Dear Editor:

We read the paper on non-invasive intracranial pressure determination by Zhang et al(1) with great interest and hope. We fully agree that the search for non-invasive intracranial pressure (ICP) evaluations is of high importance and should be continued. The Bland-Altman plot showing the difference between predicted and intracranially measured pressure looks very impressive. There are, however, still a few points and limits we would like to address concerning the anatomy of the optic nerve, the optic canal, and the basic concept the authors used.

Cerebrospinal fluid (CSF) from the intracranial subarachnoid spaces and the subarachnoid space of the optic nerve (SAS -ON) communicate via the optic canal. Using three-dimensional reconstruction of the optic canal in normal tension glaucoma (NTG) patients, this was found to be narrower than in an age-related cohort of normals,(2) thus questioning the patency of the CSF pathway between the pituitary cistern and the SAS-ON. Further, optic canal dimensions in a normal population are quite variable amongst individuals, and even between orbits within the same individual.(3) These facts largely influence the results the authors present. Further, studies in patients with NTG and patients with elevated ICP (such as patients with idiopathic intracranial hypertension) were shown to have developed an optic nerve sheath compartment syndrome. In such cases, the CSF dynamics between the intracranial CSF and the CSF in the SAS-ON differ significantly. This was proven by applying computer-assisted cisternography, diffusion-weighted MRI sequences and gradients of biochemical CSF proteins.(4-6)

In Zhang’s study, CSF pressures (CSFP) larger than 30 mm Hg were excluded. The majority of the patients in whom CSFP needs to be known are those with markedly elevated ICP. Therefore, this study should have been expanded to this group. As the compliance to pressure in the optic nerve sheath is most likely notlinear, this adds a further difficulty to measuring methods.

The authors found that CSF pressure was more significantly correlated with the area of the optic nerve sheath subarachnoid space than with the optic nerve sheath diameter. In an idealized fashion the optic nerve subarachnoid space resembles an annulus with a fractal circumference. The area A itself is not homogeneously filled with CSF but is interspersed with an interindividually variable amount of space-occupying trabeculae. The diameter in an annulus – compared to a circle - is represented twice in the formula for the area. Therefore, the diameter is involved in the SAS area as well. Its therefore difficult to understand why using the area instead of the diameter should render more accurate calculations, especially when the diameter is represented twice in the formula:
A total = π/4 (D2 -d2 ) - A trabecula = π (R2 - r2) - A trabecula

Our prior experience with formulae utilizing biophysical data such as body mass index (BMI), diastolic blood pressure (DBP), and age – all factors known to correlate to CSF pressure to variable degrees – were that they are quite poor at predicting actual CSF pressure.(7) The study excluded the analysis of formulae that utilized anatomic measures of MRI-determined optic nerve sheath width. However, as Zhang’s study is utilizing a similar method except with ultrasonographic determination of nerve sheath width, it would be assumed that the strongest predictor of CSF pressure would be the optic nerve sheath width. This subject has been repeatedly studied, and it does not seem that ultrasonographic evaluation of the optic nerve sheath diameter has ever been able to determine accurate estimates of true CSF pressures, but mostly the determination of normal versus elevated.(8-10)

Lastly, ultrasonography is a subjective method that depends on the user’s ability and reliability. In a study comparing optic nerve sheath diameter measurement between computed tomography, magnetic resonance imaging and ultrasound there was a good comparability between computed tomography and magnetic resonance imaging while the comparability between ultrasound and computed tomography or magnetic resonance tomography seems to be less reliable.(11)

We highly encourage efforts to expand our knowledge of the interrelation of cerebrospinal fluid and ophthalmic disease. But our desire to increase accessibility to study this by using formulae (in which the relationship between the variables is not understood) as opposed to current gold standard measurements of CSF pressure determination should not lead us to the path of using doubtful formulae that will confuse our body of literature.
 

