Short- and long-term prognosis of critically-ill patients referred to the ICU from the Emergency Department of a tertiary hospital

In-Hospital Mortality Prediction Model for Critically Ill Older Adult Patients Transferred from the Emergency Department to the Intensive Care Unit

Purpose

Studies on the prognosis of critically ill older adult patients admitted to the emergency department (ED) but requiring immediate admission to the intensive care unit (ICU) remain limited. This study aimed to develop an in-hospital mortality prediction model for critically ill older adult patients transferred from the ED to the ICU.

Patients and Methods

The training cohort was taken from the Medical Information Mart for Intensive Care IV (version 2.2) database, and the external validation cohort was taken from the Affiliated Dongyang Hospital of Wenzhou Medical University. In the training cohort, class balance was addressed using Random Over Sampling Examples (ROSE). Univariate and multivariate Cox regression analyses were performed to identify independent risk factors. These were then integrated into the predictive nomogram. In the validation cohort, the predictive performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, clinical utility decision curve analysis (DCA), and clinical impact curve (CIC).

Results

In the ROSE-balanced training cohort, univariate and multivariate Cox regression analysis identified that age, sex, Glasgow coma scale score, malignant cancer, sepsis, use of mechanical ventilation, use of vasoactive agents, white blood cells, potassium, and creatinine were independent predictors of in-hospital mortality in critically ill older adult patients, and were included in the nomogram. The nomogram showed good predictive performance in the ROSE-balanced training cohort (AUC [95% confidence interval]: 0.792 [0.783-0.801]) and validation cohort (AUC [95% confidence interval]: 0.780 [0.727-0.834]). The calibration curves were well-fitted. DCA and CIC demonstrated that the nomogram has good clinical application value.

Conclusion

This study developed a predictive model for early prediction of in-hospital mortality in critically ill older adult patients transferred from the ED to the ICU, which was validated by external data and has good predictive performance.

Clinical characteristics and outcomes of cancer patients requiring intensive care unit admission: a prospective study

PURPOSE

The study was aimed to describe the characteristics of cancer patients admitted to the oncological ICU and to identify clinical features associated with outcomes.

METHODS

This is a prospective study (January 2014 to December 2015) of 522 cancer patients consecutively admitted to the oncological ICU. Patients with a length of oncological ICU stay ≤ 1 day were excluded. Demographic and clinical variables were obtained at oncological ICU admission. The primary outcome of interest was hospital mortality. Logistic regression analysis was performed to identify independent risk factors for hospital mortality.

RESULTS

The study cohort consisted of 492 (94.3%) patients with solid tumours and 30 patients (5.7%) with haematological malignancies. Advanced cancer was observed in 53.3%. Unplanned admission accounted for 25.3%. Hospital mortality rate was 13.0% (n = 68), and it was higher for patients with unplanned admission than those for electively admitted patients (35.6% vs. 5.4; p < 0.0001). Stage IV of cancer (OR 5.28; 95% CI 2.71-10.28; p < 0.0001), patients from the emergency department (OR 3.33; 95% CI 1.68-6.61; p = 0.001), unplanned admission (OR 7.99; 95% CI 4.45-14.33; p < 0.0001), non-malignancy-related admission (OR 5.80; 95% CI 3.26-10.32; p < 0.0001), sepsis (OR 4.81; 95% CI 2.28-10.16; p < 0.0001), chemotherapy-induced adverse event (OR 5.64; 95% CI 2.33-13.66; p < 0.0001), and invasive mechanical ventilation (OR 18.70; 95% CI 9.93-35.21; p < 0.0001) were independently associated with increased hospital mortality in multivariate logistic regression analysis.

CONCLUSIONS

ICU admission of cancer patients should be based on potential chance of recovering from the acute problem. Clinical predictor for mortality could support this purpose (UIN: researchregistry3484).