purpose. To compare the Swedish interactive thresholding algorithm (SITA) with the full-threshold (FT) strategy for short-wavelength automated perimetry (SWAP).

methods. One eye of 286 patients with glaucomatous optic neuropathy (GON) and 289 age-matched participants without GON from the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES) were classified with optic disc stereophotographs taken within 6 months of visual field testing, conducted within a 3-month period. Six parameters were derived per test, including pattern standard deviation (PSD) and the number of pattern deviation plot (PDP) points triggered at <1%. Receiver-operating characteristic (ROC) analysis equated the tests for specificity (80%, 90%, and 95%). Sensitivities of parameters with the highest area under the curve (AUC) and STATPAC (Carl Zeiss Meditec, Inc., Dublin, CA) PSD were compared. Agreement, severity, and test duration between algorithms were assessed.

results. Sensitivities were not different between algorithms using PSD. With PDP <1%, SWAP-FT was more sensitive (35%) than SWAP-SITA (29%) at 95% specificity (*P* < 0.05). Sensitivity and specificity using the STATPAC PSD at 95% (*P* < 5%) and 99.5% (*P* < 0.05%) was similar between algorithms. Severity correlated significantly between algorithms (*P* < 0.001), although there was bias for SWAP-SITA to suggest more severe loss. SWAP-SITA required significantly less test time than did SWAP-FT (*P* < 0.001). Mean differences in PSD, PDP <1%, and MD between algorithms were not clinically significant.

conclusions. Both algorithms performed similarly when equated for specificity. The reduced test duration makes SWAP-SITA the better choice. Testing with both algorithms within a short period is recommended for confirmation of results when switching from FT to SITA.

^{ 1 }; these make up 8% to 10% of the retinal ganglion cells. SWAP has been very useful for detecting glaucoma.

^{ 2 }

^{ 3 }

^{ 4 }

^{ 5 }

^{ 6 }

^{ 7 }

^{ 8 }However, an important clinical drawback of SWAP using the full-threshold (FT) algorithm has been the lengthy test time.

^{ 9 }To decrease the test duration of SWAP, Bengtsson

^{ 10 }implemented a Bayesian-based algorithm, the Swedish interactive thresholding algorithm (SITA), in a method similar to the one she had used for standard automated perimetry (SAP).

^{ 10 }

^{ 11 }SWAP-SITA has also demonstrated higher mean sensitivities in normal eyes and lower intersubject variability than SWAP-FT.

^{ 12 }

^{ 13 }Despite these apparent advantages, the sensitivity and specificity of SWAP-SITA have not been evaluated systematically against the original SWAP-FT. Such a comparison is essential for understanding what similarities or differences might have been introduced with the SITA strategy. We compared the performance of SWAP with both algorithms on the same group of participants tested within a short time on specificity-equated and machine-derived (STATPAC; Carl Zeiss Meditec, Inc., Dublin, CA) parameters.

*n*

_{SWAP-SITA}= 2805;

*n*

_{SWAP-FT}= 2102) on either the MD or PSD measurement. Reviewers were masked to all other information about the participant.

^{2}bright yellow background to selectively test the short-wavelength–sensitive cones by decreasing the sensitivity of the long- and medium-wavelength–sensitive cones.

^{ 14 }

^{ 10 }After each response, the threshold is updated and recalculated to determine the intensity of the next target. In the 52 locations, 4-dB steps are applied until the first reversal, and then an additional 2-dB step is applied after the first reversal at points within 12° of eccentricity.

^{ 15 }was determined. Perfect classification is defined by an AUC of 1, which indicates that the outcome of the visual field test matches exactly with the stereophotograph classification. Chance discriminability is an AUC of 0.5. The AUC for the two best parameters was compared with the nonparametric Mann-Whitney

*U*test according to the method of DeLong et al.

^{ 16 }Since a test’s sensitivity varies as the criteria of specificity changes, ROC curves were used to estimate the sensitivity of each test and parameter combination at three levels of specificity: 80%, 90%, and 95%. McNemar’s test was used to compare the sensitivities of the two best parameters for each test at each specificity level. Sensitivities and specificities using machine-derived PSD at probability levels of <0.5% and <5% were also examined.

^{ 10 }which is advantageous for testing patients with more severe to end-stage visual field defects. Although the participants in this study did not include many advanced patients, we looked for any within-subject differences between algorithms across all levels of severity by using a nonparametric matched-pair comparison (two-sided Wilcoxon signed-rank test).

