Background
Methods
Systematic literature search
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MEDLINE and MEDLINE In-Process
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Embase
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Cochrane Central Register of Controlled Trials (CENTRAL)
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The Cochrane Database of Systematic Reviews (CDSR)
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NHS Economic Evaluation Database (NHS EED)
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Health Economic Evaluations Database (HEED)
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BIOSIS.
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Study types: clinical trials, observational studies, systematic reviews, meta-analyses and non-systematic reviews
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Population: human, adults (≥18 years of age) with PKD, or ADPKD specifically
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Outcomes: diagnosis or prognosis
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Language: English.
Study selection
Data extraction
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Study characteristics, such as study design, duration and location
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Patient characteristics (see ‘Definitions’)
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Clinical characteristics and outcomes linked to disease progression (see ‘Definitions’)
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Information regarding ESRD in the patient populations, such as age at ESRD onset and length of time on dialysis.
Definitions
Patient characteristics | Clinical characteristics | Outcomes |
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Demographic factors: age, gender, BMI, weight, height, ethnicity | Baseline renal function parameters: GFR, serum creatinine, proteinuria, albuminuria, creatinine clearance, albumin/creatinine ratio | Change in renal function parameters: GFR, serum creatinine, proteinuria, albuminuria, creatinine clearance, albumin/creatinine ratio |
Baseline renal size parameters: TKV, TCV, TKL, left renal volume, right renal volume, height-adjusted TKV | Change in renal size parameters: TKV, TCV, TKL, left renal volume, right renal volume, height-adjusted TKV | |
Clinical measures: presence of hypertension, renal blood flow, haematuria, UTI | Change in clinical measures: renal blood flow, hypertension | |
Genotype |
Results
Observational studies
Publication(s) | Objectives | Study details | Methodology for the assessment of disease progression and identification of prognostic indicators | Results and conclusions |
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Schrier, 2003 [40] | To assess the effect of increased research, identification of prognostic factors, and a higher number of anti-hypertensive medications on ADPKD progression | An observational study was conducted over two time periods, 255 patients from 1985 to 1992 (38 % male, age 37.5 ± 10 years, mean renal volume 701 cm3) and 258 patients from 1992 to 2001 (31 % male, age 37.5 ± 10 years, mean renal volume 704.5 cm3) | Regression analyses were performed for all patients to identify factors correlated with progression to ESRD, defined as dialysis or renal transplant | Age (P < 0.0001), renal volume (P < 0.0001), MAP (P = 0.0026) and UPE (P = 0.0051), but not gender, correlated with progression to ESRD |
To identify markers of ADPKD disease progression | A prospective, long-term observational study of 241 ADPKD patients with normal renal function who were considered at high risk of renal insufficiency | Correlations between BL characteristics (including age, gender, BMI, hypertension, MAP, TKV, TCV, RBF, RVR, GFR, serum uric acid, Cho, 24-hour urine volume, UNaE, UAE, and estimated protein intake) and ∆TKV or ∆eGFR were assessed over 3, 6 or 8 years | Factors that predict ∆TKV: | |
• BL TKV (P < 0.001) | ||||
• BL RBF, UNaE, HDL-c, and age at BL (P ≤ 0.05) | ||||
• Male gender (P = 0.0163) | ||||
