The prognosis of non‐small cell lung cancer combined with chronic obstructive pulmonary disease: A systematic review and meta‐analysis

Wu, Ke; Wang, Jing; Zhao, Limin; Wang, Pei; Duan, Qi

doi:10.1111/crj.13144

Cited by 8 publications

(9 citation statements)

References 17 publications

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“…And a 53% survival rate was reported by another study in critically ill cancer patients with respiratory failure 23 . Other respiratory disease such as chronic obstruction pulmonary diseases and emphysema can also cause poor prognosis of cancer patients admitted to ICU 24 .…”

Section: Discussionmentioning

confidence: 99%

Development of a model for predicting mortality of breast cancer admitted to Intensive Care Unit

Huang¹,

Wu²,

Guo³

et al. 2022

Afr H. Sci.

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Background: There is still not a mortality prediction model built for breast cancer admitted to intensive care unit (ICU). Objectives: We aimed to build a prognostic model with comprehensive data achieved from eICU database. Methods: Outcome was defined as all-cause in-hospital mortality. Least absolute shrinkage and selection operator (LASSO) was conducted to select important variables which were then taken into logistic regression to build the model. Bootstrap method wasthen conducted for internal validation. Results: 448 patients were included in this study and 79 (17.6%) died in hospital. Only 5 items were included in the model and the area under the curve (AUC) was 0.844 (95% confidence interval [CI]: 0.804-0.884). Calibration curve and Brier score (0.111, 95% CI: 0.090-0.127) showed good calibration of the model. After internal validation, corrected AUC and Brier score were 0.834 and 0.116. Decision curve analysis (DCA) also showed effective clinical use of the model. The model can be easily assessed on website of https://breastcancer123.shinyapps.io/BreastCancerICU/. Conclusions: The model derived in this study can provide an accurate prognosis for breast cancer admitted to ICU easily, which can help better clinical management. Keywords: Breast neoplasms; intensive care unit; prognosis.

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Section: Discussionmentioning

confidence: 99%

Development of a model for predicting mortality of breast cancer admitted to Intensive Care Unit

Huang¹,

Wu²,

Guo³

et al. 2022

Afr H. Sci.

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“…Lung cancer is a significant risk factor for the development of COPD, and COPD is a significant cause of death in lung cancer patients (70). Previous meta-analyses of the prognosis of non-small cell lung cancer with COPD have shown that COPD is an important factor in the development and progression of lung cancer and that COPD in combination with non-small cell lung cancer patients is linked to a poor prognosis, shorter survival, and higher mortality (71). Consistent with this, our study also found that COPD patients with combined malignancy had a higher mortality rate than COPD patients without combined malignancy.…”

Section: Discussionmentioning

confidence: 99%

Development of a machine learning-based nomogram for predicting one-year mortality in elderly patients with chronic obstructive pulmonary disease in intensive care unit

