Back

Prognostic value of advanced lung cancer inflammation index (ALI) combined with geriatric nutritional risk index (GNRI) in patients with chronic heart failure

Shi, T.; Wang, Y.; Peng, Y.; Wang, M.; Zhou, Y.; Gu, W.; Li, Y.; Zou, J.; Zhu, N.; Chen, L.

2023-07-09 cardiovascular medicine
10.1101/2023.07.07.23292398 medRxiv
Show abstract

BackgroundThis study was undertaken to explore the predictive value of the advanced lung cancer inflammation index (ALI) combined with the geriatric nutritional risk index (GNRI) for all-cause mortality in patients with CHF. Methods and ResultsWe enrolled 1123 patients with HF admitted to our cardiology department from January 2017 to October 2021. Patients were divided into Group 1 (ALI<24.60 and GNRI<94.41), Group 2 (ALI<24.60 and GNRI[&ge;]94.41), Group 3 (ALI[&ge;]24.60 and GNRI<94.41) and Group 4 (ALI[&ge;]24.60 and GNRI[&ge;]94.41), according to the median ALI and GNRI. From the analysis of the relationship between the ALI and GNRI, we concluded that there was a mild positive linear correlation (r= 0.348, p< 0.001) and no interaction (p=0.140) between the ALI and GNRI. Kaplan-Meier analysis showed that the cumulative incidence of all-cause mortality in patients with CHF was highest in Group 1 (log-rank {chi}2 126.244, p<0.001). Multivariate Cox proportional hazards analysis revealed that ALI and GNRI were independent predictors of all-cause mortality in CHF patients (ALI: HR 0.313, 95% CI 0.228-0.430, p <0.001; GNRI: HR 0.966, 95% CI 0.953-0.979, p <0.001). The area under the curve (AUC) for ALI combined with GNRI was 0.711 (p<0.001), according to the time-dependent ROC curve. ConclusionALI and GNRI were independent predictors of all-cause mortality in CHF patients. Patients with CHF had the highest risk of all-cause mortality when the ALI was <24.60 and the GNRI was <94.41. ALI combined with the GNRI has good predictive value for the prognosis of CHF patients.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.