Dataset Information

Significant Clinical Factors Associated with Long-term Mortality in Critical Cancer Patients Requiring Prolonged Mechanical Ventilation.

ABSTRACT: Studies about prognostic assessment in cancer patients requiring prolonged mechanical ventilation (PMV) for post-intensive care are scarce. We retrospectively enrolled 112 cancer patients requiring PMV support who were admitted to the respiratory care center (RCC), a specialized post-intensive care weaning facility, from November 2009 through September 2013. The weaning success rate was 44.6%, and mortality rates at hospital discharge and after 1 year were 43.8% and 76.9%, respectively. Multivariate logistic regression showed that weaning failure, in addition to underlying cancer status, was significantly associated with an increased 1-year mortality (odds ratio, 6.269; 95% confidence interval, 1.800-21.834; P?=?0.004). Patients who had controlled non-hematologic cancers and successful weaning had the longest median survival, while those with other cancers who failed weaning had the worst. Patients with low maximal inspiratory pressure, anemia, and poor oxygenation at RCC admission had an increased risk of weaning failure. In conclusion, cancer status and weaning outcome were the most important determinants associated with long-term mortality in cancer patients requiring PMV. We suggest palliative care for those patients with clinical features associated with worse outcomes. It is unknown whether survival in this specific patient population could be improved by modifying the risk of weaning failure.

SUBMITTER: Keng LT

PROVIDER: S-EPMC5438375 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Significant Clinical Factors Associated with Long-term Mortality in Critical Cancer Patients Requiring Prolonged Mechanical Ventilation.

Keng Li-Ta LT Chung Kuei-Pin KP Lin Shu-Yung SY Liang Sheng-Kai SK Cheng Jui-Chen JC Chen I-Chun IC Chen Yen-Fu YF Chang Hou-Tai HT Hsu Chia-Lin CL Jerng Jih-Shuin JS Wang Hao-Chien HC Kuo Ping-Hung PH Wu Huey-Dong HD Shih Jin-Yuan JY Yu Chong-Jen CJ

Scientific reports 20170519 1

Studies about prognostic assessment in cancer patients requiring prolonged mechanical ventilation (PMV) for post-intensive care are scarce. We retrospectively enrolled 112 cancer patients requiring PMV support who were admitted to the respiratory care center (RCC), a specialized post-intensive care weaning facility, from November 2009 through September 2013. The weaning success rate was 44.6%, and mortality rates at hospital discharge and after 1 year were 43.8% and 76.9%, respectively. Multivar ...[more]

PMID: 28526862

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Project description:BackgroundAmong patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.MethodsRetrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.ResultsThere were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality.DiscussionWe developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

Dataset Information

Significant Clinical Factors Associated with Long-term Mortality in Critical Cancer Patients Requiring Prolonged Mechanical Ventilation.

Publications

Significant Clinical Factors Associated with Long-term Mortality in Critical Cancer Patients Requiring Prolonged Mechanical Ventilation.

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