Predictors of Unplanned Hospitalizations in Patients With Nonmetastatic Lung Cancer During Chemotherapy

The association of body mass index with safety and effectiveness of first-line carboplatin-based chemotherapy in patients with metastatic non-small cell lung cancer

INTRODUCTION

Carboplatin is an anticancer drug used for treatment of various types of cancer including non-small cell lung cancer (NSCLC). Dosing is based on estimated glomerular filtration rate (GFR) using the Cockcroft-Gault formula. In overweight patients, the GFR is more likely overestimated, resulting in a potentially overdose of carboplatin affecting treatment response. This study investigated the association of body mass index (BMI) on overall survival (OS) and progression-free survival (PFS) in stage-IV NSCLC patients treated with first-line carboplatin-based chemotherapy. Secondary safety endpoints were thrombocytopenia and toxicity-related hospitalizations.

MATERIALS AND METHODS

This was a retrospective multicenter cohort study. Patients were categorized according to BMI<25.0 kg/m² (normal weight and reference), 25.0-29.9 kg/m² (overweight) or ≥30.0 kg/m² (obese). For survival analyses adjusted hazard ratios [aHR] were calculated using multivariate Cox regression analysis. Secondary outcomes were analyzed using multivariate logistic regression providing adjusted odd ratios [aOR].

RESULTS

Overweight patients (n=174) had a significantly better OS (aHR=0.72, 95%-CI:0.59-0.89) and PFS (aHR=0.74, 95%-CI:0.61-0.90) compared to normal weight patients (n=268). OS nor PFS were different in obese (n=51) compared to normal weight patients. However, obesity was associated with significantly higher incidences of thrombocytopenia grade ≥3 (aOR=3.47, 95%-CI:1.75-6.90).

CONCLUSION

This study shows a significantly longer survival for overweight compared to normal weight patients. Obese patients have an increased risk for grade ≥3 thrombocytopenia without a difference in survival following carboplatin-based chemotherapy. The implications for clinical practice are to use the Cockcroft-Gault formula with caution in patients with BMI≥30.0 kg/m², and to verify calculated dosing of carboplatin for appropriateness.

Association of Prognostic Understanding With Health Care Use Among Older Adults With Advanced Cancer: A Secondary Analysis of a Cluster Randomized Clinical Trial

IMPORTANCE

A poor prognostic understanding regarding curability is associated with lower odds of hospice use among patients with cancer. However, the association between poor prognostic understanding or prognostic discordance and health care use among older adults with advanced incurable cancers is not well characterized.

OBJECTIVE

To evaluate the association of poor prognostic understanding and patient-oncologist prognostic discordance with hospitalization and hospice use among older adults with advanced cancers.

DESIGN, SETTING, AND PARTICIPANTS

This was a post hoc secondary analysis of a cluster randomized clinical trial that recruited patients from October 29, 2014, to April 28, 2017. Data were collected from community oncology practices affiliated with the University of Rochester Cancer Center National Cancer Institute Community Oncology Research Program. The parent trial enrolled 541 patients who were aged 70 years or older and were receiving or considering any line of cancer treatment for incurable solid tumors or lymphomas; the patients' oncologists and caregivers (if available) were also enrolled. Patients were followed up for at least 1 year. Data were analyzed from January 3 to 16, 2021.

MAIN OUTCOMES AND MEASURES

At enrollment, patients and oncologists were asked about their beliefs regarding cancer curability (100%, >50%, 50%, <50%, and 0%; answers other than 0% reflected poor prognostic understanding) and life expectancy (≤6 months, 7-12 months, 1-2 years, 2-5 years, and >5 years; answers of >5 years reflected poor prognostic understanding). Any difference between oncologist and patient in response options was considered discordant. Outcomes were any hospitalization and hospice use at 6 months captured by the clinical research associates.

