Clinical phenotypes of delirium in patients admitted to the cardiac intensive care unit

Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units

INTRODUCTION AND OBJECTIVES

Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predictive model for delirium for patients in the CICU.

METHODS

This study included consecutive patients admitted to the CICU of the Samsung Medical Center. To assess the candidate variables for the model: we applied the following machine learning methods: random forest, extreme gradient boosting, partial least squares, and Plmnet-elastic.net. After selecting relevant variables, we performed a logistic regression analysis to derive the model formula. Internal validation was conducted using 100-repeated hold-out validation.

RESULTS

We analyzed 2774 patients, 677 (24.4%) of whom developed delirium in the CICU. Machine learning-based models showed good predictive performance. Clinically significant and frequently important predictors were selected to construct a delirium prediction scoring model for CICU patients. The model included albumin level, international normalized ratio, blood urea nitrogen, white blood cell count, C-reactive protein level, age, heart rate, and mechanical ventilation. The model had an area under the receiver operating characteristics curve (AUROC) of 0.861 (95%CI, 0.843-0.879). Similar results were obtained in internal validation with 100-repeated cross-validation (AUROC, 0.854; 95%CI, 0.826-0.883).

CONCLUSIONS

Using variables frequently ranked as highly important in four machine learning methods, we created a novel delirium prediction model. This model could serve as a useful and simple tool for risk stratification for the occurrence of delirium at the patient's bedside in the CICU.

The association of the Sequential Organ Failure Assessment score at intensive care unit discharge with intensive care unit readmission in the cardiac intensive care unit

AIMS

Unplanned intensive care unit (ICU) readmissions contribute to increased morbidity, mortality, and healthcare costs. The severity of patient illness at ICU discharge may predict early ICU readmission. Thus, in this study, we investigated the association of cardiac ICU (CICU) discharge Sequential Organ Failure Assessment (SOFA) score with unplanned CICU readmission in patients admitted to the CICU.

METHODS AND RESULTS

We retrospectively reviewed the hospital medical records of 4659 patients who were admitted to the CICU from 2012 to 18. Sequential Organ Failure Assessment scores at CICU admission and discharge were obtained. The predictive performance of organ failure scoring was evaluated by using area under the receiver operating characteristic (AUROC) curves. The primary outcome was unplanned CICU readmission. Of the 3949 patients successfully discharged from the CICU, 184 (4.7%) had an unplanned CICU readmission or they experienced a deteriorated condition but died without being readmitted to the CICU (readmission group). The readmission group had significantly higher rates of organ failure in all organ systems at both CICU admission and discharge than the non-readmission group. The AUROC of the discharge SOFA score for CICU readmission was 0.731, showing good predictive performance. The AUROC of the discharge SOFA score was significantly greater than that of either the initial SOFA score (P = 0.020) or the Acute Physiology and Chronic Health Evaluation II score (P < 0.001). In the multivariable regression analysis, SOFA score, overweight or obese status, history of heart failure, and acute heart failure as reasons for ICU admission were independent predictors of unplanned ICU readmission during the same hospital stay.

CONCLUSION

The discharge SOFA score may identify patients at a higher risk of unplanned CICU readmission, enabling targeted interventions to reduce readmission rates and improve patient outcomes.

Development and validation of a novel poisoning agitation-sedation score for predicting the need for endotracheal intubation and mechanical ventilation in acutely poisoned patients with disturbed consciousness

BACKGROUND

Accurate assessment of disturbed consciousness level (DCL) is crucial for predicting acutely poisoned patients' outcomes.

AIM

Development of a novel Poisoning Agitation-Sedation Score (PASS) to predict the need for endotracheal intubation (ETI) and mechanical ventilation (MV) in acutely poisoned patients with DCL. Validation of the proposed score on a new set of acutely poisoned patients with DCL.

METHODS

This study was conducted on 187 acutely poisoned patients with DCL admitted to hospital from June 2020 to November 2021 (Derivation cohort). Patients' demographics, toxicological data, neurological examination, calculation of the Glasgow Coma Scale (GCS), Full Outline of Unresponsiveness (FOUR) score, Richmond Agitation-Sedation Scale (RASS), and outcomes were gathered for developing a new score. The proposed score was externally validated on 100 acutely poisoned patients with DCL (Validation cohort).

RESULTS

The PASS assessing sedation consists of FOUR (reflexes and respiration) and GCS (motor) and provides a significantly excellent predictive power (AUC = 0.975) at a cutoff ≤9 with 100% sensitivity and 92.11% specificity for predicting the need for ETI and MV in sedated patients. Additionally, adding RASS (agitation) to the previous model exhibits significantly good predictive power (AUC = 0.893), 90.32% sensitivity, and 73.68% specificity at a cutoff ≤14 for predicting the need for ETI and MV in disturbed consciousness patients with agitation.

CONCLUSION

The proposed PASS could be an excellent, valid and feasible tool to predict the need for ETI and MV in acutely poisoned disturbed consciousness patients with or without agitation.