Lack of documentation of evidence-based prognostication in cancer patients by inpatient palliative care consultants

The relationship between weight gain during chemotherapy and outcomes in patients with advanced non-small cell lung cancer

BACKGROUND

This post hoc, pooled analysis examined the relationship between different weight gain categories and overall survival (OS) in patients with non-small cell lung cancer (NSCLC) receiving first-line platinum-based chemotherapy.

METHODS

Data were pooled from the control arms of three phase III clinical studies (NCT00596830, NCT00254891, and NCT00254904), and the maximum weight gain in the first 3 months from treatment initiation was categorised as >0%, >2.5%, and >5.0%. Cox proportional hazard modelling of OS was used to estimate hazard ratios (HRs) for each category, including baseline covariates, time to weight gain, and time to confirmed objective response (RECIST Version 1.0).

RESULTS

Of 1030 patients with advanced NSCLC (IIIB 11.5% and IV 88.5%), 453 (44.0%), 252 (24.5%), and 120 (11.7%) experienced weight gain from baseline of >0%, >2.5%, and >5.0%, respectively. The median time to weight gain was 23 (>0%), 43 (>2.5%), and 45 (>5.0%) days. After adjusting for a time-dependent confirmed objective response, the risk of death was reduced for patients with any weight gain (>0% vs. ≤0% [HR 0.71; 95% confidence interval-CI 0.61, 0.82], >2.5% vs. ≤2.5% [HR 0.76; 95% CI 0.64, 0.91] and >5.0% vs. ≤5.0% [HR 0.77; 95% CI 0.60, 0.99]). The median OS was 13.5 versus 8.6 months (weight gain >0% vs. ≤0%), 14.4 versus 9.4 months (weight gain >2.5% vs. ≤2.5%), and 13.4 versus 10.2 months (weight gain >5.0% vs. ≤5.0%).

CONCLUSIONS

Weight gain during treatment was associated with a reduced risk of death, independent of tumour response. The survival benefit was comparable for weight gain >0%, >2.5%, and >5.0%, suggesting that any weight gain may be an early predictor of survival with implications for the design of interventional cancer cachexia studies.

Impact of Augmented Intelligence on Utilization of Palliative Care Services in a Real-World Oncology Setting

PURPOSE

For patients with advanced cancer, timely referral to palliative care (PC) services can ensure that end-of-life care aligns with their preferences and goals. Overestimation of life expectancy may result in underutilization of PC services, counterproductive treatment measures, and reduced quality of life for patients. We assessed the impact of a commercially available augmented intelligence (AI) tool to predict 30-day mortality risk on PC service utilization in a real-world setting.

METHODS

Patients within a large hematology-oncology practice were scored weekly between June 2018 and October 2019 with an AI tool to generate insights into short-term mortality risk. Patients identified by the tool as being at high or medium risk were assessed for a supportive care visit and further referred as appropriate. Average monthly rates of PC and hospice referrals were calculated 5 months predeployment and 17 months postdeployment of the tool in the practice.

RESULTS

The mean rate of PC consults increased from 17.3 to 29.1 per 1,000 patients per month (PPM) pre- and postdeployment, whereas the mean rate of hospice referrals increased from 0.2 to 1.6 per 1,000 PPM. Eliminating the first 6 months following deployment to account for user learning curve, the mean rate of PC consults nearly doubled over baseline to 33.0 and hospice referrals increased 12-fold to 2.4 PPM.

CONCLUSION

Deployment of an AI tool at a hematology-oncology practice was found to be feasible for identifying patients at high or medium risk for short-term mortality. Insights generated by the tool drove clinical practice changes, resulting in significant increases in PC and hospice referrals.