De novo Creation and Assessment of a Prognostic Fat-Age-Inflammation Index “FAIN” in Patients With Cancer: A Multicenter Cohort Study

Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study

OBJECTIVES

The concept of possible sarcopenia (PS) was recently introduced to enable timely intervention in settings without the technologies required to make a full diagnosis of sarcopenia. This study aimed to investigate the association between PS and all-cause mortality in patients with solid cancer.

DESIGN

Retrospective observational study.

SETTING AND PARTICIPANTS

13,736 patients with 16 types of solid cancer who were ≥18 years old.

MEASUREMENTS

The presence of both a low calf circumference (men <34 cm or women <33 cm) and low handgrip strength (men <28 kg or women <18 kg) was considered to indicate PS. Harrell's C-index was used to assess prognostic value and the association of PS with mortality was estimated by calculating multivariable-adjusted hazard ratios (HRs).

RESULTS

The study enrolled 7207 men and 6529 women (median age = 57.8 years). During a median follow-up of 43 months, 3150 deaths occurred. PS showed higher Harrell's C-index (0.549, 95%CI = [0.541, 0.557]) than the low calf circumference (0.541, 95%CI = [0.531, 0.551], P = 0.037) or low handgrip strength (0.542, 95%CI = [0.532, 0.552], P = 0.026). PS was associated with increased mortality risk in both univariate (HR = 1.587, 95%CI = [1.476, 1.708]) and multivariable-adjusted models (HR = 1.190, 95%CI = [1.094, 1.293]). Sensitivity analyses showed that the association of PS with mortality was robust in different covariate subgroups, which also held after excluding those patients who died within the first 3 months (HR = 1.162, 95%CI = [1.060, 1.273]), 6 months (HR = 1.150, 95%CI = [1.039, 1.274]) and 12 months (HR = 1.139, 95%CI = [1.002, 1.296]) after enrollment.

CONCLUSION

PS could independently and robustly predict all-cause mortality in patients with solid cancer. These findings imply the importance of including PS assessment in routine cancer care to provide significant prognostic information to help mitigate sarcopenia-related premature deaths.

Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge

BACKGROUND

Diagnosing cancer cachexia relies extensively on the patient-reported historic weight, and failure to accurately recall this information can lead to severe underestimation of cancer cachexia.

OBJECTIVES

The present study aimed to develop inexpensive tools to facilitate the identification of cancer cachexia in patients without weight loss information.

METHODS

This multicenter cohort study included 12774 patients with cancer. Cachexia was retrospectively diagnosed using Fearon's framework. Baseline clinical features, excluding weight loss, were modeled to mimic a situation where the patient is unable to recall their weight history. Multiple machine learning (ML) models were trained using 75% of the study cohort to predict cancer cachexia, with the remaining 25% of the cohort used to assess model performance.

RESULTS

The study enrolled 6730 males and 6044 females (median age = 57.5 years). Cachexia was diagnosed in 5261 (41.2%) patients and most diagnoses were made based on the weight loss criterion. A 15-variable logistic regression (LR) model mainly comprising cancer types, gastrointestinal symptoms, tumor stage and serum biochemistry indices was selected among the various ML models. The LR model showed good performance for predicting cachexia in the validation data (area under the curve = 0.763, 95% confidence interval=[0.747, 0.780]). The calibration curve of the model demonstrated good agreement between predictions and actual observations (accuracy = 0.714, Kappa = 0.396, sensitivity = 0.580, specificity = 0.808, positive predictive value = 0.679, negative predictive value = 0.733). Subgroup analyses showed that the model was feasible in patients with different cancer types. The model was deployed as an online calculator and a nomogram, and was exported as predictive model markup language to permit flexible, individualized risk calculation.

CONCLUSIONS

We developed a ML model that can facilitate the identification of cancer cachexia in patients without weight loss information, which might improve decision-making and lead to the development of novel management strategies in cancer care.