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Performance of Confirmatory Tests for Diagnosing Primary Aldosteronism: a Systematic Review and Meta-Analysis

Background: Confirmatory tests are recommended for diagnosing primary aldosteronism, but the supporting evidence is unclear. Methods: We searched Medline, EMBASE, and the Cochrane Central Register of Controlled Trials. Studies evaluating any guideline-recommended confirmatory test (ie, saline infusion test, salt loading test, fludrocortisone suppression test, and captopril challenge test), compared with a reference standard were included. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the risk of bias. Meta-analyses were conducted using hierarchical summary receiver operating characteristic models. Results: Fifty-five studies were included, comprising 26 studies (3654 participants) for the recumbent saline infusion test, 4 studies (633 participants) for the seated saline infusion test, 2 studies (99 participants) for the salt loading test, 7 studies (386 participants) for the fludrocortisone suppression test, and 25 studies (2585 participants) for the captopril challenge test. Risk of bias was high, affecting more than half of studies, and across all domains. Studies with case-control sampling overestimated accuracy by 7-fold (relative diagnostic odds ratio, 7.26 [95% CI, 2.46–21.43]) and partial verification or use of inconsistent reference standards overestimated accuracy by 5-fold (5.12 [95% CI, 1.48–17.77]). There were large variations in how confirmatory tests were conducted, interpreted, and verified. Under most scenarios, confirmatory testing resulted in an excess of missed cases. The certainty of evidence underlying each test (Grading of Recommendations, Assessment, Development, and Evaluations) was very low. Conclusions: Recommendations for confirmatory testing in patients with abnormal screening tests and high probability features of primary aldosteronism are based on very low-quality evidence and their routine use should be reconsidered.

Original studies evaluating any guideline-recommended confirmatory test for PA were eligible if they included comparison to a reference standard. Studies that required multiple sequential tests to establish a diagnosis were not included if the performance of any single test could not be determined. Conference abstracts, reviews, editorials, and protocols were excluded. When the same group of patients was likely reported across several publications for the same test, only the most complete publication was included to avoid double counting.
For each study included, the number of true positive, false positive, false negative, and true negative cases were extracted (or manually calculated from available data). When the necessary data were not reported in the text or tables, they were derived from published figures using WebPlotDigitizer version 4.4 (Ankit Rohatgi, Pacifica, CA, USA). When multiple sensitivity and specificity pairs (at different thresholds) were reported for the same individuals in a single study, we only considered the threshold associated with the highest specificity (aligning with the primary purpose of the test to rule-in disease) or the one designated as "optimal" by the original investigators to avoid double counting. If variations of the same confirmatory test were performed multiple times in the same patients, the set most closely aligning to the testing protocol described by guidelines was used. 1 Meta-analyses were conducted using hierarchical summary ROC (HSROC) models that included random-effects terms for variations in accuracy and thresholds between studies, and allowed for non-symmetrical ROC curves to be fitted. 2 The diagnostic accuracies of the different tests were compared between all studies (indirect comparisons) and, where possible, head-to-head from studies that evaluated more than one test against a common reference standard (direct comparisons).
We relied on visual inspection of the coupled forest plots and summary ROC plots to describe heterogeneity, rather than using the I 2 statistic, as the latter is univariate and does not account for threshold effects. 3 We explored for potential sources of heterogeneity using meta-regression, considering differences in methodological quality and clinical characteristics between studies, and incorporated these separately as covariates in the HSROC model. 3 The likelihood ratio (LR) test was used to compare models with and without the covariate terms to formally test for differences. To quantify differences, we calculated the relative diagnostic odds ratio (DOR), which is a summary measure of the relative accuracy between two tests, assuming the summary ROC curves were parallel. 4 We assessed for publication bias using Deeks' funnel plot, noting that the statistical test has low power to detect asymmetry when heterogeneity is large. 3 Because summary statistics are only interpretable when studies share a similar threshold (but thresholds varied considerably in our current review), we estimated the sensitivities at discrete points on the summary ROC curve corresponding to the lower quartile, median, and upper quartile of the reported specificities to facilitate comparisons. 3 We calculated the number of missed cases and over-diagnosed cases per 1000 patients and presented these in a "summary of findings" table with evidence profiles adapted from the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. 5,6 Analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), Stata version 17.0 (StataCorp, College Station, TX, USA), and RevMan version 5.4.1 (The Cochrane Collaboration, Copenhagen).

Variable Description Citation
Citation.

Author
Last name of the first author.

Year
Year of publication. If the first author has published more than one article within the same year, enter the year using sequential letters (e.g., 2009a, 2009b, 2009c, etc.).

Country
Country in which the study was conducted. For multi-site trials, list all countries separated by a comma (e.g., USA, Canada, UK, and Australia). If this is not reported, use the country of origin of the first author.

Design
Select from the following options:  "Single-gate design" (single set of criteria for inclusion; entire study sample drawn from clinical population suspected to have primary aldosteronism [PA])  "Two-gate design with healthy controls" (cases and controls are sampled from 2 distinct source populations; cases are known or highly likely to have PA, and controls are healthy participants)  "Two-gate design with alternative diagnosis controls" (cases and controls are sampled from 2 distinct source populations; cases are known or highly likely to have PA, and controls have a specific alternative condition similar to PA [e.g., essential hypertension])  "Multi-gate design with healthy controls and alternative diagnosis controls" (cases and controls sampled from multiple populations; cases are known or highly likely to have PA, and compared with multiple controls, including healthy people and those with essential hypertension).