• UAE (when BL TKV was excluded, P ≤ 0.005) | ||||
• There was no significant difference in ∆TKV or ∆TCV between patients with PKD1 and PKD2 mutations (P = 0.24 and 0.79, respectively) | ||||
Factors that predict ∆GFR: | ||||
• TKV (P < 0.02, 6 years’ follow-up). TKV for patients with BL TKV > 1500 mL (P < 0.001), but not for BL TKV < 750 mL (P = 0.063) or 750–1500 mL (P = 0.57, after 3 years of follow up) | ||||
• Age (when BL GFR was excluded, P ≤ 0.02) | ||||
• SCr, BUN (both P ≤ 0.001) | ||||
• BL GFR, RBF and UNaE (P ≤ 0.02) | ||||
• UAE, BSA and 24-h urine osmolarity (when BL GFR was excluded, P ≤ 0.02) | ||||
Kistler, 2009 [92] | To assess the reliability of TKV MRI imaging over 6 months with the aim of using such timescales in studies of potential treatments | A prospective study of 100 young patients with ADPKD (63 % male, mean age 31.2 ± 6.4 years, 97 % had family history of ADPKD) with preserved GFR | Correlations between % change in TKV and clinical and demographical parameters (not specified) were calculated to identify prognostic indicators | Higher ∆TKV were observed in males (P = 0.339) and in patients with albuminuria (P = 0.005) |
To study the correlation between endogenous vasopressin concentration using plasma copeptin as a marker, and renal function | A review of data from a trial investigating the effect of an ACEI on progression of ADPKD (no treatment effect reported). Data were reviewed for 79 patients (43 % male, age 36.8 ± 10.1 years, GFR 96.8 ± 18.2 mL/min/1.73 m2) over a median of 11.2 years | ∆eGFR was used as a measure of renal function | Higher baseline copeptin was associated with a faster ∆eGFR (P < 0.01). This association was independent of age, gender and BL eGFR | |
Azurmendi, 2011 [23] | To investigate albuminuria, measured by urinary Alb/Cr, as a predictor of disease progression | 32 patients with ADPKD (mean age 26 ± 1 years) were observed over 30 ± 1 months | Yearly change in TKV, urinary MCP1 and eGFR were used as measures of renal function | In patients with high urinary Alb/Cr, yearly change in both TKV and urinary MCP1, but not eGFR, were increased compared with patients with normal urinary Alb/Cr (P < 0.05) |
Griveas, 2012 [58] | To identify patients with ADPKD who progressed and those who did not | A retrospective review of 120 patients (39 % male, age 36.7 ± 12.7 years) was conducted with a median follow-up of 52 months | Correlations were made between annual change in eGFR and the following BL characteristics: | Higher BL eGFR was associated with a faster ∆eGFR (P = 0.04). Correlations between annual ∆eGFR and other BL characteristics were not significant |
• eGFR | ||||
• Hb | ||||
• Cho | ||||
• Parathormone | ||||
• SBP and DBP | ||||
• MAP | ||||
To investigate genotypic indicators of disease progression in Japanese patients with ADPKD | A mutation search was conducted in the coding and flanking regions of PKD1/2 from 180 patients from 161 unrelated families | Multiple linear regression analyses were conducted to assess the correlations between eGFR decline and gene mutations, plasma arginine vasopressin, and urine osmolarity | • Patients with PDK1 mutations had significantly faster ∆eGFR than patients with PDK2 mutations (P < 0.01) | |
• There was no association between ∆eGFR and mutation type or position | ||||
• Lower urine osmolarity was found to correlate with ∆eGFR (significance not reported) | ||||
• Plasma arginine vasopressin was significantly associated with ∆eGFR in patients with PDK1 mutations (P = 0.018) | ||||
Panizo, 2012 [60] | To analyse factors influencing ADPKD disease progression | A retrospective observational study was conducted in 101 patients with ADPKD (mean age 43 ± 17.3 years, 43.6 % male, median follow-up 69 months, mean kidney size 14.8 ± 2.9 cm, mean eGFR 74.5 ± 32.0 mL/min/1.73 m2) | The following data were collected as potential prognostic markers using eGFR reduction as an indicator of renal function decline: | • SBP, DBP, uric acid, total and LDL-c, Cr, microalbuminuria and kidney size were significantly associated with ∆eGFR (P ≤ 0.05) |
