Key PointsQuestionCan machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?FindingsIn this cohort study of 26 525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.MeaningIn this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.
Expression of the arterial marker molecule ephrinB2 in endothelial cells is a prerequisite for adequate remodeling processes of the developing or angiogenic vasculature. Although its role in these processes has been extensively studied, the impact of ephrinB2 on the remodeling of adult arteries is largely unknown. To this end, we analyzed its expression during a biomechanically induced arteriolar remodeling process known as arteriogenesis and noted a significant increase in ephrinB2 expression under these conditions. By examining those biomechanical forces presumed to drive arteriogenesis, we identified cyclic stretch as a critical inducer of ephrinB2 expression in endo
IMPORTANCE Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncology or compared with routinely used prognostic indices.OBJECTIVE To validate an electronic health record-embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort. DESIGN, SETTING, AND PARTICIPANTSThis prognostic study comprised a prospective cohort of patients with outpatient oncology encounters between March 1, 2019, and April 30, 2019. An ML algorithm, trained on retrospective data from a subset of practices, predicted 180-day mortality risk between 4 and 8 days before a patient's encounter. Patient encounters took place in 18 medical or gynecologic oncology practices, including 1 tertiary practice and 17 general oncology practices, within a large US academic health care system. Patients aged 18 years or older with outpatient oncology or hematology and oncology encounters were included in the analysis. Patients were excluded if their appointment was scheduled after weekly predictions were generated and if they were only evaluated in benign hematology, palliative care, or rehabilitation practices.EXPOSURES Gradient-boosting ML binary classifier. MAIN OUTCOMES AND MEASURESThe primary outcome was the patients' 180-day mortality from the index encounter. The primary performance metric was the area under the receiver operating characteristic curve (AUC). RESULTS Among 24 582 patients, 1022 (4.2%) died within 180 days of their index encounter. Their median (interquartile range) age was 64.6 (53.6-73.2) years, 15 319 (62.3%) were women, 18 015 (76.0%) were White, and 10 658 (43.4%) were seen in the tertiary practice. The AUC was 0.89 (95% CI, 0.88-0.90) for the full cohort. The AUC varied across disease-specific groups within the tertiary practice (AUC ranging from 0.74 to 0.96) but was similar between the tertiary and general oncology practices. At a prespecified 40% mortality risk threshold used to differentiate high-vs low-risk patients, observed 180-day mortality was 45.2% (95% CI, 41.3%-49.1%) in the high-risk group vs 3.1% (95% CI, 2.9%-3.3%) in the low-risk group. Integrating the algorithm into the Eastern Cooperative Oncology Group and Elixhauser comorbidity index-based classifiers resulted in favorable reclassification (net reclassification index, 0.09 [95% CI, 0.04-0.14] and 0.23 [95% CI, 0.20-0.27], respectively). CONCLUSIONS AND RELEVANCEIn this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.
Coronavirus disease 2019 (COVID-19) has had a devastating impact around the world. With high rates of transmission and no curative therapies or vaccine yet available, the current cornerstone of management focuses on prevention by social distancing. This includes decreased health care contact for patients. Patients with lung cancer are a particularly vulnerable population, where the risk of mortality from cancer must now be balanced by the potential risk of a life-threatening infection. In these unprecedented times, a collaborative and multidisciplinary approach is required to streamline but not compromise care. We have developed guidelines at our academic cancer center to standardize management of patients with lung cancer across our health care system and provide guidance to the larger oncology community. We recommend that general principles of lung cancer treatment continue to be followed in most cases where delays could result in rapid cancer progression. We recognize that our recommendations may change over time based on clinical resources and the evolving nature of the COVID-19 pandemic. In principle, however, treatment paradigms must continue to be individualized, with careful consideration of risks and benefits of continuing or altering lung cancer–directed therapy.
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