A Reassessment of Weaning Parameters in Patients With Spontaneous Intracerebral Hemorrhage

Predictors of extubation failure and prolonged mechanical ventilation among patients with intracerebral hemorrhage after surgery

BACKGROUND

Spontaneous intracerebral hemorrhage (ICH) is a condition associated with high mortality and morbidity. Survivors may require prolonged intubation with mechanical ventilation (MV). The aim of this study was to analyze the predictors of extubation failure and prolonged MV in patients who undergo surgical evacuation.

METHODS

This retrospective study was conducted on adult patients with ICH who underwent MV for at least 48 h and survived > 14 days after surgery. The demographics, clinical characteristics, laboratory tests, and Glasgow Coma Scale score were analyzed.

RESULTS

A total of 134 patients with ICH were included in the study. The average age of the patients was 60.34 ± 15.59 years, and 79.9% (n = 107) were extubated after satisfying the weaning parameters. Extubation failure occurred in 11.2% (n = 12) and prolonged MV in 48.5% (n = 65) patients. Multivariable regression analysis revealed that a white blood cell count > 10,000/mm3 at the time of extubation was an independent predictor of reintubation. Meanwhile, age and initial Glasgow Coma Scale scores were predictors of prolonged MV.

CONCLUSIONS

This study provided the first comprehensive characterization and analysis of the predictors of extubation failure and prolonged MV in patients with ICH after surgery. Knowledge of potential predictors is essential to improve the strategies for early initiation of adequate treatment and prognosis assessment in the early stages of the disease.

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Background

Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.

Methods

ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.

Conclusion

This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.