^{ 17 }If the mean difference of two measurements is different from 0, there is a fixed bias such that one test measurement is typically higher or lower than the second test. If the difference between the two test results expands or contracts through the range of measurements, there is a proportional bias. Because SWAP-SITA has been reported to have an increased dynamic range compared with SWAP-FT in visual fields with greater damage,

^{ 9 }we expected to see a proportional bias. For example, we expected PSD values with SWAP-SITA to be higher than with SWAP-FT at greater PSD measurements. To formally evaluate this relationship, we regressed the difference between two test measurements on their average.

^{ 18 }The Spearman’s nonparametric correlation test was then used to assess the strength of the relationship between algorithm measurements. Both parameter measurements were also compared with the two-tailed Wilcoxon signed-rank, matched-pairs test.

*P*< 5%) and 99.5% (

*P*< 0.5%) to define abnormality. We looked at sensitivity and specificity for GON classification between ROC-derived and machine-derived PSD and the overlap in GON visual field outcomes across algorithms using Venn diagrams, and then evaluated the agreement between SWAP-FT and SWAP-SITA by using the κ statistic,

^{ 19 }which rated the strength of agreement as poor (κ = 0.00), slight (κ = 0.01–0.20), fair (κ = 0.21–0.40), moderate (κ = 0.41–0.60), substantial (κ = 0.61–0.80), or almost perfect (κ = 0.81–1.00).

^{ 10 }found a significant reduction in test time with the implementation of SITA in SWAP. Using our relatively large data set, we compared the test duration in two ways: (1) GON versus non-GON within test type, and (2) SWAP-FT versus SWAP-SITA, using dependent

*t*-tests assuming unequal variance.

*P*> 0.05). The similarity in ROC curve shape between algorithms is seen in Figure 1 . Since PDP <1% offers greater stringency than PDP <5%, it was used in addition to PSD in subsequent analyses.

*P*> 0.05, McNemar test). There was no significant difference using the PDP <1% parameter at 80% or 90% specificity (

*P*> 0.05); however, at 95% specificity, SWAP-FT was more sensitive than SWAP-SITA (

*P*= 0.032).

*P*< 5%) produced comparable results: 36.7% and 39.9% sensitivity, and 95.2% and 93.4% specificity, respectively. Abnormality set by machine-derived PSD at 99.5% (

*P*< 0.5%) for SWAP-FT produced a sensitivity of 21.0% and a specificity of 100%. For SWAP-SITA, the ROC-derived PSD set at 95% specificity produced results comparable to those of the machine-derived PSD at 99.5% (

*P*< 0.5%): 32.9% and 36.7% sensitivity, and 95.5% and 91.3% specificity, respectively. Abnormality set by machine-derived PSD at 95% (

*P*< 5%) for SWAP-SITA produced a sensitivity of 60.5% and a specificity of 70.2%.

*n*= 575). All global indices (mean ± SD) of visual field severity correlated significantly (PSD: ρ = 0.79; PDP <1%: ρ = 0.66; MD: ρ = 0.89, all

*P*< 0.001). SWAP-SITA PSD (4.01 ± 2.20) was significantly higher (

*P*= 0.011) than the SWAP-FT PSD (3.85 ± 1.85; Table 5 ). SWAP-SITA PDP <1% (5.06 ± 7.17) was also significantly higher (

*P*< 0.001) than SWAP-FT (2.96 ± 5.50; Table 5 ). There was no significant difference (

*P*= 0.209) in average MD between SWAP-FT (−5.72 ± 5.44) and SWAP-SITA (−5.83 ± 5.10) visual fields test (Table 5) .

_{diff}= 0.30; 95% confidence interval [CI]: 0.17–0.43) was significantly different from 0 (

*P*< 0.001), indicating the presence of a fixed bias such that SWAP-SITA PSD measures are consistently worse than SWAP-FT PSD measures in the GON participants (Fig. 3A) . A regression to the Bland-Altman plot was also significant (

*P*< 0.001) and indicated the presence of a proportional bias: PSD tended to be worse with SWAP-SITA than SWAP-FT at higher PSD measures in the GON participants.

_{diff}= 0.00; 95% CI: −0.09–0.10;

*P*= 0.757), indicating that no fixed bias was present (Fig. 3B) . However, a regression to the Bland-Altman plot was significant (

*P*= 0.030), indicating the existence of a proportional bias; PSD tended to be worse with SWAP-SITA than SWAP-FT at higher PSD measures in participants without GON.

_{diff}= 1.41; 95% CI: 1.05–1.76;

*P*< 0.001).

