Point prevalence of delirium among critically ill patients in Saudi Arabia: A multicenter study

Al-Qadheeb, Nada S.; Hashhoush, Mai; Maghrabi, Khaled; Rugaan, Asia; Eltatar, Faisal Saleem; Algethamy, Haifa; Abudayah, Abdullah; Ismail, Nadia; Almubarak,; Alkhatib, K; Amaani, Mohammed; Ghabashi, Alaa; Almaani, Mohammed; Amin, Reza; Alharthy, Abdulrahman; Nasim, Nasir; ElRakaiby, Galal B; Alonazi, Farhan; Alnajdi, Israa; Al-Ansari, Mariam; Ahmed, Mohammed Al; Alenazi, Ahmed O; Alruwaili, Abdulrahman; Almuslim, Osama

doi:10.4103/sccj.sccj_7_20

Cited by 2 publications

(3 citation statements)

References 20 publications

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Section: Discussionmentioning

confidence: 94%

“…This therapeutic class reduces excessive dopaminergic activity, a major contributing factor in delirium, which favors its prescription in critical care situations. 39,40 Studies have shown that delirium affects a significant proportion of older people in ICUs and that haloperidol, the predominant antipsychotic in the study sample, prevents delirium. 39,40 In patients receiving mechanical ventilation, agitation, which often results from delirium, increases the risk of extubation.…”

Section: Discussionmentioning

confidence: 95%

“…39,40 Studies have shown that delirium affects a significant proportion of older people in ICUs and that haloperidol, the predominant antipsychotic in the study sample, prevents delirium. 39,40 In patients receiving mechanical ventilation, agitation, which often results from delirium, increases the risk of extubation. Among older adults with nasoenteral tubes, confusion, which is also associated with delirium, can lead to accidental tube removal.…”

Section: Discussionmentioning

confidence: 95%

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Potentially inappropriate medications with older people in intensive care and associated factors: a historic cohort study

et al. 2024

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BACKGROUND: The epidemiology of potentially inappropriate medications (PIMs) in critical care units remains limited, especially in terms of the factors associated with their use. OBJECTIVE: To estimate the incidence and factors associated with PIMs use in intensive care units. DESIGN AND SETTING: Historical cohort study was conducted in a high-complexity hospital in Brazil. METHODS: A retrospective chart review was conducted on 314 patients aged ≥ 60 years who were admitted to intensive care units (ICUs) at a high-complexity hospital in Brazil. The dates were extracted from a “Patient Safety Project” database. A Chi-square test, Student’s t-test, and multivariable logistic regression analyses were performed to assess which factors were associated with PIMs. The statistical significance was set at 5%. RESULTS: According to Beers’ criteria, 12.8% of the identified drugs were considered inappropriate for the elderly population. The incidence rate of PIMs use was 45.8%. The most frequently used PIMs were metoclopramide, insulin, antipsychotics, non-steroidal anti-inflammatory drugs, and benzodiazepines. Factors associated with PIMs use were the number of medications (odds ratio [OR] = 1.17), length of hospital stay (OR = 1.07), and excessive potential drug interactions (OR = 2.43). CONCLUSIONS: Approximately half of the older adults in ICUs received PIM. Patients taking PIMs had a longer length of stay in the ICU, higher numbers of medications, and higher numbers of potential drug interactions. In ICUs, the use of explicit methods combined with clinical judgment can contribute to the safety and quality of medication prescriptions.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 95%

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Potentially inappropriate medications with older people in intensive care and associated factors: a historic cohort study

et al. 2024

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Using Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings

Heikal,

Saad,

Ghanime

et al. 2024

NDT

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Objective Delirium is a common and acute neuropsychiatric syndrome that requires timely intervention to prevent its associated morbidity and mortality. Yet, its diagnosis and symptoms are often overlooked due to its variable clinical presentation and fluctuating nature. Thus, in this study, we address the barriers to delirium diagnosis by utilizing a machine learning-based predictive algorithm for incident delirium that relies on archived electronic health records (EHRs) data. Methods We used the Medical Information Mart for Intensive Care (MIMIC) database to create a detailed dataset for identifying delirium in intensive care unit (ICU) patients. Our approach involved training machine learning models on this dataset to pinpoint critical clinical features for delirium detection. These features were then refined and applied to non-ICU patients using EHRs from the American University of Beirut Medical Center (AUBMC). Results Our study assessed machine learning models like Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Classification and Regression Trees (CART), Random Forest (RF), Neural Oblivious Decision Ensembles (NODE), and Logistic Regression (LR), highlighting superior delirium detection in diverse clinical settings. The CatBoost model excelled in ICU environments with an F1 Score of 89.2%, while XGBoost performed best in general hospital settings with a 75.4% F1 Score. Interpretations using Tabular Local Interpretable Model-agnostic Explanations (LIME) revealed critical indicators such as prothrombin time and hematocrit levels, enhancing model transparency and clinical applicability. These clinical insights help differentiate the delirium predictors between ICU patients, who are often sensitive to various factors. Conclusion The proposed predictive algorithm improves delirium detection rates and streamlines efficiency in hospital electronic systems, thereby enabling prompt interventions to prevent delirium progression and associated complications. The clinical indicators for delirium that we identified in general hospital settings and ICU can greatly help healthcare professionals identify potential causes of delirium and reduce misdiagnosis.

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Point prevalence of delirium among critically ill patients in Saudi Arabia: A multicenter study

Cited by 2 publications

References 20 publications

Potentially inappropriate medications with older people in intensive care and associated factors: a historic cohort study

Potentially inappropriate medications with older people in intensive care and associated factors: a historic cohort study

Using Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings

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