ArticleJCO clinical cancer informatics2019
Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data.
Article in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Prognostic Nutritional Index and Cancer Prognostic Outcomes: An Umbrella Review of Systematic Reviews and Meta-analyses of Observational Studies.Advances in nutrition (Bethesda, Md.) · 2026Pooled it
- Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study.BMJ health & care informatics · 2023Trial
- Acute Care Events During Systemic Cancer Treatment: Moving From Risk Prediction to Clinical Decision Support Using a Two-Model Approach.JCO oncology practice · 2026Article
- Article
- Acute Care Utilization Patterns During Chemotherapy and Predictive Model Development at a Rural Community Cancer Center.JCO clinical cancer informatics · 2025Article
- Article
- Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.The clinical respiratory journal · 2024Article
- Article
- Development of a claims-based risk-scoring model to predict emergency department visits in older patients receiving anti-neoplastic therapy.Scientific reports · 2024Article
- Patterns and outcome of unplanned care in lung cancer patients: an observational study in a medical oncology department.Translational lung cancer research · 2023Article
- Predicting Acute Care Events Among Patients Initiating Chemotherapy: A Practice-Based Validation and Adaptation of the PROACCT Model.JCO oncology practice · 2023Article
- Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients.JCO clinical cancer informatics · 2023Article
- Comparison of Oncologist and Model Estimates of Risk for Hospitalization During Systemic Therapy for Advanced Cancer.JCO oncology practice · 2023Article
- Association Between Remote Monitoring and Acute Care Visits in High-Risk Patients Initiating Intravenous Antineoplastic Therapy.JCO oncology practice · 2022Article
- Predictive Modeling for Adverse Events and Risk Stratification Programs for People Receiving Cancer Treatment.JCO oncology practice · 2022Review
- Applying Hospital Readmissions to Oncology: A Square Peg in a Round Hole?JCO oncology practice · 2022Article
- Prior Frequent Emergency Department Use as a Predictor of Emergency Department Visits After a New Cancer Diagnosis.JCO oncology practice · 2021Article
- Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions.JCO clinical cancer informatics · 2021Article
- Estimating the impact of the COVID-19 pandemic on diagnosis and survival of five cancers in Chile from 2020 to 2030: a simulation-based analysis.The Lancet. Oncology · 2021Article
- Building a Clinically Relevant Risk Model: Predicting Risk of a Potentially Preventable Acute Care Visit for Patients Starting Antineoplastic Treatment.JCO clinical cancer informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
purposeHospitalizations are a common occurrence during chemotherapy for advanced cancer. Validated risk stratification tools could facilitate proactive approaches for reducing hospitalizations by identifying at-risk patients. PATIENTS AND
methodsWe assembled two retrospective cohorts of patients receiving chemotherapy for advanced nonhematologic cancer; cohorts were drawn from three integrated health plans of the Cancer Research Network. We used these cohorts to develop and validate logistic regression models estimating 30-day hospitalization risk after chemotherapy initiation. The development cohort included patients in two health plans from 2005 to 2013. The validation cohort included patients in a third health plan from 2007 to 2016. Candidate predictor variables were derived from clinical data in institutional data warehouses. Models were validated based on the C-statistic, positive predictive value, and negative predictive value. Positive predictive value and negative predictive value were calculated in reference to a prespecified risk threshold (hospitalization risk ≥ 18.0%).
resultsThere were 3,606 patients in the development cohort (median age, 63 years) and 634 evaluable patients in the validation cohort (median age, 64 years). Lung cancer was the most common diagnosis in both cohorts (26% and 31%, respectively). The selected risk stratification model included two variables: albumin and sodium. The model C-statistic in the validation cohort was 0.69 (95% CI, 0.62 to 0.75); 39% of patients were classified as high risk according to the prespecified threshold; 30-day hospitalization risk was 24.2% (95% CI, 19.9% to 32.0%) in the high-risk group and 8.7% (95% CI, 6.1% to 12.0%) in the low-risk group.
conclusionA model based on data elements routinely collected during cancer treatment can reliably identify patients at high risk for hospitalization after chemotherapy initiation. Additional research is necessary to determine whether this model can be deployed to prevent chemotherapy-related hospitalizations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.