Home Hematology and Oncology Model Can Predict Outcome for Immune Checkpoint Inhibitor Treatment

Model Can Predict Outcome for Immune Checkpoint Inhibitor Treatment

Risk model with six parameters predicts outcome in patients with metastatic cancers of different origins receiving immune checkpoint inhibitors

By Elana Gotkine HealthDay Reporter

THURSDAY, Jan. 11, 2024 (HealthDay News) — A risk model comprising six inflammatory-related laboratory parameters can predict outcome in patients with metastatic cancer treated with immune checkpoint inhibitors (ICIs), according to a study published online Dec. 4 in BMC Cancer.

Satu Tiainen, from Kuopio University Hospital in Finland, and colleagues obtained laboratory parameters before initiation of ICI treatment in a real-world patient population to establish a practical prognostic risk model for the pretreatment evaluation of response and survival of 158 ICI-treated patients with different types of metastatic cancers. Six inflammation-related parameters (elevated values of neutrophils, platelets, C-reactive protein, erythrocyte sedimentation rate and lactate dehydrogenase, and the presence of anemia) were scored, each with one point. One hundred nine patients with information on all six parameters were stratified into low- and high-risk groups (0 to 3 and 4 to 6 points, respectively).

The researchers found that the risk model was strongly associated with patient outcome. The overall response rate was 30.3 and 53.9 percent in the high- and low-risk groups, respectively. In the high- and low-risk groups, the median overall survival was 10.0 and 27.3 months, and median progression-free survival was 3.9 and 6.3 months, respectively. For both overall and progression-free survival, the risk group remained a significant prognostic factor in Cox multivariate analyses.

“Since the risk model was based on routine blood tests, it is both feasible and inexpensive and thus could easily be incorporated into the clinical practice,” the authors write. “Further studies examining larger patient populations are needed to validate the risk model.”

Several authors disclosed ties to the pharmaceutical industry.

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