Predicting delirium: a review of risk-stratification models

Predicting delirium in older non-intensive care unit inpatients: development and validation of the DELIrium risK Tool (DELIKT)

BACKGROUND

Effective delirium prevention could benefit from automatic risk stratification of older inpatients using routinely collected clinical data.

AIM

Primary aim was to develop and validate a delirium prediction model (DELIKT) suitable for implementation in hospitals. Secondary aim was to select an anticholinergic burden scale as a predictor.

METHOD

We used one cohort for model development and another for validation with electronically available data collected within the first 24 h of admission. Included were patients aged ≥ 65, hospitalised ≥ 48 h with no stay > 24 h in an intensive care unit. Predictors, such as administrative and laboratory variables or an anticholinergic burden scale, were selected using a combination of feature selection filter method and forward/backward selection. The final model was based on logistic regression and the DELIKT was derived from the β-coefficients. We report the following performance measures: area under the curve, sensitivity, specificity and odds ratio.

RESULTS

Both cohorts were similar and included over 10,000 patients each (mean age 77.6 ± 7.6 years) with 11% experiencing delirium. The model included nine variables: age, medical department, dementia, hemi-/paraplegia, catheterisation, potassium, creatinine, polypharmacy and the anticholinergic burden measured with the Clinician-rated Anticholinergic Scale (CrAS). The external validation yielded an AUC of 0.795. With a cut-off at 20 points in the DELIKT, we received a sensitivity of 79.7%, specificity of 62.3% and an odds ratio of 5.9 (95% CI 5.2, 6.7).

CONCLUSION

The DELIKT is a potentially automatic tool with predictors from standard care including the CrAS to identify patients at high risk for delirium.

New-onset delirium during hospitalization in older adults: incidence and risk factors

OBJECTIVE

Delirium is a clinical diagnosis that can occur frequently in hospitalized patients. A retrospective study was completed to identify the incidence of patients aged greater than 65 developing delirium during hospitalization.

METHODS

This study was conducted at a single tertiary care teaching hospital. Charts of discharged patients from November to December 2018 were evaluated and patients less than age 65 or with delirium present on admission were excluded. The search terms altered, delirium, encephalopathy, and confusion were used to identify patients who developed delirium during the hospitalization. Characteristics of the patients with delirium were also collected.

RESULTS

The incidence of new-onset delirium in patients over age 65 during hospitalization was 10%. Patients who developed delirium during their hospital stay were found to have a higher risk of mortality (p = 0.0028) and severity of illness (p = 0.014). A strong correlation between the length of stay (LOS) and incidence of delirium was also noted.

CONCLUSION

The strong correlation between a longer LOS and a higher incidence of delirium should guide the development of new innovative strategies to shorten the LOS and thus reduce the risk of delirium, in high-risk older hospitalized patients.

Heart rate response and recovery during exercise predict future delirium risk-A prospective cohort study in middle- to older-aged adults

BACKGROUND

Delirium is a neurocognitive disorder characterized by an abrupt decline in attention, awareness, and cognition after surgical/illness-induced stressors on the brain. There is now an increasing focus on how cardiovascular health interacts with neurocognitive disorders given their overlapping risk factors and links to subsequent dementia and mortality. One common indicator for cardiovascular health is the heart rate response/recovery (HRR) to exercise, but how this relates to future delirium is unknown.

METHODS

Electrocardiogram data were examined in 38,740 middle- to older-aged UK Biobank participants (mean age = 58.1 years, range: 40-72 years; 47.3% males) who completed a standardized submaximal exercise stress test (15-s baseline, 6-min exercise, and 1-min recovery) and required hospitalization during follow-up. An HRR index was derived as the product of the heart rate (HR) responses during exercise (peak/resting HRs) and recovery (peak/recovery HRs) and categorized into low/average/high groups as the bottom quartile/middle 2 quartiles/top quartile, respectively. Associations between 3 HRR groups and new-onset delirium were investigated using Cox proportional hazards models and a 2-year landmark analysis to minimize reverse causation. Sociodemographic factors, lifestyle factors/physical activity, cardiovascular risk, comorbidities, cognition, and maximal workload achieved were included as covariates.

