Development and validation of a delirium predictive score in older people

The interrelationship between multiple long-term conditions (MLTC) and delirium: a scoping review

INTRODUCTION

Delirium and multiple long-term conditions (MLTC) share numerous risk factors and have been shown individually to be associated with adverse outcomes following hospitalisation. However, the extent to which these common ageing syndromes have been studied together is unknown. This scoping review aims to summarise our knowledge to date on the interrelationship between MLTC and delirium.

METHODS

Searches including terms for delirium and MLTC in adult human participants were performed in PubMed, EMBASE, Medline, Psycinfo and CINAHL. Descriptive analysis was used to summarise findings, structured according to Synthesis Without Meta-analysis reporting guidelines.

RESULTS

After removing duplicates, 5256 abstracts were screened for eligibility, with 313 full-texts sought along with 17 additional full-texts from references in review articles. In total, 140 met inclusion criteria and were included in the final review. Much of the literature explored MLTC as a risk factor for delirium (n = 125). Fewer studies explored the impact of MLTC on delirium presentation (n = 5), duration (n = 3) or outcomes (n = 6) and no studies explored how MLTC impacts the treatment of delirium or whether having delirium increases risk of developing MLTC. The most frequently used measures of MLTC and delirium were the Charlson Comorbidity Index (n = 98/140) and Confusion Assessment Method (n = 81/140), respectively.

CONCLUSION

Existing literature largely evaluates MLTC as a risk factor for delirium. Major knowledge gaps identified include the impact of MLTC on delirium treatment and the effect of delirium on MLTC trajectories. Current research in this field is limited by significant heterogeneity in defining both MLTC and delirium.

External validity of an automated delirium prediction model (DEMO) and comparison to the manual VMS-questions: a retrospective cohort study

BACKGROUND

It is estimated that one-third of delirium cases in hospitals could be prevented with appropriate interventions. In Dutch hospitals a manual instrument (VMS-questions) is used to identify patients at-risk for delirium. Delirium Model (DEMO) is an automated model which could support delirium prevention more efficiently. However, it has not been validated beyond the hospital it was developed in.

AIM

To externally validate the DEMO and compare its performance to the VMS-questions.

METHOD

A retrospective cohort study between July and December 2018 was conducted. Delirium cases were identified through a chart review, and the VMS-questions were extracted from the electronic health records. The DEMO was validated in patients ≥ 60 years, and a comparison with the VMS-questions was made in patients ≥ 70 years.

RESULTS

In total 1,345 admissions were included. The DEMO predicted 59 out of 75 delirium cases (sensitivity 0.79, 95% CI = 0.68-0.87; specificity 0.75, 95% CI = 0.72-0.77). Compared to the VMS-questions, the DEMO showed a lower specificity (0.64 vs. 0.72; p < 0.001) and a comparable sensitivity (0.83 vs. 0.80; p = 0.56). The VMS-questions were missing in 20% of admissions, in which the DEMO correctly predicted 10 of 12 delirium cases.

CONCLUSION

The DEMO showed acceptable performance for delirium prediction. Overall the DEMO predicted more delirium cases because the VMS-questions were missing in 20% of admissions. This study shows that automated instruments such as DEMO could play a key role in the efficient and timely deployment of measures to prevent delirium.

Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium

BACKGROUND

Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.

METHODS

This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors.

RESULTS

In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium.

CONCLUSIONS

Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review

PURPOSE

The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units.

METHODS

APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis.

RESULTS

Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability.

CONCLUSIONS

Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

Development and validation of a delirium risk prediction model using a modified version of the Delirium Elderly at Risk (mDEAR) screen in hospitalized patients aged 65 and older: A medical record review

Most delirium risk prediction models are cumbersome to use, time consuming to complete, and require education ensure accuracy. The purpose of this study was to develop and validate a risk prediction model using routinely assessed risk factors predictive of delirium including: cognitive impairment, ≥80-years old, functional dependence, sensory impairment, and chronic substance use. This retrospective study included 7999 encounters of hospitalized patients aged 65-years or older admitted from 1/1/2019 to 12/31/2019. Various models were compared, with the best tested for validation. A model, where cognitive impairment was worth 2-points and a threshold of 3-points to predict delirium, was determined to be the best model and was validated with an area-under Receiver-Operating-Characteristic curve=0.7126. Management of delirium could be enhanced by integrating a nursing admission delirium risk screening process into the workflow, triggering initiation of prevention interventions and prompt assessment for signs and symptoms of delirium for those at high risk.

Potential Risk Factors for, and Clinical Implications of, Delirium during Inpatient Rehabilitation: A Matched Case-Control Study

OBJECTIVES

To investigate the association between a wide set of baseline characteristics (age, sex, rehabilitation discipline), functional scores [Functional Independence Measure (FIM), cumulative Illness Rating Scale (CIRS)], diseases, and administered drugs and incident delirium in rehabilitation inpatients and, furthermore, to assess clinical implications of developing delirium during rehabilitation.

DESIGN

Matched case-control study based on electronic health record data.

SETTING AND PARTICIPANTS

We studied rehabilitation stays of inpatients admitted between January 1, 2015, and December 31, 2018, to ZURZACH Care, Rehaklinik Bad Zurzach, an inpatient rehabilitation clinic in Switzerland.

METHODS

We conducted unconditional logistic regression analyses to estimate adjusted odds ratios (AORs) with 95% CIs of exposures that were recorded in ≥5 cases and controls.

RESULTS

Among a total of 10,503 rehabilitation stays, we identified 125 validated cases. Older age, undergoing neurologic rehabilitation, a low FIM, and a high CIRS were associated with an increased risk of incident delirium. Being diagnosed with a bacterial infection (AOR 2.62, 95% CI 1.06-6.49), a disorder of fluid, electrolyte, or acid-base balance (AOR 2.76, 95% CI 1.19-6.38), Parkinson's disease (AOR 5.68, 95% CI 2.54-12.68), and administration of antipsychotic drugs (AOR 8.06, 95% CI 4.26-15.22), antiparkinson drugs (AOR 2.86, 95% CI 1.42-5.77), drugs for constipation (AOR 2.11, 95% CI 1.25-3.58), heparins (AOR 2.04, 95% CI 1.29-3.24), or antidepressant drugs (AOR 1.88, 95% CI 1.14-3.10) during rehabilitation, or an increased anticholinergic burden (ACB ≥ 3) (AOR 2.59, 95% CI 1.41-4.73) were also associated with an increased risk of incident delirium.

