Delirium screening for patients in the intensive care unit: A prospective validation study of the iCAM-ICU by nurse researchers and bedside nurses in routine practice

Effect of the postoperative pain management model on the psychological status and quality of life of patients in the advanced intensive care unit

OBJECTIVE

it was to explore the influence of the postoperative pain management mode on the psychological state, quality of life (QOL), and nursing satisfaction of late patients in the intensive care unit (ICU) and improve the nursing effect of late patients in the ICU.

METHODS

seventy patients who were admitted to the postoperative ICU for gastric cancer and received treatment in our hospital from March 2021 to May 2022 were selected. The patients were assigned into a research group and a control (Ctrl) group according to a random number table, with 70 cases in each group. The Ctrl group received routine nursing intervention, while research group received nursing intervention based on routine nursing intervention with postoperative pain management mode and received psychological care. Good communication was established with the patients, and the postoperative pain assessment was well conducted. The general information, state-trait anxiety (STAI) score, World Health Organization's Quality of Life Instrument (WHO QOL-BREF) score, and care satisfaction were compared.

RESULTS

the general information differed slightly, such as sex, age, and ward type, between groups, with comparability (P > 0.05). S-AI scores (13.15 ± 1.53 vs. 16.23 ± 1.24) and T-AI scores (14.73 ± 3.12 vs. 18.73 ± 3.16) in research group were inferior to those in Ctrl group (P < 0.05). The scores of patients in research group in the physiological field (78.9 ± 6.1 points vs. 72.3 ± 5.6 points), social relationship field (76.9 ± 4.5 points vs. 71.3 ± 4.8 points), psychological field (78.6 ± 6.2 points vs. 72.4 ± 5.3 points), environmental field (78.6 ± 6.7 points vs. 73.5 ± 6.4 points), and total QOL (79.5 ± 7.4 points vs. 71.6 ± 5.4 points) were higher than those in Ctrl group (P < 0.05). The total satisfaction rate with nursing care in research group (82.85%) was dramatically superior to that in Ctrl group (62.85%) (P < 0.05).

CONCLUSION

the adoption of a postoperative pain management model in postoperative nursing interventions for patients in advanced ICUs can alleviate anxiety and depression, improve patients' QOL and nursing satisfaction, and have clinical promotion value.

A narrative review of the development and performance characteristics of electronic delirium-screening tools

BACKGROUND

Electronic delirium-screening tools are an emergent area of research.

OBJECTIVE

The objective of this study was to summarise the development and performance characteristics of electronic screening tools in delirium.

METHODS

Searches were conducted in Pubmed, Embase, and CINAHL Complete databases to identify electronic delirium-screening tools.

RESULTS

Five electronic delirium-screening tools were identified and reviewed. Two were designed for and tested within a medical setting, and three were applied to intensive care. Adaptive design features, such as skip function to reduce test burden, were variably integrated into instrument design. All tools were shown to have acceptable psychometric properties, but validation studies were largely incomplete.

CONCLUSIONS

Electronic delirium-screening tools are an exciting area of development and may offer hope for improved uptake of delirium screening.

Description of an individualised delirium intervention in intensive care units for critically ill patients delivered by an artificial intelligence-assisted system: using the TIDieR checklist

Background

Delirium is a preventable and reversible complication for intensive care unit (ICU) patients, which can be linked to negative outcomes. Early intervention to cope with the risk factors of delirium is necessary. Yet no specific description of the Artificial Intelligence Assisted Prevention and Management for Delirium (AI-AntiDelirium) following the Template for Intervention Description and Replication (TIDieR) checklist was reported. This is the first study to describe a detailed process for the development of an evidence-based delirium intervention.

Aims

To describe an individualised delirium intervention which is delivered by an artificial intelligence-assisted system in the ICU for critically ill patients.

Methods and results

The TIDieR checklist improved the description of ICU delirium interventions, including several key features for improved implementation of the intervention. This descriptive research describes the AI-assisted ICU delirium interventions for improving cognitive load and adherence of nurses and reducing ICU delirium incidence. Following the TIDieR checklist, we standardised the flow chart of ICU delirium assessment tools; formed an evaluation sheet of ICU delirium risk factors; and translated the evidence-based ABCDEF bundle intervention into practice. Therefore, nurses and researchers would benefit from replicating the interventions for clinical use or experimental research.

Conclusions

The TIDieR checklist provided a systematic approach for reporting the complex ICU delirium interventions delivered in a clinical interventional trial, which contributes to the nursing practice policy for the standardisation of interventions.