Liu

Zhang

Deng³

et al. 2022

Preprint

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Objective: Predictors of one-year mortality for elderly patients referred to intensive care units (ICUs) with chronic obstructive pulmonary disease (COPD) remain poorly described. We sought to create and verify a model for predicting all-cause one-year death among COPD patients admitted to the ICU. Methods: A retrospective cohort study was designed. The Medical Information Mart for Intensive Care (MIMIC-IV) database was mined for information. A total of 2313 patients with COPD who met the inclusion criteria were screened for eligibility using registry data from the MIMIC-IV database between January 2008 and December 2019, which was randomized into training (n = 1628, 70%) and testing groups (n = 685, 30%). The tree model-based extreme gradient boosting (XGBoost), random forest (RF), and lightGBM algorithms were used in the training samples to rank feature importance and screen out its top 10 union features as independent mortality risk factors.The variance inflation factor (VIF) was used to check for collinearity in the prediction model's input variables, and multivariate logistic regression analysis was used to take a gander at factors related to mortality and then to use those factors to create a nomogram for predicting prognosis, which was then tested on the testing cohort. The AUC, calibration plot, and decision curve analysis were performed to evaluate the predictive model's discrimination, calibration, and clinical utility. Results: The results showed that ∼40.60% (939 out of 2,313) of the patients died. In multivariate logistic regression analyses, weight, BUN, RDW, MCHC, age, malignancy, and heart_rate were all independent predictors of mortality. In both groups, the nomogram showed acceptable discrimination (area under curve [AUC] = 0.7216 [95% confidence interval (CI) 0.6968–0.7465] and AUC=0.7481 [95% CI 0.7116–0.7845], respectively) and good calibration compared to SOFA, SAPS II, LODS, APS III, OASIS, SIRS, GCS, and MELD risk scores.In addition, it was discovered that calibration plots accurately predicted one-year mortality. The examination of decision curves (DCAs) revealed that the nomogram model had a greater net benefit. Conclusions: Our nomogram provides interpretable and visual explanations of customized one-year death projections in ICU-admitted elderly patients with COPD, which may aid clinical clinicians in comprehending the effects of key model characteristics and the model's decision-making for such patients.

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“… 9 Recently, Wu et al conducted a meta-analysis with 12 case-control studies and showed that patients with NSCLC concomitant COPD was associated with poorer OS than the absence of COPD. 11 …”

Section: Discussionmentioning

confidence: 99%

“… 10 Wu et al investigated the effect of NSCLC concomitant with COPD on prognosis and indicated that COPD has a deleterious effect on survival. 11 Mostly of those studies were focused on NSCLC, and studies investigating the prognostic significance of COPD for SCLC patients are limited. In addition, previous studies focused on the association of lung cancer and COPD were mainly in Caucasian populations, relevant data in Asian populations was scanty.…”

Section: Introductionmentioning

confidence: 99%

Impact of Chronic Obstructive Pulmonary Disease on the Mortality of Patients with Small Cell Lung Cancer

Liao

Hung

Lee

et al. 2021

COPD

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Background Limited studies have focused on the impact of the coexistence of small cell lung cancer (SCLC) and chronic obstructive pulmonary disease (COPD). The study was to examine the impact of COPD on mortality in SCLC patients. Methods We analyzed SCLC patients from the Taiwan Cancer Registry Database between January 1, 1997, and December 31, 2015. The COPD population was composed of patients with a COPD diagnosis before the diagnosis of SCLC. The control group was composed of randomly selected SCLC patients without COPD who were propensity score matched with those with concomitant COPD according to age, sex, index date, cancer staging and comorbidities at a 1:1 ratio. Results Among 9425 SCLC patients in the database, eligible subjects were divided into the COPD group (n = 4235) and the non-COPD group (n = 2334). Compared to patients in the non-COPD group, the patients in the COPD group were older (71.4 versus 65.7 years, p<0.0001), had a lower percentage of stage IV disease (60.1% versus 68.3%, p<0.0001) and had more comorbidities. After matching, there were 1457 patients in each group. Older age, lower body mass index (BMI), and some comorbidities were associated with higher mortality, and comorbid COPD was associated with lower 1-year mortality in SCLC patients. Multivariate analysis identified older age, lower BMI, and concomitant congestive heart failure or diabetes as risk factors for OS. Conclusion A diagnosis of COPD was associated with reduced 1-year mortality in SCLC patients, but no significant difference after 1-year in this population.

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The prognosis of non‐small cell lung cancer combined with chronic obstructive pulmonary disease: A systematic review and meta‐analysis

Cited by 8 publications

References 17 publications

Development of a model for predicting mortality of breast cancer admitted to Intensive Care Unit

Development of a model for predicting mortality of breast cancer admitted to Intensive Care Unit

Development of a machine learning-based nomogram for predicting one-year mortality in elderly patients with chronic obstructive pulmonary disease in intensive care unit

Impact of Chronic Obstructive Pulmonary Disease on the Mortality of Patients with Small Cell Lung Cancer

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