Incidence and interaction factors of delirium as an independent risk of mortality in elderly patients in the intensive units: a retrospective analysis from MIMIC-IV database

Development and internal verification of nomogram for forecasting delirium in the elderly admitted to intensive care units: an analysis of MIMIC-IV database

Background

Precise forecasting of delirium in intensive care unit (ICU) may propel effective early prevention strategies and stratification of ICU patients through delirium risks, avoiding waste of medical resources. However, there are few optimal models of delirium in critically ill older patients. This study aimed to propose and verify a nomogram for predicting the incidence of delirium in elderly patients admitted to ICU.

Methods

We performed a retrospective study using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. It included data on 13,175 older patients in total. The patients were randomly divided into a training group (n = 9,223) and an internal verification group (n = 3,452). Risk factors were screened using the least absolute shrinkage and selection operator regression. We successfully constructed a multivariate logistic regression model along with a nomogram. We conducted internal verification using 1,000 bootstrap specimens. Performance assessment was conducted using a receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

The risk factors included in the nomogram were sepsis, Sequential Organ Failure Assessment (SOFA) score, cerebrovascular disease, mechanical ventilation, sedation, severe hypothermia, and serum calcium levels. The area under the ROC curve (AUC) for the nomogram, incorporating the above-mentioned predictors for the training set was 0.762 (95% confidence interval [CI] 0.749-0.776), whereas that for the verification set was 0.756 (95% CI 0.736-0.776). Based on the calibration curve, the model forecast outcomes matched well with the actual results, and the nomogram's Brier score was 0.12 in the training set and 0.128 in the verification set. DCA and CIC showed that our model had a good net clinical benefit.

Conclusion

We developed a forecast nomogram for delirium in the critically ill elderly patients that enhances clinical decision-making. However, further verification is required.

Assessment of Geriatric Problems and Risk Factors for Delirium in Surgical Medicine: Protocol for Multidisciplinary Prospective Clinical Study

BACKGROUND

An aging population in combination with more gentle and less stressful surgical procedures leads to an increased number of operations on older patients. This collectively raises novel challenges due to higher age heavily impacting treatment. A major problem, emerging in up to 50% of cases, is perioperative delirium. It is thus vital to understand whether and which existing geriatric assessments are capable of reliably identifying risk factors, how high the incidence of delirium is, and whether the resulting management of these risk factors might lead to a reduced incidence of delirium.

OBJECTIVE

This study aimed to determine the frequency and severity of geriatric medical problems in elective patients of the Clinics of Oral and Maxillofacial Surgery, Vascular Surgery, and Orthopedics, General Surgery, and Trauma Surgery, revealing associations with the incidence of perioperative delirium regarding potential risk factors, and recording the long-term effects of geriatric problems and any perioperative delirium that might have developed later the patient's life.

METHODS

We performed both pre- and postoperative assessments in patients of 4 different surgical departments who are older than 70 years. Patient-validated screening instruments will be used to identify risk factors. A geriatric assessment with the content of basal and instrumental activities of daily living (basal activities of daily living [Katz index], instrumental activities of daily living [Lawton and Brody score], cognition [6-item screener and clock drawing test], mobility [de Morton Mobility Index and Sit-to-Stand test], sleep [Pittsburgh Sleep Quality Index and Insomnia Severity Index/STOP-BANG], drug therapy [polypharmacy and quality of medication, Fit For The Aged classification, and anticholinergic burden score], and pain assessment and delirium risk (Delirium Risk Assessment Tool) will be performed. Any medical problems detected will be treated according to current standards, and no intervention is planned as part of the study. In addition, a telephone follow-up will be performed 3, 6, and 12 months after discharge.

RESULTS

Recruitment started in August 2022, with 421 patients already recruited at the time of submission. Initial analyses of the data are to be published at the end of 2024 or the beginning of 2025.

CONCLUSIONS

In the current study, we investigate whether the risk factors addressed in the assessment are associated with an increase in the delirium rate. The aim is then to reduce this comprehensive assessment to the central aspects to be able to conduct targeted and efficient risk screening.

TRIAL REGISTRATION

German Clinical Trials Registry DRKS00028614; https://www.drks.de/search/de/trial/DRKS00028614.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/59203.

High level of systemic immune inflammation index elevates delirium risk among patients in intensive care unit

Evidence regarding the effect of systemic immune-inflammation index on delirium occurrence is limited. This study aimed to investigate the association between SII and delirium in intensive care unit (ICU) patients.

METHODS

Information was extracted from Medical Information Mart for Intensive Care-IV. Four logistic regression model was established and incorporated with subgroup analysis and restricted cubic spline (RCS). The cutoff value of SII was acquired from receiver operator characteristic curve (ROC), and propensity score matching (PSM) was utilized to attenuate the confounding effect. Survival analysis was utilized to evaluate the relationship between SII and 30-day or 90-day all-cause mortality.