CONCLUSIONS AND IMPLICATIONS

We identified a set of factors associated with an increased risk of incident delirium during inpatient rehabilitation. Our findings contribute to detect patients at risk of delirium during inpatient rehabilitation.

Identification of risk factors for delirium, cognitive decline, and dementia after cardiac surgery (FINDERI-find delirium risk factors): a study protocol of a prospective observational study

Background

Postoperative delirium is a common complication of cardiac surgery associated with higher morbidity, longer hospital stay, risk of cognitive decline, dementia, and mortality. Geriatric patients, patients undergoing cardiac surgery, and intensive care patients are at a high risk of developing postoperative delirium. Gold standard assessments or biomarkers to predict risk factors for delirium, cognitive decline, and dementia in patients undergoing cardiac surgery are not yet available.

Methods

The FINDERI trial (FINd DElirium RIsk factors) is a prospective, single-center, observational study. In total, 500 patients aged ≥ 50 years undergoing cardiac surgery at the Department of Cardiovascular and Thoracic Surgery of the University of Göttingen Medical Center will be recruited. Our primary aim is to validate a delirium risk assessment in context of cardiac surgery. Our secondary aims are to identify specific preoperative and perioperative factors associated with delirium, cognitive decline, and accelerated dementia after cardiac surgery, and to identify blood-based biomarkers that predict the incidence of postoperative delirium, cognitive decline, or dementia in patients undergoing cardiac surgery.

Discussion

This prospective, observational study might help to identify patients at high risk for delirium prior to cardiac surgery, and to identify important biological mechanisms by which cardiac surgery is associated with delirium. The predictive value of a delirium screening questionnaire in cardiac surgery might be revealed. Finally, the identification of specific blood biomarkers might help to predict delirium, cognitive decline, and dementia in patients undergoing cardiac surgery.

Trial registration: Ethics approval for this study was obtained from the IRB of the University of Göttingen Medical Center. The investigators registered this study in the German Clinical Trials Register (DRKS; https://www.drks.de) (DRKS00025095) on April 19th, 2021.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02732-4.

Head-to-head comparison of 14 prediction models for postoperative delirium in elderly non-ICU patients: an external validation study

OBJECTIVES

Delirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models.

SETTING

Single-site university hospital.

DESIGN

Secondary analysis of prospective cohort study.

PARTICIPANTS AND INCLUSION

CPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients.

PRIMARY OUTCOME MEASURES

Discrimination, calibration and clinical usefulness.

RESULTS

14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from -0.02 to 0.34, slopes from -0.74 to 1.96 and scaled Brier from -1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai et al (c-index: 0.739; (95% CI: 0.664 to 0.813); intercept: -0.018; slope: 1.96; scaled Brier: 0.049) and Litaker et al (c-index: 0.706 (95% CI: 0.590 to 0.823); intercept: -0.015; slope: 0.995; scaled Brier: 0.088). For the remaining CPMs, model discrimination was considered poor with corresponding c-indices <0.70.

CONCLUSION

Our head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.

Delirium prediction in the ICU: designing a screening tool for preventive interventions

Introduction

Delirium occurrence is common and preventive strategies are resource intensive. Screening tools can prioritize patients at risk. Using machine learning, we can capture time and treatment effects that pose a challenge to delirium prediction. We aim to develop a delirium prediction model that can be used as a screening tool.

Methods

From the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care version III (MIMIC-III) database, patients with one or more Confusion Assessment Method-Intensive Care Unit (CAM-ICU) values and intensive care unit (ICU) length of stay greater than 24 h were included in our study. We validated our model using 21 quantitative clinical parameters and assessed performance across a range of observation and prediction windows, using different thresholds and applied interpretation techniques. We evaluate our models based on stratified repeated cross-validation using 3 algorithms, namely Logistic Regression, Random Forest, and Bidirectional Long Short-Term Memory (BiLSTM). BiLSTM represents an evolution from recurrent neural network-based Long Short-Term Memory, and with a backward input, preserves information from both past and future. Model performance is measured using Area Under Receiver Operating Characteristic, Area Under Precision Recall Curve, Recall, Precision (Positive Predictive Value), and Negative Predictive Value metrics.

Results

We evaluated our results on 16 546 patients (47% female) and 6294 patients (44% female) from eICU-CRD and MIMIC-III databases, respectively. Performance was best in BiLSTM models where, precision and recall changed from 37.52% (95% confidence interval [CI], 36.00%–39.05%) to 17.45 (95% CI, 15.83%–19.08%) and 86.1% (95% CI, 82.49%–89.71%) to 75.58% (95% CI, 68.33%–82.83%), respectively as prediction window increased from 12 to 96 h. After optimizing for higher recall, precision and recall changed from 26.96% (95% CI, 24.99%–28.94%) to 11.34% (95% CI, 10.71%–11.98%) and 93.73% (95% CI, 93.1%–94.37%) to 92.57% (95% CI, 88.19%–96.95%), respectively. Comparable results were obtained in the MIMIC-III cohort.

Conclusions

Our model performed comparably to contemporary models using fewer variables. Using techniques like sliding windows, modification of threshold to augment recall and feature ranking for interpretability, we addressed shortcomings of current models.

Accuracy of delirium risk factors in adult intensive care unit patients

OBJECTIVE

To assess the accuracy measurements for predisposing and precipitating Risk Factors for delirium in an adult Intensive Care Unit.

METHOD

Cohort, prospective study with patients over 18 who had been hospitalized for over 24 hours and were able to communicate. The patients were assessed once a day until the onset of delirium or permanence in the Intensive Care Unit. Instruments were employed to track delirium, characterize the sample, and identify the risk factors. Descriptive statistics was employed for sample characterization and accuracy tests for risk factors.

RESULTS

The included patients amounted to 102, 31 of which presented delirium. The predisposing predictive risk factors were hypoalbuminemia, American Society of Anesthesiology over three, severity, altered tissue perfusion, dehydration, and being a male, whereas precipitating predictive factors were physical restraint, infection, pharmacological agent, polypharmacy, anemia, altered renal function, dehydration, invasive devices, altered tissue perfusion and altered quality and quantity of sleep.

CONCLUSION

An accurate identification of predisposing and precipitating risk factors may contribute to planning preventive measures against delirium.