*P*< 0.001), as well as in those without GON (ρ = 0.63,

*P*< 0.001). The PDP <1% parameter was also significantly correlated in the GON (ρ = 0.80,

*P*< 0.001) and non-GON (ρ = 0.38,

*P*< 0.001) participants (correlation plots not shown).

*P*< 0.001); no significant difference in MD was found (

*P*= 0.161; Table 5 ). The non-GON group had a significantly larger PDP <1% with SWAP-SITA than with SWAP-FT (

*P*< 0.001) and a significantly larger MD with SWAP-FT than SWAP-SITA (

*P*= 0.002); no significant difference was found in PSD (

*P*= 0.757; Table 5 ).

*n*= 286). The ROC-derived PSD and PDP <1% parameters set at 95% specificity produced similar patterns of overlap (Figs. 4A 4B) . Abnormality overlap was 29% (PSD) and 24% (PDP <1%), and normality overlap was 59% (PSD) and 61% (PDP <1%). The overall normal and abnormal overlap in visual field outcome of the GON fields was 88% using ROC-derived PSD and 85% ROC-derived PDP <1%. Agreement was substantial using both parameters (ROC-PSD: κ = 0.75 ± 0.04; ROC-PDP <1%: κ = 0.65 ± 0.05).

*P*< 5%), agreement was moderate (κ = 0.52 ± 0.05): there were 38% overlapping abnormal fields and 37% overlapping normal fields for a 75% total overlap in the outcome of GON fields between algorithms; for the nonoverlapping GON fields, there were 23% that were abnormal by SWAP-SITA only, and 2% that were abnormal by SWAP-FT only (Fig. 3C) . With the machine-derived ROC at 99.5% (

*P*< 0.5%), agreement was substantial (κ = 0.62 ± 0.05). There were 20% overlapping abnormal fields and 64% overlapping normal fields for a total 84% overlap in GON field outcome between both algorithms. For the nonoverlapping GON fields, there were 15% that were abnormal by SWAP-SITA only and less than 1% abnormal by SWAP-FT only (Fig. 4D) .

*t*-tests assuming unequal variance: (1) test duration between the GON and non-GON study groups within SWAP-FT or SWAP-SITA and (2) test duration between algorithms. For the first set of comparisons within algorithm type, we found that the GON group required more time than the non-GON group did when tested with SWAP-SITA (GON: 4:15 ± 00:52 [min:sec] versus non-GON: 3:50 ± 0:41,

*P*< 0.001). No difference was found with SWAP-FT (GON: 11:39 ± 1:53 versus non-GON: 11:44 ± 1:25,

*P*= 0.481). In the second set of comparisons within study group, we found that all participants needed significantly more time to perform SWAP-FT than SWAP-SITA (

*P*< 0.001).

*P*< 5%), a more clinically relevant parameter, had a sensitivity similar to the ROC-derived PSD at 95% specificity for SWAP-FT; whereas the sensitivity for SWAP-SITA was lower. Using the machine-derived PSD at 95%, however, SWAP-SITA had a higher sensitivity (61%) than did SWAP-FT (40%). The sensitivity of the machine-derived PSD at 95% (

*P*< 5%) for SWAP-FT was similar to what SWAP-SITA would provide at a machine-derived PSD of 99.5% (

*P*< 0.5%). These variations are probably due in part to differences in the normative databases for the two SWAP tests, even though similar criteria were used to select participants for each respective normative database. This is a limitation in the study, but is an important comparison, as clinically relevant parameters for each test were derived with different normative databases.

^{ 20 }

^{ 21 }

^{ 22 }However, some misclassification is likely to occur, so that some of the GON classifications may be false-negatives or -positives, not necessarily indicative of glaucomatous damage.

^{ 23 }These factors contributed to the relatively low sensitivities reported herein. It is important to note that the main purpose of this widely used gold standard is to equate visual field tests for specificity to allow a fair comparison and not to draw conclusions about the individual test’s efficacy, since the true state of the eye may not be known without longitudinal validation.

^{ 24 }and to compare SWAP-FT to SAP-SITA.

^{ 25 }These studies found no significant differences in using SWAP over SAP-SITA for separating groups of eyes with GON from those without. Specifically, in a study by Tafreshi et al.

^{ 24 }of 174 GON and 164 non-GON eyes, the diagnostic accuracy of SAP-SITA was similar to that of SWAP-SITA (AUROC 0.692 for SAP-SITA; 0.693 for SWAP-SITA). In a study by Sample et al.

^{ 25 }of 111 GON and 51 non-GON participants, the diagnostic accuracy of SAP-SITA was similar to that of SWAP-FT (AUROC 0.713 for SAP-SITA; 0.733 for SWAP-SITA).