RESULTS

During a median follow-up period of 11 years, 348 participants (9/1000) newly developed delirium. Compared with the high HRR group (16/1000), the risk for delirium was almost doubled in those with low HRR (hazard ratio = 1.90, 95% confidence interval (95%CI): 1.30-2.79, p = 0.001) and average HRR (hazard ratio = 1.54, 95%CI: 1.07-2.22, p = 0.020)). Low HRR was equivalent to being 6 years older, a current smoker, or ≥3 additional cardiovascular disease risks. Results were robust in sensitivity analysis, but the risk appeared larger in those with better cognition and when only postoperative delirium was considered (n = 147; hazard ratio = 2.66, 95%CI: 1.46-4.85, p = 0.001).

CONCLUSION

HRR during submaximal exercise is associated with future risk for delirium. Given that HRR is potentially modifiable, it may prove useful for neurological risk stratification alongside traditional cardiovascular risk factors.

Neurophysiologic predictors of individual risk for post-operative delirium after elective surgery

BACKGROUND

Post-surgical delirium is associated with increased morbidity, lasting cognitive decline, and loss of functional independence. Within a conceptual framework that delirium is triggered by stressors when vulnerabilities exist in cerebral connectivity and plasticity, we previously suggested that neurophysiologic measures might identify individuals at risk for post-surgical delirium. Here we demonstrate the feasibility of the approach and provide preliminary experimental evidence of the predictive value of such neurophysiologic measures for the risk of delirium in older persons undergoing elective surgery.

METHODS

Electroencephalography (EEG) and transcranial magnetic stimulation (TMS) were collected from 23 patients prior to elective surgery. Resting-state EEG spectral power ratio (SPR) served as a measure of integrity of neural circuits. TMS-EEG metrics of plasticity (TMS-plasticity) were used as indicators of brain capacity to respond to stressors. Presence or absence of delirium was assessed using the confusion assessment method (CAM). We included individuals with no baseline clinically relevant cognitive impairment (MoCA scores ≥21) in order to focus on subclinical neurophysiological measures.

RESULTS

In patients with no baseline cognitive impairment (N = 20, age = 72 ± 6), 3 developed post-surgical delirium (MoCA = 24 ± 2.6) and 17 did not (controls; MoCA = 25 ± 2.4). Patients who developed delirium had pre-surgical resting-state EEG power ratios outside the 95% confidence interval of controls, and 2/3 had TMS-plasticity measures outside the 95% CI of controls.

CONCLUSIONS

Consistent with our proposed conceptual framework, this pilot study suggests that non-invasive and scalable neurophysiologic measures can identify individuals at risk of post-operative delirium. Specifically, abnormalities in resting-state EEG spectral power or TMS-plasticity may indicate sub-clinical risk for post-surgery delirium. Extension and confirmation of these findings in a larger sample is needed to assess the clinical utility of the proposed neurophysiologic markers, and to identify specific connectivity and plasticity targets for therapeutic interventions that might minimize the risk of delirium.

Recipe for primary prevention of delirium in hospitalized older patients

Delirium is an acute fluctuating syndrome characterized by a change in consciousness, perception, orientation, cognition, sleep-wake rhythm, psychomotor skills, and the mood and feelings of a patient. Delirium and delirium prevention remain a challenge for healthcare professionals, especially nurses who form the basis of patient care. It also causes distress for patients, their caregivers and healthcare professionals. However, delirium is preventable in 30-40% of cases. The aim of this article is to summarize the delirium risk models, delirium screening tools, and (non-pharmacological) delirium prevention strategies. A literature search of review articles supplemented by original articles published in PubMed, Cinahl, and Cochrane between 1 January 2000 and 31 December 2020 was carried out. Among the older patients, delirium is a common condition with major consequences in terms of mortality and morbidity, but prevention is possible. Despite the fact that delirium risk models, delirium screening scales and non-pharmacological prevention are available for the development of a hospital delirium prevention programme, such a programme is still not commonly used on a daily basis.