Association between the reactivity of local cerebral oxygen saturation after hypo-to-hypercapnic tests and delirium after abdominal surgery in older adults: A prospective study

OBJECTIVE

This study aimed to investigate the correlation between changes in regional cerebral oxygen saturation (rSO2) and postoperative delirium in older adults undergoing major abdominal surgery.

MATERIALS AND METHODS

This prospective study enrolled older adults scheduled for elective major abdominal surgery at the Second Affiliated Hospital of Anhui Medical University from August 2021 to January 2022. The change in rSO2 from baseline was determined using the hypo-to-hypercapnic test. The main study outcome was the occurrence of postoperative delirium.

RESULTS

A total of 101 participants were included for analysis, of whom 16 (15.8%) developed postoperative delirium. Compared with non-delirium participants, the mean arterial pressure and heart rate were not significantly different in the postoperative delirium group at T0, T1, T2, T3, T4, and T6 (all P_interaction > 0.05), but the delirium group had lower pH, lower PaO2, and higher lactate levels at T4, T5, and T6 (all P_interaction < 0.05). rSO2 at T0, T1, T2, T3, T4, and T6 was 69.0 (63.2-75.2), 70.7 ± 7.3, 68.2 ± 7.5, 72.1 ± 8.0, 69.9 ± 7.8, 67.4 ± 7.2, and 71.7 ± 8.1, respectively. The postoperative change in rSO2 during the hypercapnia test (TΔrSO2%) was 6.62 (5.31-9.36). Multivariable analysis showed that the Cumulative Illness Rating Scale (odd ratio, OR = 1.89, 95% confidence interval, CI: 1.10-3.25, P = 0.021), preoperative albumin levels (OR = 0.67, 95% CI: 0.48-0.94, P = 0.022), rSO2 at T4 (OR = 0.61, 95% CI: 0.41-0.89, P = 0.010), and postoperative TΔrSO2% (OR = 0.80, 95% CI: 0.66-0.98, P = 0.028) were independently associated with postoperative delirium in older adults undergoing elective abdominal surgery.

CONCLUSION

The rSO2 measured at T4 and postoperative TΔrSO2% were independently associated with postoperative delirium in older adults undergoing elective abdominal surgery.

Developing a machine learning model to identify delirium risk in geriatric internal medicine inpatients

PURPOSE

To develop a machine learning model that predicts delirium risk in geriatric internal medicine inpatients.

METHODS

A prospective cohort study of internal medicine wards in a tertiary care hospital in China. Blinded observers assessed delirium using the Confusion Assessment Method (CAM). The data set was randomly divided into a training set (70%) and a test set (30%). The model was trained on the training set using the decision tree and the five-fold cross-validation, and then the model performance was evaluated on the test set. Under-sampling was used to address the class imbalance. The discriminatory power of the model was measured by the area under the receiver operating characteristic curve (AUC) and F1 score. The data set comprised 740 patients from March 2016 to January 2017.

RESULTS

The training set included 518 patients; the median (IQR) age was 84 (79-87) years; 364 (70.3%) were men; 71 (13.7%) with delirium. The test set included 222 patients; the median (IQR) age was 84.5 (79-87) years; 163 (73.4%) were men; 30 (13.5%) with delirium. In total, the data set included 740 hospital admissions with a median (IQR) age of 84 (79-87) years, 527 (71.2%) were men, and 101 (13.6%) with delirium. From 32 potential predictors, we included five variables in the predictive model: depression, cognitive impairment, types of drugs, nutritional status, and activity of daily life (ADL). The mean AUC on the training set was 0.967, the AUC and F1 score on the test set was 0.950 and 0.810, respectively. The model achieved 93.3% sensitivity, 94.3% specificity, 71.8% positive predictive value, 98.9% negative predictive value, and 94.1% accuracy on the test set.

CONCLUSION

This machine learning model may allow more precise targeting of delirium prevention and could support clinical decision making in geriatric internal medicine wards.

The short IQCODE as a predictor for delirium in hospitalized geriatric patients

BACKGROUND

Delirium is a serious complication, which occurs frequently in older patients with pre-existing cognitive impairment. There is a need for a simple tool to assess chronic cognitive impairment and the associated risk of delirium during hospitalization.

AIMS

To assess the usefulness of the short IQCODE questionnaire in predicting delirium during hospitalization in older patients in a geriatric ward.

METHODS

A prognostic study in the Geriatric Department at Aarhus University Hospital, Aarhus Denmark. Consecutive patients were enrolled during March to December, 2017. After consent of the patient, the staff interviewed the relatives by phone using the short IQCODE questionnaire. Delirium was assessed morning and evening until discharge by the Confusion Assessment Method. The ability of short IQCODE to predict delirium was examined.

RESULTS

Three hundred and fifty-three patients were eligible, and 306 completed the IQCODE. Delirium occurred among 19% of the patients during hospitalization. The IQCODE score was associated with the risk of delirium with a receiver operating characteristic (ROC) area of 0.72. A cut-point of 3.3 could separate the patients in a larger group with a risk of approximately 26% to develop delirium and a smaller group having a risk of approximately 6%.

CONCLUSION

The IQCODE is a useful tool to predict delirium among older inpatients, but it may not stand alone. It can be a useful supplement to other clinical information and observations in detecting patients needing dementia-friendly treatment and care.

Episodes of psychomotor agitation among medical patients: findings from a longitudinal multicentre study

BACKGROUND

The management of delirium among older in-hospital patients is a challenge, leading to worse outcomes, including death. Specifically, psychomotor agitation, one of the main characteristics of hyperactive delirium, requires a significant amount of medical and nursing surveillance. However, despite its relevance, to date incidence and/or prevalence of psychomotor agitation, its predictors and outcomes have not been studied among Italian older patients admitted in medical units.

AIMS

To describe the incidence and the prevalence of psychomotor agitation among patients aged > 65 years admitted to medical units and identify predictors at the individual, nursing care and hospital levels.

METHODS

A longitudinal multicentre study was conducted involving 12 medical units in 12 northern Italian hospitals. Descriptive, bivariate and multivariate logistic regression analyses were performed.