RESULTS

Among the 541 patients, the mean (SD) age was 76.6 (5.2) years, 264 of 540 (49%) were female, and 486 of 540 (90%) were White. Poor prognostic understanding regarding curability was reported for 59% (206 of 348) of patients, and poor prognostic understanding regarding life expectancy estimates was reported for 41% (205 of 496) of patients. Approximately 60% (202 of 336) of patient-oncologist dyads were discordant regarding curability, and 72% (356 of 492) of patient-oncologist dyads were discordant regarding life expectancy estimates. Poor prognostic understanding regarding life expectancy estimates was associated with lower odds of hospice use (adjusted odds ratio, 0.30; 95% CI, 0.16-0.59). Discordance regarding life expectancy estimates was associated with greater odds of hospitalization (adjusted odds ratio, 1.64; 95% CI, 1.01-2.66).

CONCLUSIONS AND RELEVANCE

This study highlights different constructs of prognostic understanding and the need to better understand the association between prognostic understanding and health care use among older adult patients with advanced cancer.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02107443.

Predict multicategory causes of death in lung cancer patients using clinicopathologic factors

BACKGROUND

Random forests (RF) is a widely used machine-learning algorithm, and outperforms many other machine learning algorithms in prediction-accuracy. But it is rarely used for predicting causes of death (COD) in cancer patients. On the other hand, multicategory COD are difficult to classify in lung cancer patients, largely because they have multiple labels (versus binary labels).

METHODS

We tuned RF algorithms to classify 5-category COD among the lung cancer patients in the surveillance, epidemiology and end results-18, whose lung cancers were diagnosed in 2004, for the completeness in their follow-up. The patients were randomly divided into training and validation sets (1:1 and 4:1 sample-splits). We compared the prediction accuracy of the tuned RF and multinomial logistic regression (MLR) models.

RESULTS

We included 42,257 qualified lung cancers in the database. The COD were lung cancer (72.41%), other causes or alive (14.43%), non-lung cancer (6.85%), cardiovascular disease (5.35%), and infection (0.96%). The tuned RF model with 300 iterations and 10 variables outperformed the MLR model (accuracy = 69.8% vs 64.6%, 1:1 sample-split), while 4:1 sample-split produced lower prediction-accuracy than 1:1 sample-split. The top-10 important factors in the RF model were sex, chemotherapy status, age (65+ vs < 65 years), radiotherapy status, nodal status, T category, histology type and laterality, all of which except T category and laterality were also important in MLR model.

CONCLUSION

We tuned RF models to predict 5-category CODs in lung cancer patients, and show RF outperforms MLR in prediction accuracy. We also identified the factors associated with these COD.

Unplanned hospitalizations in older patients with cancer: Occurrence and predictive factors

BACKGROUND

This study aims to investigate the occurrence of unplanned hospitalizations in older patients with cancer and to determine predictive factors.

METHODS

A prospective Belgian multicentre (n = 22), observational cohort study was performed. Patients ≥70 years with a malignant tumor were included. Patients underwent G8 screening followed by geriatric assessment (GA) if abnormal at baseline and were followed for unplanned hospitalizations at approximately three months. Uni- and multivariable regression models were performed to determine predictive factors associated with unplanned hospitalizations in older patients with an abnormal G8.

RESULTS

In total, 7763 patients were included in the current analysis of which 2409 (31%) patients with a normal G8 score and 5354 (69%) with an abnormal G8 score. Patients with an abnormal G8 were hospitalized more frequently than patients with a normal G8 (22.9% versus 12.4%; p < 0.0001). Reasons for unplanned hospitalizations were most frequently cancer related (25.7%) or cancer therapy related (28%). In multivariable analysis, predictive factors for unplanned hospitalizations in older patients with cancer and an abnormal G8 were female gender, absence of surgery, chemotherapy, ADL dependency, malnutrition and presence of comorbidities.

CONCLUSION

Older patients with cancer and an abnormal G8 screening present a higher risk (23%) for unplanned hospitalizations. Predictive factors for these patients were identified and include not only patient and treatment related factors but also GA related factors.

Hospitalization and definitive radiotherapy in lung cancer: incidence, risk factors and survival impact

BACKGROUND

Unplanned hospitalization during cancer treatment is costly, can disrupt treatment, and affect patient quality of life. However, incidence and risks factors for hospitalization during lung cancer radiotherapy are not well characterized.