A cluster-randomized controlled trial of a nurse-led artificial intelligence assisted prevention and management for delirium (AI-AntiDelirium) on delirium in intensive care unit: Study protocol

BACKGROUND

Delirium is a common complication among intensive care unit (ICU) patients that is linked to negative clinical outcomes. However, adherence to the Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS guidelines), which recommend the use of the ABCDEF bundle, is sub-optimal in routine clinical care. To address this issue, AI-AntiDelirium, a nurse-led artificial intelligence-assisted prevention and management tool for delirium, was developed by our research team. Our pilot study yielded positive findings regarding the use of AI-AntiDelirium in preventing patient ICU delirium and improving activities of daily living and increasing intervention adherence by health care staff.

METHODS

The proposed large-scale pragmatic, open-label, parallel-group, cluster randomized controlled study will assess the impact of AI-AntiDelirium on the incidence of ICU delirium and delirium-related outcomes. Six ICUs in two tertiary hospitals in China will be randomized in a 1:1 ratio to an AI-AntiDelirium or a PADIS guidelines group. A target sample size of 1,452 ICU patients aged 50 years and older treated in the ICU for at least 24 hours will be included. The primary outcome evaluated will be the incidence of ICU delirium and the secondary outcomes will be the duration of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, patient activities of daily living, and ICU nurse adherence to the ABCDEF bundle.

DISCUSSION

If this large-scale trial provides evidence of the effectiveness of AI-AntiDelirium, an artificial intelligence-assisted system tool, in decreasing the incidence of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, and patient activities of daily living while increasing ICU nurse adherence to the ABCDEF bundle, it will have a profound impact on the management of ICU delirium in both research and clinical practice.

CLINICAL TRIAL REGISTRATION

ChiCTR1900023711 (Chinese Clinical Trial Registry).

Development and Usability Testing of a Mobile App-Based Clinical Decision Support System for Delirium: Randomized Crossover Trial

Background

The 3-Minute Diagnostic Interview for Confusion Assessment Method-Defined Delirium (3D-CAM) is an instrument specially developed for the assessment of delirium in general wards, with high reported sensitivity and specificity. However, the use of the 3D-CAM by bedside nurses in routine practice showed relatively poor usability, with multiple human errors during assessment.

Objective

This study aimed to develop a mobile app-based delirium assessment tool based on the 3D-CAM and evaluate its usability among older patients by bedside nurses.

Methods

The Delirium Assessment Tool With Decision Support Based on the 3D-CAM (3D-DST) was developed to address existing issues of the 3D-CAM and optimize the assessment process. Following a randomized crossover design, questionnaires were used to evaluate the usability of the 3D-DST among older adults by bedside nurses. Meanwhile, the performances of both the 3D-DST and the 3D-CAM paper version, including the assessment completion rate, time required for completing the assessment, and the number of human errors made by nurses during assessment, were recorded, and their differences were compared.

Results

The 3D-DST included 3 assessment modules, 9 evaluation interfaces, and 16 results interfaces, with built-in reminders to guide nurses in completing the delirium assessment. In the usability testing, a total of 432 delirium assessments (216 pairs) on 148 older adults were performed by 72 bedside nurses with the 3D-CAM paper version and the 3D-DST. Compared to the 3D-CAM paper version, the mean usability score was significantly higher when using the 3D-DST (4.35 vs 3.40; P<.001). The median scores of the 6 domains of the satisfactory evaluation questionnaire for nurses using the 3D-CAM paper version and the 3D-DST were above 2.83 and 4.33 points, respectively (P<.001). The average time for completing the assessment reduced by 2.1 minutes (4.4 vs 2.3 min; P<.001) when the 3D-DST was used.

Conclusions

This study demonstrated that the 3D-DST significantly improved the efficiency of delirium assessment and was considered highly acceptable by bedside nurses.

Validity and predictability of the confusion assessment method for the intensive care unit for delirium among critically ill patients in the intensive care unit: A systematic review and meta-analysis

OBJECTIVE

To identify the validity and predictability of the confusion assessment method for the intensive care unit (CAM-ICU) for delirium in critically ill patients in the ICU.

METHODS

In this systematic review, PubMed, Embase, Cochrane Central Register of Controlled Trials, and MEDLINE databases were searched for observational studies investigating delirium screening tools for ICU patients. In the meta-analysis, we combined the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve (AUC) of SROC to analysis the predictive value of CAM-ICU.

RESULTS

Twenty-nine articles met the inclusion criteria. The pooled sensitivity and specificity values were 0.82 (95% confidence interval [CI]: 0.75-0.87) and 0.95 (95% CI: 0.93-0.97), respectively. The AUC point estimate of the SROC curve was 0.96 (95% CI: 0.94-0.97). Race (Asian or Others) could affect the pooled sensitivity and specificity, and the analysis method (Patient- or Scan-based) and study design were not sources of heterogeneity for pooled sensitivity and specificity.