RESULTS

Among the 1464 patients included in the study, two hundred (13.6%) have manifested episode(s), with an average of 3.46/patient (95% confidence of interval [CI] 2.73-4.18). In 108 (54.0%) patients, episode(s) were present also in the week prior to hospitalisation: therefore, in-hospital-acquired psychomotor agitation was reported in 92 patients (46%). The multivariate logistic regression analysis explained the 25.4% of the variance and identified the following variables as psychomotor agitation predictors: the risk of falls (relative risk [RR] 1.314, 95% CI 1.218-1.417), the amount of missed nursing care (RR 1.078, 95% CI 1.037-1.12) and the patient's age (RR 1.018, 95% CI 1.002-1.034). Factors preventing the occurrence of episode(s) were: the amount of care received from graduated nurses (RR 0.978; 95% CI 0.965-0.992) and the lower functional dependence at admission (RR 0.987, 95% CI 0.977-0.997).

CONCLUSIONS

A considerable number of elderly patients admitted in medical units develop psychomotor agitation; its predictors need to be identified early to inform decisions regarding the personal care needed to prevent its occurrence, especially by acting on modifiable factors, such as the risk of falls, missed nursing care and functional dependence.

Predicting medication-associated altered mental status in hospitalized patients: Development and validation of a risk model

PURPOSE

This study presents a medication-associated altered mental status (AMS) risk model for real-time implementation in inpatient electronic health record (EHR) systems.

METHODS

We utilized a retrospective cohort of patients admitted to 2 large hospitals between January 2012 and October 2013. The study population included admitted patients aged ≥18 years with exposure to an AMS risk-inducing medication within the first 5 hospitalization days. AMS events were identified by a measurable mental status change documented in the EHR in conjunction with the administration of an atypical antipsychotic or haloperidol. AMS risk factors and AMS risk-inducing medications were identified from the literature, drug information databases, and expert opinion. We used multivariate logistic regression with a full and backward eliminated set of risk factors to predict AMS. The final model was validated with 100 bootstrap samples.

RESULTS

During 194,156 at-risk days for 66,875 admissions, 262 medication-associated AMS events occurred (an event rate of 0.13%). The strongest predictors included a history of AMS (odds ratio [OR], 9.55; 95% confidence interval [CI], 5.64-16.17), alcohol withdrawal (OR, 3.34; 95% CI, 2.18-5.13), history of delirium or psychosis (OR, 3.25; 95% CI, 2.39-4.40), presence in the intensive care unit (OR, 2.53; 95% CI, 1.89-3.39), and hypernatremia (OR, 2.40; 95% CI, 1.61-3.56). With a C statistic of 0.85, among patients scoring in the 90th percentile, our model captured 159 AMS events (60.7%).

CONCLUSION

The risk model was demonstrated to have good predictive ability, with all risk factors operationalized from discrete EHR fields. The real-time identification of higher-risk patients would allow pharmacists to prioritize surveillance, thus allowing early management of precipitating factors.

Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study

OBJECTIVES

We aimed to develop and validate a postoperative delirium (POD) prediction model for patients admitted to the intensive care unit (ICU).

DESIGN

A prospective study was conducted.

SETTING

The study was conducted in the surgical, cardiovascular surgical and trauma surgical ICUs of an affiliated hospital of a medical university in Heilongjiang Province, China.

PARTICIPANTS

This study included 400 patients (≥18 years old) admitted to the ICU after surgery.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measure was POD assessment during ICU stay.

RESULTS

The model was developed using 300 consecutive ICU patients and was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological and Operative Severity Score for the enumeration of Mortality and morbidity; acid-base disturbance and history of coma, diabetes or hypertension. The model had an area under the receiver operating characteristics curve of 0.852 (95% CI 0.802 to 0.902), Youden index of 0.5789, sensitivity of 70.73% and specificity of 87.16%. The Hosmer-Lemeshow goodness of fit was 5.203 (p=0.736). At a cutoff value of 24.5%, the sensitivity and specificity were 71% and 69%, respectively.

CONCLUSIONS

The model, which used readily available data, exhibited high predictive value regarding risk of ICU-POD at admission. Use of this model may facilitate better implementation of preventive treatments and nursing measures.

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

Background

Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited.

Objective

This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance.

Methods

We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees.

Results

Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03).

Conclusions

Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes.

Delirium risk in non-surgical patients: systematic review of predictive tools

OBJECTIVE

Delirium is a common, serious condition associated with poor hospital outcomes. Guidelines recommend screening for delirium risk to target diagnostic and/or prevention strategies. This study critically reviews multicomponent delirium risk prediction tools in adult non-surgical inpatients.

STUDY DESIGN

Systematic review of studies incorporating at least two clinical factors in a multicomponent tool predicting risk of delirium during hospital admission. Derivation and validation studies were included. Study design, risk factors and tool performance were extracted and tabulated, and study quality was assessed by CHARMS criteria.

DATA SOURCES

PubMed, Embase, PsycINFO, and Cumulative Index to Nursing Health Literature (CINAHL) to 11^th March 2018.

DATA SYNTHESIS

22 derivation studies enrolling 38,874 participants (9 with a validation component) and 4 additional validation studies were identified, from a range of ward types. All studies had at least moderate risk of bias. Older age and cognitive, functional and sensory impairment were important predisposing factors. Precipitating risk factors included infection, illness severity, renal and electrolyte disturbances. Tools mostly did not differentiate between predisposing and precipitating risk factors mathematically or conceptually Most tools showed fair to good discrimination, and identified more than half of older inpatients at risk.

CONCLUSIONS

Several validated delirium risk prediction tools can identify patients at increased risk of delirium, but do not provide clear advice for clinical application. Most recommended cut-points are sensitive but have low specificity. Implementation studies demonstrating how risk screening can better direct clinical interventions in specific clinical settings are needed to define the potential value of these tools.

Postoperative delirium in critically ill surgical patients: incidence, risk factors, and predictive scores

BACKGROUND

A common postoperative complication found among patients who are critically ill is delirium, which has a high mortality rate. A predictive model is needed to identify high-risk patients in order to apply strategies which will prevent and/or reduce adverse outcomes.

OBJECTIVES

To identify the incidence of, and the risk factors for, postoperative delirium (POD) in surgical intensive care unit (SICU) patients, and to determine predictive scores for the development of POD.

METHODS

This study enrolled adults aged over 18 years who had undergone an operation within the preceding week and who had been admitted to a SICU for a period that was expected to be longer than 24 h. The CAM - ICU score was used to determine the occurrence of delirium.