References:
1) Non-invasive intracranial pressure estimation using ultrasonographic measurement of area of optic nerve subarachnoid space.Zhang Y, Cao K, Pang R, Wang N, Qu X, Kang J, Wang N, Liu H. Br J Ophthalmol. 2022 Aug 24:bjophthalmol-2022-321065.
2) The Optic Canal: A Bottleneck for Cerebrospinal Fluid Dynamics in Normal-Tension Glaucoma?Pircher A, Montali M, Berberat J, Remonda L, Killer HE. Front Neurol. 2017 Feb 23;8:47
3) Evaluation of optic canal anatomy and symmetry. Zhang X, Lee Y, Olson D, Fleischman D. BMJ Open Ophthalmology 2019;4:e000302
4) Cerebrospinal fluid dynamics between the intracranial and the subarachnoid space of the optic nerve. Is it always bidirectional? Killer HE, Jaggi GP, Flammer J, Miller NR, Huber AR, Mironov A. Brain. 2007 Feb;130(Pt 2):514-20
5) Case Report: Cerebrospinal Fluid Dynamics in the Optic Nerve Subarachnoid Space and the Brain Applying Diffusion Weighted MRI in Patients With Idiopathic Intracranial Hypertension-A Pilot Study. Berberat J, Pircher A, Gruber P, Lovblad KO, Remonda L, Killer HE. Front Neurol. 2022 Apr 15;13:862808
6) The optic nerve: a new window into cerebrospinal fluid composition? Killer HE, Jaggi GP, Flammer J, Miller NR, Huber AR. Brain. 2006 Apr;129(Pt 4):1027-30
7) Analysis of cerebrospinal fluid pressure estimation using formulae derived from clinical data. Fleischman D, Bicket AK, Stinnett SS, Berdahl JP, Jonas JB, Wang N, Fautsch MP, Allingham RR. Invest Ophthalmol Vis Sci. 2016;57:5625-5630.
8) Effect of intracranial pressure on the diameter of the optic nerve sheath. Watanabe A, Kinouchi H, Horiokoshi T, Uchida M, Ishigame K. J Neurosurg 2008 109(2): 255-8.
9) Optic nerve ultrasound for the detection of raised intracranial pressure. Rajajee V, Vanaman M, Fletcher JJ, Jacobs TL. Neurocrit Care 2011;15(3):506-15.
10) Sonographic assessment of the optic nerve sheath in idiopathic intracranial hypertension. Bauerle J, Nedelmann M. J Neurol 2011; 258(11):2014-9.
11) Measurement of Optic Nerve Sheath Diameter: Differences between Methods? A Pilot Study. Giger-Tobler C, Eisenack J, Holzmann D, Pangalu A, Sturm V, Killer HE, Landau K, Jaggi GP. Klin Monbl Augenheilkd. 2015 Apr;232(4):467-70

Dear Editor.

