23.9) | 229 (20.9) | 194 (20.3) | 256 (22.2) | 449 (25.0) |
|
| |||||
| Chronic kidney disease (CKD) | 186 (18.3) | 200 (18.3) | 183 (19.1) | 247 (21.5) | 512 (28.5) |
|
| |||||
| Worsening kidney function | 154 (15.1) | 180 (16.4) | 169 (17.6) | 220 (19.1) | 265 (14.8) |
|
| |||||
| Diabetic kidney disease | 111 (10.9) | 117 (10.7) | 104 (10.9) | 76 (6.6) | 118 (6.6) |
|
| |||||
| Kidney dysfunction | 79 (7.8) | 81 (7.4) | 83 (8.7) | 70 (6.1) | 74 (4.1) |
|
| |||||
| Haematuria | 69 (6.8) | 77 (7.0) | 54 (5.6) | 90 (7.8) | 69 (3.9) |
|
| |||||
| Others | 40 (3.9) | 50 (4.6) | 39 (4.1) | 49 (4.3) | 100 (5.6) |
|
| |||||
| Acute kidney injury | 31 (3.0) | 23 (2.1) | 30 (3.1) | 37 (3.2) | 55 (3.1) |
|
| |||||
| Abnormal kidney imaging | 18 (1.8) | 31 (2.8) | 14 (1.5) | 29 (2.5) | 28 (1.6) |
|
| |||||
| Rare or genetic causes of CKD | 24 (2.4) | 22 (2.0) | 22 (2.3) | 17 (1.5) | 29 (1.6) |
|
| |||||
| Hyperkalaemia | 15 (1.5) | 21 (1.9) | 14 (1.5) | 16 (1.4) | 22 (1.2) |
|
| |||||
| Anaemia | 13 (1.3) | 13 (1.2) | 7 (0.7) | 5 (0.4) | 16 (0.9) |
|
| |||||
| Renal artery stenosis | 8 (0.8) | 15 (1.4) | 16 (1.7) | 13 (1.1) | 22 (1.2) |
|
| |||||
| Nephrotic syndrome | 8 (0.8) | 8 (0.7) | 13 (1.4) | 14 (1.2) | 14 (0.8) |
|
| |||||
| Transfer of care | 13 (1.3) | 18 (1.6) | 7 (0.7) | 3 (0.3) | 9 (0.5) |
|
| |||||
| Urinary tract infection | 0 (0) | 1 (0.1) | 4 (0.4) | 6 (0.5) | 4 (0.2) |
|
| |||||
| Uncontrolled hypertension | 5 (0.5) | 6 (0.6) | 5 (0.5) | 3 (0.3) | 5 (0.3) |
|
| |||||
| Second opinion | 2 (0.2) | 3 (0.3) | 0 (0) | 0 (0) | 3 (0.2) |
The UPCR results were available for 2,819 patients within 1 year before referral. Excluding the results beyond detection limits, the median UPCR result of 2,668 patients was 157 mg/mmol (IQR: 55.0, 359) [Table 1]. Urine microscopy for white blood cells showed a median of 3 cells per high-power field (IQR: 0, 13). Out of the 359 patients referred for either gross or microscopic haematuria, 271 patients had microscopic haematuria with a median of 20 red blood cells per high-power field (IQR: 8, 75) and 66 patients had urine dipstick tests positive for blood 1+ to 4+. A total of 5,572 patients had serum potassium tests (mean 4.4 ± 0.5 mmol/L), and 84 (1.5%) patients had serum potassium >5.5 mmol/L.
Prescription of angiotensin-converting enzyme inhibitors (ACE-I) and/or angiotensin receptor blockers (ARB) in proteinuric patients in the early stages of CKD, when the risks of acute kidney injury or hyperkalaemia are less likely to limit their use, was explored. There were 1,828 proteinuric patients with CKD stages G1-G3 (UPCR ≥15 mg/mmol and eGFR ≥30 mL/min/1.73 m2), of whom 64.6% (n = 1,181) were taking ACE-I and/or ARB. Of those who were not on ACE-I or ARB (n = 647), 62.8% (n = 406) were on alternative antihypertensive agents such as beta-blockers (n = 248, 38.3%), calcium channel blockers (n = 295, 45.6%), vasodilators (n = 23, 3.6%) and alpha-blockers (n = 15, 2.3%). Among the patients with eGFR <45 mL/min/1.73 m2, 861 (27.0%) were prescribed at least one diuretic. Loop diuretics were used more frequently in patients with GFR <45 mL/min/1.73 m2 than those with GFR ≥45 mL/min/1.73 m2 (14.3% vs. 10.7%, P < 0.001), but this was not observed in the use of sodium bicarbonate and phosphate binders. The use of sodium–glucose co-transporter 2 inhibitors in diabetic and proteinuric patients with CKD was also explored to understand the prevalence of their use in an era that predated publication of trials demonstrating their renal protective effects. Using cut-offs adapted from the Dapagliflozin in Patients with Chronic Kidney Disease (DAPA-CKD) trial, it was found that of diabetic patients with proteinuria (UPCR ≥15 mg/mmol) and eGFR ≥25 mL/min/1.73 m2, only 0.27% (4/1438) received sodium–glucose co-transporter 2 inhibitors.[11] Malay and Indian patients had poorer diabetes control compared to Chinese and other ethnic groups (glycated haemoglobin [HbA1c] 7.8% vs. 7.4%, P < 0.001).
