New Index May Better Predict All-cause Mortality at AAV Diagnosis
A novel index that computes five simple laboratory results at the time of diagnosis may improve predictions of all-cause mortality among patients with ANCA-associated vasculitis (AAV), a South Korean study reports.
These lab results include the levels of immune neutrophils, lymphocytes, and monocytes, and of proteins C-reactive protein and albumin.
The study, “Novel mortality-predicting index at diagnosis can effectively predict all-cause mortality in patients with antineutrophil cytoplasmic antibody-associated vasculitis,” was published in the Journal of Clinical Laboratory Analysis.
AAV is a group of diseases in which certain autoantibodies, called anti-neutrophil cytoplasmic antibodies (ANCAs), target proteins at the surface of immune cells, leading them to erroneously damage blood vessels.
As AAV progression varies widely among patients, researchers need parameters to help predict poor outcomes in patients and identify those needing more aggressive therapies.
Some blood tests, such as the neutrophil-to-lymphocyte ratio (NLR) and the C-reactive protein-to-albumin ratio (CAR), have proved useful at predicting poor outcomes in AAV patients. Both tests are used as biomarkers of broad inflammation.
The product of NLR and CAR was also shown to have success in predicting outcomes for lung cancer patients, but it has not yet been assessed in people with AAV. This index is called the inflammatory prognostic index (IPI).
Now, researchers in South Korea have developed a new index that is calculated by multiplying NLR, CAR, and monocyte counts at diagnosis. The index was called the mortality predicting index (MPI), and was tested for its ability to predict the risk of all-cause mortality among AAV patients.
The team examined medical records of 223 patients with AAV (mean age 59 years). Most (54.7%) had microscopic polyangiitis, followed by granulomatosis with polyangiitis (25.6%) and eosinophilic granulomatosis with polyangiitis (19.7%).
A total of 25 patients (11.2%) died over a median follow-up of 36.5 months. No differences in medication were observed in patients who died and those who survived.
Results showed that MPI was better at predicting all-cause mortality compared to any other test — NLR alone, CAR alone, or IPI. While MPI had an accuracy of 69.1%, the accuracy of the remaining tests ranged between 63.1% and 68.6%.
The researchers then estimated the optimal cutoff values that better predicted mortality for each index. For MPI, for example, patients with a score of 8367.82 or greater were significantly more likely to die during follow-up than those with lower scores.
When this cutoff was used, the MPI index could accurately detect 76% of patients who would die from any cause, and 59.1% of patients who would survive during follow-up. For IPI, these percentages were 80% and 57.6%, respectively.
When accounting for additional factors associated with mortality, the researchers found that all indexes, except CAR, were significant predictors of all-cause mortality.
In the model using MPI, smoking history and the five-factor score — a measure of prognosis in AAV — were also associated with death from all causes.
“This study developed a novel indicator, MPI, that uses the existing NLR and CAR indices and proved they could predict all-cause mortality in AAV patients,” the researchers wrote.
“New prognosis-predicting indices at diagnosis are expected to improve the prognosis of AAV,” they concluded.
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