RESULTS

Among the 7,518 participants, 1,685 cases of delirium occurred. Individuals in the highest quartile of SII exhibited a heightened delirium risk, with a significant multivariable-adjusted odds ratio (OR) of 3.12(2.24,4.33). Tendency analysis, subgroup analysis and PSM together confirmed the positive relationship. Results of Cox regression displayed the risk of both 30-day and 90-day mortality increased about 50% in the higher-SII group.

CONCLUSION

Higher levels of SII is positively associated with the occurrence of delirium and increased all-cause mortality risk.

Machine Learning for Prediction of Postoperative Delirium in Adult Patients: A Systematic Review and Meta-analysis

PURPOSE

This meta-analysis aimed to evaluate the performance of machine learning (ML) models in predicting postoperative delirium (POD) and to provide guidance for clinical application.

METHODS

PubMed, Embase, Cochrane Library, and Web of Science databases were searched from inception to April 29, 2024. Studies reported ML models for predicting POD in adult patients were included. Data extraction and risk of bias assessment were performed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis - AI (TRIPOD-AI) and Prediction model Risk Of Bias ASsessment Tool (PROBAST) tools. Meta-analysis with the area under the curve (AUC) was performed using MedCalc software.

FINDINGS

A total of 23 studies were included after screening. Age (n = 20, 86.95%) and Random Forest (RF) (n = 24, 17.27%) were the most frequently used feature and ML algorithm, respectively. The meta-analysis showed an overall AUC of 0.792. The ensemble models (AUC = 0.805) showed better predictive performance than single models (AUC = 0.782). Additionally, considerable variations in AUC were found among different ML algorithms, with AdaBoost (AB) demonstrating good performance with AUC of 0.870. Notably, the generalizability of these models was uncertain due to limitations in external validation and bias assessment.

IMPLICATIONS

The performance of ensemble models were higher than single models, and the AB algorithms demonstrated better performance, compared with other algorithms. However, further research was needed to enhance the generalizability and transparency of ML models.

Risk factors for postoperative delirium in frail elderly patients undergoing on-pump cardiac surgery and development of a prediction model-a prospective observational study

Background

To identify the risk factors for postoperative delirium (POD) after cardiac surgery in frail elderly patients and develop a receiver operating characteristic (ROC) prediction model to confirm the effectiveness.

Methods

This was a prospective observational study, patients were assessed preoperatively according to the frailty index (FI) scale. Cerebral (SctO2) was assessed at different time points using near-infrared spectroscopy (NIRS). On the basis of the occurrence of POD within 7 days after surgery, patients were divided into POD and non-POD groups. Risk factors were analyzed using logistic regression analysis, while their predictive values were evaluated using the receiver operating characteristic curve analysis.

Results

POD was significantly associated with frailty, lower preoperative MMSE scores, hyperlipidemia, diabetes, cerebrovascular disease, lower hemoglobin level, lower albumin level, longer operation time, longer CPB time, lower SctO2 at T5, and lower SctO2baseline (P < 0.05). SrtO2 and SmtO2 did not differ significantly between groups. FI, preoperative MMSE score, and operation time as independent risk factors (P < 0.05). Significant predictive value was demonstrated in all 3 variables (P < 0.001; respectively). Among them, high sensitivity and specificity were observed with the FI (cut-off value 0.27, sensitivity 75%, specificity 73.5%) and operation time (cut-off value 237.5, sensitivity 62.5%, specificity 78.6%).

Conclusions

The FI, preoperative MMSE score, and operation time were independent risk factors for POD in elderly patients after cardiac surgery, with high predictive value observed with the FI and operation time. Cerebral oxygen saturation was associated with POD but was not an independent risk factor.

Clinical Trial Registration

Chinese Clinical Trail Registry, No: chictr2200056038.

A qualitative study of geriatric specialist nurses' experiences to navigate delirium in the elderly

BACKGROUND

The experiences and perceptions of geriatric specialist nurses are pivotal to understanding the complexities of managing delirium and to developing effective nursing interventions. This qualitative study aims to explore these experiences and perceptions to inform the enhancement of clinical geriatric nursing and care practices.

METHODS

Utilizing a qualitative exploratory design, this research engaged a convenience sample of geriatric specialist nurses at a tertiary hospital in Shanghai, China through focus groups and semi-structured interviews. Data were rigorously analyzed using Colaizzi's phenomenological method, which facilitated the identification of themes that emerged from the narratives of the geriatric specialist nurses.