RESULTS

Of the 250 patients enrolled, delirium was found in 61 (24.4%). The independent risk factors for delirium that were identified by a multivariate analysis comprised age, diabetes mellitus, severity of disease (SOFA score), perioperative use of benzodiazepine, and mechanical ventilation. A predictive score (age + (5 × SOFA) + (15 × Benzodiazepine use) + (20 × DM) + (20 × mechanical ventilation) + (20 × modified IQCODE > 3.42)) was created. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 (95% CI: 0.786 to 0.897). The cut point of 125 demonstrated a sensitivity of 72.13% and a specificity of 80.95%, and the hospital mortality rate was significantly greater among the delirious than the non-delirious patients (25% vs. 6%, p < 0.01).

CONCLUSIONS

POD was experienced postoperatively by a quarter of the surgical patients who were critically ill. A risk score utilizing 6 variables was able to predict which patients would develop POD. The identification of high-risk patients following SICU admission can provide a basis for intervention strategies to improve outcomes.

TRIAL REGISTRATION

Thai Clinical Trials Registry TCTR20181204006 . Date registered on December 4, 2018. Retrospectively registered.

Development and validation of a dynamic delirium prediction rule in patients admitted to the Intensive Care Units (DYNAMIC-ICU): A prospective cohort study

BACKGROUND

Delirium is one of the most common cognitive complications among patients admitted to the intensive care units (ICU).

OBJECTIVE

To develop and validate a DYNAmic deliriuM predICtion rule for ICU patients (DYNAMIC-ICU) and to stratify patients into different risk levels among patients in various types of ICUs.

DESIGN

Prospective cohort study.

SETTING AND PARTICIPANTS

A total of 560 (median age of 66 years, 62.5% male) consecutively enrolled patients from four ICUs were included in the study. The patients were randomly assigned into either the derivation (n = 336, 60%) or the validation (n = 224, 40%) cohort by stratified randomization based on delirium/non-delirium and types of ICU.

METHODS

The simplified Chinese version of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) was used to assess delirium until patients were discharged from the ICUs. Potential predisposing, disease-related, and iatrogenic and environmental risk factors as well as data on patients' outcomes were collected prospectively.

RESULTS

Of the enrolled patients, 20.2% and 20.5% developed delirium in the derivation and validation cohorts, respectively. Predisposing factors (history of chronic diseases, hearing deficits), disease-related factors (infection, higher APACHE II scores at admission), and iatrogenic and environmental factors (the use of sedatives and analgesics, indwelling catheter, and sleep disturbance) were identified as independent predictors of delirium. Points were assigned to each predictor according to their odds ratio to create a prediction rule which was internally validated based on total scores and by bootstrapping (AUCs of 0.907 [95% CI 0. 871 -0.944], 0.888 [95% CI 0.845-0.932], and 0.874 [95% CI 0.828-0.920]), respectively. The total score of the DYNAMIC-ICU ranged from 0 to 33 and patients were divided into low risk (0-9), moderate risk (10-17), high risk (18-33) groups in developing delirium according to their total score with incidence of delirium at 2.8%, 16.8% and 75.9% in the derivation group, respectively. The DYNAMIC-ICU and its performance of risk level stratification were further validated in the validation cohort (AUC = 0.900 [95% CI 0.858-0.941]). The all-cause mortality was increased and the length of hospital stay was prolonged dramatically with the increase of delirium risk levels in both derivation (p = 0.034, p < 0.001) and validation cohorts (p < 0.001, p < 0.001).

CONCLUSIONS

Seven predictors for ICU delirium were identified to create DYNAMIC-ICU, which could well stratify ICU patients into three different delirium risk levels, tailor risk level changes, and predict in-hospital outcomes by a dynamic assessment approach.

Risk Factors for Delirium in Older Medical Inpatients in Tanzania

BACKGROUND

The risk factors for prevalent delirium in older hospitalised adults in Sub-Saharan Africa (SSA) remain poorly characterised.

METHODS

A total of 510 consecutive admissions of adults aged ≥60 years to acute medical wards of Kilimanjaro Christian Medical Centre in northern Tanzania were recruited. Patients were assessed within 24 h of admission with a risk factor questionnaire, physiological observations, neurocognitive assessment, and informant interview. Delirium and dementia diagnoses were made according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM V) and DSM IV respectively, by an expert panel.

RESULTS

Being male, current alcohol use, dementia, and physiological markers of illness severity were significant independent risk factors for delirium on multivariable analysis.

CONCLUSIONS

The risk factors for prevalent delirium in older medical inpatients in SSA include pre-existing dementia, and are similar to those identified in high-income countries. Our data could help inform the development of a delirium risk stratification tool for older adults in SSA.

Systematic review of prediction models for delirium in the older adult inpatient

OBJECTIVE

To identify existing prognostic delirium prediction models and evaluate their validity and statistical methodology in the older adult (≥60 years) acute hospital population.

DESIGN

Systematic review.

DATA SOURCES AND METHODS

PubMed, CINAHL, PsychINFO, SocINFO, Cochrane, Web of Science and Embase were searched from 1 January 1990 to 31 December 2016. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and CHARMS Statement guided protocol development.

INCLUSION CRITERIA

age >60 years, inpatient, developed/validated a prognostic delirium prediction model.

EXCLUSION CRITERIA

alcohol-related delirium, sample size ≤50. The primary performance measures were calibration and discrimination statistics. Two authors independently conducted search and extracted data. The synthesis of data was done by the first author. Disagreement was resolved by the mentoring author.

RESULTS

The initial search resulted in 7,502 studies. Following full-text review of 192 studies, 33 were excluded based on age criteria (<60 years) and 27 met the defined criteria. Twenty-three delirium prediction models were identified, 14 were externally validated and 3 were internally validated. The following populations were represented: 11 medical, 3 medical/surgical and 13 surgical. The assessment of delirium was often non-systematic, resulting in varied incidence. Fourteen models were externally validated with an area under the receiver operating curve range from 0.52 to 0.94. Limitations in design, data collection methods and model metric reporting statistics were identified.

CONCLUSIONS

Delirium prediction models for older adults show variable and typically inadequate predictive capabilities. Our review highlights the need for development of robust models to predict delirium in older inpatients. We provide recommendations for the development of such models.