We read with interest the manuscript published by Sakamoto et al, on behalf of the Japanese Retina and Vitreous Society, titled: Increased incidence of endophthalmitis after vitrectomy relative to face mask-wearing during COVID-19 pandemic”.[1] In this manuscript, the authors discuss their results after comparing the total prevalence of infectious endophthalmitis among patients that underwent ocular surgery, before and after the peak of the SARS-CoV-2 pandemic in Japan.[1] The authors should be commended due to the level of complexity and significant effort needed to coordinate several centers simultaneously, as well as the detailed description provided in the manuscript regarding the clinical presentation, microbiological results, and outcomes of all cases. Interestingly and despite the low rate of positive vitreous cultures, the authors were able to isolate oral bacteria among several of the cases that developed endophthalmitis during the pandemic, including one caused by Staphylococcus lugdunensis; a pathogen typically hard to eliminate with mechanical washing bacteria, because it accumulates behind the auricle.[1] With all this evidence, the authors provided a compelling argument regarding the inappropriate wearing of face masks could increase the risk of postoperative endophthalmitis. Nevertheless, we believe that there are a few important considerations that the authors may need to address before making such an assumption.
As a start, we can mention a few methodological irregularities that are hallmarks of any retrospective study and thus unavoidable. However, some may carry a significant relevance to the outcome such as the lack of rigor in the definition of postoperative endophthalmitis in the inclusion/exclusion criteria. In their manuscript, Sakamoto et al considered postoperative endophthalmitis, any intraocular infection that developed within 42 days after surgery. Although we agree with this definition, we must clarify that this definition refers to the maximum time elapsed between the invasion of the intraocular space by the offending bacteria, which is during surgery, and the development of the first clinical symptoms. Which, depending on the bacteria's virulence, could be up to 6 weeks. The latest optical coherence tomography and ultrasound biomicroscopy evidence have shown that sclerotomies after a pars plana vitrectomy seal between 8 and 15 days after surgery, even after a sutureless approach.[2 3] Therefore, it is highly unlikely that the source of infection originated after this time, as the authors seem to imply. Moreover, although the authors’ argument that face masking may increase the contamination of the periocular area makes sense, laboratory evidence has shown that there is no difference between bacterial dispersion toward the ocular surface when comparing different types of masks and masking techniques (tape in the superior border of the mask and no tape).[4] Nevertheless, we do agree that wearing a face mask before or during surgery may induce ocular surface changes such as dry eye disease and subclinical infectious keratitis, which might hypothetically increase the risk of endophthalmitis. If we consider the possible exhaustion of the surgical team (human error), scarcity of surgical disinfectants, and other factors that occurred during the peak of the SARS-CoV-2 pandemic, this should place the source of the infection during surgery and not after it. The high prevalence of Streptococcal endophthalmitis in the SARS-CoV-2 mask period (as defined by the authors), supports indeed the notion that contamination may come from the oral flora, very similar to the reports of post intravitreal injection endophthalmitis. However, bacteria such as Streptococcus mitis and Streptococcus salivarius are usually described as low-virulence or opportunistic pathogens. Therefore, the onset time of the clinical symptoms of endophthalmitis, information not described in the manuscript, could have helped the reader to infer if the contamination occurred during surgery or during the postoperative time.
Finally, the result observed regarding the prevalence of postoperative endophthalmitis in the only-phacoemulsification group, is not consistent with the main hypothesis suggested by the authors, and points in the opposite direction. If how the patient uses the mask during the postoperative period is indeed a determinant factor in endophthalmitis development and, considering that the prevalence of endophthalmitis after vitrectomy is usually lower in comparison to other surgical procedures [5]; we should have expected a proportional increase in the prevalence in all three groups. The latter might be the result of a lack of a sample calculation and therefore an error type 1, which should have been mentioned in the limitation section. A throughout analysis of the surgical circumstance per group is also lacking. Consequently, it is not clear at this time if other significant risk factors (trauma, intraocular foreign body, posterior capsule rupture) for endophthalmitis were present or not during surgery. A multivariate analysis, controlling for several other risk factors should be enough to shed light on this matter.
We congratulate Sakamoto et al for this outstanding contribution. We will look forward to their reply.