Health Care 4.0: A Vision for Smart and Connected Health Care

Industry 4.0 has transformed manufacturing industry into a new paradigm. In a manner similar to manufacturing, health care delivery is at the dawn of a foundational change into the new era of smart and connected health care, referred to as Health Care 4.0. In this paper, we discuss the historical evolution of Health Care 1.0 to 4.0, describe the characteristics of smart and connected care in Health Care 4.0, identify multiple research challenges and opportunities of Health Care 4.0 in terms of data, model, dynamics, and integration, and outline the implications of people, process, system and health outcomes. Finally, conclusions and recommendations are presented in the areas of (1) involvement of multiple disciplines and perspectives, (2) development of technologies and methodologies with combination of quantitative and qualitative approaches, (3) closed-loop integration of sociotechnical system, and (4) design of person-centered system with specific attention to human needs and health equity.

Delirium in Intensive Care

Purpose of Review

Delirium in the intensive care unit (ICU) has become increasingly acknowledged as a significant problem for critically ill patients affecting both the actual course of illness as well as outcomes. In this review, we focus on the current evidence and the gaps in knowledge.

Recent Findings

This review highlights several areas in which the evidence is weak and further research is needed in both pharmacological and non-pharmacological treatment. A better understanding of subtypes and their different response to therapy is needed and further studies in aetiology are warranted. Larger studies are needed to explore risk factors for developing delirium and for examining long-term consequences. Finally, a stronger focus on experienced delirium and considering the perspectives of both patients and their families is encouraged.

Summary

With the growing number of studies and a better framework for research leading to stronger evidence, the outcomes for patients suffering from delirium will most definitely improve in the years to come.

Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool: Development and Validation of a Risk-Stratification Model

OBJECTIVE

To develop a delirium risk-prediction tool that is applicable across different clinical patient populations and can predict the risk of delirium at admission to hospital.

METHODS

This retrospective study included 120,764 patients admitted to Mayo Clinic between January 1, 2012, and December 31, 2017, with age 50 and greater. The study group was randomized into a derivation cohort (n=80,000) and a validation cohort (n=40,764). Different risk factors were extracted and analyzed using least absolute shrinkage and selection operator (LASSO) penalized logistic regression.

RESULTS

The area under the receiver operating characteristic curve (AUROC) for Mayo Delirium Prediction (MDP) tool using derivation cohort was 0.85 (95% confidence interval [CI], .846 to .855). Using the regression coefficients obtained from the derivation cohort, predicted probability of delirium was calculated for each patient in the validation cohort. For the validation cohort, AUROC was 0.84 (95% CI, .834 to .847). Patients were classified into 1 of the 3 risk groups, based on their predicted probability of delirium: low (≤5%), moderate (6% to 29%), and high (≥30%). In the derivation cohort, observed incidence of delirium was 1.7%, 12.8%, and 44.8% (low, moderate, and high risk, respectively), which is similar to the incidence rates in the validation cohort of 1.9%, 12.7%, and 46.3%.

CONCLUSION

The Mayo Delirium Prediction tool was developed from a large heterogeneous patient population with good validation results and appears to be a reliable automated tool for delirium risk prediction with hospitalization. Further prospective validation studies are required.

[Descriptive study of delirium in the emergency department]

Objetivo

Conocer mejor las variables clínicas, funcionales y analíticas que se asocian al síndrome confusional agudo (SCA) en urgencias y la evolución de las mismas con el fin de obtener una mejora en el abordaje terapéutico del paciente anciano previniendo así la morbimortalidad en este tipo de pacientes.

Diseño

Se trata de un estudio descriptivo prospectivo de SCA en urgencias.

Emplazamiento

Hospital General Universitario de Ciudad Real.

Participantes

Se incluyó, en el intervalo de las 24 h siguientes al ingreso en el Servicio de Geriatría, a todos los pacientes procedentes del Servicio de Urgencias con diagnóstico de SCA.

Mediciones principales

Se realizó un análisis de las variables del conjunto de datos (variables sociodemográficas y clínicas), calculando tablas de frecuencias para variables de tipo cualitativo y estadísticos descriptivos para las variables cuantitativas. Posteriormente, se han empleado técnicas de inferencia estadística.

