Neutrophil to Lymphocyte Ratio (NLR): Predicting Outcomes in COVID

Could a CBC w/diff tell us who’s going to do poorly with COVID? Using the neutrophil to lymphocyte ratio could be quite helpful. Can we identify who’s going to get incredibly ill and who isn’t from COVID? I know we struggle with this significantly as we make phone calls to family members every single day and wonder when their loved one is going to finally turn the corner or if they’re going to crash on us.

Why use the Neutrophil to Lymphocyte Ratio (NLR)

What if there’s a calculation right underneath our noses that we can obtain from the trusty CBC w/differential we mindlessly order every single day for our patients? Well it turns out that this is something that could be potentially useful to know who’s going to become a severe case or even know who may die from this horrendous virus. This article (which you should read for yourself and not trust me) describes the neutrophil to lymphocyte ratio and the multitude of its uses historically to predict disease severity. The reason behind the ratio of neutrophils to lymphocytes is that it can be used as a marker to predict systemic inflammation. Since COVID-19 is an inflammatory issue above all else at the time when people arrive at the hospital, it makes only sense that the neutrophil to lymphocyte ratio has been investigated by researchers out there to help predict outcomes. If we can predict outcomes from a simple CBC with differential which costs a few bucks and we obtain it on everyone anyway, then let’s use it to the best of our ability!

Appraising the Study

This paper was a systematic review and meta-analysis. To those unfamiliar with the terms, a systematic review is, in my own words, an extensive google search (but using more official means of course) on the topic. One has to uncover every single article/paper/study on the matter. Then, a meta-analysis is that they combine the data of the studies that meet their quality threshold and do some fancy number crunching. These authors identified 298 studies but ended up crunching the numbers using 19 studies. There’s a lot of junk out there that one needs to weed through.

Area Under the Curve

I’ve gone over the AUC (area under the curve) before but bottom line is that it’s excellent for predicting disease severity and mortality. For those of you who are not familiar with the Receiver operating curve (ROC) or AUC, it basically takes into account the sensitivity and specificity of a test and combines them. An AUC or ROC of 1 means it’s a perfect test. If it is 0.5, that means it’s a coin flip. In the literature that is suggestive no discrimination. Not helpful. In medicine, we consider an AUC of greater than 0.9 to be outstanding. If it’s between 0.8 to 0.9 then it’s excellent. If it’s between 0.7 to 0.8 it’s acceptable. Less than 0.7 isn’t really a good test. It’s a gray area where we really don’t know what to do with it. There’s more data and deeper explanations on this that are beyond the scope of this blog post. I have linked an article that’s free for you to download below in the citations titled “Receiver Operating Characteristic Curve in Diagnostic Test Assessment” by Jayawant N.Mandrekar, PhD.

Is the Neutrophil to Lymphocyte Ratio (NLR) any good?

In this article, the ability of the Neutrophil to Lymphocyte Ratio to predict disease severity the AUC was 0.78 when they combined the outcomes of several different studies for this meta-analysis. That’s excellent. Mortality fared even better. The pooled AUC of the Neutrophil to Lymphocyte Ratio for mortality was 0.90 which is excellent bordering on outstanding.

What are the Neutrophil to Lymphocyte Ratio Cutoffs?

So the next question is how could we use these NLR numbers in our practice to predict outcomes? The authors conducted a subgroup analysis of different cutoffs for both disease severity as well as mortality with their respective AUCs to see what numbers we should be using to help out our patients. Using a cutoff of NLR ≥ 4.5 has an AUC of 0.86 (which is excellent) to predict disease severity. If it’s <4.5 then the AUC of 0.82 suggests that the patient may do well. With regards to mortality, they found that using ≥6.5 is highly predictive of mortality (AUC of 0.92) while < 6.5 is suggestive that they are not going to die (AUC of 0.84).

Wrapping it up

If you’re working in a COVID floor or ICU, calculate the NLR of your patients and let me know what you find in your unit. I crunched the numbers on my ICU patients and they were all in double digits. There are some caveats to this, of course. For example, one would have to go study by study in the meta-analysis to see what therapies the patients received. For example, did they receive steroids? How does this affect certain subgroups like the elderly and the younger patients? It could all get pretty muddy but it’s quite interesting overall. There also isn’t prospective data on this. It’s ultimately just a guide. Thanks to everyone taking care of these patients. We are all in this together. Much love.

A 🎩tip to the authors. CLICK HERE for other content I’ve created on COVID-19.

Li, X., Liu, C., Mao, Z. et al. Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis. Crit Care 24, 647 (2020).
Link to Article

Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010 Sep;5(9):1315-6. doi: 10.1097/JTO.0b013e3181ec173d. PMID: 20736804.
Link to Article

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