Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

Abstract

PURPOSE Hospitalizations 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 METHODS We 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%). RESULTS There 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. CONCLUSION A 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.

Development and Validation of a Score to Predict Acute Care Use After Initiation of Systemic Therapy for Cancer

“…Brooks et al 4 developed a logistic regression model to predict hospitalizations in a cohort of 1579 patients receiving palliative chemotherapy at a single institution, with a C statistic of 0.71 based on internal bootstrapping. In a subsequent study, Brooks et al 14 used logistic regression to predict hospitalizations among 4240 patients with stage IV or recurrent solid tumors within 3 Kaiser Permanente regional health systems. Their model included 2 variables, albumin and sodium, and had a C statistic of 0.69 in the validation cohort.…”

“…Although several existing risk scores predict specific toxic effects from chemotherapy such as febrile neutropenia, 10,11,12 prediction scores for acute care use have focused only on hospitalizations after starting palliative chemotherapy for solid tumors and have not used population-based data. 13,14 To our knowledge, to date, population-based studies on acute care use during systemic treatment for cancer have focused mostly on a single cancer type or considered few risk factors and have not generated a predictive model. 15,16,17,18,19…”

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

“… 9 In most countries, cancer treatments for diagnosed patients have been postponed, especially immunosuppressive therapies and invasive procedures, such as surgery, which require hospitalisation and admission to intensive care units in some cases. 10 WHO has reported that cancer services have been partially or completely disrupted in a third of countries in Europe. 11 For example, the Netherlands experienced a 25% decrease in cancer diagnoses during the first COVID-19 wave in February to April, 2020.…”

System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning–Directed Clinical Evaluations During Radiation and Chemoradiation

“…This strategy may also be appropriate in other settings, including for patients undergoing systemic therapy, which has been the subject of many prediction models. 9,10,27,28 We additionally evaluated reasons for visits to determine if certain types of acute care were preventable. CMS-designated preventable diagnoses during RT were less common reasons for acute care in the intervention arm, though more data may be required to understand which acute care visits are truly preventable.…”

“…We previously developed a ML algorithm utilizing patient electronic health record (EHR) data, which retrospectively demonstrated strong predictive ability to identify patients at high risk for acute care, 8 comparing favorably to other models for this complex problem. 9,10 Despite a growing body of retrospective medical ML literature, 11,12 prospective evaluation remains tremendously limited, primarily to diagnostic fields. [13][14][15][16] Despite the need for prospective interventional trials, 17 there have been few, including a recent trial effectively predicting intraoperative hypotension utilizing arterial catheter sensor data.…”