RESULTS

The thematic analysis yielded three major themes that encapsulate the nurses' experiences and perceptions. Theme 1: Understanding of Delirium, highlighted the nurses' awareness of the condition's significance, yet it was often deprioritized due to the pressing demands of managing more acute and immediately life-threatening conditions. Theme 2: Barriers in Application, brought to light the multifaceted challenges faced by nurses, including language barriers, the frequency and consistency of delirium assessments, the social determinants of health, and the nurses' own competencies in assessment. Theme 3: Evolution of Nursing Approaches, detailed the adaptive strategies employed by nurses, such as managing nursing adverse events, improving communication with patients' families, and adopting a proactive stance towards long-term patient outcomes.

CONCLUSIONS

The findings suggest that while geriatric specialist nurses recognize the importance of delirium assessment, there are several barriers to effective application. The study underscores the imperative for the advancement of more refined delirium assessment and care protocols, tailored to address the unique requirements of geriatric nursing care.

Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

AIMS AND OBJECTIVES

To investigate whether a low Braden Skin Score (BSS), reflecting an increased risk of pressure injury, could predict the risk of delirium in older patients in the intensive care unit (ICU).

BACKGROUND

Delirium, a common acute encephalopathy syndrome in older ICU patients, is associated with prolonged hospital stay, long-term cognitive impairment and increased mortality. However, few studies have explored the relationship between BSS and delirium.

DESIGN

Multicenter cohort study.

METHODS

The study included 24,123 older adults from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and 1090 older adults from the eICU Collaborative Research Database (eICU-CRD), all of whom had a record of BSS on admission to the ICU. We used structured query language to extract relevant data from the electronic health records. Delirium, the primary outcome, was primarily diagnosed by the Confusion Assessment Method for the ICU or the Intensive Care Delirium Screening Checklist. Logistic regression models were used to validate the association between BSS and outcome. A STROBE checklist was the reporting guide for this study.

RESULTS

The median age within the MIMIC-IV and eICU-CRD databases was approximately 77 and 75 years, respectively, with 11,195 (46.4%) and 524 (48.1%) being female. The median BSS at enrollment in both databases was 15 (interquartile range: 13, 17). Multivariate logistic regression showed a negative association between BSS on ICU admission and the prevalence of delirium. Similar patterns were found in the eICU-CRD database.

CONCLUSIONS

This study found a significant negative relationship between ICU admission BSS and the prevalence of delirium in older patients.

RELEVANCE TO CLINICAL PRACTICE

The BSS, which is simple and accessible, may reflect the health and frailty of older patients. It is recommended that BSS assessment be included as an essential component of delirium management strategies for older patients in the ICU.

NO PATIENT OR PUBLIC CONTRIBUTION

This is a retrospective cohort study, and no patients or the public were involved in the design and conduct of the study.

Clinical Significance of the Prognostic Nutritional Index in Predicting Delirium among Critically Ill Patients: A Retrospective Cohort Study

Delirium is a serious and complex problem facing critically ill patients. This retrospective study aimed to explore the association between prognostic nutrition index (PNI) and delirium in critically ill patients in the intensive care unit (ICU). This study was based on the Medical Information Mart Intensive Care IV (MIMIC IV) database. Patients over 18 years of age were enrolled. Univariate and multivariate logistic regression analyses were performed to explore the association between PNI and delirium. Tendency analysis, subgroups analysis, and restricted cubic spline (RCS) were selected to further certify the association. The receiver operating characteristic curve (ROC) was adopted to assess the predictability of PNI to delirium. Propensity score matching (PSM) was used to reduce the interference of confounders. A total of 3,105 patients participated in this study. As the grade of malnutrition increases, the incidence of delirium increases in all models. The odds ratios (OR) of delirium in the fully adjusted model were 1.00 (reference), 1.04 (0.80, 1.36), 1.53 (1.17, 1.99), and 1.93 (1.44, 2.59). Strong U-shaped curves were found with RCS analysis between delirium and PNI in the subgroups of patients over 60 years of age and patients with chronic obstructive pulmonary disease (COPD). After PSM, the ORs of delirium were 1.44 (1.16, 1.79) and 1.53 (1.22, 1.93), respectively, in the univariate and multivariate logistic regression models. PNI is negatively associated with the prevalence of delirium in critically ill adults in the ICU. PNI is an independent risk factor for the incidence of delirium in adults in the ICU.

Predictive value of the geriatric nutrition risk index for postoperative delirium in elderly patients undergoing cardiac surgery

AIMS

The aims of the study were to determine the relationship between preoperative geriatric nutritional risk index (GNRI) and the occurrence of postoperative delirium (POD) in elderly patients after cardiac surgery and to evaluate the additive value of GNRI for predicting POD.