• Kidney size | • Younger age at diagnosis was also associated with rapid ∆eGFR (P = 0.010) | |||
• SBP and DBP | ||||
• Concomitant medications | ||||
• Hb | ||||
• Cr | ||||
• Uric acid | ||||
• Total Cho, HDL-c and LDL-c | ||||
• Triglycerides | ||||
• Calcium | ||||
• Phosphorus | ||||
• Parathyroid hormone | ||||
• Proteinuria (microalbuminuria) | ||||
• Haematuria | ||||
CRISP: Warner, 2012 [56] | To assess the association between CPSA and decline in eGFR to determine whether this is a better indicator of ADPKD progression than TKV | Patients were randomly selected from the CRISP cohort: 10 rapid progressors, 10 slow progressors, and 4 atypical cases with large TKV and a small number of cysts at baselinea | • When the atypical cases were excluded from the analysis, BL lnCPSA and lnTKV correlated equally well with ∆eGFR over 6 years (P = 0.0003) | |
• When atypical cases were included, baseline lnCPSA correlated better than lnTKV with ∆eGFR (P < 0.0001 and P = 0.0008, respectively) | ||||
Hwang, 2013 [97] | To investigate the association between asymptomatic pyuria and the development of UTIs and the deterioration of renal function | Retrospective case control study of 256 patients with ADPKD (52 % male, mean age 48.1 ± 12.8 years, mean eGFR 91.1 ± 29.2 mL/min/1.73 m2) in South Korea observed over 1 year | ∆GFR was used as a measure of renal function | Patients with chronic asymptomatic pyuria, who were predominantly female (58.5 %) exhibited a significantly faster ∆GFR (P = 0.01) than patients without pyuria or with transient pyuria |
Lacquaniti, 2013 [98] | To quantify the predictive potential of apelin (marker of vasopressin) and copeptin (antagonist of vasopressin signalling) in ADPKD disease progression | A prospective observational study of 52 patients with ADPKD (60 % male, mean age 43 ± 10 years, mean TKV 1057.3 ± 417.9 mL, mean mGFR 47.7 ± 35.6 mL/min/1.73 m2) and 50 matched healthy control patients (50 % male, mean age 40.3 ± 9.6 years, mean mGFR 116.6 ± 17 mL/min/1.73 m2) were followed for 24 months | Renal function was assessed by combination of ∆mGFR and ∆TKV (>5 % per year) | Concentrations of apelin < 68.5 pg/mL (P = 0.0002) and copeptin > 9.5 pmol/L (P = 0.02) were each associated with a faster decline in renal function |
Ozkok, 2013 [59] | To investigate the importance of clinical characteristics and biochemical data on disease progression | 323 patients with ADPKD (46 % male, mean age 53 ± 15 years) were followed for a mean of 100 ± 38 months | In Cox regression analysis, the following factors were assessed as potential predictors of ∆GFR: | Age, hypertension, hernia, proteinuria, and urinary stone were significantly associated with faster ∆GFR (P ≤ 0.04) |
• Age | ||||
• Gender | ||||
• BL SCr | ||||
• Smoking history | ||||
• History of hypertension | ||||
• Abdominal wall hernia | ||||
• Hepatic cyst | ||||
• Familial history of ADPKD | ||||
• Macroscopic haematuria | ||||
• Proteinuria | ||||
• Urinary stone | ||||
• Palpable kidney | ||||
• Use of ACEIs and/or ARBs | ||||
Spithoven, 2013 [99] | To measure CCr(TS) in patients with ADPKD compared with healthy adults | A case control study of 125 patients with ADPKD (56 % male, mean age 40.4 ± 10.8 years, mean TKV 1470 mL, mean mGFR 77.7 ± 30.1 mL/min/1.73 m2) and 215 healthy controls (48 % male, mean age 53.1 ± 10.3 years, mean eGFR 97.7 ± 17.0 mL/min/1.73 m2) | CCr(TS) was used as a measure of renal function | • CCr(TS) was significantly higher for patients with ADPKD than for controls (P < 0.001), which may be due to cyst formation |
• In patients with ADPKD, CCr(TS) correlated with BMI (P = 0.003), BL mGFR (P = 0.03) and age (P = 0.07), but was not associated with TKV, female sex, filtration fraction, serum albumin, albuminuria (all P > 0.1) | ||||
Thong & Ong, 2013 [100] | To analyse the natural history of ADPKD progression | A retrospective study of 210 patients with ADPKD (48.6 % male, mean age 45.6 ± 16.2 years) | Regression analyses were performed to identify risk factors for ∆eGFR for 55 patients who had eGFR and kidney length measurements recorded over 5 years | ∆eGFR was significantly associated with age at diagnosis and with mean kidney length (both P < 0.05). Gender, hypertension, haematuria, proteinuria, UTIs, and liver cysts were not significantly associated with renal function decline |
