Respiratory Failure after Open Descending Aortic Aneurysm Repair: Risk Factors and Outcomes

Percutaneous dilatation tracheotomy in patients on extracorporeal membrane oxygenation after cardiac surgery

BACKGROUND

Current practices regarding percutaneous dilatational tracheostomy in adult patients treated with extracorporeal membrane oxygenation (ECMO) after cardiac surgery is not completely defined. This study aimed to evaluate the safety of the percutaneous dilatational tracheostomy in patients with ECMO after cardiac surgery.

METHODS

Between July 2017 and May 2021, 371 ECMO procedures were performed in more than 35,000 adult patients who underwent cardiac surgery in our hospital. Sixty-two patients underwent percutaneous dilatational tracheostomy (PDT) during or after ECMO. A retrospective analysis was performed comparing the incidence of complications and clinical outcomes of the two groups.

RESULTS

Of the 371 patients treated with ECMO after adult cardiac surgery during the enrollment period, 22 (7.1%) and 40 (12.8%) underwent PDT during or after ECMO, respectively. The platelet count (PLT) of the day was significantly lower in the PDT during ECMO group (54 (34, 68) vs. 108 (69, 162) (thousands), p < 0.001)). The prothrombin time (PT) and activated partial thromboplastin time (APTT) of the day were longer in the PDT during ECMO group (15.8 (14.6, 19.9) vs. 13.8 (13.2, 15.2) seconds, p = 0.001, 43.8 (38.0, 49.4) vs. 35.2 (28.2, 40.9) seconds, p < 0.001, respectively). There was no significant difference in tracheotomy-related complications between the two groups. Significantly decreased ventilator time was observed in the PDT during ECMO group.

CONCLUSIONS

Despite poor coagulation of the day, PDT during ECMO is safe and can appropriately reduce the duration of mechanical ventilation compared with PDT after ECMO weaning in adult patients who have undergone cardiac surgery.

A prediction model for in-hospital mortality in intensive care unit patients with metastatic cancer

Aim

To identify predictors for in-hospital mortality in patients with metastatic cancer in intensive care units (ICUs) and established a prediction model for in-hospital mortality in those patients.

Methods

In this cohort study, the data of 2,462 patients with metastatic cancer in ICUs were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to identify the predictors for in-hospital mortality in metastatic cancer patients. Participants were randomly divided into the training set (n = 1,723) and the testing set (n = 739). Patients with metastatic cancer in ICUs from MIMIC-IV were used as the validation set (n = 1,726). The prediction model was constructed in the training set. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were employed for measuring the predictive performance of the model. The predictive performance of the model was validated in the testing set and external validation was performed in the validation set.

Results

In total, 656 (26.65%) metastatic cancer patients were dead in hospital. Age, respiratory failure, the sequential organ failure assessment (SOFA) score, the Simplified Acute Physiology Score II (SAPS II) score, glucose, red cell distribution width (RDW) and lactate were predictors for the in-hospital mortality in patients with metastatic cancer in ICUs. The equation of the prediction model was ln(P/(1 + P)) = -5.9830 + 0.0174 × age + 1.3686 × respiratory failure + 0.0537 × SAPS II + 0.0312 × SOFA + 0.1278 × lactate - 0.0026 × glucose + 0.0772 × RDW. The AUCs of the prediction model was 0.797 (95% CI,0.776-0.825) in the training set, 0.778 (95% CI, 0.740-0.817) in the testing set and 0.811 (95% CI, 0.789-0.833) in the validation set. The predictive values of the model in lymphoma, myeloma, brain/spinal cord, lung, liver, peritoneum/pleura, enteroncus and other cancer populations were also assessed.

Conclusion

The prediction model for in-hospital mortality in ICU patients with metastatic cancer exhibited good predictive ability, which might help identify patients with high risk of in-hospital death and provide timely interventions to those patients.