Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment

PURPOSE

For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diagnosis. We evaluated whether a prognostic machine learning system could promote early PC, given existing capacity.

METHODS

Using population-level administrative data in Ontario, Canada, we assembled a cohort of patients with incurable cancer who received palliative-intent systemic therapy between July 1, 2014, and December 30, 2019. We developed a machine learning system that predicted death within 1 year of each treatment using demographics, cancer characteristics, treatments, symptoms, laboratory values, and history of acute care admissions. We trained the system in patients who started treatment before July 1, 2017, and evaluated the potential impact of the system on PC in subsequent patients.

RESULTS

Among 560,210 treatments received by 54,628 patients, death occurred within 1 year of 45.2% of treatments. The machine learning system recommended the same number of PC consultations observed with usual care at the 60.0% 1-year risk of death, with a first-alarm positive predictive value of 69.7% and an outcome-level sensitivity of 74.9%. Compared with usual care, system-guided care could increase early PC by 8.5% overall (95% CI, 7.5 to 9.5; P < .001) and by 15.3% (95% CI, 13.9 to 16.6; P < .001) among patients who live 6 months beyond their first treatment, without requiring more PC consultations in total or substantially increasing PC among patients with a prognosis exceeding 2 years.

CONCLUSION

Prognostic machine learning systems could increase early PC despite existing resource constraints. These results demonstrate an urgent need to deploy and evaluate prognostic systems in real-time clinical practice to increase access to early PC.

Risk Prediction of Emergency Department Visits in Patients With Lung Cancer Using Machine Learning: Retrospective Observational Study

BACKGROUND

Patients with lung cancer are among the most frequent visitors to emergency departments due to cancer-related problems, and the prognosis for those who seek emergency care is dismal. Given that patients with lung cancer frequently visit health care facilities for treatment or follow-up, the ability to predict emergency department visits based on clinical information gleaned from their routine visits would enhance hospital resource utilization and patient outcomes.

OBJECTIVE

This study proposed a machine learning-based prediction model to identify risk factors for emergency department visits by patients with lung cancer.

METHODS

This was a retrospective observational study of patients with lung cancer diagnosed at Seoul National University Bundang Hospital, a tertiary general hospital in South Korea, between January 2010 and December 2017. The primary outcome was an emergency department visit within 30 days of an outpatient visit. This study developed a machine learning-based prediction model using a common data model. In addition, the importance of features that influenced the decision-making of the model output was analyzed to identify significant clinical factors.

RESULTS

The model with the best performance demonstrated an area under the receiver operating characteristic curve of 0.73 in its ability to predict the attendance of patients with lung cancer in emergency departments. The frequency of recent visits to the emergency department and several laboratory test results that are typically collected during cancer treatment follow-up visits were revealed as influencing factors for the model output.

CONCLUSIONS

This study developed a machine learning-based risk prediction model using a common data model and identified influencing factors for emergency department visits by patients with lung cancer. The predictive model contributes to the efficiency of resource utilization and health care service quality by facilitating the identification and early intervention of high-risk patients. This study demonstrated the possibility of collaborative research among different institutions using the common data model for precision medicine in lung cancer.

Biceps brachii muscle cross-sectional area measured by ultrasonography is independently associated with one-month mortality: A prospective observational study

PURPOSE

Studies examining mortality in palliative care units are limited. We aimed to investigate the mortality and associated factors including ultrasonographic muscle parameters in hospitalized palliative patients with a subgroup analysis of older patients.

METHODS

A prospective-observational study. We recorded the demographics, number of diseases, diagnoses, and the Charlson comorbidity index (CCI), palliative performance scale (PPS), and nutritional risk screening-2002 (NRS-2002) scores. We noted the nutritional parameters and mortality. We measured the subcutaneous fat thickness (SFT), muscle thickness (MT), and cross-sectional area (CSA) of the rectus femoris and biceps brachii using ultrasonography.

RESULTS

We enrolled 100 patients (mean age: 73.2 ± 16.4 years, 53%: female). One-month mortality was 42%. The non-survivors had significantly higher malignancy, increased CCI and NRS-2002 scores, lower required energy intake, calorie sufficiency rate, and biceps brachii SFT, MT, and CSA than the survivors. The independent mortality predictors were malignancy and biceps brachii CSA while the PPS score and malignancy were significantly associated with mortality in the older subgroup.

CONCLUSION

The malignancy and biceps brachii CSA might have prognostic value in predicting mortality in palliative patients. This was the first study investigating the mortality-associated factors including ultrasonographic muscle measurements of both the lower and upper limbs in a palliative care center.