Dry Eye Risk Score: A Formula, Many Hypotheses


As I prepare to submit another paper about the association between meibomian gland atrophy and electronic screen-time viewing in children, now with controls (which were very hard to find), the effect of certain factors on chronic dry eye pain risk is becoming clearer.  There are key risk factors which determine one’s risk for meibomian gland atrophy and chronic dry eye pain.

It will take years to prove causation with most of these below and a little less time to “prove” an association. In my mind and in many other eye surgeons’ and doctors’ minds, who take care of dry eye patients, the below are the key risk factors for having Meibomian Gland Atrophy

Here is my first attempt to create a formula, as inadequate as it is, to determine one’s risk of dry eye.

Of note, this is a condition that is an equal opportunity debilitator: it no longer matters (as much) if you are young or old (dry eye used to be considered an “old lady’s disease,” educated or not, rich or poor, live in a rich country or poor–this condition is hitting all continents. It is key to not ignore eye symptoms or to pretend it will go away.

I have many patients that say they had been in a state of denial for years until they could no longer live with the pain and found me and my team. The vast majority have received relief, but not everyone, so it is important to pick up Meibomian Gland atrophy early! We are looking to give 100% of patients 100% relief. We are looking for the cure. There is hope on the horizon. We just need time and research (and research funding).

The risk factors are listed in the order that I perceive them to have an effect in the general population of dry eye patients I have seen (eg, in terms of how often I have seen the risk be a key issue in dry eye pain.)

For instance, not all the patients I see who have used Accutane have dry eye or dry eye pain or MG atrophy. Those that were on Accutane and have a couple of other risk factors tend to be tipped over the edge faster than others: why? I am not sure, but the formula below may shed light.

Each factor has a different weight in the Risk Score. The amount of time, for instance, one has used contact lenses or views electronic screens could make a difference in a Total Risk Score.

Also, many of these factors are modifiable: you can only use contact lenses for “contact lens worth” events; you can use the computer or iphone for only “eye worthy” things; blepharitis and allergies can be controlled.

Thus by knowing all the factors, each person can start to think about what their key risk factors are and try to do something about it.

Meibomian Gland Atrophy & Chronic Dry Eye Pain Risk = Autoimmune Disease (Y/N)+ Age + Sex (M/F) + Low Average Blink Rate (< every 4 seconds) + “Excess” partial blinks + Electronic Screen Time >4hr/day (days/year) + Years of Extensive Reading >5 hrs/day + Chronic blepharitis (ie, Demodex) (years with) + Chronic Allergies (years with) + Contact Lens Use (years with) + European Descent (+Rosacea/Family History of Dry eye) + Lasik/PRK/PTK (Y/N) + Accutane (Y/N) + Topical Drops with preservative (year used) + Oral Drugs that dry out eye (years used) + Diabetes (Y/N) + Chemotherapy (Y/N) + Radiation (Y/N) + Trauma or surgery to eyelid or periorbital area + Diet (“A lot of” Omega 3 Y/N) + Eyelid issues (ie, tight lids, floppy eyelids) + CPAP used (Y/N) + Insomnia history or sleep apnea (Y/N) + Other diagnoses (ie, Anxiety, Psychological diagnosis requiring medications) + Profession (ie, computer programmers [highest] vs cloistered nuns[lowest])+ How much Omega 3 you eat each day (low vs high doses [ie 4000mg/day] + Personality (perfectionist vs laid back) 

That is what I know/suspect so far. 

Some of these are controversial and not based yet on a case-control study, such as the last one.  I tend to see many patients who are and have been for years, laser-focused on their work and electronic screens or worked hard to get their masters, Ph.D., etc only to now struggled with chronic dry eye. I wonder if they are so intense in their work that they blink less and have done so for years. Also no one knows what is the definition of “excessive” partial blinks. 

How often should you blink?
This is also very unclear. The average blink rate is about every 6 seconds. Most patients decrease their blink rate to every 13 seconds when they read. Likely the blink rate decrease even less than 13sec/minute when one is on an electronic screen. 