^{ 25 }Similarly, in the present study of 286 GON and 289 non-GON participants, no difference was found between the diagnostic accuracy of SAP SITA and SWAP SITA (data not shown). However, because SWAP tests an aspect of visual function that SAP does not, it provides additional information about the status of the visual system for a given individual. In addition, when a defect is present on both tests, it falls within the same retinal area. Tafreshi et al.

^{ 24 }found that confirmation of a SAP-SITA defect with either another SAP-SITA or with a SWAP-SITA offers a similar combination of sensitivity and specificity.

^{ 26 }examined the number of points triggered at <5% on the pattern deviation plot and found no significant difference. In our study, a statistically significant difference was found in the number of points triggered at <1% on the PDP; but again, the 3-point difference may not be clinically significant.

**M. Ng**, None;

**L. Racette**, None;

**J.P. Pascual**, None;

**J.M. Liebmann**, Carl Zeiss Meditec, Inc. (F);

**C.A. Girkin**, Carl Zeiss Meditec, Inc. (F), Heidelberg Engineering (F), OptoVue (F);

**S.L. Lovell**, None;

**L.M. Zangwill**, Allergan, Inc. (F), Carl Zeiss Meditec, Inc. (F), Heidelberg Engineering (F), OptoVue (F);

**R.N. Weinreb**, Carl Zeiss Meditec, Inc. (F, C), Heidelberg Engineering (F);

**P.A. Sample**, Carl Zeiss Meditec, Inc. (F), Haag-Streit (F), Welch-Allyn (F)

GON (n = 286) | Non-GON (n = 289) | |
---|---|---|

Mean age ± SD (y) | 65.6 ± 12.9 | 63.5 ± 12.2 |

Median age (y) | 67.2 | 64.8 |

Age range (y) | 20.5–92.7 | 21.4–88.6 |

Sex (% male) | 44.1 | 38.1 |

Eye (% OD) | 53.8 | 53.6 |

SAP-SITA MD (mean ± SD) | −4.49 ± 6.31 | −1.01 ± 1.87 |

SAP-SITA MD range | −31.46–2.44 | −12.41–2.04 |

SAP-SITA PSD (mean ± SD) | 4.35 ± 3.73 | 2.06 ± 1.05 |

SAP-SITA PSD range | 1.08–17.00 | 1.05–6.72 |

Parameter | SWAP-FT (95% Confidence Interval) | SWAP-SITA (95% Confidence Interval) | P-value |
---|---|---|---|

PSD | 0.715 (0.673–0.758) | 0.722 (0.681–0.767) | 0.323 |

Points triggered on PDP <1% | 0.695 (0.654–0.735) | 0.712 (0.670–0.753) | 0.180 |

Points triggered on PDP <5% | 0.709 (0.667–0.752) | 0.689 (0.646–0.731) | 0.115 |

Points triggered on TDP <1% | 0.691 (0.649–0.733) | 0.680 (0.637–0.723) | 0.196 |

Points triggered on TDP <5% | 0.640 (0.596–0.685) | 0.645 (0.601–0.690) | 0.335 |

MD | 0.634 (0.589–0.680) | 0.658 (0.614–0.703) | 0.012 |

**Figure 1.**

**Figure 1.**

Parameter | Test | 80% Specificity | 90% Specificity | 95% Specificity | ||||||
---|---|---|---|---|---|---|---|---|---|---|

Sensitivity | Criterion | Sensitivity | Criterion | Sensitivity | Criterion | |||||

PSD | SWAP-FT | 53.2 (80.3) | 3.83 | 41.6 (90.3) | 4.31 | 36.7 (95.2) | 4.79 | |||

SWAP-SITA | 52.8 (80.3) | 3.91 | 44.1 (90.3) | 4.37 | 32.9 (95.2) | 5.24 | ||||

Points triggered on PDP <1% | SWAP-FT | 53.5 (79.58) | 2 | 45.45 (88.6) | 3 | 34.6 (95.5) | 5 | |||

SWAP-SITA | 55.9 (78.2) | 4 | 40.9 (90.0) | 7 | 28.7 (95.5) | 10 | ||||

Points triggered on PDP <5% | SWAP-FT | 56.3 (78.9) | 6 | 42.3 (90.0) | 9 | 32.9 (95.2) | 12 | |||

SWAP-SITA | 45.8 (78.6) | 12 | 36.36 (88.9) | 15 | 28.0 (94.8) | 19 | ||||

Points triggered on TDP <1% | SWAP-FT | 50.7 (78.6) | 7 | 38.8 (89.3) | 13 | 21.0 (95.2) | 25 | |||