Chen, 2014 [14] | To identify parameters that predict cyst growth and decline in renal function | A prospective, longitudinal observational study was performed in 541 Chinese patients with ADPKD and eGFR ≥30 mL/min/1.73 m2 (54 % male, mean age 39.7 ± 12.1 years, eGFR 100.4 ± 20.1 mL/min/1.73 m3) over a median follow-up of 14.3 ± 10.6 months | Analyses were performed for 279 patients with measurements for all variables of the correlation between yearly change in eGFR or yearly % growth in TKV and: | The following parameters correlated with yearly eGFR: |
• Age | • Age (P = 0.016) | |||
• Sex | • History of hypertension (P = 0.056) | |||
• Observation time | • Use of anti-hypertensive drugs (P = 0.102) | |||
• History of hypertension | • BL eGFR (P = 0.290) | |||
• Use of anti-hypertensive drugs | • Log10 Pr/Cr (P < 0.001) | |||
• BP | • Log10 BL TKV (P < 0.001) | |||
• Macrohaematuria | • BL thrombocyte count (P = 0.031) | |||
• BL eGFR | ||||
• Pr/Cr | ||||
• BL TKV | ||||
• BL thrombocyte count | The following parameters correlated significantly with yearly TKV: | |||
• Age (P < 0.001) | ||||
• Male sex (P = 0.023) | ||||
• Observation time (P = 0.072) | ||||
• Use of anti-hypertensive drugs (P = 0.015) | ||||
• DBP (P = 0.041) | ||||
• BL eGFR (P = 0.173) | ||||
• Log10 Pr/Cr (P = 0.050) | ||||
• Log10 BL TKV (P = 0.092) | ||||
• BL thrombocyte count (P = 0.042) | ||||
Higashihara, 2014 [101] | To assess the relationship between TKV and kidney function (measured by eGFR) | An observational study of 64 patients with ADPKD who completed ≥3 measurements and did not have any clinical conditions affecting kidney volume (33 % male, mean age 47.0 ± 14.1 years, mean TKV 1681.1 ± 1001.1 mL, mean eGFR 60.2 ± 27.38 mL/min/1.73 m2) | TKV, GFR, SCr, Cr clearance, UPE, and BP were measured over 5 years | • TKV, height-adjusted TKV, BSA-adjusted TKV and log-TKV significantly correlated with eGFR (all P < 0.0001) |
• BL TKV, age, and final eGFR were significantly associated with the yearly change in eGFR (P = 0.0349, P < 0.001, P = 0.0011, respectively), but the relationship between BL eGFR and the yearly change in eGFR was not significant (P = 0.4007) | ||||
• Although there was no significant correlation between age and the TKV parameters investigated (P > 0.1), there was a significant relationship between age and both the yearly % change in TKV and change in log-TKV (P < 0.01) | ||||
• There was a significant correlation between BL and final TKV and the yearly change in TKV (both P < 0.001) |
Prognostic indicator | Number of publications reporting prognostic indicator | Publications reporting a significant association between prognostic indicator and rate of decrease in renal function (GFR) | Publications reporting a significant association between prognostic indicator and rate of increase in renal volume |
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Age e.g. age at diagnosis (years) | 10 | Torres, 2011 [15] | |
Chen, 2014 [14] | |||
Higashihara, 2014 [101] | |||
Baseline TKV e.g. log10 baseline TKV (cm3) | 10 | Chen, 2014 [14]d | |
Grantham, 2006 [46]e | |||
Higashihara, 2014 [101]i | |||
Baseline GFR (either estimated or measured) | 6 | Chen, 2014 [14] | |
Proteinuria/albuminuria e.g. baseline albuminuria (mg/L) | 5 | Kistler, 2009 [92] | |
Torres, 2007 [16]b | |||
Blood pressure e.g. mean arterial pressure (mmHg) | 4 | Chen, 2014 [14]q | |
Male gender | 3 | None | Harris, 2006 [17] |
Kistler, 2009 [92] | |||
Chen, 2014 [14] | |||
Urine sodium concentration | 3 | Torres, 2007 [16] | |
Irazabal, 2012 [48] | |||
Protein/creatinine ratio | 2 | Azurmendi, 2011 [23]r | Chen, 2014 [14] |
Chen, 2014 [14] | Azurmendi, 2011 [23]s | ||
PKD1 genotype | 2 | None | |
Cholesterol e.g. serum HDL-c (mg/dL) | 1 | None | Torres, 2011 [15]s |
Othert,u | 14 | Chen, 2014 [14] |