Supplementary Online Content Wu HY, Peng YS, Chiang CK, et al. Diagnostic performance of random urine samples using albumin concentration vs ratio of albumin to creatinine for microalbuminuria screening in patients with diabetes mellitus: a systematic review and meta-analysis. JAMA Intern Med. Published online May 5, 2014. doi:10.1001/jamainternmed.2014.1363 eappendix. Study Protocol and Search Strategies ereferences. References for Study Protocol efigure 1. Summary of Study Identification and Selection efigure 2. Summary for Risk of Bias of Included Studies efigure 3. Risk of Bias Graph of Included Studies efigure 4. Funnel Plot for Publication Bias Assessment of Studies efigure 5. Paired Forest Plots of the Sensitivity and Specificity of Random Urine Albumin Concentration for the Detection of Microalbuminuria in Patients With Diabetes efigure 6. Paired Forest Plots of the Sensitivity and Specificity of Random Urine Albumin to Creatinine Ratio for the Detection of Microalbuminuria in Patients With Diabetes This supplementary material has been provided by the authors to give readers additional information about their work. 20141
eappendix. Study Protocol and Search Strategies Study protocol for systematic review and meta-analysis to compare random urine albumin concentration with random urine albumin to creatinine ratio for diagnostic performance in detecting microalbuminuria among patients with diabetes Objective To compare the diagnostic performance of the different screening modalities of urine albumin concentration (UAC) and urine albumin to creatinine ratio (ACR) of random urine samples, for detecting microalbuminuria in patients with diabetes using meta-analysis strategy. Inclusion Criteria Study type We will include studies assessing UAC or ACR of a random urine sample as an index test (i.e., the test under investigation) to evaluate patients with diabetes for the presence of microalbuminuria. The reference standard (i.e., the gold standard) of microalbuminuria is defined as urine albumin excretion rate 30-300 mg/day by 24-hour timed urine collection. Studies will be eligible regardless of whether patients with diabetes were referred for suspected or known microalbuminuria. Participants Eligible studies should include patients with diabetes. Eligible studies should include patients older than 18 years. Eligible studies should include patients with urine albumin excretion rate 300 mg/day by 24-hour timed urine collection. We will include studies of patients with any type of diabetes. Outcome measures Eligible studies should report cases in absolute numbers of true positive, false positive, false negative, and true negative results, or these data are derivable from the presented results. If a study presents multiple cutoff values of an index test and, as a consequence, reports multiple pairs of sensitivity and specificity estimates, the data with the best estimates will be extracted. If a study presents different cutoff values of an index test for male and female patients, the data 20142
of different sex will be analyzed as different individual studies. Publication type Full-length articles or letters in peer-reviewed English-language journals will be eligible. Data extraction Two investigators (Hon-Yen Wu, Yu-Sen Peng) will independently perform data extraction. Extracted information should include location of study, details of the study design, numbers of patients enrolled and excluded, as well as patient demographic characteristics (age, sex, and the type of diabetes), and prevalence of microalbuminuria. When relevant information regarding design or outcomes is unclear, or when doubt exists for duplicate publications, the original authors will be contacted for clarifications. Disagreements between the two authors will be resolved by discussion. If the disagreement persists, two other senior investigators (Kuan-Yu Hung, Kwan-Dun Wu) will be consulted to attain consensus. Quality Assessment The methodological quality of the eligible studies will be evaluated independently by two investigators (Hon-Yen Wu, Yu-Sen Peng) using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool. 1 Disagreements between the two authors will be resolved by discussion. If the disagreement persists, two other senior investigators (Kuan-Yu Hung, Kwan-Dun Wu) will be consulted to attain consensus. Data Synthesis and Analysis All data from each eligible study will be extracted and entered into a spreadsheet software (Microsoft Excel 2007; Microsoft Corp, Redmond, WA, USA). Categorical variables will be presented as frequencies or percentages and continuous variables will be presented as mean values, unless stated otherwise. Measures of diagnostic performance will be reported as point estimates with 95% confidence intervals. On the basis of true-positive, false-positive, false-negative, and true-negative rates, we will calculate sensitivity, specificity, as well as the diagnostic odds ratios (DORs). 2 Between-study statistical heterogeneity will be assessed by using I 2 and the Cochrane Q test on the basis of the random-effects analysis. 3 Publication bias will be examined using the effective sample size funnel plot and associated regression test of asymmetry described by Deeks and colleagues. 4 We will use the bivariate random-effects model for the analysis and pooling of diagnostic performance measures across studies, as well as the comparisons between different index tests. 5, 6 The bivariate model estimates pairs of logit-transformed sensitivity and specificity from studies, thereby incorporating the correlation that might exist between sensitivity and specificity. The random-effects approach allows for heterogeneity between studies. 7, 8 We will also use the model to create hierarchical 20143
