Development and validation of a predictive score for ICU delirium in critically ill patients

Abstract

Background The incidence of delirium in intensive care unit (ICU) patients is high and associated with a poor prognosis. We validated the risk factors of delirium to identify relevant early and predictive clinical indicators and developed an optimized model. Methods In the derivation cohort, 223 patients were assigned to two groups (with or without delirium) based on the CAM-ICU results. Multivariate logistic regression analysis was conducted to identify independent risk predictors, and the accuracy of the predictors was then validated in a prospective cohort of 81 patients. Results A total of 304 patients were included: 223 in the derivation group and 81 in the validation group, 64(21.1%)developed delirium. The model consisted of six predictors assessed at ICU admission: history of hypertension (RR = 4.367; P = 0.020), hypoxaemia (RR = 3.382; P = 0.018), use of benzodiazepines (RR = 5.503; P = 0.013), deep sedation (RR = 3.339; P = 0.048), sepsis (RR = 3.480; P = 0.018) and mechanical ventilation (RR = 3.547; P = 0.037). The mathematical model predicted ICU delirium with an accuracy of 0.862 (P < 0.001) in the derivation cohort and 0.739 (P < 0.001) in the validation cohort. No significant difference was found between the predicted and observed cases of ICU delirium in the validation cohort (P > 0.05). Conclusions Patients’ risk of delirium can be predicted at admission using the early prediction score, allowing the implementation of early preventive interventions aimed to reduce the incidence and severity of ICU delirium.

Evaluation of Suvorexant and Lemborexant for the Prevention of Delirium in Adult Critically Ill Patients at an Advanced Critical Care Center

“…The factors identified in this study that were involved in the development of delirium were consistent with existing risk factors for developing delirium in the ICU and included higher age, higher clinical frailty score, lower BMI, greater dementia, high APACHE II score, increases ventilator use, and greater sleep disturbances. [39][40][41][42][43][44] Findings of previous studies suggest that dexmedetomidine prevents delirium. [45][46][47] However, in the present study, adjusted Cox regression analyses revealed a lack of delirium prevention by dexmedetomidine.…”

Sepsis-associated brain injury: underlying mechanisms and potential therapeutic strategies for acute and long-term cognitive impairments

“…Delirium is commonly seen in critically ill patients and is clinically diagnosed using screening tools, such as the Confusion Assessment Method for the ICU (CAM-ICU) or the Intensive Care Delirium Screening Checklist (ISDSC). Zhang et al reported sepsis as a risk factor for delirium in ICU patients; others reported risk factors include a history of hypertension, hypoxemia, use of benzodiazepines, deep sedation, and mechanical ventilation [98].…”

Diagnóstico de delirium em pacientes admitidos em Unidade de Terapia Intensiva: uma revisão integrativa

“…A presença de pelo menos um desses achados anormais de EEG foi associada a um aumento significativo na chance de delirium (42 vs 15%, p=0,006). Zhang et al (2021) desenvolveram e validaram um modelo preditivo de delirium, que inclui seis preditores avaliados na admissão na UTI: (1) história de hipertensão, (2) hipoxemia, (3) uso de benzodiazepínicos, (4) sedação profunda, ( 5) sepse e (6) ventilação mecânica. O modelo matemático previu o delirium na UTI de forma confiável (0,862, p<0,001) na coorte de derivação e 0,739 (P < 0,001) na coorte de validação; contribuindo com o diagnóstico de delirium e a tomada de medidas precoces.…”