Clinical outcomes in patients with lung cancer admitted to intensive care units

Characteristics and outcomes of cancer patients admitted to intensive care units in cancer specialized hospitals in China

PURPOSE

Standard intensive care unit (ICU) admission policies and treatment strategies for patients with cancer are still lacking. To depict the current status of admission, characteristics, and outcomes of patients with cancer in the ICU.

METHODS

A multicenter cross-sectional study was performed from May 10, 2021 to July 10, 2021, in the ICU departments of 37 cancer-specialized hospitals in China. Clinical records of all admitted patients aged ≥ 14 years and ICU duration > 24 h with complete data were included. Demographic information, clinical history, severity score at admission, ICU critical condition diagnosis and treatment, ICU and in-hospital outcomes and 90 days survival were also collected. A total of 1455 patients were admitted and stayed for longer than 24 h. The most common primary cancer diagnoses included lung, colorectal, esophageal, and gastric cancer.

RESULTS

Patients with lung cancer were admitted more often because of worsening complications that occurred in the clinical ward. However, other cancer patients may be more likely to be admitted to the ICU because of postoperative care. ICU-admitted patients with lung or esophageal cancer tended to have more ICU complications. Patients with lung cancer had a poor overall survival prognosis, whereas patients with colorectal cancer appeared to benefit the most according to 90 days mortality rates.

CONCLUSION

Patients with lung cancer require more ICU care due to critical complications and the overall survival prognosis is poor. Colorectal cancer may benefit more from ICU management. This information may be considered in ICU admission and treatment strategies.

Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit

BACKGROUNDS

The in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making.

METHODS

Data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature.

RESULTS

Overall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model.

CONCLUSION

The ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians' decision-making in advance.

Prognostic factors for advanced lung cancer patients with do-not-intubate order in intensive care unit: a retrospective study

Background

The survival of patients with lung cancer undergoing critical care has improved. An increasing number of patients with lung cancer have signed a predefined do-not-intubate (DNI) order before admission to the intensive care unit (ICU). These patients may still be transferred to the ICU and even receive non-invasive ventilation (NIV) support. However, there is still a lack of prognostic predictions in this cohort. Whether patients will benefit from ICU care remains unclear.

Methods

We retrospectively collected data from patients with advanced lung cancer who had signed a DNI order before ICU admission in a tertiary medical center between 2014 and 2016. The clinical characteristics and survival outcomes were discussed.

Results

A total of 140 patients (median age, 73 years; 62.1% were male) were included, had been diagnosed with stage III or IV non-small cell lung cancer (NSCLC) (AJCC 7th edition), and signed a DNI. Most patients received NIV during ICU stay. The median APACHE II score was 14 (standard error [SE], ± 0.66) and the mean PaO2/FiO2 ratio (P/F ratio) was 174.2 (SD, ± 104 mmHg). The APACHE II score was significantly lower in 28-day survivors (survivor: 12 (± 0.98) vs. non-survivor: 15 (± 0.83); p = 0.019). The P/F ratio of the survivors was higher than that of non-survivors (survivors: 209.6 ± 111.4 vs. non-survivors: 157.9 ± 96.7; p = 0.006). Patients with a P/F ratio ≥ 150 had better 28-day survival (p = 0.005). By combining P/F ratio ≥ 150 and APACHE II score < 16, those with high P/F ratios and low APACHE II scores during ICU admission had a notable 28-day survival compared with the rest (p < 0.001). These prognostic factors could also be applied to 90-day survival (p = 0.003). The prediction model was significant for those with driver mutations in 90-day survival (p = 0.021).

Conclusions

P/F ratio ≥ 150 and APACHE II score < 16 were significant prognostic factors for critically ill patients with lung cancer and DNI. This prediction could be applied to 90-day survival in patients with driver mutations. These findings are informative for clinical practice and decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02042-7.

Differences in ICU Outcomes According to the Type of Anticancer Drug in Lung Cancer Patients

Purpose

We investigated the intensive care unit (ICU) outcomes of patients who used targeted therapy compared to those who received cytotoxic chemotherapy.

Materials and Methods

This study was based on Korean administrative health insurance claims from 2015 to 2019. We extracted data on lung cancer patients (&gt;18 years old) who were admitted to the ICU after receiving chemotherapy.

Results

6,930 lung cancer patients who received chemotherapy within 30 days before ICU admission were identified; the patients received cytotoxic chemotherapy (85.4%, n = 5,919) and molecular targeted therapy (14.5%, n = 1,011). Grade 4 neutropenia was identified only in the cytotoxic chemotherapy group (0.6%). Respiratory failure requiring ventilator treatment was more common in the cytotoxic chemotherapy group than in the targeted therapy group (HR, 3.30; 95% CI, 2.99–3.63), and renal failure requiring renal replacement therapy was not significantly different between the two groups (HR, 1.57; 95% CI, 1.36–1.80). Patients who received targeted chemotherapy stayed longer in the ICU than the cytotoxic chemotherapy. The 28-day mortality was 23.4% (HR, 0.79; 95% CI, 0.67–0.90, p &lt; 0.05) among patients who received targeted agents compared with 29.6% among patients who received cytotoxic chemotherapy.

Conclusion

Targeted chemotherapy for lung cancer may contribute to increasing access to critical care for lung cancer patients, which may play a role in improving critical care outcomes of lung cancer patients.