References:
1. Sakamoto T, Terasaki H, Yamashita T, et al. Increased incidence of endophthalmitis after vitrectomy relative to face mask wearing during COVID-19 pandemic. Br J Ophthalmol 2022 doi: 10.1136/bjophthalmol-2022-321357[published Online First: Epub Date]|.
2. Keshavamurthy R, Venkatesh P, Garg S. Ultrasound biomicroscopy findings of 25 G Transconjuctival Sutureless (TSV) and conventional (20G) pars plana sclerotomy in the same patient. BMC Ophthalmol 2006;6:7 doi: 10.1186/1471-2415-6-7[published Online First: Epub Date]|.
3. Sawada T, Kakinoki M, Sawada O, Kawamura H, Ohji M. Closure of sclerotomies after 25- and 23-gauge transconjunctival sutureless pars plana vitrectomy evaluated by optical coherence tomography. Ophthalmic Res 2011;45(3):122-8 doi: 10.1159/000318875[published Online First: Epub Date]|.
4. Angaramo S, Law JC, Maris AS, et al. Potential impact of oral flora dispersal on patients wearing face masks when undergoing ophthalmologic procedures. BMJ Open Ophthalmol 2021;6(1):e000804 doi: 10.1136/bmjophth-2021-000804[published Online First: Epub Date]|.
5. AlBloushi B, Mura M, Khandekar R, et al. Endophthalmitis Post Pars Plana Vitrectomy Surgery: Incidence, Organisms' Profile, and Management Outcome in a Tertiary Eye Hospital in Saudi Arabia. Middle East Afr J Ophthalmol 2021;28(1):1-5 doi: 10.4103/meajo.MEAJO_424_20[published Online First: Epub Date]|.

We thank Dr. Carkeet for his comments on our paper,1 which is now over 15 years old.

As discussed with Dr. Carkeet in personal correspondence recently, the discrepancy between his results and ours occurred because we simplified the 1 exam/year and 3 exam/years conditions by linearly scaling the outputs from the 2 exam/year condition. We repeated the simulations under the conditions Dr. Carkeet has outlined, and we agree with the result. The simulations yield approximately the same time required to detect the various rates of change for 2 exams per year, and slightly different values for 1 and 3 exams per year. He has pointed out discrepancies in the 1 and 3 exam per year conditions which appear large only in extreme conditions and are not realistic in clinical practice, for example, detecting a -0.25 dB/y change with high variability, where we estimated 30 years and Dr. Carkeet estimated 18 years.

In the final analysis simulations are only simulations that can be made with conditions assumed to reflect reality. The precision with which these estimates is made can be low. Ultimately, the message in our paper was that it takes a long time to detect a small amount of change if visual field results are variable and the testing frequency is low.

Our paper has been used to inform guidelines from various organizations and is based on one of the key messages of the paper, i.e., that ruling out fast progression (worse -2 dB/y or worse) requires 6 visual fields in the first 2 years if the exams are equally spaced. None of the other testing frequencies/amounts of change in our paper have been incorporated into the guidelines. Since our paper was published, others have built upon our work and performed simulations with more nuanced assumptions,2 3 such as clustering exam frequency and spacing. Nonetheless, this key guideline, that is requires 6 examinations in the first 2 years to rule out fast progression remains true. Whether the real number is 5 or 6 or 7 is immaterial. Given that in many jurisdictions, patients diagnosed with glaucoma receive less than 1 exam per year,4 5 arguing about subtleties in simulation conditions that impact marginal conditions seems trivial.

References

1. Chauhan BC, Garway-Heath DF, Goni FJ, et al. Practical recommendations for measuring rates of visual field change in glaucoma. Br J Ophthalmol 2008;92(4):569-73. doi: 10.1136/bjo.2007.135012 [published Online First: 20080122]
2. Crabb DP, Garway-Heath DF. Intervals between visual field tests when monitoring the glaucomatous patient: wait-and-see approach. Invest Ophthalmol Vis Sci 2012;53(6):2770-6. doi: 10.1167/iovs.12-9476 [published Online First: 20120517]
3. Wu Z, Saunders LJ, Daga FB, et al. Frequency of Testing to Detect Visual Field Progression Derived Using a Longitudinal Cohort of Glaucoma Patients. Ophthalmology 2017;124(6):786-92. doi: 10.1016/j.ophtha.2017.01.027 [published Online First: 20170306]
4. Fung SS, Lemer C, Russell RA, et al. Are practical recommendations practiced? A national multi-centre cross-sectional study on frequency of visual field testing in glaucoma. Br J Ophthalmol 2013;97(7):843-7. doi: 10.1136/bjophthalmol-2012-302903 [published Online First: 20130423]
5. Stagg BC, Stein JD, Medeiros FA, et al. The Frequency of Visual Field Testing in a US Nationwide Cohort of Individuals with Open-Angle Glaucoma. Ophthalmol Glaucoma 2022;5(6):587-93. doi: 10.1016/j.ogla.2022.05.002 [published Online First: 20220520]