A Proactive Approach to High Risk Delirium Patients Undergoing Total Joint Arthroplasty

BACKGROUND

Delirium is a common complication among elderly patients undergoing total joint arthroplasty (TJA). Its incidence has been reported from 4% to 53%. The Centers for Medicare and Medicaid Services consider delirium following TJA a "never-event." The purpose of this study is to evaluate a simple perioperative protocol used to identify delirium risk patients and prevent its incidence following TJA.

METHODS

Our group developed a protocol to identify and prevent delirium in patients undergoing TJA. All patients were screened and scored in the preoperative assessment, on criteria such as age, history of forgetfulness, history of agitation or visual hallucinations, history of falls, history of postoperative confusion, and inability to perform higher brain functions. Patients were scored on performance in a simple mental examination. The patients were classified as low, medium, or high risk. Patients who were identified as high risk were enrolled in a delirium avoidance protocol that minimized narcotics and emphasized nursing involvement and fluids administration.

RESULTS

Five of 7659 (0.065%) consecutive TJA patients from 2010 to 2015 developed delirium. A total of 422 patients were identified as high risk. All 5 patients who suffered delirium were within the high risk group. No low or medium risk patients suffered a delirium complication. Three (0.039%) patients suffered drug-induced delirium, 1 (0.013%) had delirium related to alcohol withdrawal, and 1 (0.013%) had delirium after a systemic infection.

CONCLUSION

This protocol is effective in identifying patients at high delirium risk and diminishing the incidence of this complication by utilizing a simple screening tool and perioperative protocol.

Nursing assessment as an effective tool for the identification of delirium risk in older in-patients: A case-control study

AIMS AND OBJECTIVES

To evaluate the usefulness of comprehensive nursing assessment as a strategy for determining the risk of delirium in older in-patients from a model of care needs based on variables easily measured by nurses.

BACKGROUND

There are many scales of assessment and prediction of risk of delirium, but they are little known and infrequently used by professionals. Recognition of delirium by doctors and nurses continues to be limited.

DESIGN AND METHODS

A case-control study. A specific form of data collection was designed to include the risk factors for delirium commonly identified in the literature and the care needs evaluated from the comprehensive nursing assessment based on the Virginia Henderson model of care needs. We studied 454 in-patient units in a basic general hospital. Data were collected from a review of the records of patients' electronic clinical history.

RESULTS

The areas of care that were significant in patients with delirium were dyspnoea, problems with nutrition, elimination, mobility, rest and sleep, self-care, physical safety, communication and relationships. The specific risk factors identified as independent predictors were as follows: age, urinary incontinence, urinary catheter, alcohol abuse, previous history of dementia, being able to get out of bed/not being at rest, habitual insomnia and history of social risk.

CONCLUSIONS

Comprehensive nursing assessment is a valid and consistent strategy with a multifactorial model of delirium, which enables the personalised risk assessment necessary to define a plan of care with specific interventions for each patient to be made.

RELEVANCE TO CLINICAL PRACTICE

The identification of the risk of delirium is particularly important in the context of prevention. In a model of care based on needs, nursing assessment is a useful component in the risk assessment of delirium and one that is necessary for developing an individualised care regime.

Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study

OBJECTIVES

Delirium is an underdiagnosed, severe and costly disorder, and 30%-40% of cases can be prevented. A fully automated model to predict delirium (DEMO) in older people has been developed, and the objective of this study is to validate the model in a hospital setting.

SETTING

Secondary care, one hospital with two locations.

DESIGN

Observational study.

PARTICIPANTS

The study included 450 randomly selected patients over 60 years of age admitted to Zuyderland Medical Centre. Patients who presented with delirium on admission were excluded.

PRIMARY OUTCOME MEASURES

Development of delirium through chart review.

RESULTS

A total of 383 patients were included in this study. The analysis was performed for delirium within 1, 3 and 5 days after a DEMO score was obtained. Sensitivity was 87.1% (95% CI 0.756 to 0.939), 84.2% (95% CI 0.732 to 0.915) and 82.7% (95% CI 0.734 to 0.893) for 1, 3 and 5 days, respectively, after obtaining the DEMO score. Specificity was 77.9% (95% CI 0.729 to 0.882), 81.5% (95% CI 0.766 to 0.856) and 84.5% (95% CI 0.797 to 0.884) for 1, 3 and 5 days, respectively, after obtaining the DEMO score.

CONCLUSION

DEMO is a satisfactory prediction model but needs further prospective validation with in-person delirium confirmation. In the future, DEMO will be applied in clinical practice so that physicians will be aware of when a patient is at an increased risk of developing delirium, which will facilitate earlier recognition and diagnosis, and thus will allow the implementation of prevention measures.

Predicting inpatient delirium: The AWOL delirium risk-stratification score in clinical practice

Inpatient delirium improves with multicomponent interventions by hospital staff, though the resources needed are often limited. Risk-stratification to predict delirium is a useful first step to help triage resources, but the performance of risk-stratification as part of a functioning multicomponent pathway has not been assessed. We retrospectively studied the performance of a validated delirium prediction rule, the AWOL score, as a part of a multicomponent delirium care pathway in practice on a university hospital ward. We reviewed the hospitalizations of patients 50 years or older for evidence of delirium and extracted the AWOL score from nursing documentation (n = 347). The area under the receiver operating characteristic curve (AUC) was 0.83 (95% CI 0.77-0.89) for all cases and 0.73 (95% CI 0.60-0.85) when cases of prevalent delirium were removed. Involving minimal additional assessment, this nursing-based risk stratification score performed well as part of a multicomponent delirium care pathway.

Models for Predicting Incident Delirium in Hospitalized Older Adults: A Systematic Review

Purpose

The purpose of this systematic review is to summarize the reported risk prediction models and identify the most prevalent factors for incident delirium in older inpatient populations (age ≥ 65 years). In the future, these risk factors could be used to develop a delirium risk prediction model in the electronic health record that can be used by the Hospital Elder Life Program to reduce the incidence of delirium.

Methods

A medical librarian customized and conducted a search strategy for all published articles on delirium prediction models using an array of electronic databases and specific inclusion and exclusion criteria. Then, a geriatrician and two research associates assessed the quality of the selected studies using the Newcastle-Ottawa Scale (NOS).

Results

A total of 4,351 articles were identified from initial literature search. After review, data were extracted from 12 studies. The quality of these studies was assessed using NOS and ranged from 4 to 8. The most common risk factors reported were dementia, decreased functional status, high blood urea nitrogen-to-creatinine ratio, infection and severe illness.

