Association of Early Palliative Care With Chemotherapy Intensity in Patients With Advanced Stage Lung Cancer: A National Cohort Study

Lammers, Austin; Slatore, Christopher G.; Fromme, Erik K.; Vranas, Kelly C.; Sullivan, Donald R.

doi:10.1016/j.jtho.2018.09.029

Cited by 19 publications

(16 citation statements)

References 51 publications

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“…www.nature.com/scientificreports www.nature.com/scientificreports/ Retrospective studies in a single institution have shown that some T3N0 patients may not require postoperative radiotherapy 20 . Furthermore, among patients with advanced cancer, early palliative care may optimize patient selection for chemotherapy reducing the use of high-intensity therapy by focusing on quality of life in accordance with patients' performance, preferences and care goals 21 . Additionally, no clear linear pattern between adjuvant chemotherapy and better adjusted relative survival in colon cancer was observed 22 .…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection

Tong

et al. 2020

Sci Rep

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the aim of this study is to explore the feasibility of using machine learning (ML) technology to predict postoperative recurrence risk among stage iV colorectal cancer patients. four basic ML algorithms were used for prediction-logistic regression, decision tree, GradientBoosting and lightGBM. the research samples were randomly divided into a training group and a testing group at a ratio of 8:2. 999 patients with stage 4 colorectal cancer were included in this study. In the training group, the GradientBoosting model's AUC value was the highest, at 0.881. The Logistic model's AUC value was the lowest, at 0.734. The GradientBoosting model had the highest F1_score (0.912). In the test group, the AUC Logistic model had the lowest AUC value (0.692). The GradientBoosting model's AUC value was 0.734, which can still predict cancer progress. However, the gbm model had the highest AUC value (0.761), and the gbm model had the highest F1_score (0.974). The GradientBoosting model and the gbm model performed better than the other two algorithms. the weight matrix diagram of the GradientBoosting algorithm shows that chemotherapy, age, LogCEA, CEA and anesthesia time were the five most influential risk factors for tumor recurrence. the four machine learning algorithms can each predict the risk of tumor recurrence in patients with stage iV colorectal cancer after surgery. Among them, GradientBoosting and gbm performed best. Moreover, the GradientBoosting weight matrix shows that the five most influential variables accounting for postoperative tumor recurrence are chemotherapy, age, LogCEA, ceA and anesthesia time. Colorectal cancer is a common malignant tumor with high morbidity and mortality in clinical practice. It ranks third in mortality among all tumors 1. Approximately 1.4 million new cases are diagnosed every year, and about half of the new cases are in the progressive stage. The 5-year survival rate is 30% ~ 40%, due primarily to postoperative recurrence and metastasis, of which 10% ~ 30% have abdominal cavity metastasis, with a median survival of 7 months. In China, the incidence and mortality of colorectal cancer rank third and fifth, respectively, among systemic tumors. Currently, the main clinical approach is surgical treatment assisted with multidisciplinary methods such as radiotherapy, chemotherapy and targeted therapy. However, a meta-analysis of 18 clinical trials shows that patients have a recurrence rate of 80.00% within 3 years after surgery 2. With early diagnosis and treatment, the prognosis of early stage colorectal cancer patients is optimistic, and the middle and long-term survival rate is usually high. However, as early symptoms are not typical, they are easily ignored by patients, leading to progression to the middle and late stages when they are finally admitted to hospitals. This inhibits treatment and reduces long-term survival rates. Recent machine learning (ML) methods have shown accurate predictive ability, and have been increasingly used in the diagnosis and prognosis of various diseases and hea...

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Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection

Tong

et al. 2020

Sci Rep

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“…Evidently, the decision-making process is complicated for both the patient and their medical team. The judicious use of chemotherapy only for patients who are likely to derive benefit has been shown to improve survival [ 6 , 15 , 16 ]. Additionally, enhanced communication of patient preferences may improve symptom management, helping reduce unintended harms of aggressive treatment leading to potential survival benefits [ 6 ].…”

Section: Discussionmentioning

confidence: 99%

“…The judicious use of chemotherapy only for patients who are likely to derive benefit has been shown to improve survival [ 6 , 15 , 16 ]. Additionally, enhanced communication of patient preferences may improve symptom management, helping reduce unintended harms of aggressive treatment leading to potential survival benefits [ 6 ]. Incorporating goals of care discussions, fostering better communication among the multidisciplinary care team, and setting realistic patient expectations for cancer treatment, may lead to improved patient selection for a treatment regimen most likely to derive benefit than harm [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, the use of chemotherapy in stage IV NSLSC is not curative, and only results in a small improvement in patient overall survival and quality of life [ 5 ]. Overall, patients diagnosed with advanced-stage NSLSC typically have a median survival of less than one year despite treatment and a five-year survival of less than 20% [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Pattern of Treatment Initiation and Outcomes for Patients With Metastatic Non-small Cell Lung Cancer in Ontario