Most of the patients were assigned to general nephrology clinics. However, 7.6% of all patients did not turn up for appointments [Figure 2]. The most common reported reason for deferred referrals was ‘Declined speciality follow-up’ (n = 187, 20.5%). Patients who did not show up for appointments (n = 459) were younger (62.3 ± 15.7 vs. 64.1 ± 15.6 years, P = 0.015) and consisted of proportionately more Indian (12.85% vs. 6.05%, P < 0.01) and Malay (33.55% vs. 21.10%, P < 0.01) patients.
Figure 2.
Chart shows patient outcomes.
DISCUSSION
A disproportionately large number of Malay patients are referred for kidney disease, and they have high metabolic disease burden and poorer diabetes control. They are also more likely to be referred late at eGFR <30 mL/min/1.73 m2 and miss their first nephrology appointment. These findings are consistent with studies that have demonstrated higher prevalence of CKD and end-stage kidney disease in the Malay population, with diabetes mellitus and hyperlipidaemia accounting for greater population-attributable risk of CKD.[4,12] Population studies have identified socioeconomic and behavioural factors such as lower education level, poor awareness of chronic disease diagnoses, and smoking and alcohol consumption as contributory factors.[12,13,14] Poor awareness of metabolic and kidney disease complications may perpetuate undesirable lifestyle habits. Population-specific interventions targeting these modifiable risk factors need to be implemented both at primary and tertiary care.
The polyclinics have established programmes for diabetes mellitus and hypertension that include monitoring serum electrolytes (sodium and potassium), serum creatinine, HbA1c, and urine albumin and UPCR. This works well in CKD stages G1–G3a for monitoring the effects of medications and trajectory of kidney function and identifying new-onset or worsening proteinuria. However, as CKD progresses to stage G3b or higher, tests for detecting the complications of CKD, including anaemia, mineral bone disorders and metabolic acidosis, are less routinely performed. Our data support this contention as the use of sodium bicarbonate for metabolic acidosis and the use of phosphate binders are similar when analysed by eGFR >45 and <45 mL/min/1.73 m2. To improve CKD identification and management, the Ministry of Health, Singapore, implemented the HALT-CKD programme in polyclinics in 2018.[9] Patients with CKD stages G3b, G4 and G5 were automatically referred to nephrology, accounting for the significant increase in referrals for CKD management. However, a proportion of patients who miss nephrology appointments may continue to receive CKD management in primary care, even with advanced CKD. Moreover, a large proportion of referrals to nephrology came from intrahospital and interdepartment hospital-based doctors. Thus, it is important to not only develop protocols for CKD management in primary care, but also educate tertiary care practitioners on thresholds for nephrology referral.
Renin–angiotensin–aldosterone system (RAAS) blockade with ACE-I or ARB is the mainstay treatment for patients with proteinuric CKD.[15,16,17] In our cohort of patients who had a result of UPCR ≥15 mg/mmol and eGFR ≥30 mL/min/1.73 m2, only 64.6% were found to be on ACE-I and/or ARB medications. Out of those patients not on RAAS blockade, most (62.8%) were on alternative antihypertensive treatment. By the time patients with moderately severe and severe CKD (stages G4–5) are referred, there is often difficulty in optimising RAAS blockade due to the risks of hyperkalaemia and acute kidney injury. More emphasis needs to be placed on increasing the use and optimising the dose of ACE-I or ARB during the early stages of CKD. Sodium–glucose co-transporter 2 inhibitors, which in recent years have been demonstrated to be renoprotective for both diabetic and nondiabetic CKD with eGFR down to 25 mL/min/1.73 m2, are a recent addition to our therapeutic armamentarium against CKD progression. Their use needs to be encouraged in both primary and tertiary care.[11,18,19]
There are limited published data on the referral patterns to the nephrology speciality service and their implications on Asian healthcare systems. Our study is the first to investigate the referral patterns to nephrology in a multi-ethnic Asian population. The strength of our study is the prospectively collected database of over 6,000 patients. Our study is limited by adjudication of reasons for referrals, which is complicated and may not show the entire disease pattern. However, most patients were referred for multiple problems, and an order of classification (Supplementary data) was used for this analysis. Urine albumin to creatinine ratio was not collected and UPCR below or above detection limits was excluded, limiting the understanding of the true degree of patients with proteinuric CKD. Data on body mass index and control of metabolic risk factors were not collected. We recommend for future population-based studies to further explore the identified ethnic disparities in kidney disease and metabolic disease burden.
In summary, we showed that a large number of ethnic Malay patients were referred for kidney disease, which is out of proportion to their numbers in the general population. These patients also have higher metabolic disease burden, tend to miss appointments and are referred at lower eGFR. Reasons underpinning these associations should be identified to facilitate population-specific interventions, to promote kidney health for all.
Conflicts of interest
There are no conflicts of interest.
Supplemental digital content
Appendix at http://links.lww.com/SGMJ/A186
Funding Statement
Nil.
REFERENCES
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