METHODS

Patients treated with definitive intent radiation (≥45 Gy) for lung cancer between 2008 and 2018 at a tertiary academic institution were identified. In addition to patient, tumor, and treatment related characteristics, specific baseline frailty markers (Charlson comorbidity index, ECOG, patient reported weight loss, BMI, hemoglobin, creatinine, albumin) were recorded. All cancer-related hospitalizations during or within 30 days of completing radiation were identified. Associations between baseline variables and any hospitalization, number of hospitalizations, and overall survival were identified using multivariable linear regression and multivariable Cox proportional-hazards models, respectively.

RESULTS

Of 270 patients included: median age was 66.6 years (31-88), 50.4% of patients were male (n = 136), 62% were Caucasian (n = 168). Cancer-related hospitalization incidence was 17% (n = 47), of which 21% of patients hospitalized (n = 10/47) had > 1 hospitalization. On multivariable analysis, each 1 g/dL baseline drop in albumin was associated with a 2.4 times higher risk of any hospitalization (95% confidence interval (CI) 1.2-5.0, P = 0.01), and baseline hemoglobin ≤10 was associated with, on average, 2.7 more hospitalizations than having pre-treatment hemoglobin > 10 (95% CI 1.3-5.4, P = 0.01). After controlling for baseline variables, cancer-related hospitalization was associated with 1.8 times increased risk of all-cause death (95% CI: 1.02-3.1, P = 0.04).

CONCLUSIONS

Our data show baseline factors can predict those who may be at increased risk for hospitalization, which was independently associated with increased mortality. Taken together, these data support the need for developing further studies aimed at early and aggressive interventions to decrease hospitalizations during treatment.

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

IMPORTANCE

Emergency department visits and hospitalizations after starting systemic therapy for cancer are frequent, undesirable, and costly. A score to quantify the risk of needing acute care can inform decision-making and facilitate the development of preventive interventions.

OBJECTIVE

To develop and validate a score to predict early use of acute care after initiating systemic therapy for cancer.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective population-based cohort study was conducted between July 1, 2014, and June 30, 2015. Patients with cancer were eligible if they started a new systemic therapy for cancer, regardless of line of therapy. A total of 12 162 patients in Southwestern Ontario, Canada, formed the development cohort and 15 845 patients in Northeastern Ontario formed the validation cohort. Data analysis was conducted from December 1, 2016, to August 10, 2019.

EXPOSURES

The Prediction of Acute Care Use During Cancer Treatment (PROACCT) score was created based on logistic regression in the development cohort. Combinations of cancer type and regimens were grouped into quintiles based on risk of needing acute care. The score was assessed in the validation cohort.

MAIN OUTCOMES AND MEASURES

At least 1 emergency department visit or hospitalization within 30 days after starting systemic therapy for cancer identified from administrative databases.

RESULTS

Among the 12 162 patients in the development cohort, 6903 were women and 5259 were men (mean [SD] age, 62.9 [12.6] years); among the 15 845 patients in the validation cohort, 9025 were women and 6820 were men (mean [SD] age, 62.9 [12.6] years). Use of acute care occurred within 30 days after initiation of systemic therapy in 3039 patients (25.0%) in the development cohort and 4212 patients (26.6%) in the validation cohort. Three characteristics predicted early use of acute care and formed the PROACCT score: combination of cancer type and treatment regimen, age, and emergency department visits in the prior year (C statistic, 0.67; 95% CI, 0.66-0.69; P < .001). Other characteristics including patient-reported symptoms did not improve performance. In the validation cohort, the PROACCT score was associated with use of acute care (odds ratio per point increase, 1.22; 95% CI, 1.20-1.24; P < .001), had a C statistic of 0.61 (95% CI, 0.60-0.62; P < .001), was reasonably calibrated, and provided net benefit in decision curve analysis.

CONCLUSIONS AND RELEVANCE

The PROACCT score predicted the risk of early use of acute care in patients starting systemic treatment for cancer and could be incorporated at the point of care to select patients for preventive interventions. Future studies should validate the PROACCT score in other settings.