Conclusions

The most prevalent factors associated with incidence of delirium in hospitalized older patients identified by this systematic review could be used to develop an electronic health record-generated risk prediction model to identify inpatients at risk of developing delirium.

Delirium is a Strong Predictor of Mortality in Patients Receiving Non-invasive Positive Pressure Ventilation

PURPOSE

Non-invasive positive pressure ventilation (NIPPV) has gained popularity over the years in the treatment of acute respiratory failure (ARF). Preliminary evidence suggests that delirium is an important factor contributing to NIPPV failure and death. This study was conducted to evaluate delirium and other associated factors of deaths in patients with ARF requiring the use of NIPPV.

METHODS

A prospective observational study was conducted in a specialised NIPPV unit. Consecutive patients admitted for ARF requiring NIPPV were assessed by a psychiatrist for presence of delirium using the Diagnostic and Statistical Manual Version IV (DSM-IV). APACHE II score, co-morbidities-, and lung function were also assessed. Patients were followed until their deaths for a minimum of 1 year. Univariate and multivariate Cox's regression analyses were performed to explore predictive factors for death.

RESULTS

A total of 153 subjects were recruited, 49 (32.0 %) of whom had delirium. On univariate analysis, higher APACHE II score, lower BMI, presence of delirium, higher Charlson's co-morbidity index but not FEV₁ were associated with earlier death. On multivariate analysis, delirium (HR 4.4; 95 % CI 2.6-7.4; p < 0.001) and lower BMI (HR 0.92; 95 % CI 0.86-0.98; p = 0.013) were independently associated with earlier death within 1 year.

CONCLUSIONS

There is a high prevalence of delirium in patients requiring NIPPV. The presence of delirium is a strong predictor of mortality. There is strong need to identify and manage these high-risk patients to improve their mortality. The collaboration between psychiatrists and physicians should be strengthened.

The development of an automated ward independent delirium risk prediction model

Background A delirium is common in hospital settings resulting in increased mortality and costs. Prevention of a delirium is clearly preferred over treatment. A delirium risk prediction model can be helpful to identify patients at risk of a delirium, allowing the start of preventive treatment. Current risk prediction models rely on manual calculation of the individual patient risk. Objective The aim of this study was to develop an automated ward independent delirium riskprediction model. To show that such a model can be constructed exclusively from electronically available risk factors and thereby implemented into a clinical decision support system (CDSS) to optimally support the physician to initiate preventive treatment. Setting A Dutch teaching hospital. Methods A retrospective cohort study in which patients, 60 years or older, were selected when admitted to the hospital, with no delirium diagnosis when presenting, or during the first day of admission. We used logistic regression analysis to develop a delirium predictive model out of the electronically available predictive variables. Main outcome measure A delirium risk prediction model. Results A delirium risk prediction model was developed using predictive variables that were significant in the univariable regression analyses. The area under the receiver operating characteristics curve of the "medication model" model was 0.76 after internal validation. Conclusions CDSSs can be used to automatically predict the risk of a delirium in individual hospitalised patients' by exclusively using electronically available predictive variables. To increase the use and improve the quality of predictive models, clinical risk factors should be documented ready for automated use.

Risk factors and clinical aspects of delirium in elderly hospitalized patients in Iran

BACKGROUND

Recognition of the risk factors of delirium has been clearly advantageous in preventing and managing it as it occurs.

AIMS

The main aims of this study were to investigate the occurrence of delirium and identify the associated risk factors in a sample of hospitalized elderly in Southwestern Iran.

METHODS

A cross-sectional, hospital-based study was performed on a total of 200 elderly patients, admitted to a general hospital for various health reasons. Data were gathered over a 3-month period of time in 2010. Abbreviated Mental Test score (AMTs) used for delirium detection in post-admission days 1, 3, and 5, followed by clinical diagnostic confirmation according to the DSM-IV-TR criteria for delirium. Information regarding physical, cognitive, emotional, and functional states of the participants was collected, too.

RESULTS

Delirium developed in 22 % of the participants. The demographic characteristics of the patients with delirium indicated that they were typically single, older men who lived alone and had a lower level of education and poorer functional status. Among other variables, the following were significantly associated with delirium: hemoglobin ≤12 (P < 0.001); Blood urea nitrogen/creatinine ratio ≥1/20 (P < 0.005); and positive C-reactive protein (P = 0.022); depressive symptoms (P < 0.001), and previous cognitive decline (P < 0.001). Patients with more than six different categories of medications were at high risk for delirium as well.

CONCLUSIONS

Delirium is a serious and common problem in people over 60 years of age who are admitted to hospitals. Understanding risk factors and clinical aspects of delirium in elderly hospitalized patients will provide us with a better delirium management strategy.

Delirium risk stratification in consecutive unselected admissions to acute medicine: validation of externally derived risk scores

Background: reliable delirium risk stratification will aid recognition, anticipation and prevention and will facilitate targeting of resources in clinical practice as well as identification of at-risk patients for research. Delirium risk scores have been derived for acute medicine, but none has been prospectively validated in external cohorts. We therefore aimed to determine the reliability of externally derived risk scores in a consecutive cohort of older acute medicine patients.

Methods: consecutive patients aged ≥65 over two 8-week periods (2010, 2012) were screened prospectively for delirium using the Confusion Assessment Method (CAM), and delirium was diagnosed using the DSM IV criteria. The reliability of existing delirium risk scores derived in acute medicine cohorts and simplified for use in routine clinical practice (USA, n = 2; Spain, n = 1; Indonesia, n = 1) was determined by the area under the receiver operating characteristic curve (AUC). Delirium was defined as prevalent (on admission), incident (occurring during admission) and any (prevalent + incident) delirium.

Results: among 308 consecutive patients aged ≥65 (mean age/SD = 81/8 years, 164 (54%) female), existing delirium risk scores had AUCs for delirium similar to those reported in their original internal validations ranging from 0.69 to 0.76 for any delirium and 0.73 to 0.83 for incident delirium. All scores performed better than chance but no one score was clearly superior.

Conclusions: externally derived delirium risk scores performed well in our independent acute medicine population with reliability unaffected by simplification and might therefore facilitate targeting of multicomponent interventions in routine clinical practice.