Sampling
Select from the following options:  Consecutive patients  Random sample  Case-control (non-consecutive, non-random)  Unclear

Data collection
Select from the following options:  Prospective (e.g., consent was obtained prior to testing)  Retrospective (e.g., chart review)  Unclear N total Total number of participants in all groups.

N disease
Total number of people with PA.

N unilateral
Total number of people with PA that were reported to have unilateral disease (either by presence of adrenal mass, lateralization, or surgery-as defined by study).

TP
Number of true positive cases.

FP
Number of false positive cases.

FN
Number of false negative cases.

TN
Number of true negative cases.

Mean age
Mean age of all participants

Range age
If mean age not reported (or cannot be estimated), report age range when available.

Number male
Number of males of all participants.

Number hypokalemia
Number of participants with hypokalemia.

ARR threshold
Minimum ARR required for inclusion in study.

Confirmatory test
Select from the following options:

Confirmatory test interpretation
Describe how confirmatory test was interpreted.

Aldosterone assay
Type of laboratory assay for aldosterone

Renin assay
Type of laboratory assay for renin

Renin type
Plasma renin activity (PRA) vs. direct renin concentration (DRC) Reference Reference standard ("gold standard") used for disease verification: For different reference tests, it captures the situation where a different definition of PA is applied depending on the results of the confirmatory test (e.g., abnormal confirmatory testing gets AVS, but normal confirmatory results receives another confirmatory test).

Patient selection risk of bias
Risk of bias assessment for patient selection.
 Low = "single-gate design," enrolling patients suspected (but not proven) to have PA.  High = "two-gate design" or case-control studies at risk of spectrum bias (e.g., patients with florid disease were compared with those who were entirely normal).  Unclear = not enough data to make judgment.

Patient selection applicability
Concerns about applicability for patient selection.
 Low = patients represent those that would likely receive a confirmatory test in clinical practice.  High = patients are highly selected and unlikely to reflect those who would receive a confirmatory test in clinical practice.  Unclear = not enough data to make judgment.

Index test risk of bias
Risk of bias assessment for index test.  Low = confirmatory test was interpreted without knowledge of reference standard and/or the interpretation threshold was prespecified.  High = there was potential of subjective interpretation of the confirmatory test (e.g., some patients were already deemed to have diagnosis of PA, then threshold for positive/negative test was determined afterwards).  Unclear = not enough data to make judgment.

Index test applicability
Concerns about applicability of index test.  Low = confirmatory test similar to what is expected to be used in clinical practice (as per guidelines), or derived from objective standard.  High = confirmatory test significantly different than what is done in clinical practice.  Unclear = not enough data to make judgment.

Reference standard risk of bias
Risk of bias assessment for reference standard.
 Low = classification of disease was most likely correct and interpreted independently of index test (e.g., clinical response to targeted treatment). It is reasonable to assume that any disagreements between the reference standard and index test is because of misclassification from the index test.  High = significant potential of misclassification of disease and/or inconsistent reference standard (e.g., AVS lateralization may miss bilateral forms of PA; histopathology may miss cases that did not undergo surgery and bilateral forms of PA that underwent surgery; another confirmatory test may be subject to false positive/negative results).  Unclear = not enough data to make judgment.

Reference standard applicability
Concerns about applicability of reference standard.
 Low = interpretation of the reference standard is similar to what is expected in clinical practice.  High = interpretation of the reference standard is significantly different than usual clinical practice.  Unclear = not enough data to make judgment.

Flow and timing risk of bias
Risk of bias assessment for study flow and timing.
 Low = adequate time was provided for verification of disease status (e.g., clinical outcome following treatment); all patients received the same reference standard; all patients were accounted for in the analysis.
 High = inadequate time was provided for verification of disease status; only some patients received a reference standard and/or inconsistent reference standards were used; some patients were unaccounted for in the analysis.  Unclear = not enough data to make judgment.

Other comments
Additional notes.     Table S7. Meta-regression analysis for potential sources of diagnostic test accuracy variability. Figure S2. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) plot. Figure S3. Summary receiver operating characteristics curves for studies that compared two confirmatory tests with a common reference standard (direct comparisons). There is a line joining tests that were compared. Curves were only plotted when there were more than 2 studies available. To avoid extrapolation beyond the data, the curves were drawn within the range of observed specificities. Comparisons were made for the recumbent SIT vs. CCT in 5 studies (panel A); recumbent SIT vs. FST in 1 study (panel B); seated SIT vs. CCT in 1 study (panel C); and recumbent SIT vs. seated SIT in 2 studies (panel D). Abbreviations: CCT, captopril challenge test; FST, fludrocortisone suppression test; SIT, intravenous saline infusion test.

2A)
2C) Figure S4. Deeks' funnel plot and asymmetry test for publication bias for the intravenous recumbent saline infusion test, p=0.11 (panel A); seated saline infusion test, p=0.70 (panel B); fludrocortisone suppression test, p=0.38 (panel C); and captopril suppression test, p=0.42 (panel D). The oral salt loading test was not examined for publication bias because there were only two studies.