CONCLUSIONS

The CAM-ICU is a valid and reliable tool for delirium prediction among ICU patients. When introducing CAM-ICU to assess delirium, it is necessary to localize its language and content to improve its predictive efficacy in different countries and different ethnic groups.

RELEVANCE TO CLINICAL PRACTICE

In clinical practice, nurses can use CAM-ICU to evaluate delirium in critically ill patients in ICU. However, it is necessary to debug the language and content according to the application population.

Effect of the clinical decision assessment system on clinical outcomes of delirium in hospitalized older adults: study protocol for a pair-matched, parallel, cluster randomized controlled superiority trial

BACKGROUND

Prompt recognition of delirium is the first key step in its proper management. A previous study has demonstrated that nurses' delirium screening using the usual paper version assessment tool has no effect on clinical outcomes. Clinical decision assessment systems have been demonstrated to improve patients' adherence and clinical outcomes. Therefore, We developed a clinical decision assessment system (3D-DST) based on the usual paper version (3-min diagnostic interview for CAM-defined delirium), which was developed for assessing delirium in older adults with high usability and accuracy. However, no high quality evidence exists on the effectiveness of a 3D-DST in improving outcomes of older adults compared to the usual paper version.

METHODS

A pair-matched, open-label, parallel, cluster randomized controlled superiority trial following the SPIRIT checklist. Older patients aged 65 years or older admitted to four medical wards of a geriatric hospital will be invited to participate in the study. Prior to the study, delirium prevention and treatment interventions will be delivered to nurses in both the intervention and control groups. The nurses in the intervention group will perform routine delirium assessments on the included older patients with 3D-DST, while the nurses in the control group will perform daily delirium assessments with the usual paper version. Enrolled patients will be assessed twice daily for delirium by a nurse researcher using 3D-DST. The primary outcome is delirium duration. The secondary outcomes are delirium severity, incidence of delirium, length of stay, in-hospital mortality, adherence to delirium assessment, prevention, and treatment of medical staff.

DISCUSSION

This study will incorporate the 3D-DST into clinical practice for delirium assessment. If our study will demonstrate that 3D-DST will improve adherence with delirium assessment and clinical outcomes in older patients, it will provide important evidence for the management of delirium in the future.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, Identifier: ChiCTR1900028402. https://www.chictr.org.cn/showproj.aspx?proj=47127 .

PROTOCOL VERSION

1, 29/7/22.

Accuracy of a clinical decision support system based on the 3-minute diagnostic interview for CAM-defined delirium: A validation study✰

OBJECTIVE

To evaluate the accuracy of the 3D-DST for delirium assessment in older adults by the nurse researcher.

METHODS

The 3D-DST was administered by a trained nurse researcher to assess delirium among eligible older adults (aged ≥70 years). The criteria for identifying delirium was based on the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V).

RESULTS

A total of 95 older adults were enrolled in the current study, and 23 patients were identified as positive for delirium by the psychiatrist. The sensitivity and specificity of the 3D-DST were 96% and 94%, respectively. High sensitivities of the 3D-DST were also observed among patients with hypoactive delirium (95%) and those with cognitive impairment (93%).

CONCLUSION

The 3D-DST was demonstrated as an appropriate instrument with highly acceptable sensitivities and specificities for delirium detection in hospitalized older patients.

A Cross-Sectional Survey on the Clinical Management of Emergence Delirium in Adults: Knowledge, Attitudes, and Practice in Mainland China

Background: Emergence delirium (ED) occurs immediately after emergence from general anesthesia, which may have adverse consequences. This cross-sectional survey assessed Chinese physicians’ and nurses’ knowledge of, attitudes towards, and practice regarding ED in adults. Methods: Electronic questionnaires were sent to 93 major academic hospitals across mainland China and both attending anesthesiologists and anesthesia nurses were recommended to complete them. Results: A total of 243 anesthesiologists and 213 anesthesia nurses participated in the survey. Most of the participants considered it a very important issue; however, less than one-third of them routinely assessed ED. In terms of screening tools, anesthesiologists preferred the Confusion Assessment Method, while anesthesia nurses reported using multiple screening tools. Divergence also appeared with regard to the necessity of monitoring the depth of anesthesia. Anesthesiologists considered it only necessary in high-risk patients, while the nurses considered that it should be carried out routinely. No unified treatment strategy nor medication was reported for ED treatment during the recovery period. Conclusions: This study illustrated that there are high awareness levels among both Chinese anesthesiologists and anesthesia nurses regarding the importance of ED. However, a specific practice in terms of routine delirium assessment, anesthesia depth monitoring, and a standardized treatment algorithm needs to be implemented to improve ED management.