Chauhan and co-workers [1] have provided Table 1, showing times taken to detect significant field progression with 80% power, based on a number of modelling parameters: frequency of examinations, rate of field progression, intrasession variability of field assessment. They have also provided Table 2 showing the number of annual eye examinations required to detect different total visual field changes, for different time periods, and for moderate variability. I have checked the calculations of Chauhan and co-workers, using Monte Carlo modelling, assuming a one-tailed significance value of 0.025. Of the 36 outcome values in Table 1, 33 are incorrect. Of the 12 outcome values in Table 2, 11 are incorrect.

Chauhan and co-workers have made 2 main errors in their calculations for Table 1. The first is in applying their estimates of power. The curves shown in Figure 2 (statistical power plotted against number of field examinations) are appropriate for the case of 2 field examinations per year, but Chauhan and co-workers appear to have incorrectly also used them for the cases of 1 examination per year and 3 examinations per year. Separate sets of curves should have been calculated for those conditions. The effect on Table 1 is that the time taken to detect a field change is incorrectly reported as being inversely proportional to the number of examinations per year. This anomalous relationship was commented on by Albert Alm in his 2008 Rapid Response, “Is a field every 4 month a significant improvement over a field every 6 months?”. He wrote, “… increasing the duration is much more efficient than increasing the frequency of examinations. Thus, 5 examinations in 1.7 years will not be able to detect the same slope as 5 examinations in 5 years!” My calculations for the values in Table 1 show that increasing examination frequency, from once per year to 3 times per year, reduces time to detect a field change to between 71% and 50%, not 33% as implied by the original paper.

The second error made by Chauhan and co-workers is in converting from the number of fields (S) required to the duration required (D) for a given annual frequency of testing (f). From equation 5 in another paper,[2] this relationship is: D=(S-1)/f. Chauhan and co-workers appear to have used D=S/f.
In Table 1, three values are correct. One of those correct values, that it takes 2 years to detect fast progression (MD rate =-2dB/Year) with moderate field variability (SD=1dB) with 3 examinations/year, seems to be the basis for the paper’s recommendation that “six visual field examinations should be performed in the first 2 years.” This is slightly incorrect. A testing frequency of 3 examinations per year over 2 years will yield 7 examinations.

Table 2 contains significant errors. For example, its results imply that detecting a total field change of 1 dB will require between 15 and 21 measurements with SD= 1dB, depending on the number of years measurements are spread over. My calculations give an estimate of 95 total measurements required (irrespective of the time span).
This paper has been, and continues to be, influential. For example, Table 1 has been included in clinical glaucoma management guidelines.[3] The journal’s article metrics show 371 citations for the paper at the time of writing. Yet, the paper incorrectly overemphasises the value of increasing visual field test frequency. That is a core message for the paper. Given the paper’s influence those errors should be corrected.

References
1. Chauhan BC, Garway-Heath DF, Goñi FJ, et al. Practical recommendations for measuring rates of visual field change in glaucoma. Br J Ophthalmol 2008;92(4):569-73. doi: 10.1136/bjo.2007.135012 [published Online First: 2008/01/24]
2. Schlesselman JJ. Planning a longitudinal study. II. Frequency of measurement and study duration. J Chronic Dis 1973;26(9):561-70. doi: 10.1016/0021-9681(73)90061-1
3. NHMRC. NHMRC Guidelines for the screening, prognosis, diagnosis, management and prevention of glaucoma 2010: National Health and Medical Research Council, Canberra., 2010.