SWAP-SITA | 48.6 (78.2) | 9 | 32.2 (90.0) | 19 | 20.6 (95.2) | 31 | ||||

Points triggered on TDP <5% | SWAP-FT | 42.0 (79.6) | 25 | 28.3 (90.0) | 35 | 17.5 (95.2) | 43 | |||

SWAP-SITA | 40.2 (78.2) | 31 | 27.6 (90.0) | 41 | 13.6 (95.5) | 48 | ||||

MD | SWAP-FT | 43.7 (80.3) | −7.36 | 32.5 (90.3) | −9.02 | 24.5 (95.2) | −10.84 | |||

SWAP-SITA | 45.8 (80.3) | −7.08 | 33.9 (90.3) | −8.82 | 25.9 (95.2) | −10.66 |

Classification | ROC PSD 95% Specificity | Machine PSD 95% (P < 5%) | Machine PSD 99.5% (P < 0.5%) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Abnormal | Normal | Abnormal | Normal | Abnormal | Normal | ||||||||||

SWAP-FT | |||||||||||||||

GON | 105 | 181 | 286 | 114 | 172 | 286 | 60 | 226 | 286 | ||||||

36.7^{*} | 63.3 | 39.9^{*} | 60.1 | 21.0^{*} | 79.0 | ||||||||||

Non-GON | 14 | 275 | 289 | 19 | 270 | 289 | 0 | 289 | 289 | ||||||

4.8 | 95.2^{, †} | 6.6 | 93.4^{, †} | 0.0 | 100.0^{, †} | ||||||||||

SWAP-SITA | |||||||||||||||

GON | 94 | 192 | 286 | 173 | 113 | 286 | 105 | 181 | 286 | ||||||

32.9^{*} | 67.1 | 60.5^{*} | 39.5 | 36.7^{*} | 63.3 | ||||||||||

Non-GON | 13 | 276 | 289 | 86 | 203 | 289 | 25 | 264 | 289 | ||||||

4.5 | 95.2^{, †} | 29.8 | 70.2^{, †} | 8.7 | 91.3^{, †} |

**Figure 2.**

**Figure 2.**

Range | Median | Mean ± SD | P-value | |
---|---|---|---|---|

All participants (n = 575) | ||||

PSD | ||||

SWAP-FT | 1.35–13.56 | 3.28 | 3.85 ± 1.85 | 0.011 |

SWAP-SITA | 1.41–13.42 | 3.23 | 4.01 ± 2.20 | |

PDP <1% | ||||

SWAP-FT | 0–33 | 1 | 2.96 ± 5.50 | <0.001 |

SWAP-SITA | 0–37 | 2 | 5.06 ± 7.17 | |

MD | ||||

SWAP-FT | −25.22–6.10 | −5.20 | −5.83 ± 5.10 | 0.209 |

SWAP-SITA | −28.04–5.25 | −4.87 | −5.72 ± 5.44 | |

GON (n = 286) | ||||

PSD | ||||

SWAP-FT | 1.35–13.56 | 3.92 | 4.61 ± 2.22 | <0.001 |

SWAP-SITA | 1.48–13.42 | 4.03 | 4.91 ± 2.67 | |

PDP <1% | ||||

SWAP-FT | 0–33 | 2 | 4.99 ± 7.01 | <0.001 |

SWAP-SITA | 0–37 | 4 | 7.80 ± 8.73 | |

MD | ||||

SWAP-FT | −25.22–5.80 | −6.48 | −7.17 ± 5.80 | 0.161 |

SWAP-SITA | −28.04–5.25 | −6.21 | −7.40 ± 6.28 | |

Non-GON (n = 289) | ||||

PSD | ||||

SWAP-FT | 1.55–6.37 | 2.98 | 3.11 ± 0.91 | 0.7567 |

SWAP-SITA | 1.41–7.20 | 2.89 | 3.11 ± 1.01 | |

PDP <1% | ||||

SWAP-FT | 0–14 | 0 | 0.95 ± 1.88 | <0.001 |

SWAP-SITA | 0–24 | 1 | 2.36 ± 3.50 | |

MD | ||||

SWAP-FT | −21.52–6.10 | −4.31 | −4.510 ± 3.889 | 0.002 |

SWAP-SITA | −18.92–4.43 | −3.66 | −4.072 ± 3.801 |

**Figure 3.**

**Figure 3.**

**Figure 4.**

**Figure 4.**