Ding¹,

Hussein²,

Huang³

et al. 2022

Cureus

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Introduction: The impact of diagnosis and treatment delay on outcomes in advanced non-small cell lung carcinoma (NSCLC) is not well understood. In this study, we examined the effect of the length of time to the first chemotherapy treatment initiation and the other factors affecting overall survival. Methods: This retrospective study used data from the Institute of Clinical Evaluative Sciences and identified 4520 patients in Ontario who were diagnosed with stage IV NSCLC between 2007 and 2016, treated using chemotherapy. We adjusted the analysis for location (rural vs urban), gender, distance from the nearest cancer center, first chemotherapy treatment used, income, and age. Results: Type of the chemotherapy, length of time to the first treatment, and distance from the nearest cancer center had a statistically significant impact on survival. Paclitaxel was associated with decreased risk of death compared to vinorelbine (Hazard Ratio (HR)=0.835, 95%CI 0.753-0.925), gemcitabine (HR=0.916, 95%CI 0.998-0.826), and docetaxel (HR=0.771, 95%CI 0.994-0.513). Every additional 10 km distance from the nearest cancer center was associated with a 0.5% increased risk of death (HR=1.005, 95%CI 1.000-1.010). A longer time to the first treatment was associated with increased survival. In fact, every 10 days increase in wait time was associated with a 0.5% decrease in the risk of death (HR=0.995, 95%CI 0.993-0.998). Conclusion: Chemotherapy treatment using paclitaxel and living closer to the cancer center is associated with better survival. A longer time between diagnosis and treatment leading to better survival could perhaps be explained by patients on the "sicker" end of the spectrum receiving treatment sooner.

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“…Second, numerous studies conducted across different care settings have provided clear evidence to support the earlier integration of palliative care, sometimes even delivered concurrently with (curative) treatment [41,42]. This has led to an increased interest with regards to the earlier integration as well as official endorsement by clinical guidelines [43].…”

Section: Discussionmentioning

confidence: 99%

The distress thermometer as a prognostic tool for one-year survival among patients with lung cancer

et al. 2019

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The use of patient-reported outcome measures is increasingly advocated to support high-quality cancer care. We therefore investigated the added value of the Distress Thermometer (DT) when combined with known predictors to assess one-year survival in patients with lung cancer. Methods: All patients had newly diagnosed or recurrent lung cancer, started systemic treatment, and participated in the intervention arm of a previously published randomised controlled trial. A Cox proportional hazards model was fitted based on five selected known predictors for survival. The DT-score was added to this model and contrasted to models including the EORTC-QLQ-C30 global QoL score (quality of life) or the HADS total score (symptoms of anxiety and depression). Model performance was evaluated through improvement in the-2 log likelihood, Harrell's C-statistic, and a risk classification. Results: In total, 110 patients were included in the analysis of whom 97 patients accurately completed the DT. Patients with a DT score ≥5 (N = 51) had a lower QoL, more symptoms of anxiety and depression, and a shorter median survival time (7.6 months vs 10.0 months; P = 0.02) than patients with a DT score < 5 (N = 46). Addition of the DT resulted in a significant improvement in the accuracy of the model to predict one-year survival (P < 0.001) and the discriminatory value (C-statistic) marginally improved from 0.69 to 0.71. The proportion of patients correctly classified as high risk (≥85% risk of dying within one year) increased from 8% to 28%. Similar model performance was observed when combining the selected predictors with QoL and symptoms of anxiety or depression. Conclusions: Use of the DT allows clinicians to better identify patients with lung cancer at risk for poor outcomes, to further explore sources of distress, and subsequently personalize care accordingly.

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Association of Early Palliative Care With Chemotherapy Intensity in Patients With Advanced Stage Lung Cancer: A National Cohort Study

Cited by 19 publications

References 51 publications

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection

Pattern of Treatment Initiation and Outcomes for Patients With Metastatic Non-small Cell Lung Cancer in Ontario

The distress thermometer as a prognostic tool for one-year survival among patients with lung cancer

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