Risk of Pre-and Post-Operative Delirium and the Delirium Elderly At Risk (DEAR) Tool in Hip Fracture Patients

BACKGROUND AND PURPOSE

Delirium is common after hip fracture. Previous work has shown that a simple delirium risk factor tool, the Delirium Elderly At Risk instrument (DEAR), has a high inter-rater reliability in this population. Little research has looked at the ability of risk factor screening tools to identify patients at high risk of pre-operative delirium. This study investigates the ability of the DEAR to identify patients at high risk of pre-operative delirium, as well as reporting its performance in a post-operative validation sample. Associations between delirium risk factors and pre-operative delirium are explored.

METHODS

This prospective cohort study took place on an orthopedic in-patient service at a University-affiliated tertiary care hospital. Patients aged 65 and older who were admitted for surgical repair of hip fracture (N = 283) were assessed pre-operatively for 5 delirium risk factors (cognitive impairment, sensory impairment, functional dependence, substance use, age) using the DEAR. Patients were assessed for delirium using the Mini-Mental State Examination and the Confusion Assessment Method pre-operatively and on post-operative days 1, 3 and 5. Characteristics of patients who developed delirium were compared with the characteristics of those who did not.

RESULTS

Delirium was present in 58% (95% CI = 52-63%) of patients pre-operatively and 42% (95% CI = 36-48%) post-operatively. Individually, sensory impairment (χ(2) = 21.7, p = .0001), functional dependence (χ(2) = 24.1, p = .0001), cognitive impairment (χ(2) = 55.5, p = .0001) and substance use (χ(2) = 7.5, p = .007) were significantly associated with pre-operative delirium, as was wait-time for surgery (t = 3.1, p = .003) and length of stay (t = 2.8, p =.03). In multivariate modeling, the strongest association with pre-operative delirium was cognitive impairment.

CONCLUSIONS

The DEAR, a simple, delirium risk factor screening tool, can be used to identify hip fracture patients at risk of both pre-operative and post-operative delirium, which may allow targeted implementation of delirium prevention strategies.

Cognitive and functional status predictors of delirium and delirium severity after coronary artery bypass graft surgery: an interim analysis of the Neuropsychiatric Outcomes After Heart Surgery study

BACKGROUND

Cognitive and functional impairment increase risk for post-coronary artery bypass graft (CABG) surgery delirium (PCD), but how much impairment is necessary to increase PCD risk remains unclear.

METHODS

The Neuropsychiatric Outcomes After Heart Surgery (NOAHS) study is a prospective, observational cohort study of participants undergoing elective CABG surgery. Pre-operative cognitive and functional status based on Clinical Dementia Rating (CDR) scale and neuropsychological battery are assessed. We defined mild cognitive impairment (MCI) based on either (1) CDR global score 0.5 (CDR-MCI) or (2) performance 1.5 SD below population means on any cognitive domain on neurocognitive battery (MCI-NC). Delirium was assessed daily post-operative day 2 through discharge using the confusion assessment method (CAM) and delirium index (DI). We investigate whether MCI - either definition - predicts delirium or delirium severity.

RESULTS

So far we have assessed 102 participants (mean age 65.1 ± 9; male: 75%) for PCD. Twenty six participants (25%) have MCI-CDR; 38 (62% of those completing neurocognitive testing) met MCI-NC criteria. Fourteen participants (14%) developed PCD. After adjusting for age, sex, comorbidity, and education, MCI-CDR, MMSE, and Lawton IADL score predicted PCD on logistic regression (OR: 5.6, 0.6, and 1.5, respectively); MCI-NC did not (OR [95% CI]: 11.8 [0.9, 151.4]). Using similarly adjusted linear regression, MCI-CDR, MCI-NC, CDR sum of boxes, MMSE, and Lawton IADL score predicted delirium severity (adjusted R(2): 0.26, 0.13, 0.21, 0.18, and 0.32, respectively).

CONCLUSIONS

MCI predicts post-operative delirium and delirium severity, but MCI definition alters these relationships. Cognitive and functional impairment independently predict post-operative delirium and delirium severity.

Predicting delirium: a review of risk-stratification models

BACKGROUND

Delirium is a common condition in hospitalized patients and is associated with multiple adverse outcomes. There is increasing evidence to support interventions that prevent delirium, so the identification of patients at high risk is of significant clinical value. Numerous risk factors have been identified, including both premorbid patient characteristics and acute precipitants. Despite this, predicting the occurrence of delirium remains a clinical challenge.

OBJECTIVE

This article reviews studies of validated risk-stratification models for delirium. We discuss possible barriers to use of these models and future directions for research.

METHODS

A comprehensive review of the literature was completed using PubMed and Embase. The resulting citations were filtered in a structured process. Inclusion criteria were original research, adult medical inpatient population and presence of a validation group in the study design.

RESULTS

Ten cohort studies met inclusion criteria. The quality of the studies was moderate to good. All studies proposed models using clinical data to predict the risk of patients' developing delirium.

CONCLUSION

The most common risk factors identified were preexisting cognitive impairment, medical comorbidity, elevated Blood Urea Nitrogen, and impairment in activities of daily living. While multiple validated predictive models exist, there is substantial heterogeneity between models, and only one replication study has been performed. In addition, difficulties in implementation may be a barrier to broader use of these models. There is limited support for an accurate and reliable tool to predict inpatient delirium. Further research is needed in this clinically important area.

Delirium diagnosis, screening and management

Purpose of review

Our review focuses on recent developments across many settings regarding the diagnosis, screening and management of delirium, so as to inform these aspects in the context of palliative and supportive care.

Recent findings

Delirium diagnostic criteria have been updated in the long-awaited Diagnostic Statistical Manual of Mental Disorders, fifth edition. Studies suggest that poor recognition of delirium relates to its clinical characteristics, inadequate interprofessional communication and lack of systematic screening. Validation studies are published for cognitive and observational tools to screen for delirium. Formal guidelines for delirium screening and management have been rigorously developed for intensive care, and may serve as a model for other settings. Given that palliative sedation is often required for the management of refractory delirium at the end of life, a version of the Richmond Agitation-Sedation Scale, modified for palliative care, has undergone preliminary validation.

Summary

Although formal systematic delirium screening with brief but sensitive tools is strongly advocated for patients in palliative and supportive care, it requires critical evaluation in terms of clinical outcomes, including patient comfort. Randomized controlled trials are needed to inform the development of guidelines for the management of delirium in this setting.