Resultados

El antecedente médico más frecuente fueron la enfermedad neurológica y la HTA, seguida de las enfermedades reumatológicas. Los motivos de consulta principales fueron el deterioro del estado general, la disnea, la disminución del nivel de consciencia y la fiebre. Se debe destacar la incidencia de la polifarmacia, especialmente de fármacos como los diuréticos, benzodiacepinas o hipnóticos. En relación con la etiología principal, destaca el papel de las infecciones de tipo urinario y respiratorio.

Conclusiones

Se destaca el papel fundamental de las enfermedades neurológicas (especialmente la demencia), la HTA, la polifarmacia (uso inadecuado de benzodiacepinas e hipnóticos) y las infecciones urinarias y respiratorias como factores tratables o prevenibles del delirium en el paciente de Atención Primaria en nuestro medio.

Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study

OBJECTIVE

Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting.

MATERIALS AND METHODS

Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 predictions were analyzed. In addition, 119 predictions for internal medicine patients were compared with ratings of clinical experts in a blinded and nonblinded setting.

RESULTS

During clinical application, the algorithm achieved a sensitivity of 74.1% and a specificity of 82.2%. Discrimination on prospective data (area under the receiver-operating characteristic curve = 0.86) was as good as in the test dataset, but calibration was poor. The predictions correlated strongly with delirium risk perceived by experts in the blinded (r = 0.81) and nonblinded (r = 0.62) settings. A major advantage of our setting was the timely prediction without additional data entry.

DISCUSSION

The implemented machine learning algorithm achieved a stable performance predicting delirium in high agreement with expert ratings, but improvement of calibration is needed. Future research should evaluate the acceptance of implemented machine learning algorithms by health professionals.

CONCLUSIONS

Our study provides new insights into the implementation process of a machine learning algorithm into a clinical workflow and demonstrates its predictive power for delirium.

Risk factors associated with postoperative intensive care unit delirium in patients undergoing invasive mechanical ventilation following acute exacerbation of chronic obstructive pulmonary disease

OBJECTIVE

This study was performed to determine the risk factors associated with intensive care unit delirium (ICUD) in patients undergoing invasive mechanical ventilation (IMV) secondary to acute exacerbation of chronic obstructive pulmonary disease (COPD).

METHODS

Data involving 620 patients undergoing IMV secondary to acute exacerbation of COPD from 2009 to 2019 at the First Hospital of Hebei Medical University were retrospectively analysed. The primary endpoint was the risk factors associated with developing ICUD. Univariable and multivariable logistic regression analyses were used to identify these risk factors.

RESULTS

Of 620 patients, 93 (15.0%) developed ICUD. In the multivariable analysis, risk factors that were significantly associated with ICUD were increased age, male sex, alcoholism with active abstinence, current smoking, stage 3 acute kidney injury (AKI), and an American Society of Anesthesiologists (ASA) physical status of III.

CONCLUSION

This study showed that increasing age, male sex, alcoholism with active abstinence, current smoking, stage 3 AKI, and an ASA physical status of III might be associated with a risk of developing ICUD. Even if these risk factors are unaltered, they provide a target population for quality improvement initiatives.

Delirium risk prediction models for intensive care unit patients: A systematic review

OBJECTIVE

To systematically review the delirium risk prediction models for intensive care unit (ICU) patients.

METHODS

A systematic review was conducted. The Cochrane Library, PubMed, Ovid and Web of Science were searched to collect studies on delirium risk prediction models for ICU patients from database establishment to 31 March 2019. Two reviewers independently screened the literature according to the pre-determined inclusion and exclusion criteria, extracted the data and evaluated the risk of bias of the included studies using the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) checklist. A descriptive analysis was used to describe and summarise the data.

RESULTS

A total of six models were included. All studies reported the area under the receiver operating characteristic curve (AUROC) of the prediction models in the derivation and (or) validation datasets as over 0.7 (from 0.75 to 0.9). Five models reported calibration metrics. Decreased cognitive reserve and the Acute Physiology and Chronic Health Evaluation II (APACHE-II) score were the most commonly reported predisposing and precipitating factors, respectively, of ICU delirium among all models. The small sample size, lack of external validation and the absence of or unreported blinding method increased the risk of bias.

