ArticleJCO oncology practice2023
Comparison of Oncologist and Model Estimates of Risk for Hospitalization During Systemic Therapy for Advanced Cancer.
Article in JCO oncology practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Acute Care Events During Systemic Cancer Treatment: Moving From Risk Prediction to Clinical Decision Support Using a Two-Model Approach.JCO oncology practice · 2026Article
- Predicting Acute Care Events Among Patients Initiating Chemotherapy: A Practice-Based Validation and Adaptation of the PROACCT Model.JCO oncology practice · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
purposeA validated risk model with inputs of pretreatment sodium and albumin can identify patients at risk for hospitalization during cancer treatment. We evaluated how the model compares with risk estimates from treating oncologists.
methodsWe evaluated the 30-day risk of hospitalization or death in patients starting palliative-intent systemic therapy for solid tumor malignancy. For each patient, we prospectively recorded categorical estimates of 30-day hospitalization risk (bottom third, middle third, top third) generated by a treating oncologist and by the two-variable model; a third hybrid risk estimate represented a composite of the oncologist and model risk assessments. We analyzed the agreement of oncologist and model-based risk estimates and compared discrimination, sensitivity, and specificity of each risk assessment method.
resultsWe collected oncologist, model, and hybrid estimates of hospitalization risk for 120 patients. The 30-day rate of hospitalization or death was 20%. There was minimal agreement between oncologist and model risk estimates (weighted kappa = 0.27). The c-statistic (a measure of discrimination) was 0.69 (95% CI, 0.57 to 0.81) for the clinician assessment, 0.77 for the model assessment (CI, 0.67 to 0.86;
conclusionA model with inputs of pretreatment sodium and albumin improves oncologists' predictions of hospitalization risk during cancer treatment.
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