Administrative healthcare data to predict performance status in lung cancer patients

Andreano, Anita; Russo, Antonio

doi:10.1016/j.dib.2021.107559

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Performance status (PS), as a subjective composite to evaluate the patient’s wellness, is a key factor reflecting the patient’s ability to carry on normal activities. Several previous studies have reported the role of PS as a prognostic signature impacting the survival rate in different age categories [ 38 – 41 ]. Regarding medical urgency associated with late diagnosis of advanced disease, we found that SVCS, as well as pleurisy syndrome, were all associated with poor survival in patients at the different stage categories of the disease.…”

Section: Discussionmentioning

confidence: 99%

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

Tafenzi,

Choulli,

Adjade

et al. 2023

BMC Cancer

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Background Nomogram is a graphic representation containing the expressed factor of the mathematical formula used to define a particular phenomenon. We aim to build and internally validate a nomogram to predict overall survival (OS) in patients diagnosed with lung cancer (LC). Methods We included 1200 LC patients from a single institution registry diagnosed from 2013 to 2021. The independent prognostic factors of LC patients were identified via cox proportional hazard regression analysis. Based on the results of multivariate cox analysis, we constructed the nomogram to predict the OS of LC patients. Results We finally included a total of 1104 LC patients. Age, medical urgency at diagnosis, performance status, radiotherapy, and surgery were identified as prognostic factors, and integrated to build the nomogram. The model performance in predicting prognosis was measured by receiver operating characteristic curve. Calibration plots of 6-, 12-, and 24- months OS showed optimal agreement between observations and model predictions. Conclusion We have developed and validated a unique predictive tool that can offer patients with LC an individual OS prognosis. This useful prognostic model could aid doctors in making decisions and planning therapeutic trials.

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Section: Discussionmentioning

confidence: 99%

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

Tafenzi,

Choulli,

Adjade

et al. 2023

BMC Cancer

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“…In previous research, Andreano et al [53] proposed a Logistic Regression model for predicting the ECOG PS in lung cancer patients using administrative healthcare data including 4488 patients with 11 features. The target feature was dichotomized as "poor" (ECOG PS between 3 and 5) and "good" (ECOG PS between 0 and 2) based on all other factors in the dataset.…”

Section: Discussionmentioning

confidence: 99%

Prediction of ECOG Performance Status of Lung Cancer Patients Using LIME-Based Machine Learning

Nguyen

Byeon

2023

Mathematics

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The Eastern Cooperative Oncology Group (ECOG) performance status is a widely used method for evaluating the functional abilities of cancer patients and predicting their prognosis. It is essential for healthcare providers to frequently assess the ECOG performance status of lung cancer patients to ensure that it accurately reflects their current functional abilities and to modify their treatment plan accordingly. This study aimed to develop and evaluate an AdaBoost classification (ADB-C) model to predict a lung cancer patient’s performance status following treatment. According to the results, the ADB-C model has the highest “Area under the receiver operating characteristic curve” (ROC AUC) score at 0.7890 which outperformed other benchmark models including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, XGBoost, and TabNet. In order to achieve model prediction explainability, we combined the ADB-C model with a LIME-based explainable model. This explainable ADB-C model may assist medical professionals in exploring effective cancer treatments that would not negatively impact the post-treatment performance status of a patient.

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Immune checkpoint inhibitors at any treatment line in advanced NSCLC: Real-world overall survival in a large Italian cohort

Andreano¹,

Bergamaschi²,

Russo³

2021

Lung Cancer

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To estimate the average treatment effect of immune checkpoint inhibitors in any line of treatment in a 2016-2018 population-based cohort of patients with advanced non-small-cell lung cancer (NSCLC). Materials and methods: The cohort, and information on the tumor, were derived from the cancer registry of the Agency for Health Protection of Milan, Italy. Inclusion criteria were adult age, microscopically confirmed NSCLC, stage IIIB or IV at diagnosis, and having received at least one line of treatment. Treatment with all licensed anti PD-1/PD-L1 inhibitors was derived from inpatients and outpatients' pharmaceutical databases of the ATS and vital status at 31 December 2019 from the health registry office of the Lombardy region. We investigated, with a causal approach, the relationship between survival and anti PD-1/PD-L1 treatment at any line constructing a directed acyclic graph and fitting a Marginal Structural Cox Model (MSCM). Results: Of 1673 subjects, 324 received anti PD-1/PD-L1 at any treatment line. Overall, one-year survival was 61.1% (95 %CI, 55.6-66.2%) in the group treated with anti PD-1/PD-L1 at any line and 31.1% (95 %CI, 28.6-33.5%) among not treated. One-year hazard ratio (HR) of death for not treated vs. treated was 2.15 (95 % CI, 1.91-2.41), decreasing to 1.23 (95 %CI, 1.03-1.46) at two years and reaching one in the third year. Conclusion:In un unselected population-based cohort with advanced lung cancer, treatment with anti PD-1/PD-L1 at any line lowered the hazard of death up to two-years from date of diagnosis, confirming the efficacy of immunotherapy outside clinical trials.

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Administrative healthcare data to predict performance status in lung cancer patients

Cited by 4 publications

References 5 publications

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

Prediction of ECOG Performance Status of Lung Cancer Patients Using LIME-Based Machine Learning

Immune checkpoint inhibitors at any treatment line in advanced NSCLC: Real-world overall survival in a large Italian cohort

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