Frequency, Risk Factors, and Outcomes of Unplanned Readmission to the Neurological Intensive Care Unit after Spontaneous Intracerebral Hemorrhage

Predicting ICU readmission risks in intracerebral hemorrhage patients: Insights from machine learning models using MIMIC databases

BACKGROUND

Intracerebral hemorrhage (ICH) is a stroke subtype characterized by high mortality and complex post-event complications. Research has extensively covered the acute phase of ICH; however, ICU readmission determinants remain less explored. Utilizing the MIMIC-III and MIMIC-IV databases, this investigation develops machine learning (ML) models to anticipate ICU readmissions in ICH patients.

METHODS

Retrospective data from 2242 ICH patients were evaluated using ICD-9 codes. Recursive feature elimination with cross-validation (RFECV) discerned significant predictors of ICU readmissions. Four ML models-AdaBoost, RandomForest, LightGBM, and XGBoost-underwent development and rigorous validation. SHapley Additive exPlanations (SHAP) elucidated the effect of distinct features on model outcomes.

RESULTS

ICU readmission rates were 9.6% for MIMIC-III and 10.6% for MIMIC-IV. The LightGBM model, with an AUC of 0.736 (95% CI: 0.668-0.801), surpassed other models in validation datasets. SHAP analysis revealed hydrocephalus, sex, neutrophils, Glasgow Coma Scale (GCS), specific oxygen saturation (SpO2) levels, and creatinine as significant predictors of readmission.

CONCLUSION

The LightGBM model demonstrates considerable potential in predicting ICU readmissions for ICH patients, highlighting the importance of certain clinical predictors. This research contributes to optimizing patient care and ICU resource management. Further prospective studies are warranted to corroborate and enhance these predictive insights for clinical utilization.

Perioperative risk factors associated with unplanned neurological intensive care unit readmission following elective supratentorial brain tumor resection

OBJECTIVE

The aim of this study was to describe the clinical and procedural risk factors associated with the unplanned neurosurgical intensive care unit (NICU) readmission of patients after elective supratentorial brain tumor resection and serves as an exploratory analysis toward the development of a risk stratification tool that may be prospectively applied to this patient population.

METHODS

This was a retrospective observational cohort study. The electronic medical records of patients admitted to an institutional NICU between September 2018 and November 2021 after elective supratentorial brain tumor resection were reviewed. Demographic and perioperative clinical factors were recorded. A prognostic model was derived from the data of 4892 patients recruited between September 2018 and May 2021 (development cohort). A nomogram was created to display these predictor variables and their corresponding points and risks of readmission. External validation was evaluated using a series of 1118 patients recruited between June 2021 and November 2021 (validation cohort). Finally, a decision curve analysis was performed to determine the clinical usefulness of the prognostic model.

RESULTS

Of the 4892 patients in the development cohort, 220 (4.5%) had an unplanned NICU readmission. Older age, lesion type, Karnofsky Performance Status (KPS) < 70 at admission, longer duration of surgery, retention of endotracheal intubation on NICU entry, and longer NICU length of stay (LOS) after surgery were independently associated with an unplanned NICU readmission. A total of 1118 patients recruited between June 2021 and November 2021 were included for external validation, and the model's discrimination remained acceptable (C-statistic = 0.744, 95% CI 0.675-0.814). The decision curve analysis for the prognostic model in the development and validation cohorts showed that at a threshold probability between 0.05 and 0.8, the prognostic model showed a positive net benefit.

CONCLUSIONS

A predictive model that included age, lesion type, KPS < 70 at admission, duration of surgery, retention of endotracheal intubation on NICU entry, and NICU LOS after surgery had an acceptable ability to identify elective supratentorial brain tumor resection patients at high risk for an unplanned NICU readmission. These risk factors and this prediction model may facilitate better resource allocation in the NICU and improve patient outcomes.

Status and Risk Factors in Patients Requiring Unplanned Intensive Care Unit Readmission Within 48 Hours: A Retrospective Propensity-Matched Study in China

Aim

This study investigated the current status and related risk factors of 48-hour unplanned return to the intensive care unit (ICU) to reduce the return rate and improve the quality of critical care management.

Methods

Data were collected from 2365 patients discharged from the comprehensive ICU. Multivariate and 1:1 propensity score matching analyses were performed.

Results

Forty patients (1.69%) had unplanned readmission to the ICU within 48 hours after transfer. The primary reason for return was respiratory failure (16 patients, 40%). Furthermore, respiratory failure (odds ratio [OR] = 5.994, p = 0.02) and the number of organ failures (OR = 5.679, p = 0.006) were independent risk factors for unplanned ICU readmission. Receiver operating characteristic curves were drawn for the predictive value of the number of organ injuries during a patient's unplanned transfer to the ICU (area under the curve [AUC] = 0.744, sensitivity = 60%, specificity = 77.5%).

Conclusion

The reason for patient transfer and the number of organ injuries during the process were independent risk factors for patients who were critically ill. The number of organs damaged had a predictive value on whether the patient would return to the ICU within 48 hours.

Impact of respiratory therapists-driven assess-and-treat protocol on unplanned adult neurovascular ICU readmissions: a quality improvement initiative

ICU readmission is associated with increased mortality, resource utilisation and hospital expenditure. In the general population, respiratory-related event is one of the most common causes of unexpected ICU readmission. Patients with neurological deficits faced an increased risks of ICU readmissions due to impaired mentation, protective reflexes and other factors. A retrospective review revealed that the leading cause of unexpected ICU readmissions in adult neurovascular patients admitted to our hospital was respiratory related. A respiratory therapists-driven assessment-and-treat protocol was developed for proactively assessing and treating adult neurovascular patients. On-duty respiratory therapists assessed all neurovascular patients on admission, assigned a respiratory severity score to each patient and then recommended interventions based on a standardised algorithm.

Our quality improvement initiative had no effect on the rate of unexpected ICU readmissions in adult neurovascular patients. When compared with the baseline population, patients enrolled in the intervention group were significantly older ((79, 68–85 years) vs (71, 56–81 years)), but they spent comparable amount of time in the ICU (4.5 vs 4 days, p=0.42). When the respiratory severity score was trended in the intervention group, patients demonstrated significant improvement in their respiratory function, with a greater proportion of patients scoring in the minimal and mild categories and smaller proportion in the moderate category (p<0.01).