CONCLUSION

According to the discrimination and calibration statistics reported in the original studies, six prediction models may have moderate power in predicting ICU delirium. However, this finding should be interpreted with caution due to the risk of bias in the included studies. More clinical studies should be carried out to validate whether these tools have satisfactory predictive performance in delirium risk prediction for ICU patients.

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 incident delirium diagnoses using data from primary-care electronic health records

IMPORTANCE

risk factors for delirium in hospital inpatients are well established, but less is known about whether delirium occurring in the community or during an emergency admission to hospital care might be predicted from routine primary-care records.

OBJECTIVES

identify risk factors in primary-care electronic health records (PC-EHR) predictive of delirium occurring in the community or recorded in the initial episode in emergency hospitalisation. Test predictive performance against the cumulative frailty index.

DESIGN

Stage 1: case-control; Stages 2 and 3: retrospective cohort.

SETTING

clinical practice research datalink: PC-EHR linked to hospital discharge data from England.

SUBJECTS

Stage 1: 17,286 patients with delirium aged ≥60 years plus 85,607 controls. Stages 2 and 3: patients ≥ 60 years (n = 429,548 in 2015), split into calibration and validation groups.

METHODS

Stage 1: logistic regression to identify associations of 110 candidate risk measures with delirium. Stage 2: calibrating risk factor weights. Stage 3: validation in independent sample using area under the curve (AUC) receiver operating characteristic.

RESULTS

fifty-five risk factors were predictive, in domains including: cognitive impairment or mental illness, psychoactive drugs, frailty, infection, hyponatraemia and anticholinergic drugs. The derived model predicted 1-year incident delirium (AUC = 0.867, 0.852:0.881) and mortality (AUC = 0.846, 0.842:0.853), outperforming the frailty index (AUC = 0.761, 0.740:0.782). Individuals with the highest 10% of predicted delirium risk accounted for 55% of incident delirium over 1 year.

CONCLUSIONS

a risk factor model for delirium using data in PC-EHR performed well, identifying individuals at risk of new onsets of delirium. This model has potential for supporting preventive interventions.

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.

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.

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.

Mesures de prévention non pharmacologiques du delirium de réanimation

Le delirium est défini par un changement brutal ou rapidement progressif de l’état mental ou une modification de l’humeur associés à une baisse des capacités de concentration, à une désorganisation de la pensée, à une confusion et à une altération du niveau de conscience. L’incidence du delirium en réanimation est variable d’environ 4 à 83 %, selon les études. Cela est probablement lié à la variété des outils de mesure employés, au niveau d’entraînement des professionnels de santé établissant ces scores, à la profondeur de la sédation et aux différences de populations étudiées. Son étiologie semble être multifactorielle. Il a été montré que la survenue du delirium a un fort impact sur le pronostic vital et fonctionnel des patients en réanimation, car son incidence est associée à une augmentation de la mortalité hospitalière précoce et tardive, et le déclin cognitif qui lui est associé peut persister à distance du séjour en réanimation. Il est important de souligner que la prise en charge dans les soins critiques est très hétérogène. Néanmoins, de nouvelles données de la littérature apportent des éléments concrets sur la prise charge de ce syndrome et fournissent un guide utile à la pratique paramédicale dans la prévention et le dépistage de ce trouble cognitif. L’objectif de ce travail est d’apporter une synthèse autour de la littérature disponible dans ce domaine, mettant en lumière le rôle clé de la profession paramédicale dans ce contexte afin d’identifier des éléments diagnostiques et thérapeutiques susceptibles de modifier pertinemment les pratiques soignantes.