summary receiver-operating characteristic (HSROC) curves and estimate the area under the curve (AUC). 9 When statistical heterogeneity is substantial, we will identify its source by performing meta-regression. The selection of covariates for meta-regression will be based on potential sources of bias, 10 such as the location, year of publication, cutoff value, time point of urine collection, quality of the studies, as well as the age, sex, number, and prevalence of microalbuminuria of the study participants. Pooled estimates will also be calculated for subgroups of studies that are defined according to specific study designs, such as studies comparing UAC with ACR, or studies with specific time point of urine collection. A two-sided P value 0.05 will be considered statistically significant. Statistical analyses will be performed with SAS (version 9.2, SAS Institute, Cary, North Carolina, USA) and Stata software (version 11.1, StataCorp LP, College Station, TX, USA). Search Strategies We will search the following electronic databases: MEDLINE PubMed Scopus We will search additional studies in the reference lists of all identified publications, including relevant meta-analyses and systematic reviews. MEDLINE: Search using the Ovid interface from the earliest available date of indexing through 31 Jul 2012 (1) Diabetic Nephropathies.mp. or exp Diabetic Nephropathies/; (2) Diabetes Mellitus.mp. or exp Diabetes Mellitus/; (3) exp Kidney Failure, Chronic/ or chronic kidney disease.mp. or exp Renal Insufficiency, Chronic/; (4) Albuminuria.mp. or exp Albuminuria/; (5) Urine Specimen Collection.mp. or exp Urine Specimen Collection/; (6) albumin creatinine ratio.mp.; (7) (Sensitivity and Specificity).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]; (8) Mass Screening.mp. or exp Mass Screening/; (9) 1 or (2 and 3); (10) 4 or 5 or 6; (11) 7 or 8; (12) 9 and 10 and 11; (13) limit 12 to English language. PubMed: Search using the NCBI interface from the earliest available date of indexing through 31 Jul 2012 Diabetic Nephropathies OR (Diabetes Mellitus AND ((renal Insufficiency, chronic) OR (kidney Failure, chronic) OR "chronic kidney disease")) AND (Albuminuria OR Urine Specimen Collection OR "albumin creatinine ratio") AND ((Sensitivity and Specificity) OR Mass Screening) AND (English[lang]) 20144
Scopus: Search using the Elsevier interface from the earliest available date of indexing through 31 Jul 2012 Diabetic Nephropathies OR (Diabetes Mellitus AND ((renal Insufficiency, chronic) OR (kidney Failure, chronic) OR "chronic kidney disease")) AND (Albuminuria OR Urine Specimen Collection OR "albumin creatinine ratio") AND ((Sensitivity and Specificity) OR Mass Screening) AND LANGUAGE(English) 20145
ereferences. References for Study Protocol 1. Whiting P, Rutjes AW, Reitsma JB, Bossuyt PM, Kleijnen J. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol. 2003;3:25. 2. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol. 2003;56(11):1129-1135. 3. Thompson SG. Why sources of heterogeneity in meta-analysis should be investigated. BMJ. 1994;309(6965):1351-1355. 4. Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol. 2005;58(9):882-893. 5. Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58(10):982-990. 6. Hamza TH, van Houwelingen HC, Stijnen T. The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol. 2008;61(1):41-51. 7. Gatsonis C, Paliwal P. Meta-analysis of diagnostic and screening test accuracy evaluations: methodologic primer. AJR Am J Roentgenol. 2006;187(2):271-281. 8. Riley RD, Abrams KR, Sutton AJ, Lambert PC, Thompson JR. Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Med Res Methodol. 2007;7:3. 9. Rutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med. 2001;20(19):2865-2884. 10. Lijmer JG, Mol BW, Heisterkamp S, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA. 1999;282(11):1061-1066. 20146
efigure 1. Summary of Study Identification and Selection 20147
efigure 2. Summary for Risk of Bias of Included Studies The green symbols represent low risk of bias, the yellow symbols represent unclear risk of bias, and the red symbols represent high risk of bias. The figure was generated using Review Manager Version 5.1. 20148
efigure 3. Risk of Bias Graph of Included Studies Each methodological quality item is presented as percentages across all included studies. The figure was generated using Review Manager Version 5.1. 20149
efigure 4. Funnel Plot for Publication Bias Assessment of Studies (A) random urine albumin concentration and (B) random urine albumin to creatinine ratio, for the detection of microalbuminuria in patients with diabetes. 2014 10
efigure 5. Paired Forest Plots of the Sensitivity and Specificity of Random Urine Albumin Concentration for the Detection of Microalbuminuria in Patients With Diabetes 2014 11
efigure 6. Paired Forest Plots of the Sensitivity and Specificity of Random Urine Albumin to Creatinine Ratio for the Detection of Microalbuminuria in Patients With Diabetes 2014 12