METHODS

The data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-IV) database. Patients who underwent cardiac surgery and were aged 65 or older were included. The relationship between preoperative GNRI and POD was investigated using logistic regression. We determined the added predictive value of preoperative GNRI for POD by measuring the changes in the area under the receiver operating characteristic curve (AUC) and calculating the net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

A total of 4286 patients were included in the study, and 659 (16.1%) developed POD. Patients with POD had significantly lower GNRI scores than patients without POD (median 111.1 vs. 113.4, p < 0.001). Malnourished patients (GNRI ≤ 98) had a significantly higher risk of POD (odds ratio, 1.83, 90% CI, 1.42-2.34, p < 0.001) than those without malnutrition (GNRI > 98). This correlation remains after adjusting for confounding variables. The addition of GNRI to the multivariable models slightly but not significantly increases the AUCs (all p > 0.05). Incorporating GNRI increases NRIs in some models and IDIs in all models (all p < 0.05).

CONCLUSIONS

Our results showed a negative association between preoperative GNRI and POD in elderly patients undergoing cardiac surgery. The addition of GNRI to POD prediction models may improve their predictive accuracy. However, these findings were based on a single-center cohort and will need to be validated in future studies involving multiple centers.

Promising Effects of Montelukast for Critically Ill Asthma Patients via a Reduction in Delirium

Background: Montelukast (MTK), a potent antagonist of cysteinyl leukotriene receptor 1, has shown therapeutic promise for the treatment of neuropsychiatric disorders. Delirium, a common complication in critically ill patients, lacks effective treatment. This study aims to explore the impact of pre-intensive care unit (ICU) MTK use on in-hospital delirium incidence and, subsequent, prognosis in critically ill patients. Methods: A retrospective cohort study (n = 6344) was conducted using the MIMIC-IV database. After propensity score matching, logistic/Cox regression, E-value sensitivity analysis, and causal mediation analysis were performed to assess associations between pre-ICU MTK exposure and delirium and prognosis in critically ill patients. Results: Pre-ICU MTK use was significantly associated with reduced in-hospital delirium (OR: 0.705; 95% CI 0.497-0.999; p = 0.049) and 90-day mortality (OR: 0.554; 95% CI 0.366-0.840; p = 0.005). The association was more significant in patients without myocardial infarction (OR: 0.856; 95% CI 0.383-0.896; p = 0.014) and could be increased by extending the duration of use. Causal mediation analysis showed that the reduction in delirium partially mediated the association between MTK and 90-day mortality (ACME: -0.053; 95% CI -0.0142 to 0.0002; p = 0.020). Conclusions: In critically ill patients, MTK has shown promising therapeutic benefits by reducing the incidence of delirium and 90-day mortality. This study highlights the potential of MTK, beyond its traditional use in respiratory disease, and may contribute to the development of novel therapeutic strategies for delirium.

The nomogram to predict the occurrence of sepsis-associated encephalopathy in elderly patients in the intensive care units: A retrospective cohort study

Background

Sepsis-associated encephalopathy (SAE) is a critical and common problem in elderly patients with sepsis, which is still short of efficient predictive tools. Therefore, this study aims to screen the risk factors and establish a useful predictive nomogram for SAE in elderly patients with sepsis in the intensive care unit (ICU).

Patients and methods

Elderly patients (age ≥ 65 years) with sepsis were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Data from demographics and laboratory examinations were collected on the first day of admission to the ICU. SAE was defined by two criteria in the presence of sepsis: ① a Glasgow Coma Scale (GCS) score of < 15 or ② delirium. Differences in demographics and laboratory tests were calculated between SAE and non-SAE groups. Participants were randomly divided into a training set and a validation set without replacement at a ratio of 6:4. A predictive nomogram was constructed in the training set by logistic regression analysis and then validated. The predictive capability of the nomogram was demonstrated by receiver operating characteristic (ROC) analysis and calibration curve analysis.

Results

A total of 22,361 patients were selected, of which 2,809 patients (12.7%) died in the hospital and 8,290 patients (37.1%) had SAE. In-hospital mortality in the SAE group was higher than that in the non-SAE group (18.8 vs. 8.9%, p < 0.001). Based on the results of logistic regression analysis, a nomogram integrating age, Na⁺, Sequential Organ Failure Assessment (SOFA) score, heart rate, and body temperature were constructed. The area under the curve (AUC) of the nomogram was 80.2% in the training set and 80.9% in the validation set. Calibration curve analysis showed a good predictive capacity of the nomogram.

Conclusion

SAE is an independent risk of in-hospital mortality in elderly patients in the intensive care unit. The nomogram has an excellent predictive capability of SAE and helps in clinical practice.