The ability to determine the prognosis of a patient is an important aspect of medical care. In the face of a chronic disease, such as idiopathic pulmonary fibrosis (IPF), patients and their carers want to know: what is the estimated life expectancy? What kind of quality of life can they realistically expect? 

Our ability as clinicians to provide a healthy estimate on issues regarding prognosis is an important part of medical education. By helping patients understand the likely progression of their disease, we are helping them set realistic expectations and allowing them to make appropriate life plans best suited to their specific condition. 

Biomarkers as Prognostic Tools

Over the years, scientists have uncovered more tools that help them determine prognosis more accurately. Many of these are biomarkers. It is important to remember that prognostic biomarkers are never apparent without clinical investigation; it is only when scientists study them individually do we know how much they can tell us about the prognosis of a patient with a particular illness.

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In the International Journal of Molecular Sciences, Buonacera and colleagues wrote about how the neutrophil-to-lymphocyte ratio (NLR) has emerged as an important biomarker elucidating the relationship between the immune system and various diseases. The NLR is, as the name suggests, the ratio between neutrophil and lymphocyte count in peripheral blood.

Read more about IPF prognosis

“[The NLR] is a biomarker which conjugates two faces of the immune system: the innate immune response, mainly due to neutrophils, and adaptive immunity, supported by lymphocytes,” Buonacera et al wrote. 

Scientists have observed that the NLR is elevated in a number of conditions such as stroke, infection, cancer, postsurgical complications, and indeed any tissue damage that activates a systemic inflammatory response syndrome. Importantly, studies indicate that the NLR has prognostic value in predicting mortality in the general population. 

Studies cited by Buonacera and colleagues indicate that the NLR correlates with a higher overall mortality, in addition to specific causes of mortality (such as heart disease, respiratory disease, and kidney disease, among others). This makes it an incredibly valuable prognostic marker for life expectancy. In addition, it is easy to obtain, reproducible, and inexpensive. 

Nonetheless, its specific cutoff value is yet to be determined. There have been various estimates of what constitutes a normal NLR range; studies variably provide an estimate between 0.78 and 3.53. Also, it is important to note that scientists have uncovered comorbidities that can create a “false” increase in NLR; these include obesity, emotional stress, and various diseases. 

“NLR could be considered a robust prognostic marker of disease severity and predictor of mortality, bearing in mind the effect of confounders and taking into account the specific context of the disease, comorbidities and therapeutic strategy,” Buonacera and colleagues wrote.

Predicting IPF Outcomes 

In eClinicalMedicine, Mikolasch and colleagues sought to use the NLR as the basis for stratifying risk and predicting mortality in IPF. 

“There is an urgent need for biomarkers to better stratify patients with idiopathic pulmonary fibrosis (IPF) for clinical trials and transplant allocation,” they wrote. “The search for viable biomarkers has taken advantage of the rapidly expanding knowledge of IPF immunopathogenesis.” 

Read more about IPF etiology 

The research team recruited 71 patients with IPF who had baseline pulmonary function tests and a complete blood count. Patients with a malignancy, hematological disorder, or active infection were excluded. The primary outcome measure was transplant-free survival from the time of blood investigation until all-cause mortality or transplant. 

In this study, a high NLR was set at 2.9 or more; a low NLR was anything below that figure. They discovered that patients with a low NLR had a median survival of 62.1 months; patients with a high NLR had a median survival of 24.3 months. This means that NLR can significantly predict survival outcomes in patients with IPF. 

The research team also compared the merits of NLR as a prognostic marker to the Gender, Age, Physiology (GAP) index, which is used to assess treatment response and identify rapidly deteriorating patients. They found that the NLR had a strong correlation with GAP clinical scoring and that predicted mortality was similar using both methodologies. 

“NLR can significantly refine the predictive capacity of the clinical GAP index,” the research team wrote. 

Mikolasch and colleagues proposed an NLR-modified GAP calculation that relies on a modification of GAP scoring dependent on low (+0) and high (+1) NLR. This modified GAP scoring system is memorable and stabilizes its ability to monitor disease progression and predict mortality. 

Read more about IPF treatment 

The findings of this study further validate the role of NLR as an important emerging prognostic biomarker. Using the NLR allows physicians to develop more refined management plans for their patients. This study also demonstrates how the NLR can be used in conjunction with existing prognostic scoring systems (in this case, the GAP index) to help physicians better understand the overall trajectory of a disease category. 

Further studies should investigate the use of NLR in other diseases and to determine the best cutoff points for predicting outcomes. Physicians should consider utilizing the NLR in the clinical management of their patients to determine the suitability of certain therapeutic regimens. 


Buonacera A, Stancanelli B, Colaci M, Malatino L. Neutrophil to lymphocyte ratio: an emerging marker of the relationships between the immune system and diseasesInt J Mol Sci. 2022;23(7):3636. doi:10.3390/ijms23073636

Mikolasch TA, George PM, Sahota J, et al. Multi-center evaluation of baseline neutrophil-to-lymphocyte (NLR) ratio as an independent predictor of mortality and clinical risk stratifier in idiopathic pulmonary fibrosisEClinicalMedicine. 2022;55:101758. doi:10.1016/j.eclinm.2022.101758