Relationship between PaO2/FiO2 and delirium in intensive care: A cross-sectional study

Gong, Fang; Ai, Yuhang; Zhang, Lina; Peng, Qianyi; Zhou, Quan; Gui, Chunmei

doi:10.1016/j.jointm.2022.08.002

Cited by 6 publications

(6 citation statements)

References 39 publications

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“…The literature reported that low MAP was a risk factor for ICU-onset delirium, 8 like our results. Hypoxia is an independent risk factor for acute brain injury that can lead to long-term cognitive impairment 9,29,30 . In our cohort, patients with a low pH value and Pa o 2 /F io 2 were at higher risk of ICU delirium.…”

Section: Discussionmentioning

confidence: 67%

“…Hypoxia is an independent risk factor for acute brain injury that can lead to long-term cognitive impairment. 9,29,30 In our cohort, patients with a low pH value and PaO 2 /FIO 2 were at higher risk of ICU delirium. A recent review reported a low hematocrit level July/August 2023 as a risk factor for ICU delirium.…”

Section: Risk Factors and Outcomesmentioning

confidence: 66%

“…7 Several risk factors such as low Glasgow Coma Scale (GCS) score, low mean arterial pressure (MAP), high creatinine level, low hematocrit level, low pH, and low PaO 2 /FIO 2 contribute to the onset of ICU delirium. [8][9][10][11][12] Early detection of high-risk patients for delirium would facilitate the early use of nonpharmacological interventions to manage and reduce the duration of ICU delirium and its impact on health outcomes. 7,13,14 The Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption also suggest using nonpharmacologic interventions to reduce modifiable risk factors for ICU delirium, such as early mobilization and programs for sleep optimization.…”

mentioning

confidence: 99%

“…Misdiagnosis and underestimation of the phenomenon lead to higher care costs and long-suffering patients 7 . Several risk factors such as low Glasgow Coma Scale (GCS) score, low mean arterial pressure (MAP), high creatinine level, low hematocrit level, low pH, and low Pa o 2 /F io 2 contribute to the onset of ICU delirium 8-12 …”

mentioning

confidence: 99%

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Calibration of the PREdiction of DELIRium in ICu Patients (PRE-DELIRIC) Score in a Cohort of Critically Ill Patients

Gravante

Giannarelli

Pucci

et al. 2023

Dimens Crit Care Nurs

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Background: To predict delirium in intensive care unit (ICU) patients, the Prediction of Delirium in ICU Patients (PRE-DELIRIC) score may be used. This model may help nurses to predict delirium in high-risk ICU patients.Objectives: The aims of this study were to externally validate the PRE-DELIRIC model and to identify predictive factors and outcomes for ICU delirium.Method: All patients underwent delirium risk assessment by the PRE-DELIRIC model at admission. We used the Intensive Care Delirium Screening Check List to identify patients with delirium. The receiver operating characteristic curve measured discrimination capacity among patients with or without ICU delirium. Calibration ability was determined by slope and intercept.Results: The prevalence of ICU delirium was 55.8%. Discrimination capacity (Intensive Care Delirium Screening Check List score ≥4) expressed by the area under the receiver operating characteristic curve was 0.81 (95% confidence interval, 0.75-0.88), whereas sensitivity was 91.3% and specificity was 64.4%. The best cut-off was 27%, obtained by the max Youden index. Calibration of the model was adequate, with a slope of 1.03 and intercept of 8.14. The onset of ICU delirium was associated with an increase in ICU length of stay ( P < .0001), higher ICU mortality ( P = .008), increased duration of mechanical ventilation ( P < .0001), and more prolonged respiratory weaning ( P < .0001) compared with patients without delirium.Discussion: The PRE-DELIRIC score is a sensitive measure that may be useful in early detection of patients at high risk for developing delirium. The baseline PRE-DELIRIC score could be useful to trigger use of standardized protocols, including nonpharmacologic interventions.

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Section: Discussionmentioning

confidence: 67%

Section: Risk Factors and Outcomesmentioning

confidence: 66%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Calibration of the PREdiction of DELIRium in ICu Patients (PRE-DELIRIC) Score in a Cohort of Critically Ill Patients

Gravante

Giannarelli

Pucci

et al. 2023

Dimens Crit Care Nurs

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“…For many heart failure patients, particularly those with advanced organ dysfunction or severe complications, ICUs are necessary to provide advanced, high-tech, life-saving care [ 10 , 11 ]. ICUs have a high-intensity staffing model with high nurse and physician-to-patient ratios [ 12 , 13 ]. In the USA, approximately 10%–51% of hospitalized heart failure patients are admitted to an ICU [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

Huang

2023

PLoS ONE

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Background There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality. Objective The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency. Methods Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map. Results Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics. Conclusion Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

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Erratum to “Relationship between PaO2/FiO2 and delirium in intensive care: A cross-sectional study” [Journal of Intensive Medicine volume 3 (2023) 73–78.]

Gong,

Ai,

Zhang

et al. 2024

Journal of Intensive Medicine

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Relationship between PaO2/FiO2 and delirium in intensive care: A cross-sectional study

Cited by 6 publications

References 39 publications

Calibration of the PREdiction of DELIRium in ICu Patients (PRE-DELIRIC) Score in a Cohort of Critically Ill Patients

Calibration of the PREdiction of DELIRium in ICu Patients (PRE-DELIRIC) Score in a Cohort of Critically Ill Patients

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

Erratum to “Relationship between PaO2/FiO2 and delirium in intensive care: A cross-sectional study” [Journal of Intensive Medicine volume 3 (2023) 73–78.]

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