Inter-clinician variance in glaucoma diagnostic decisions Lisa Collins and Adrian R. Hill

1. Inter-clinician variance in glaucoma diagnostic decisions Lisa Collins and Adrian R. Hill Gloucestershire Eye Service Cheltenham General Hospital, Cheltenham GL53 7AN & Heriot-Watt University, Edinburgh, UK

2. Acknowledgements Thanks are due to: Prof. Andrew McNaught, Mr James Nairne, Prof. Richard Wormald, Prof. Rupert Bourne, Mr Ted Garway-Heath, Dr Paul Spry, Prof. Peter Aspinall, Prof. Baljean Dhillon and Mrs Frances Reilly Corresponding author’s e-mail: lisa.collins@glos.nhs.uk 1. INTRODUCTION The diagnosis of open-angle glaucoma is multi-factorial and there are many instances where clinical uncertainty exists; particularly in the early stages of the disease. In order to explore the clinical criteria used by ophthalmologists in making diagnostic decisions for glaucoma, we observed real-life patterns of clinical behaviour, rather than self-reported criteria, across a range of cases. 2. AIMS To examine the nature of individual similarities and differences in the clinical criteria used by a group of expert ophthalmologists when diagnosing glaucoma.

3. 3. METHOD • The sample comprised 36 participants, age range 51-91 yrs (mean 70.2 yrs), 13 males (mean 70.9 yrs) and 23 females (mean 69.9 yrs). The IOP range was 11-33 mmHg and the anterior angle grade II-III (Van Herick). • 32 participants had been newly referred from primary-care optometrists for suspected glaucoma, 4 participants were recruited as known “normal”. • 5 consultant glaucoma specialists from different UK hospitals, examined each of the 36 participants in a four hour session. • Real-life clinical information was presented for each participant. • All participants underwent pupil dilation and were examined by each doctor (ophthalmologist) in an order defined by a Latin Square experimental design. • Doctors did not confer with each other or with the participants. • Doctors recorded the presence of observed features of the optic nerve head, the visual field status, and their diagnostic and treatment decisions.

4. 4. RESULTS Only right eye data are presented; similar findings were obtained for the left eye. Table 1: An example cross-tabulation of diagnostic decisions All pairs of doctors gave Kappa statistics of agreement for the 3 diagnostic categories of K = 0.374 to 0.684 (all sig. at p ≤ 0.01). Only 2 pairs of doctors showed reasonable agreement at K > 0.60 and there were instances where eyes were classified as “normal” by one doctor and “glaucoma” by another.

5. Table 2 (next slide) This table shows the significant variables at p ≤ 0.01 (df = 2) for each doctor which provided effective discrimination for the diagnostic categories of “glaucoma”, “suspected glaucoma” and “normal”. Different sub-sets of variables or indicators (present v absent) were used by each of the doctors, and not all used IOP or visual field status (“normal” v “abnormal”) as significant indicators for their diagnosis. Three variables (i.e. discriminators) were common to all doctors’ decisions; these were: C:D ratio estimate, Failure of the ISNT rule(present v absent), Generalised assessment of the optic nerve head (“normal” v “abnormal”). Additionally, the following variables were significant to at least four doctors: notching of the neuro-retinal rim, flame haemorrhage, and visual fields’ status.

6. Table 3: Summary Kruskal-Wallis one-way ANOVAs by doctor Table 2: Summary Kruskal-Wallis one-way ANOVAs by doctor Chi square significant variables shown in red at p ≤ 0.01 (df = 2) (p values rounded down)

7. Non-parametric clustering analyses (Latent Class analysis) were performed for each doctor to determine the Bayesian probabilities associated with the principal discriminating variables associated with diagnostic decisions. Data for Doctor 2 gave a highly discriminating 3-cluster model consistent with the three diagnostic decision categories of “normal”, “suspect” and “glaucoma” (Fig. 1). Clinical decisions for four of the doctors gave different 2-cluster models which distinguished decisions for “normal” (cluster 1) from a combined cluster of “glaucoma and suspect glaucoma” (cluster 2). See example in Fig. 2. Fig. 1 and Fig. 2 on the next two sides Bayesian probability (P) profiles comparing a 3-cluster model for Doctor 2 and a 2-cluster model for Doctor 5 For ease of comparison, both sets of probability profiles in Figures 1 and 2 are presented for the same eight variables, although four other variables were significant for Doctor 2 and two other variables significant for Doctor 5 (see Table 2).

8. Fig. 1: Graph ordinates are Bayesian conditional probabilities: P = p (IOP high | cluster group membership) P = p (CD ratio high | cluster group membership) P = p (other variable features absent | cluster group membership) The wider the separation of the profiles, the greater the discrimination. P Doctor 2 “Normal” P=0.89 “Suspect” P=0.91 “Glaucoma” P=0.97

9. Fig. 2: Graph ordinates are Bayesian conditional probabilities: P = p (IOP high | cluster group membership) P = p (CD ratio high | cluster group membership) P = p (other variable features absent | cluster group membership) The wider the separation of the profiles, the greater the discrimination. P Doctor 5 “Normal” P=0.74 “Glaucoma” P=0.54 and “Suspect” P=0.44

10. 5. CONCLUSIONS • This study has demonstrated that glaucoma specialist consultant ophthalmologists use different sub-sets of clinical variables for diagnosing glaucoma. • Only three significant optic disc diagnostic discriminators were • common to all five ophthalmologists. • The greater the number of variables used as significant • discriminators for diagnosis, the more complex are the doctor’s • judgements. • Inter-correlations in Latent Class Analysis showed that either a • two or three cluster model optimally described the diagnostic • decisions for each doctor. • These findings highlight a need for identifying common diagnostic criteria to improve the: • detection and referral of patients with suspected • glaucoma by primary care clinicians, • consistency of patient management between • ophthalmologists and allied clinicians, • clinical training and decision-making for junior • ophthalmologists and allied clinicians.