Meibomian Gland Atrophy Risk= Autoimmune Disease (Y/N)+ Age + Sex (M/F) + Low Average Blink Rate (< every 4 seconds) + “Excess” partial blinks + Electronic Screen Time >4hr/day (days/year) + Years of Extensive Reading >5 hrs/day + Chronic blepharitis (ie, Demodex) (years with) + Chronic Allergies (years with) + Contact Lens Use (years with) + European Descent (+Rosacea/Family History of Dry eye) + Lasik/PRK/PTK (Y/N) + Accutane (Y/N) + Topical Drops with preservative (year used) + Oral Drugs that dry out eye (years used) + Chemotherapy (Y/N) + Radiation (Y/N) + Trauma or surgery to eyelid or periorbital area + Diet (“A lot of” Omega 3 Y/N) + Eyelid issues (ie, tight lids, floppy eyelids) + CPAP used (Y/N) + Insomnia history or sleep apnea (Y/N) + Other diagnoses (ie, Anxiety, Psychological diagnosis requiring medications) + Profession (ie, computer programmers [highest] vs cloistered nuns[lowest])+ How much Omega 3 you eat each day (low vs high doses [ie 4000mg/day] + Personality (perfectionist vs laid back) 

Red means higher risk is well known with some publications to show the association.
Green means low risk factors. 

Chronic Dry Eye Pain Risk = Autoimmune Disease (Y/N)+ Age + Sex (M/F) + Low Average Blink Rate (< every 4 seconds) + “Excess” partial blinks + Electronic Screen Time >4hr/day (days/year) + Years of Extensive Reading >5 hrs/day + Chronic blepharitis (ie, Demodex) (years with) + Chronic Allergies (years with) + Contact Lens Use (years with) + European Descent (+Rosacea/Family History of Dry eye) + Lasik/PRK/PTK (Y/N) + Accutane (Y/N) + Topical Drops with preservative (year used) + Oral Drugs that dry out eye (years used) + Chemotherapy (Y/N) + Radiation (Y/N) + Trauma or surgery to eyelid or periorbital area + Diet (“A lot of” Omega 3 Y/N) + Eyelid issues (ie, tight lids, floppy eyelids) + CPAP used (Y/N) + Insomnia history or sleep apnea (Y/N) + Other diagnoses (ie, Anxiety, Psychological diagnosis requiring medications) + Profession (ie, computer programmers [highest] vs cloistered nuns[lowest])+ How much Omega 3 you eat each day (low vs high doses [ie 4000mg/day] + Personality (perfectionist vs laid back) 
References:

Analysis of blink rate patterns in normal subjects

First published: 04 November 2004

Cited by: 152

Abstract

The present study measured the normal blink rate (BR) variations in relation to behavioral tasks in 150 healthy volunteers (70 males and 80 females; aged 35.9 ± 17.9 years, range 5–87 years). The subjects were videotaped in a standard setting while performing three different tasks: resting quietly, reading a short passage, talking freely. The mean BR was computed during each task; the data were compared by means of analysis of variance and Student’s t tests. Mean BR at rest was 17 blinks/min, during conversation it increased to 26, and it was as low as 4.5 while reading. As compared with rest, BR decreased by −55.08% while reading (p < 1 × 1015) and increased by 99.70% during conversation (p < 1 × 109). As compared with reading, BR increased during conversation by 577.8% (p < 1 × 10 17). The distribution curves were highly reproducible in each task. The best curve fit was represented by a long‐normal distribution, with the upper tail of each curve having a normal distribution. Eye color and eyeglass wearing did not influence BR. Women had higher BR than men just while reading. No age‐related differences were found. The most common BR pattern was conversation > rest > reading, which occurred in 101 subjects (67.3%); 34 subjects (22.7%) had the pattern rest > conversation > reading; 12 (8.0%) had the pattern conversation > reading > rest. This study identified three normal behavioral BR patterns and showed that BR is more influenced by cognitive processes than by age, eye color, or local factors. The present findings provide a normal reference for the analysis of BR in movement disorders such as dystonia or tics.



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