Refining Delirium: A Transtheoretical Model of Delirium Disorder with Preliminary Neurophysiologic Subtypes

The development of delirium indicates neurophysiologic disruption and predicts unfavorable outcomes. This relationship between delirium and its outcomes has inspired a generation of studies aimed at identifying, predicting, and preventing both delirium and its associated sequelae. Despite this, evidence on delirium prevention and management remains limited. No medication is approved for the prevention or treatment of delirium or for its associated psychiatric symptoms. This unmet need for effective delirium treatment calls for a refined approach. First, we explain why a one-size-fits-all approach based on a unitary biological model of delirium has contributed to variance in delirium studies and prevents further advance in the field. Next, in parallel with the shift from dementia to "major neurocognitive disorder," we propose a transtheoretical model of "delirium disorder" composed of interactive elements-precipitant, neurophysiology, delirium phenotype, and associated psychiatric symptoms. We explore how these relate both to the biopsychosocial factors that promote healthy cognition ("procognitive factors") and to consequent neuropathologic sequelae. Finally, we outline a preliminary delirium typology of specific neurophysiologic disturbances. Our model of delirium disorder offers several avenues for novel insights and clinical advance: it univocally differentiates delirium disorder from the phenotype of delirium, highlights delirium neurophysiology as a treatment target, separates the core features of delirium from associated psychiatric symptoms, suggests how procognitive factors influence the core elements of delirium disorder, and makes intuitive predictions about how delirium disorder leads to neuropathologic sequelae and cognitive impairment. Ultimately, this model opens several avenues for modern neuroscience to unravel this disease of antiquity.

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.

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.

Statin and Its Association With Delirium in the Medical ICU

OBJECTIVES

To examine the association between statin use and the risk of delirium in hospitalized patients with an admission to the medical ICU.

DESIGN

Retrospective propensity-matched cohort analysis with accrual from September 1, 2012, to September 30, 2015.

SETTING

Hartford Hospital, Hartford, CT.

PATIENTS

An initial population of patients with an admission to a medical ICU totaling 10,216 visits were screened for delirium by means of the Confusion Assessment Method. After exclusions, a population of 6,664 was used to match statin users and nonstatin users. The propensity-matched cohort resulted in a sample of 1,475 patients receiving statin matched 1:1 with control patients not using statin.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Delirium defined as a positive Confusion Assessment Method assessment was the primary end point. The prevalence of delirium was 22.3% in the unmatched cohort and 22.8% in the propensity-matched cohort. Statin use was associated with a significant decrease in the risk of delirium (odds ratio, 0.47; 95% CI, 0.38-0.56). Considering the type of statin used, atorvastatin (0.51; 0.41-0.64), pravastatin (0.40; 0.28-0.58), and simvastatin (0.33; 0.21-0.52) were all significantly associated with a reduced frequency of delirium.

CONCLUSIONS

The use of statins was independently associated with a reduction in the risk of delirium in hospitalized patients. When considering types of statins used, this reduction was significant in patients using atorvastatin, pravastatin, and simvastatin. Randomized trials of various statin types in hospitalized patients prone to delirium should validate their use in protection from delirium.

Characterizing and predicting rates of delirium across general hospital settings

OBJECTIVE

To better understand variation in reported rates of delirium, this study characterized delirium occurrence rate by department of service and primary admitting diagnosis.

METHOD

Nine consecutive years (2005-2013) of general hospital admissions (N=831,348) were identified across two academic medical centers using electronic health records. The primary admitting diagnosis and the treating clinical department were used to calculate occurrence rates of a previously published delirium definition composed of billing codes and natural language processing of discharge summaries.

RESULTS

Delirium rates varied significantly across both admitting diagnosis group (X²₁₀=12786, p<0.001) and department of care (X²₆=12106, p<0.001). In both cases obstetrical admissions showed the lowest incidences of delirium (86/109764; 0.08%) and neurological admissions the greatest (2851/25450; 11.2%). Although the rate of delirium varied across the two hospitals the relative rates within departments (r=0.96, p<0.001) and diagnostic categories (r=0.98, p<0.001) were consistent across the two institutions.

CONCLUSIONS

The frequency of delirium varies significantly across admitting diagnosis and hospital department. Both admitting diagnosis and department of care are even stronger predictors of risk than age; as such, simple risk stratification may offer avenues for targeted prevention and treatment efforts.